31 research outputs found
Existence of strong solutions for a system of interaction between a compressible viscous fluid and a wave equation
In this article, we consider a fluid-structure interaction system where the fluid is viscous and compressible and where the structure is a part of the boundary of the fluid domain and is deformable. The fluid is governed by the barotropic compressible Navier-Stokes system whereas the structure displacement is described by a wave equation. We show that the corresponding coupled system admits a unique strong solution for an initial fluid density and an initial fluid velocity in and for an initial deformation and an initial deformation velocity in and respectively. The reference configuration for the fluid domain is a rectangular cuboid with the elastic structure being the top face. We use a modified Lagrangian change of variables to transform the moving fluid domain into the rectangular cuboid and then analyze the corresponding linear system coupling a transport equation (for the density), a heat-type equation, and a wave equation. The corresponding results for this linear system and estimations of the coefficients coming from the change of variables allow us to perform a fixed point argument and to prove the existence and uniqueness of strong solutions for the nonlinear system, locally in time
Edge Generation Scheduling for DAG Tasks using Deep Reinforcement Learning
Directed acyclic graph (DAG) tasks are currently adopted in the real-time
domain to model complex applications from the automotive, avionics, and
industrial domain that implement their functionalities through chains of
intercommunicating tasks. This paper studies the problem of scheduling
real-time DAG tasks by presenting a novel schedulability test based on the
concept of trivial schedulability. Using this schedulability test, we propose a
new DAG scheduling framework (edge generation scheduling -- EGS) that attempts
to minimize the DAG width by iteratively generating edges while guaranteeing
the deadline constraint. We study how to efficiently solve the problem of
generating edges by developing a deep reinforcement learning algorithm combined
with a graph representation neural network to learn an efficient edge
generation policy for EGS. We evaluate the effectiveness of the proposed
algorithm by comparing it with state-of-the-art DAG scheduling heuristics and
an optimal mixed-integer linear programming baseline. Experimental results show
that the proposed algorithm outperforms the state-of-the-art by requiring fewer
processors to schedule the same DAG tasks.Comment: Under revie
RDMA-Based Deterministic Communication Architecture for Autonomous Driving
Autonomous driving is a big challenge for nextgeneration vehicles and requires multiple computationallyintensive deep neural networks (DNNs) to be implemented on
distributed automotive platforms. Distributed software—enabling
autonomous functionalities—has strict timing requirements, e.g.,
low and deterministic end-to-end latency. Such timings rely on
the communication technologies used in the automotive platform,
as much on the computation performance of CPUs, GPUs, TPUs,
and FPGAs. Hence, we advocate the use of Remote Direct
Memory Access (RDMA) technology—typically used in data
centers—in automotive platforms. As shown by our experiments
with real hardware, Soft-RoCE (software implementation of
RDMA) offers low latency communication because of minimal
CPU involvement and reduced memory copies. Simultaneously,
we show that the native implementation of RDMA does not
support determinism, i.e., there is a high variation in communication delays in the presence of interfering data packets.
To mitigate this issue, we propose a multi-layer communication
stack comprising a deterministic scheduler on top of the SoftRoCE layer. Further, we have developed a C++ library that offers
easy-to-use communication interfaces for distributed applications
while implementing the proposed architecture. Experiments show
that our library (i) reduces the end-to-end latency of distributed
object detection by nearly 9% while having an implementation
overhead of less than 1.5% and (ii) minimizes the effects of other
data traffic on the delay in high-priority communication
An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges
Automatic analysis of colonoscopy images has been an active field of research
motivated by the importance of early detection of precancerous polyps. However,
detecting polyps during the live examination can be challenging due to various
factors such as variation of skills and experience among the endoscopists, lack
of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning
has emerged as a promising solution to this challenge as it can assist
endoscopists in detecting and classifying overlooked polyps and abnormalities
in real time. In addition to the algorithm's accuracy, transparency and
interpretability are crucial to explaining the whys and hows of the algorithm's
prediction. Further, most algorithms are developed in private data, closed
source, or proprietary software, and methods lack reproducibility. Therefore,
to promote the development of efficient and transparent methods, we have
organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI:
Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We
present a comprehensive summary and analyze each contribution, highlight the
strength of the best-performing methods, and discuss the possibility of
clinical translations of such methods into the clinic. For the transparency
task, a multi-disciplinary team, including expert gastroenterologists, accessed
each submission and evaluated the team based on open-source practices, failure
case analysis, ablation studies, usability and understandability of evaluations
to gain a deeper understanding of the models' credibility for clinical
deployment. Through the comprehensive analysis of the challenge, we not only
highlight the advancements in polyp and surgical instrument segmentation but
also encourage qualitative evaluation for building more transparent and
understandable AI-based colonoscopy systems