39 research outputs found
It is not a panacea! The conditional effect of bricolage in SME opportunity exploitation
No full textConcerns and perceived obstacles for females with regards to cardiac surgery residency—A survey
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Hydrologic Scenario Simulation and Related Techniques Forecasting Land Use/Land Cover Changes
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An automatic plant leaf stoma detection method based on YOLOv5
No full textAbstract The stomata on the leaf surface are mainly responsible for the material exchange between the internal and external environments of the plant, a large number of methods have been proposed to automatically measure the distribution position and number of stomatal, but few methods could achieve both stomatal count and open/closed‐state judgment. Therefore, this study proposes an automatic detection method for leaf stomatal morphology analysis based on an attention mechanism and deep learning. In order to obtain more stomatal feature information and send it to the network for learning, the proposed method adds a coordinate attention (CA) mechanism to the YOLOV5 backbone part. At the same time, in order to avoid the overfitting of the model during the training process, the authors added the training trick of label smoothing. Finally, the detection ability of the proposed method for stomata is verified on the broad bean leaves stomata dataset. The experimental results show that our method achieves a detection accuracy of 0.934 and an mAP of 0.968. By comparing with other state‐of‐the‐art algorithms, the detection capability of our method has been significantly improved. The generalization of the model is verified on the wheat leaf stomatal dataset. The experimental results show that our method can achieve a detection accuracy of 0.894 and an mAP of 0.907
Comparison of outcomes of root replacement procedures and supracoronary techniques for surgical repair of acute aortic dissection
No full textBACKGROUND: Surgical approach to type A acute aortic dissection (AADA) is usually dictated by the presenting anatomy. We compared long-term outcomes of AADA repaired with a root replacement versus a supracoronary tube graft, regardless of the proximal extent of the intimal tear. METHODS: A single-centre, retrospective cohort of consecutive patients undergoing repair of AADA between December 1999 and March 2012 were stratified based on the proximal surgical procedure performed: supracoronary tube graft or root replacement. Imaging, chart reviews and clinical follow-ups were analyzed to identify the presenting anatomy and clinical outcomes. RESULTS: We included the cases of 75 patients in our analysis: 54 received a supracoronary tube graft and 21 received a root replacement. The proximal tear was identified below the sinotubular junction in all patients in the root group and in 61% of patients in the supracoronary group. We detected no differences between the groups for in-hospital mortality, length of stay, or complications. However, the root group had significantly increased renal failure (0% v. 9.5%, p = 0.018), cardiopulmonary bypass time (198.4 ± 80.0 min v. 316.5 ± 102.5 min, p < 0.001), cross-clamp time (91.6 ± 34.9 min v. 191.3 ± 52.8 min, p < 0.001), duration of surgery (457.5 ± 129.9 min v. 611.6 ± 197.8 min, p < 0.001), and platelet transfusions (8.1 ± 7.6 v. 12.8 ± 8.7 units, p = 0.021) than the supracoronary group. Long-term follow-up demonstrated a greater incidence of 2+ aortic regurgitation among patients in the supracoronary group than the root group (29.7% v. 0.0%, p = 0.006); however, there was no difference between the groups in symptoms or reoperation. CONCLUSION: In AADA, aortic root replacement involves a longer procedure with increased risk of early renal impairment. Long-term follow-up identified significantly more aortic regurgitation and root dilation in the supracoronary group than the root group, with a trend toward worse long-term survival. However, we found no difference between the groups in mortality, reoperation or New York Heart Association class
DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
No full textConstrained policy search (CPS) is a fundamental problem in offline
reinforcement learning, which is generally solved by advantage weighted
regression (AWR). However, previous methods may still encounter
out-of-distribution actions due to the limited expressivity of Gaussian-based
policies. On the other hand, directly applying the state-of-the-art models with
distribution expression capabilities (i.e., diffusion models) in the AWR
framework is intractable since AWR requires exact policy probability densities,
which is intractable in diffusion models. In this paper, we propose a novel
approach, (dubbed
DiffCPS), which tackles the diffusion-based constrained policy search with the
primal-dual method. The theoretical analysis reveals that strong duality holds
for diffusion-based CPS problems, and upon introducing parameter approximation,
an approximated solution can be obtained after number
of dual iterations, where denotes the representation ability of the
parametrized policy. Extensive experimental results based on the D4RL benchmark
demonstrate the efficacy of our approach. We empirically show that DiffCPS
achieves better or at least competitive performance compared to traditional
AWR-based baselines as well as recent diffusion-based offline RL methods. The
code is now available at https://github.com/felix-thu/DiffCPS.Comment: 22 pages, 9 figures, 6 tables. Submitted to ICML 2024. arXiv admin
note: text overlap with arXiv:1910.13393 by other author
One‐Dimensional Nanostructured Pseudocapacitive Materials: Design, Synthesis and Applications in Supercapacitors
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