In the process of project construction, problems from the target control can cause conflict because of conflicts between the owner and the contractor, such as quality, cost and est. It will eventually lead to deviation from the objectives of the project or even failure, if the conflict cannot be promptly and effectively addressed. This paper introduces the theory of game theory, to study the evolutionary stable strategy of owner and contractor in the conflict management by establishing a model of static game of complete information to simulate the process of conflict. Some practical guidance on effectively conflict management was proposed by the analysis of static and evolutionary game theory.
Numerical results for the one loop correction to (ϕ 4 ) 2 are compared to results obtained from a derivative expansion and an expansion in inverse powers of the effective mass. We vary the scalar background field to illustrate when and why these expansions succeed, and how they break down. It is shown that both expansions behave like asymptotic series, with the approximation improving until higher order corrections grow in magnitude.
Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data. However, as GNNs typically define the graph convolution by the orthonormal basis for the graph Laplacian, they suffer from high computational cost when the graph size is large. This paper introduces Haar basis which is a sparse and localized orthonormal system for a coarse-grained chain on graph. The graph convolution under Haar basis, called Haar convolution, can be defined accordingly for GNNs. The sparsity and locality of the Haar basis allow Fast Haar Transforms (FHTs) on graph, by which a fast evaluation of Haar convolution between graph data and filters can be achieved. We conduct experiments on GNNs equipped with Haar convolution, which demonstrates state-of-the-art results on graph-based regression and node classification tasks.
Abstract Reversible simulation of irreversible algorithms is analyzed in the stylized form of a ‘reversible’pebble game. The reacheable reversible simulation instantaneous descriptions (pebble configurations)are characterized completely. As a corollary we obtain the reversible simulation by Bennett and thatamong all simulations that can be modelled by the pebble game, Bennett’s simulation is optimal inthat it uses the least auxiliary space for the greatest number of simulated steps. One can reducethe auxiliary storage overhead incurred by the reversible simulation at the cost of allowing limitederasing leading to an irreversibility-space tradeoff. We show that in this resource-bounded setting thelimited erasing needs to be performed at precise instants during the simulation. We show that thereversible simulation can be modified so that it is applicable also when the simulated computation timeis unknown. 1 Introduction The ultimate limits of miniaturization of computing devices, and therefore the speed of computation, areconstrained by the increasing density of switching elements in the device. Linear speed up by shorteninginterconnects on a two-dimensional device is attended by a cubing of dissipated energy per unit areaper second. Namely, we square the number of switching elements per area unit and linearly increase thenumber of switching events per switch per time unit. In the long run, the attending energy dissipation onthis scale cannot be compensated for by cooling. Ignoring architectural improvements, reduction of theenergy dissipation per elementary computation step therefore determines future advances in computingpower.J. von Neumann reputedly thought that a computer operating at temperature T must dissipate atleast kT ln2 Joule per elementary bit operation [Burks, 1966]. R. Landauer [Landauer, 1961] demon-strated that it is only the ‘logically irreversible’ operations in a physical computer that are required todissipate energy by generating a corresponding amount of entropy for each bit of information that getsirreversibly erased. As a consequence, any arbitrarily large reversible computation can be performed onan appropriate physical device using only one unit of physical energy
In this short paper, we study a symmetric covariant tensor in Finsler geometry, which is called the mean Berwald curvature. We first investigate the geometry of the fibres as the submanifolds of the tangent sphere bundle on a Finsler manifold. Then we prove that if the mean Berwald curvature is isotropic along fibres, then the Berwald scalar curvature is constant along fibres.
RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification. Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 2 of RibFrac dataset, including 120 CTs and the corresponding annotations. Files include: ribfrac-train-images-2.zip: 120 chest-abdomen CTs in NII format (nii.gz). ribfrac-train-labels-2.zip: 120 annotations in NII format (nii.gz). ribfrac-train-info-2.csv: labels in the annotation NIIs. public_id: anonymous patient ID to match images and annotations. label_id: discrete label value in the NII annotations. label_code: 0, 1, 2, 3, 4, -1 0: it is background 1: it is a displaced rib fracture 2: it is a non-displaced rib fracture 3: it is a buckle rib fracture 4: it is a segmental rib fracture -1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as: Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (DOI) or using bibtex @article{ribfrac2020, title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet}, author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming}, journal={EBioMedicine}, year={2020}, publisher={Elsevier} } The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Euarthropods are an extremely diverse phylum in the modern, and have been since their origination in the early Palaeozoic. They grow through moulting the exoskeleton (ecdysis) facilitated by breaking along lines of weakness (sutures). Artiopodans, a group that includes trilobites and their non-biomineralizing relatives, dominated arthropod diversity in benthic communities during the Palaeozoic. Most trilobites – a hyperdiverse group of tens of thousands of species -moult by breaking the exoskeleton along cephalic sutures, a strategy that has contributed to their high diversity during the Palaeozoic. However, the recent description of similar sutures in early diverging non-trilobite artiopodans mean that it is unclear whether these sutures were evolved deep within Artiopoda, or evolved multiple times within the group. Here we describe new well-preserved material of Acanthomeridion, a putative early diverging artiopodan, including hitherto unknown details of its ventral anatomy and appendages revealed through CT scanning, highlighting additional possible homologous features between Acanthomeridion and trilobites. We used two coding strategies treating structures as homologous or independently derived across multiple phylogenetic analysis techniques (parsimony or Bayesian inference), and showed that regardless of these variables, the sutures crucial for the success and growth of the hyperdiverse trilobites evolved multiple times within Artiopoda.
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