399 research outputs found
A Novel Model of Working Set Selection for SMO Decomposition Methods
In the process of training Support Vector Machines (SVMs) by decomposition
methods, working set selection is an important technique, and some exciting
schemes were employed into this field. To improve working set selection, we
propose a new model for working set selection in sequential minimal
optimization (SMO) decomposition methods. In this model, it selects B as
working set without reselection. Some properties are given by simple proof, and
experiments demonstrate that the proposed method is in general faster than
existing methods.Comment: 8 pages, 12 figures, it was submitted to IEEE International
conference of Tools on Artificial Intelligenc
PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Message passing Graph Neural Networks (GNNs) are known to be limited in
expressive power by the 1-WL color-refinement test for graph isomorphism. Other
more expressive models either are computationally expensive or need
preprocessing to extract structural features from the graph. In this work, we
propose to make GNNs universal by guiding the learning process with exact
isomorphism solver techniques which operate on the paradigm of
Individualization and Refinement (IR), a method to artificially introduce
asymmetry and further refine the coloring when 1-WL stops. Isomorphism solvers
generate a search tree of colorings whose leaves uniquely identify the graph.
However, the tree grows exponentially large and needs hand-crafted pruning
techniques which are not desirable from a learning perspective. We take a
probabilistic view and approximate the search tree of colorings (i.e.
embeddings) by sampling multiple paths from root to leaves of the search tree.
To learn more discriminative representations, we guide the sampling process
with particle filter updates, a principled approach for sequential state
estimation. Our algorithm is end-to-end differentiable, can be applied with any
GNN as backbone and learns richer graph representations with only linear
increase in runtime. Experimental evaluation shows that our approach
consistently outperforms leading GNN models on both synthetic benchmarks for
isomorphism detection as well as real-world datasets.Comment: Published as a conference paper at ICLR 202
Risk Assessment of Nautical Navigational Environment Based on Grey Fixed Weight Cluster
In order to set up a mathematical model suitable for nautical navigational environment risk evaluation and systematically master the navigational environment risk characteristics of the Qiongzhou Strait in a quantitative way, a risk assessment model with approach steps is set up based on the grey fixed weight cluster (GFWC). The evaluation index system is structured scientifically through both literature review and expert investigation. The relative weight of each index is designed to be obtained via fuzzy analytic hierarchy process (FAHP); Index membership degree of every grey class is proposed to be achieved by fuzzy statistics (FS) to avoid the difficulty of building whiten weight functions. By using the model, nautical navigational environment risk of the Qiongzhou Strait is determined at a “moderate” level according to the principle of maximum membership degree. The comprehensive risk evaluation of the Qiongzhou Strait nautical navigational environment can provide theoretical reference for implementing targeted risk control measures. It shows that the constructed GFWC risk assessment model as well as the presented steps are workable in case of incomplete information. The proposed strategy can excavate the collected experts’ knowledge mathematically, quantify the weight of each index and risk level, and finally lead to a comprehensive risk evaluation result. Besides, the adoptions of probability and statistic theory, fuzzy theory, aiming at solving the bottlenecks in case of uncertainty, will give the model a better adaptability and executability.</p
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
Implicit Graph Neural Networks (GNNs) have achieved significant success in
addressing graph learning problems recently. However, poorly designed implicit
GNN layers may have limited adaptability to learn graph metrics, experience
over-smoothing issues, or exhibit suboptimal convergence and generalization
properties, potentially hindering their practical performance. To tackle these
issues, we introduce a geometric framework for designing implicit graph
diffusion layers based on a parameterized graph Laplacian operator. Our
framework allows learning the metrics of vertex and edge spaces, as well as the
graph diffusion strength from data. We show how implicit GNN layers can be
viewed as the fixed-point equation of a Dirichlet energy minimization problem
and give conditions under which it may suffer from over-smoothing during
training (OST) and inference (OSI). We further propose a new implicit GNN model
to avoid OST and OSI. We establish that with an appropriately chosen
hyperparameter greater than the largest eigenvalue of the parameterized graph
Laplacian, DIGNN guarantees a unique equilibrium, quick convergence, and strong
generalization bounds. Our models demonstrate better performance than most
implicit and explicit GNN baselines on benchmark datasets for both node and
graph classification tasks.Comment: 57 page
Natural Disaster Impacts on U.S. Banks
We examine how natural disasters affect bank performance during the 2000-2017 period. The results suggest bank offices in affected counties raise loan rates more than deposit rates. However, we find that community banks, not non-community banks, drive the results, and by being located in disaster-prone areas, they contribute to helping communities recover from natural disasters without any evidence of price gouging. This contributes to higher returns on assets and net interest margins for community banks. Yet, the banks\u27 resulting higher return on assets is not large enough that their offices in disaster-prone communities contribute to economically meaningful profits. Moreover, banks increase their use of brokered deposits after natural disasters to help offset any withdrawal of deposits by individuals and firms in affected communities
Evaluating urban land carrying capacity based on the ecological sensitivity analysis : a case study in Hangzhou, China
In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-use types, the intensity of urban development, and ecological environmental sensitivity to create reasonable evaluation indicators to analyze urban land carrying capacity based on ecological sensitivity in the rapidly developing megacity of Hangzhou, China. In the study, ecological sensitivity is grouped into four levels: non-sensitive, lightly sensitive, moderately sensitive, and highly sensitive. The results show that the ecological sensitivity increases progressively from the center to the periphery. The results also show that ULCC is determined by ecologically sensitive levels and that the ULCC is categorized into four levels. Even though it is limited by the four levels, the ULCC still has a large margin if compared with the current population numbers. The study suggests that the urban ecological environment will continue to sustain the current population size in the short-term future. However, it is necessary to focus on the protection of distinctive natural landscapes so that decision makers can adjust measures for ecological conditions to carry out the sustainable development of populations, natural resources, and the environment in megacities like Hangzhou, China
Evaluating urban land carrying capacity based on the ecological sensitivity analysis: a case study in Hangzhou, China.
In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-use types, the intensity of urban development, and ecological environmental sensitivity to create reasonable evaluation indicators to analyze urban land carrying capacity based on ecological sensitivity in the rapidly developing megacity of Hangzhou, China. In the study, ecological sensitivity is grouped into four levels: non-sensitive, lightly sensitive, moderately sensitive, and highly sensitive. The results show that the ecological sensitivity increases progressively from the center to the periphery. The results also show that ULCC is determined by ecologically sensitive levels and that the ULCC is categorized into four levels. Even though it is limited by the four levels, the ULCC still has a large margin if compared with the current population numbers. The study suggests that the urban ecological environment will continue to sustain the current population size in the short-term future. However, it is necessary to focus on the protection of distinctive natural landscapes so that decision makers can adjust measures for ecological conditions to carry out the sustainable development of populations, natural resources, and the environment in megacities like Hangzhou, China
Aqua(furan-2-carboxylato-κO)(furan-2-carboxylato-κ2 O,O′)(1,10-phenanthroline-κ2 N,N′)copper(II) methanol hemisolvate
The asymmetric unit of the title compound, [Cu(C5H3O3)2(C12H8N2)2(H2O)]·0.5CH3OH, contains two CuII complex molecules and one methanol solvent molecule with the metal centres in strongly distorted octahedral coordination. The coordinated water molecule is involved in intermolecular O—H⋯O hydrogen bonding, which links the complex molecules into chains propagating along the c axis. Neighbouring chains interact further via π–π interactions between the aromatic rings of 1,10-phenanthroline fragments [centroid–centroid distances = 3.726 (4) and 3.750 (4) Å]
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