498 research outputs found
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
Study on the Affection of Drilling Tools’ Abrasion to the Regular Pattern of Tensile Strength
Drilling tool is a necessary tool in oil drilling engineering, its performance directly affects the penetration rate of drilling engineering, to a well, reasonable selection of drilling tools will be conductive to high-quality, efficient and fast drilling construction. Reasonable optimization of drilling tools, based on the field testing of drilling tools in use, and according to the results of field testing, correctly guide the selection of drilling tools. According to the different drilling conditions, the drilling tools should be reasonably allocated to match the drilling performance with the drilling conditions. Analyze the problems and causes of drilling tools found in testing, and determine the rational solutions and preventive measures. The abrasion and thinning of drilling tools often occurs in the drilling process of oil and gas, which affects the bearing capacity of drilling tools. Tensile load is one of the main load-bearing modes of drilling tools and an important evaluation index of drilling tool safety. By simplifying the drilling tool model, confirm the tensile strength of the wearing drilling tool and the stress state at different well depths, and reach the relationship between the wearing degree of drilling tool and the safe well depth
Kūkai 空海 (774–835) and Saichō’s 最澄 (766–822) theories on gotra 種姓 (caste)
In this article, I argue that although the Pusa yingluo benye jing 瓔珞 本業經 [Sutra of the Diadem of the Primary Activities of the Bodhisattvas] utilised the theory of zhongxing 種姓 (Skt. gotra; Jp. shushō; caste) in the Pusa dichi jing 菩薩地持經 [Sutra of Stages of Bodhisattvas], the Pusa yingluo benye jing changed the explanation of zhongxing with the stages of bodhisattvas. According to Kūkai and Saichō’s interpretations of shushō related issues, the Pusa dichi jing and the Pusa yingluo benye jing were still mainstream. The theory of zhongxing in these two texts strongly influenced their thoughts on shushō
Backdoors in Satisfiability Problems
Although satisfiability problems (SAT) are NP-complete, state-of-the-art SAT solvers are able to solve large practical instances. The notion of backdoors has been introduced to capture structural properties of instances. Backdoors are a set of variables for which there exists some value assignment that leads to a polynomial-time solvable sub-problem. I address in this thesis the problem of finding all minimal backdoors, which is essential for studying value and variable ordering mistakes. I discuss our definition of sub-solvers and propose algorithms for finding backdoors. I implement our proposed algorithms by modifying a state-of-the-art SAT solver, Minisat. I analyze experimental results comparing our proposed algorithms to previous algorithms applied to random 3SAT, structured, and real-world instances. Our proposed algorithms improve over previous algorithms for finding backdoors in two ways. First, our algorithms often find smaller backdoors. Second, our algorithms often find a much larger number of backdoors
Physics Informed Token Transformer
Solving Partial Differential Equations (PDEs) is the core of many fields of
science and engineering. While classical approaches are often prohibitively
slow, machine learning models often fail to incorporate complete system
information. Over the past few years, transformers have had a significant
impact on the field of Artificial Intelligence and have seen increased usage in
PDE applications. However, despite their success, transformers currently lack
integration with physics and reasoning. This study aims to address this issue
by introducing PITT: Physics Informed Token Transformer. The purpose of PITT is
to incorporate the knowledge of physics by embedding partial differential
equations (PDEs) into the learning process. PITT uses an equation tokenization
method to learn an analytically-driven numerical update operator. By tokenizing
PDEs and embedding partial derivatives, the transformer models become aware of
the underlying knowledge behind physical processes. To demonstrate this, PITT
is tested on challenging 1D and 2D PDE neural operator prediction tasks. The
results show that PITT outperforms popular neural operator models and has the
ability to extract physically relevant information from governing equations.Comment: 22 pages, 5 figure
A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
Machine learning models are gaining increasing popularity in the domain of
fluid dynamics for their potential to accelerate the production of
high-fidelity computational fluid dynamics data. However, many recently
proposed machine learning models for high-fidelity data reconstruction require
low-fidelity data for model training. Such requirement restrains the
application performance of these models, since their data reconstruction
accuracy would drop significantly if the low-fidelity input data used in model
test has a large deviation from the training data. To overcome this restraint,
we propose a diffusion model which only uses high-fidelity data at training.
With different configurations, our model is able to reconstruct high-fidelity
data from either a regular low-fidelity sample or a sparsely measured sample,
and is also able to gain an accuracy increase by using physics-informed
conditioning information from a known partial differential equation when that
is available. Experimental results demonstrate that our model can produce
accurate reconstruction results for 2d turbulent flows based on different input
sources without retraining
Scalable Transformer for PDE Surrogate Modeling
Transformer has shown state-of-the-art performance on various applications
and has recently emerged as a promising tool for surrogate modeling of partial
differential equations (PDEs). Despite the introduction of linear-complexity
variant, applying attention to a large number of grid points can result in
instability and is still expensive to compute. In this work, we propose
Factorized Transformer(FactFormer), which is based on an axial factorized
kernel integral. Concretely, we introduce a learnable projection operator that
decomposes the input function into multiple sub-functions with one-dimensional
domain. These sub-functions are then evaluated and used to compute the
instance-based kernel with an axial factorized scheme. We showcase that the
proposed model is able to simulate 2D Kolmogorov flow on a 256 by 256 grid and
3D smoke buoyancy on a 64 by 64 by 64 grid with good accuracy and efficiency.
In addition, we find out that with the factorization scheme, the attention
matrices enjoy a more compact spectrum than full softmax-free attention
matrices
Graph Neural Networks for Molecules
Graph neural networks (GNNs), which are capable of learning representations
from graphical data, are naturally suitable for modeling molecular systems.
This review introduces GNNs and their various applications for small organic
molecules. GNNs rely on message-passing operations, a generic yet powerful
framework, to update node features iteratively. Many researches design GNN
architectures to effectively learn topological information of 2D molecule
graphs as well as geometric information of 3D molecular systems. GNNs have been
implemented in a wide variety of molecular applications, including molecular
property prediction, molecular scoring and docking, molecular optimization and
de novo generation, molecular dynamics simulation, etc. Besides, the review
also summarizes the recent development of self-supervised learning for
molecules with GNNs.Comment: A chapter for the book "Machine Learning in Molecular Sciences". 31
pages, 4 figure
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Graph neural networks (GNNs) have been widely used in various graph-related
problems such as node classification and graph classification, where the
superior performance is mainly established when natural node features are
available. However, it is not well understood how GNNs work without natural
node features, especially regarding the various ways to construct artificial
ones. In this paper, we point out the two types of artificial node
features,i.e., positional and structural node features, and provide insights on
why each of them is more appropriate for certain tasks,i.e., positional node
classification, structural node classification, and graph classification.
Extensive experimental results on 10 benchmark datasets validate our insights,
thus leading to a practical guideline on the choices between different
artificial node features for GNNs on non-attributed graphs. The code is
available at https://github.com/zjzijielu/gnn-exp/.Comment: This paper has been accepted to the Sixth International Workshop on
Deep Learning on Graphs (DLG-KDD'21) (co-located with KDD'21
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