278 research outputs found
STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph
The key to traffic prediction is to accurately depict the temporal dynamics
of traffic flow traveling in a road network, so it is important to model the
spatial dependence of the road network. The essence of spatial dependence is to
accurately describe how traffic information transmission is affected by other
nodes in the road network, and the GNN-based traffic prediction model, as a
benchmark for traffic prediction, has become the most common method for the
ability to model spatial dependence by transmitting traffic information with
the message passing mechanism. However, existing methods model a local and
static spatial dependence, which cannot transmit the global-dynamic traffic
information (GDTi) required for long-term prediction. The challenge is the
difficulty of detecting the precise transmission of GDTi due to the uncertainty
of individual transport, especially for long-term transmission. In this paper,
we propose a new hypothesis\: GDTi behaves macroscopically as a transmitting
causal relationship (TCR) underlying traffic flow, which remains stable under
dynamic changing traffic flow. We further propose spatial-temporal Granger
causality (STGC) to express TCR, which models global and dynamic spatial
dependence. To model global transmission, we model the causal order and causal
lag of TCRs global transmission by a spatial-temporal alignment algorithm. To
capture dynamic spatial dependence, we approximate the stable TCR underlying
dynamic traffic flow by a Granger causality test. The experimental results on
three backbone models show that using STGC to model the spatial dependence has
better results than the original model for 45 min and 1 h long-term prediction.Comment: 14 pages, 16 figures, 4 table
DeepGAR: Deep Graph Learning for Analogical Reasoning
Analogical reasoning is the process of discovering and mapping
correspondences from a target subject to a base subject. As the most well-known
computational method of analogical reasoning, Structure-Mapping Theory (SMT)
abstracts both target and base subjects into relational graphs and forms the
cognitive process of analogical reasoning by finding a corresponding subgraph
(i.e., correspondence) in the target graph that is aligned with the base graph.
However, incorporating deep learning for SMT is still under-explored due to
several obstacles: 1) the combinatorial complexity of searching for the
correspondence in the target graph; 2) the correspondence mining is restricted
by various cognitive theory-driven constraints. To address both challenges, we
propose a novel framework for Analogical Reasoning (DeepGAR) that identifies
the correspondence between source and target domains by assuring cognitive
theory-driven constraints. Specifically, we design a geometric constraint
embedding space to induce subgraph relation from node embeddings for efficient
subgraph search. Furthermore, we develop novel learning and optimization
strategies that could end-to-end identify correspondences that are strictly
consistent with constraints driven by the cognitive theory. Extensive
experiments are conducted on synthetic and real-world datasets to demonstrate
the effectiveness of the proposed DeepGAR over existing methods.Comment: 22nd IEEE International Conference on Data Mining (ICDM 2022
Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms
Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms
Open-ended Commonsense Reasoning with Unrestricted Answer Scope
Open-ended Commonsense Reasoning is defined as solving a commonsense question
without providing 1) a short list of answer candidates and 2) a pre-defined
answer scope. Conventional ways of formulating the commonsense question into a
question-answering form or utilizing external knowledge to learn
retrieval-based methods are less applicable in the open-ended setting due to an
inherent challenge. Without pre-defining an answer scope or a few candidates,
open-ended commonsense reasoning entails predicting answers by searching over
an extremely large searching space. Moreover, most questions require implicit
multi-hop reasoning, which presents even more challenges to our problem. In
this work, we leverage pre-trained language models to iteratively retrieve
reasoning paths on the external knowledge base, which does not require
task-specific supervision. The reasoning paths can help to identify the most
precise answer to the commonsense question. We conduct experiments on two
commonsense benchmark datasets. Compared to other approaches, our proposed
method achieves better performance both quantitatively and qualitatively.Comment: Findings of EMNLP 202
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