655 research outputs found
Modern service industry agglomeration and its influencing factors: spatial interaction in Chinese cities
From the perspective of spatial interaction, the impact of the
modern service industry’s agglomeration on today’s increasingly
connected cities is worth studying. This study uses a spatial
econometric model to test the development trends and factors
influencing the modern service industry’s agglomeration in the
Yangtze River Delta city group. The results show that the industry
has the highest agglomeration in leasing and business and
the lowest in education. The overall concentration of the industry
is generally low, implying a more fragmented distribution.
Moreover, the agglomeration has a significant positive spatial correlation with economic development, knowledge intensity, and
city size. However, it has a negative correlation with information
technology level and transportation infrastructure, inconsistent
with existing research. This study argues that the development of
the information technology level and transportation infrastructure
in a city could lead to the ‘virtual agglomeration’ of the modern
service industry and gradual decentralisation in geographical distribution. This is a new paradox that city groups may face when
improving their infrastructure and developing modern services.
This study uses the spatial interaction perspective to propose policy recommendations for promoting the modern service industry’s
agglomeration and coordinated regional development
A Joint Replication-Migration-based Routing in Delay Tolerant Networks
Abstract—Delay tolerant networks (DTNs) use mobility-assisted routing, where nodes carry, store, and forward data to each other in order to overcome the intermittent connectivity and limited network capacity of this type of network. In this paper, we propose a routing protocol that includes two mechanisms: message replication and message migration. Each mechanism has two steps: message selection and node selection. In message repli-cation, we choose the smallest hop-count message to replicate. The hop-count threshold is used to control the replication speed. We propose a metric called 2-hop activity level to measure the relay node’s transmission capacity, which is used in node selection. Our protocol includes a novel message migration policy that is used to overcome the limited buffer space and bandwidth of DTN nodes. We validate our protocol via extensive simulation experiments; we use a combination of synthetic and real mobility traces. Index Terms—Buffer management, delay tolerant networks (DTNs), message migration, message replication, routing. I
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search
Recent advances have shown the success of using reinforcement learning and
search to solve NP-hard graph-related tasks, such as Traveling Salesman
Optimization, Graph Edit Distance computation, etc. However, it remains unclear
how one can efficiently and accurately detect the occurrences of a small query
graph in a large target graph, which is a core operation in graph database
search, biomedical analysis, social group finding, etc. This task is called
Subgraph Matching which essentially performs subgraph isomorphism check between
a query graph and a large target graph. One promising approach to this
classical problem is the "learning-to-search" paradigm, where a reinforcement
learning (RL) agent is designed with a learned policy to guide a search
algorithm to quickly find the solution without any solved instances for
supervision. However, for the specific task of Subgraph Matching, though the
query graph is usually small given by the user as input, the target graph is
often orders-of-magnitude larger. It poses challenges to the neural network
design and can lead to solution and reward sparsity. In this paper, we propose
NSUBS with two innovations to tackle the challenges: (1) A novel
encoder-decoder neural network architecture to dynamically compute the matching
information between the query and the target graphs at each search state; (2) A
novel look-ahead loss function for training the policy network. Experiments on
six large real-world target graphs show that NSUBS can significantly improve
the subgraph matching performance
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