Graphs are naturally used to describe the structures of various real-world systems in biology, society, computer science etc., where subgraphs or motifs as basic blocks play an important role function expression and information processing. However, existing research focuses on statistics certain motifs, largely ignoring connection patterns among them. Recently, a subgraph network (SGN) model is...

Recent deep network-based compressive sensing (CS) methods have achieved great success. However, most of them regard different sampling matrices as independent tasks and need to train a specific model for each target matrix. Such practices give rise inefficiency in computing suffer from poor generalization ability. In this paper, we propose novel COntrollable Arbitrary-Sampling neTwork, dubbed ...

We introduce a method for extracting meaningful entanglement measures of tensor network states in general dimensions. Current methods require the explicit reconstruction density matrix, which is highly demanding, or contraction replicas, requires an effort exponential number replicas and costly terms memory. In contrast, our stochastic sampling matrix elements classically represented reduced wi...

Artificial intelligence using neural networks has achieved remarkable success. However, optimization procedures of the learning algorithms require global and synchronous operations variables, making it difficult to realize neuromorphic hardware, a promising candidate low-cost energy-efficient artificial intelligence. The also fails explain recently observed criticality brain. Cortical neurons s...

Abstract Reciprocity—the mutual provisioning of support/goods—is a pervasive feature social life. Directed networks provide way to examine the structure reciprocity in community. However, measuring involves assumptions about what relationships matter and how elicit them, which may impact observed reciprocity. In particular, practice aggregating multiple sources data on same relationship (e.g., ...

The sampling method has been paid much attention in the field of complex network in general and statistical physics in particular. This paper presents two new sampling methods based on the perspective that a small part of vertices with high node degree can possess the most structure information of a network. The two proposed sampling methods are efficient in sampling the nodes with high degree....

Accurate traffic characterization by packet source is needed to predict network behavior and to properly allocate network resources to achieve a desired Quality of Service for all network users. As networks have become faster, the processing load required for complete packet sampling has also grown. In some cases, for example Gigabit Ethernet, the network can deliver packets faster than a netwo...