We present an algorithm that an agent can use for determining which of its nested, recursive models of other agents are important to consider when choosing an action. Pruning away less important models allows an agent to take its “best” action in a timely manner, given its knowledge, computational capabilities, and time constraints. We describe a theoretical framework, based on situations, for ...

Recently, we have shown that the differential properties of the surfaces represented by 3D volumic images can be recovered using their partial derivatives. For instance, the crest lines can be characterized by the first, second and third partial derivatives of the grey levrl function I(x,y,z) . This paper deals with the following points : the computation of the partial derivatives of an image c...

We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank sentences for multi-document summarization. It formulates the sentence ranking task as a hierarchical regression process, which simultaneously measures the salience of a sentence and its constituents (e.g., phrases) in the parsing tree. This enables us to draw on word-level to sentence-level supervisions derived from r...

We present an algorithm that an agent can use for determining which of its nested, recursive models of other agents are important to consider when choosingan action. Pruning away less important models allows an agent to take its “best” action in a timely manner, given its knowledge, computational capabilities, and time constraints. We describe a theoretical framework, based on situations, for t...

The problem of computing the set prime implicants to represent a Boolean function is classical that still considered running for research because all known approaches have limitations. article reviews existing methods and highlights their limitations, particularly multi-output functions limited scalability due growth in memory required complete computation. Then it proposes recursive ternary-ba...

The Least Mean Squares Blind Adaptive Multi-user Detection algorithm, the Recursive Least Squares Blind Adaptive Multi-user Detection algorithm, and the Kalman Filter Blind Adaptive Multi-user Detection algorithm, for under-ice communication networks with rising and diving interfering users, are investigated in this paper. Under-ice random asynchronous multi-user communication experiments with ...

The efficiency of deep learning-based fault diagnosis methods for bearings is affected by the sample size labeled data, which might be insufficient in engineering field. Self-training a commonly used semi-supervised method, usually limited accuracy features unlabeled data screening. It significant to design an efficient training mechanism extract accurate and novel feature fusion ensure that fu...