In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling methods, we also visualize the learned pooling masks and the embeddings of the internal feature responses for different pooling operations. Our proposed pooling operations are easy to implement and can be applied within various deep neural network architectures.
In many problem domains data may come from multiple sources (or views), such as video and audio from a camera or text on and links to a web page. These multiple views of the data are often not directly comparable to one another, and thus a principled method for their integration is warranted. In this paper we develop a new algorithm to leverage information from multiple views for unsupervised clustering by constructing a custom kernel. We generate a multipartite graph (with the number of parts given by the number of views) that induces a kernel we then use for spectral clustering. Our algorithm can be seen as a generalization of co-clustering and spectral clustering and a relative of Kernel Canonical Correlation Analysis. We demonstrate the algorithm on four data sets: an illustrative artificial data set, synthetic fMRI data, voxels from an fMRI study, and a collection of web pages. Finally, we compare its performance to common alternatives.
The 9/2+ ground state of "Mo has been found to decay with a half-life of 2.15~0.20 min to the ground state and levels at 658.6, 1155, and 1272 keV in "Nb. Spin and parity assignments are discussed as are the levels of the N = 48 and N = 52 odd Z isotones.
RADIOACTIVITYMo~f rom Mo(p, p3n), measured Q~2, F",I~. Nb deduced levels. Ge detector. Enriched target.
The half-life of the 1/2-isomer in 89Mo has been determined in an experiment where an enriched 92Mo target was irradiated by 350 or 700 ms bursts of 60 MeV protons.Analysis of the gamma-ray spectra collected as a function of time between irradiations revealed gamma rays at 118.8 and 268.5 keV decaying with a 190_+ 15 ms half-life. The hindrance of E 3 transitions in N = 47 and Z = 47 nuclides is discussed.Radioactive Decay: 93Mo(p, p'3n)89mMo; measured E~, Iv, tl/2. 89Mo deduced levels, J, ~. Ge(Li) detector. Enriched targets.
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