The Structure Transfer Machine Theory and Applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared to state-of-the-art CNN architectures, we achieve the better results on several commonly used benchmarks\footnote{The source code is available. https://github.com/stmstmstm/stm }

Hadron structure at small momentum transfer

Giving three examples, the form factors of the nucleon, the polarisability of the charged pion and the interference of the $S_{11}(1535)$ with the $D_{13}(1520)$ excitation of the nucleon in the $\eta p$-decay channel, it is argued that the hadron structure at low momentum transfer is highly significant for studying QCD.Comment: 7 pages, 9 figures. Contribution to the International School of Nuclear Physics, 29th Ccourse, "Quarks in Hadrons and Nuclei", Erice, Sicily, 16 - 24 September 200

Solvent Induced Proton Hopping at a Water-Oxide Interface

Despite widespread interest, a detailed understanding of the dynamics of proton transfer at interfaces is lacking. Here we use ab initio molecular dynamics to unravel the connection between interfacial water structure and proton transfer for the widely studied and experimentally well-characterized water-ZnO$(10\bar{1}0)$ interface. We find that upon going from a single layer of adsorbed water to a liquid multilayer changes in the structure are accompanied by a dramatic increase in the proton transfer rate at the surface. We show how hydrogen bonding and rather specific hydrogen bond fluctuations at the interface are responsible for the change in the structure and proton transfer dynamics. The implications of this for the chemical reactivity and for the modelling of complex wet oxide interfaces in general are also discussed.Comment: 6 pages, 5 figure

Covariant representations for matrix-valued transfer operators

Motivated by the multivariate wavelet theory, and by the spectral theory of transfer operators, we construct an abstract affine structure and a multiresolution associated to a matrix-valued weight. We describe the one-to-one correspondence between the commutant of this structure and the fixed points of the transfer operator. We show how the covariant representation can be realized on $\mathbb{R}^n$ if the weight satisfies some low-pass condition.Comment: new version, motivation adde

F-structure transfer-based statistical machine translation

In this paper, we describe a statistical deep syntactic transfer decoder that is trained fully automatically on parsed bilingual corpora. Deep syntactic transfer rules are induced automatically from the f-structures of a LFG parsed bitext corpus by automatically aligning local f-structures, and inducing all rules consistent with the node alignment. The transfer decoder outputs the n-best TL f-structures given a SL f-structure as input by applying large numbers of transfer rules and searching for the best output using a log-linear model to combine feature scores. The decoder includes a fully integrated dependency-based tri-gram language model. We include an experimental evaluation of the decoder using different parsing disambiguation resources for the German data to provide a comparison of how the system performs with different German training and test parses

Dimerization-assisted energy transport in light-harvesting complexes

We study the role of the dimer structure of light-harvesting complex II (LH2) in excitation transfer from the LH2 (without a reaction center (RC)) to the LH1 (surrounding the RC), or from the LH2 to another LH2. The excited and un-excited states of a bacteriochlorophyll (BChl) are modeled by a quasi-spin. In the framework of quantum open system theory, we represent the excitation transfer as the total leakage of the LH2 system and then calculate the transfer efficiency and average transfer time. For different initial states with various quantum superposition properties, we study how the dimerization of the B850 BChl ring can enhance the transfer efficiency and shorten the average transfer time.Comment: 11 pages, 6 figure