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 }

Goal-Driven Context-aware Service Composition

Two important aspects are associated with service composition. One is to understand the needs and constraints for a new added-value composite service, and otherwise it would lead to an ad-hoc effort for service composition. The second is to reflect the changes of computing environment to the service composition to catch up the on-demand of users. This paper introduces a goal-driven approach to specify the user requirements and demands that guides the service composition, and proposes context awareness to adapt to a dynamically changing environment. Computing contexts, including physical context, user profile and computed results, are gathered by various services, and imported into an ontology based a context repository. A Goal Description Language, Context Condition/Effect are designed to describe the dynamic semantics of goal requirements and service capability. A planner is designed and implemented to dynamically compose services based on the current contexts, and a service runner is designed and implemented to invoke proper services based on the contexts and interactions with users. ?2010 IEEE.EI

Building a Context World for Dynamic Service Composition

Dynamic service composition requires responding and adapting to changes in the computing environment when orchestrating existing services into one or more new services that fit better to a composite application. This paper abstracts the changes of the environment as a context world to store the physical contexts of the computing environment, user profiles and computed results of services as well. We use ontology techniques to model the domain concepts of application contexts. Context Condition/Effect Description Language is designed to describe the dynamic semantics of the requirements and capabilities of goals and services in a concise and editable manner. Goal-driven and planning techniques are used to dynamically implement the service composition according to the domain knowledge and facts in the context world. ?2010 IEEE.EI

Noise suppression of on-chip mechanical resonators by chaotic coherent feedback

We propose a method to decouple the nanomechanical resonator in optomechanical systems from the environmental noise by introducing a chaotic coherent feedback loop. We find that the chaotic controller in the feedback loop can modulate the dynamics of the controlled optomechanical system and induce a broadband response of the mechanical mode. This broadband response of the mechanical mode will cut off the coupling between the mechanical mode and the environment and thus suppress the environmental noise of the mechanical modes. As an application, we use the protected optomechanical system to act as a quantum memory. It's shown that the noise-decoupled optomechanical quantum memory is efficient for storing information transferred from coherent or squeezed light

Band dependence of charge density wave in quasi-one-dimensional Ta2NiSe7 probed by orbital magnetoresistance

Ta2NiSe7 is a quasi-one-dimensional (quasi-1D) transition-metal chalcogenide with Ta and Ni chain structure. An incommensurate charge-density wave (CDW) in this quasi-1D structure was well studied previously using tunnelling spectrum, X-ray and electron diffraction, whereas its transport property and the relation to the underlying electronic states remain to be explored. Here we report our results of magnetoresistance (MR) on Ta2NiSe7. A breakdown of the Kohler's rule is found upon entering the CDW state. Concomitantly, a clear change of curvature in the field dependence of MR is observed. We show that the curvature change is well described by two-band orbital MR, with the hole density being strongly suppressed in the CDW state, indicating that the $p$ orbitals from Se atoms dominate the change in transport through the CDW transition

The structure transfer machine theory and applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. In this paper, we propose a new representation learning method, named 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 with state-of-the-art CNN architectures, we achieve better results on several commonly used public benchmarks

A Common Variant in CLDN14 is Associated with Primary Biliary Cirrhosis and Bone Mineral Density.

Primary biliary cirrhosis (PBC), a chronic autoimmune liver disease, has been associated with increased incidence of osteoporosis. Intriguingly, two PBC susceptibility loci identified through genome-wide association studies are also involved in bone mineral density (BMD). These observations led us to investigate the genetic variants shared between PBC and BMD. We evaluated 72 genome-wide significant BMD SNPs for association with PBC using two European GWAS data sets (n = 8392), with replication of significant findings in a Chinese cohort (685 cases, 1152 controls). Our analysis identified a novel variant in the intron of the CLDN14 gene (rs170183, Pfdr = 0.015) after multiple testing correction. The three associated variants were followed-up in the Chinese cohort; one SNP rs170183 demonstrated consistent evidence of association in diverse ethnic populations (Pcombined = 2.43 × 10(-5)). Notably, expression quantitative trait loci (eQTL) data revealed that rs170183 was correlated with a decline in CLDN14 expression in both lymphoblastoid cell lines and T cells (Padj = 0.003 and 0.016, respectively). In conclusion, our study identified a novel PBC susceptibility variant that has been shown to be strongly associated with BMD, highlighting the potential of pleiotropy to improve gene discovery

Auger-assisted electron transfer from photoexcited semiconductor quantum dots

Although quantum confined nanomaterials, such as quantum dots (QDs) have emerged as a new class of light harvesting and charge separation materials for solar energy conversion, theoretical models for describing photoinduced charge transfer from these materials remain unclear. In this paper, we show that the rate of photoinduced electron transfer from QDs (CdS, CdSe, and CdTe) to molecular acceptors (anthraquinone, methylviologen, and methylene blue) increases at decreasing QD size (and increasing driving force), showing a lack of Marcus inverted regime behavior over an apparent driving force range of ∼0-1.3 V. We account for this unusual driving force dependence by proposing an Auger-assisted electron transfer model in which the transfer of the electron can be coupled to the excitation of the hole, circumventing the unfavorable Franck-Condon overlap in the Marcus inverted regime. This model is supported by computational studies of electron transfer and trapping processes in model QD-acceptor complexes