Weakly Supervised Learning of Objects, Attributes and Their Associations

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10605-2_31]”

Multivariate Regression on the Grassmannian for Predicting Novel Domains

This work was supported by EPSRC (EP/L023385/1), and the European Union’s Horizon 2020 research and innovation program under grant agreement No 640891

Effect of superlattice modulation of electronic parameters on the density of states of cuprate superconductors

Recent scanning tunneling microscopy on BSCCO 2212 has revealed a substantial spatial supermodulation of the energy gap in the superconducting state. We propose that this gap modulation is due to the superlattice modulations of the atoms in the structure, and hence the parameters in a microscopic model of the CuO2 plane. The gap modulation is estimated using renormalized mean field theory for a t- t′ -J model on a superlattice. The results compare well with experiment. © 2007 The American Physical Society.published_or_final_versio

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Copyright @ Springer Science + Business Media B.V. 2010.Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant No. 70431003 and Grant No. 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, the National Support Plan of China under Grant No. 2006BAH02A09 and the Ministry of Education, science, and Technology in Korea through the Second-Phase of Brain Korea 21 Project in 2009, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and the Hong Kong Polytechnic University Research Grants under Grant G-YH60

Adaptive targeting in online advertisement: models based on relative influence of factors

We consider the problem of adaptive targeting for real-time bidding for internet advertisement. This problem involves making fast decisions on whether to show a given ad to a particular user. For demand partners, these decisions are based on information extracted from big data sets containing records of previous impressions, clicks and subsequent purchases. We discuss several criteria which allow us to assess the significance of different factors on probabilities of clicks and conversions. We then devise simple strategies that are based on the use of the most influential factors and compare their performance with strategies that are much more computationally demanding. To make the numerical comparison, we use real data collected by Crimtan in the process of running several recent ad campaigns

Actor-Critic Sequence Training for Image Captioning.

Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users about what it is seeing. Such image captioning methods are typically trained by maximising the likelihood of ground-truth annotated caption given the image. While simple and easy to implement, this approach does not directly maximise the language quality metrics we care about such as CIDEr. In this paper we investigate training image captioning methods based on actor-critic reinforcement learning in order to directly optimise non-differentiable quality metrics of interest. By formulating a per-token advantage and value computation strategy in this novel reinforcement learning based captioning model, we show that it is possible to achieve the state of the art performance on the widely used MSCOCO benchmark

Exploration of a potent PI3 kinase/mTOR inhibitor as a novel anti-fibrotic agent in IPF

© 2016 BMJ Publishing Group Ltd & British Thoracic Society.Rationale Idiopathic pulmonary fibrosis (IPF) is the most rapidly progressive and fatal of all fibrotic conditions with no curative therapies. Common pathomechanisms between IPF and cancer are increasingly recognised, including dysfunctional pan-PI3 kinase (PI3K) signalling as a driver of aberrant proliferative responses. GSK2126458 is a novel, potent, PI3K/mammalian target of rapamycin (mTOR) inhibitor which has recently completed phase I trials in the oncology setting. Our aim was to establish a scientific and dosing framework for PI3K inhibition with this agent in IPF at a clinically developable dose. Methods We explored evidence for pathway signalling in IPF lung tissue and examined the potency of GSK2126458 in fibroblast functional assays and precision-cut IPF lung tissue. We further explored the potential of IPF patient-derived bronchoalveolar lavage (BAL) cells to serve as pharmacodynamic biosensors to monitor GSK2126458 target engagement within the lung. Results We provide evidence for PI3K pathway activation in fibrotic foci, the cardinal lesions in IPF. GSK2126458 inhibited PI3K signalling and functional responses in IPF-derived lung fibroblasts, inhibiting Akt phosphorylation in IPF lung tissue and BAL derived cells with comparable potency. Integration of these data with GSK2126458 pharmacokinetic data from clinical trials in cancer enabled modelling of an optimal dosing regimen for patients with IPF. Conclusions Our data define PI3K as a promising therapeutic target in IPF and provide a scientific and dosing framework for progressing GSK2126458 to clinical testing in this disease setting. A proof-ofmechanism trial of this agent is currently underway. Trial registration number NCT01725139, pre-clinical

Direct observation of delithiation as the origin of analog memristance in LixNbO2

The discovery of analog LixNbO2 memristors revealed a promising new memristive mechanism wherein the diffusion of Li+ rather than O2- ions enables precise control of the resistive states. However, directly correlating lithium concentration with changes to the electronic structure in active layers remains a challenge and is required to truly understand the underlying physics. Chemically delithiated single crystals of LiNbO2 present a model system for correlating lithium variation with spectroscopic signatures from operando soft x-ray spectroscopy studies of device active layers. Using electronic structure modeling of the x-ray spectroscopy of LixNbO2 single crystals, we demonstrate that the intrinsic memristive behavior in LixNbO2 active layers results from field-induced degenerate p-type doping. We show that electrical operation of LixNbO2-based memristors is viable even at marginal Li deficiency and that the analog memristive switching occurs well before the system is fully metallic. This study serves as a benchmark for material synthesis and characterization of future LixNbO2-based memristor devices and suggests that valence change switching is a scalable alternative that circumvents the electroforming typically required for filamentary-based memristors

End-to-End Joint Antenna Selection Strategy and Distributed Compress and Forward Strategy for Relay Channels

Multi-hop relay channels use multiple relay stages, each with multiple relay nodes, to facilitate communication between a source and destination. Previously, distributed space-time codes were proposed to maximize the achievable diversity-multiplexing tradeoff, however, they fail to achieve all the points of the optimal diversity-multiplexing tradeoff. In the presence of a low-rate feedback link from the destination to each relay stage and the source, this paper proposes an end-to-end antenna selection (EEAS) strategy as an alternative to distributed space-time codes. The EEAS strategy uses a subset of antennas of each relay stage for transmission of the source signal to the destination with amplify and forwarding at each relay stage. The subsets are chosen such that they maximize the end-to-end mutual information at the destination. The EEAS strategy achieves the corner points of the optimal diversity-multiplexing tradeoff (corresponding to maximum diversity gain and maximum multiplexing gain) and achieves better diversity gain at intermediate values of multiplexing gain, versus the best known distributed space-time coding strategies. A distributed compress and forward (CF) strategy is also proposed to achieve all points of the optimal diversity-multiplexing tradeoff for a two-hop relay channel with multiple relay nodes.Comment: Accepted for publication in the special issue on cooperative communication in the Eurasip Journal on Wireless Communication and Networkin

Forward-backward Asymmetry and Branching Ratio of B \rar K_1 \ell^+ \ell^- Transition in Supersymmetric Models

The mass eigen states $K_1(1270)$ and $K_1(1400)$ are mixture of the strange members of two axial-vector SU(3) octet, $^3P_1(K_1^A)$ and $^1P_1(K_1^B)$. Taking into account this mixture, the forward-backward asymmetry and branching ratio of B \rar K_1(1270,1400) \ell^+ \ell^- transitions are studied in the framework of different supersymmetric models. It is found that the results have considerable deviation from the standard model predictions. Any measurement of these physical observables and their comparison with the results obtained in this paper can give useful information about the nature of interactions beyond the standard model.Comment: 14 pages, 4 figure