A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape

Search for Zs1+Z^{+}_{s1} and Zs2+Z^{+}_{s2} strangeonium-like structures

Theoretically, it has been presumed from an effective Lagrangian calculation that there could exist two charged strangeonium-like molecular states $Z^{+}_{s1}$ and $Z^{+}_{s2}$, with $K\bar{K}^{*}$ and $K^{*}\bar{K}^{*}$ configurations respectively. In the framework of QCD sum rules, we predict that masses of $Z^{+}_{s1}$ ($K\bar{K}^{*}$) and $Z^{+}_{s2}$ ($K^{*}\bar{K}^{*}$) are $1.85\pm0.14 GeV$ and $2.02\pm0.15 GeV$ respectively, which are both above their respective two meson thresholds. We suggest to put in practice the search for these two charged strangeonium-like structures in future experiments.Comment: 7 pages, 4 eps figures; the version accepted for publication in PRD. arXiv admin note: text overlap with arXiv:1203.070

Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015

Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis

We propose a large deformation diffeomorphic metric mapping algorithm to align multiple b-value diffusion weighted imaging (mDWI) data, specifically acquired via hybrid diffusion imaging (HYDI), denoted as LDDMM-HYDI. We then propose a Bayesian model for estimating the white matter atlas from HYDIs. We adopt the work given in Hosseinbor et al. (2012) and represent the q-space diffusion signal with the Bessel Fourier orientation reconstruction (BFOR) signal basis. The BFOR framework provides the representation of mDWI in the q-space and thus reduces memory requirement. In addition, since the BFOR signal basis is orthonormal, the L2 norm that quantifies the differences in the q-space signals of any two mDWI datasets can be easily computed as the sum of the squared differences in the BFOR expansion coefficients. In this work, we show that the reorientation of the $q$-space signal due to spatial transformation can be easily defined on the BFOR signal basis. We incorporate the BFOR signal basis into the LDDMM framework and derive the gradient descent algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally, we extend the previous Bayesian atlas estimation framework for scalar-valued images to HYDIs and derive the expectation-maximization algorithm for solving the HYDI atlas estimation problem. Using real HYDI datasets, we show the Bayesian model generates the white matter atlas with anatomical details. Moreover, we show that it is important to consider the variation of mDWI reorientation due to a small change in diffeomorphic transformation in the LDDMM-HYDI optimization and to incorporate the full information of HYDI for aligning mDWI

Nonlocal magnon-polaron transport in yttrium iron garnet

The spin Seebeck effect (SSE) is observed in magnetic insulator|heavy metal bilayers as an inverse spin Hall effect voltage under a temperature gradient. The SSE can be detected nonlocally as well, viz. in terms of the voltage in a second metallic contact (detector) on the magnetic film, spatially separated from the first contact that is used to apply the temperature bias (injector). Magnon-polarons are hybridized lattice and spin waves in magnetic materials, generated by the magnetoelastic interaction. Kikkawa et al. [Phys. Rev. Lett. \textbf{117}, 207203 (2016)] interpreted a resonant enhancement of the local SSE in yttrium iron garnet (YIG) as a function of the magnetic field in terms of magnon-polaron formation. Here we report the observation of magnon-polarons in \emph{nonlocal} magnon spin injection/detection devices for various injector-detector spacings and sample temperatures. Unexpectedly, we find that the magnon-polaron resonances can suppress rather than enhance the nonlocal SSE. Using finite element modelling we explain our observations as a competition between the SSE and spin diffusion in YIG. These results give unprecedented insights into the magnon-phonon interaction in a key magnetic material.Comment: 5 pages, 6 figure

HETEROGENEITY IN RISK FACTORS FOR COGNITIVE IMPAIRMENT, NO DEMENTIA: Population-Based Longitudinal Study From the Kungsholmen Project.

OBJECTIVES: The objectives of this study were to investigate the relation of vascular, neuropsychiatric, social, and frailty-related factors with "Cognitive impairment, no dementia" (CIND) and to verify their effect independently of future progression to Alzheimer disease (AD). METHODS: Seven hundred eighteen subjects aged 75+ years who attended baseline, 3- and 6-year follow-up examinations of the Kungsholmen Project, a Swedish prospective cohort study, were studied. CIND was defined according to the performance on the Mini-Mental State Examination. Potential risk factors were collected at baseline and clustered according to four research hypotheses (frailty, vascular, neuropsychiatric, and social hypothesis), each representing a possible pathophysiological mechanism of CIND independently of subsequent development of AD. RESULTS: Over a mean 3.4 years of follow up, 82 participants (11.4%) developed CIND. When the population was subsequently followed for a mean of 2.7 years, subjects with CIND had a threefold increased risk to progress to AD. After multiple adjustments, including adjustment for the development of AD at the 6-year follow up, risk factors for CIND were hip fracture, polypharmacy, and psychoses. CONCLUSIONS: The results suggest that not only the AD-type neurodegenerative process, but also neuropsychiatric- and frailty-related factors may induce cognitive impairment in nondemented elderly. These findings may have relevant preventive and therapeutic implications