Experimental Proof of Quantum Nonlocality without Squeezing

It is shown that the ensemble $\{p (\alpha),|\alpha>|\alpha^*>\}$ where $p (\alpha)$ is a Gaussian distribution of finite variance and $| \alpha>$ is a coherent state can be better discriminated with an entangled measurement than with any local strategy supplemented by classical communication. Although this ensemble consists of products of quasi-classical states, it exhibits some quantum nonlocality. This remarkable effect is demonstrated experimentally by implementing the optimal local strategy together with a joint nonlocal strategy that yields a higher fidelity.Comment: 4 pages, 2 figure

Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion

The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available. Keywords: Functional Connectivity, Cortical Surface, Task Activation, Target Subject, Intrinsic ConnectivityCongressionally Directed Medical Research Programs (U.S.) (Grant PT100120)Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (R01HD067312)Neuroimaging Analysis Center (U.S.) (P41EB015902)Oesterreichische Nationalbank (14812)Oesterreichische Nationalbank (15929)Seventh Framework Programme (European Commission) (FP7 2012-PIEF-GA-33003

A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints

We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression. We use this observation to drive the end-to-end learning of keypoints tailored for the registration task, and without knowledge of ground-truth keypoints. This framework not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the final alignment. Moreover, KeyMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. Finally, we show how multiple deformation fields can be computed efficiently and in closed-form at test time corresponding to different transformation variants. We demonstrate the proposed framework in solving 3D affine and spline-based registration of multi-modal brain MRI scans. In particular, we show registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at https://github.com/alanqrwang/keymorph.Comment: Accepted to Medical Image Analysis 202

Out-of-plane dynamic stability analysis of curved beams subjected to uniformly distributed radial loading

The out-of-plane stability of tapered cross-sectioned thin curved beams under uniformly distributed radial loading is investigated by using the Finite Element Method. Solutions referred to as Bolotin’s approach are investigated for the dynamic stability analysis and the first unstable regions are examined. Out-of-plane vibrations and out-plane buckling analyses are also considered. In addition, the results obtained in this study are compared with the results of other investigators in existing literature for the fundamental frequency and critical lateral buckling load. The effects of subtended angle, variations of cross-section and dynamic load parameter on the stability regions are shown in graphics.За допомогою методу скінчених елементів проаналізовано бокову стійкість викривленої звуженої у кінці тонкої балки при однорідно розподіленому радіальному навантаженні.Розв‘язки у вигляді наближення Болотіна досліджені в рамках аналізу динамічної стійкості. Також вивчені бокові коливання і бокове випучення балки. Отримані результати щодо основної частоти та критичного навантаження при боковому випученні порівняні з іншими опублікованими результатами. На рисунках показано вплив утвореного дугою кута зміни поперечного перерізу та параметру динамічного навантаження на область стійкості

Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Environmental Assisted Quantum Information Correction for Continuous Variables

Quantum information protocols are inevitably affected by decoherence which is associated with the leakage of quantum information into an environment. In this paper we address the possibility of recovering the quantum information from an environmental measurement. We investigate continuous variable quantum information, and we propose a simple environmental measurement that under certain circumstances fully restores the quantum information of the signal state although the state is not reconstructed with unit fidelity. We implement the protocol for which information is encoded into conjugate quadratures of coherent states of light and the noise added under the decoherence process is of Gaussian nature. The correction protocol is tested using both a deterministic as well as a probabilistic strategy. The potential use of the protocol in a continuous variable quantum key distribution scheme as a means to combat excess noise is also investigated.Comment: Submitted to PR

Probing Nanoparticle Interactions in Cell Culture Media

Nanoparticle research is often performed in vitro with little emphasis on the potential role of cell culture medium. In this study, gold nanoparticle interactions with cell culture medium and two cancer cell lines (human T-cell leukemia Jurkat and human pancreatic carcinoma PANC1) were investigated. Gold nanoparticles of 10, 25, 50, and 100 nm in diameter at fixed mass concentration were tested. Size distributions and zeta potentials of gold nanoparticles suspended in deionized (DI) water and Dulbecco\u27s Modified Eagle\u27s Media (DMEM) supplemented with fetal calf serum (FCS) were measured using dynamic light scattering (DLS) technique. In DI water, particle size distributions exhibited peaks around their nominal diameters. However, the gold nanoparticles suspended in DMEM supplemented with FCS formed complexes around 100 nm, regardless of their nominal sizes. The DLS and UV-vis spectroscopy results indicate gold nanoparticle agglomeration in DMEM that is not supplemented by FCS. The zeta potential results indicate that protein rich FCS increases the dispersion quality of gold nanoparticle suspensions through steric effects. Cellular uptake of 25 and 50 nm gold nanoparticles by Jurkat and PANC1 cell lines were investigated using inductively coupled plasma-mass spectroscopy. The intracellular gold level of PANC1 cells was higher than that of Jurkat cells, where 50 nm particles enter cells at faster rates than the 25 nm particles