Chromaticity of Gravitational Microlensing Events

In this paper, we investigate the color changes of gravitational microlensing events caused by the two different mechanisms of differential amplification for a limb-darkened extended source and blending. From this investigation, we find that the color changes of limb-darkened extended source events (color curves) have dramatically different characteristics depending on whether the lens transits the source star or not. We show that for a source transit event, the lens proper motion can be determined by simply measuring the turning time of the color curve instead of fitting the overall color or light curves. We also find that even for a very small fraction of blended light, the color changes induced by the blending effect is equivalent to those caused by the limb-darkening effect, causing serious distortion in the observed color curve. Therefore, to obtain useful information about the lens and source star from the color curve of a limb-darkened extended source event, it will be essential to eliminate or correct for the blending effect. We discuss about the methods for the efficient correction of the blending effect.Comment: total 18 pages, including 5 figures and no table, MNRAS, submitte

Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.Comment: presented at the 32nd Conference on Neural Information Processing Systems (NIPS 2018), 2nd Workshop on Machine Learning on the Phone and other Consumer Devices (MLPCD 2), Montr\'eal, Canad

Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

In machine learning, one of the most popular deep learning methods is the convolutional neural network (CNN), which utilizes shared local filters and hierarchical information processing analogous to the brain’s visual system. Despite its popularity in recognizing two-dimensional (2D) images, the conventional CNN is not directly applicable to semi-regular geometric mesh surfaces, on which the cerebral cortex is often represented. In order to apply the CNN to surface-based brain research, we propose a geometric CNN (gCNN) that deals with data representation on a mesh surface and renders pattern recognition in a multi-shell mesh structure. To make it compatible with the conventional CNN toolbox, the gCNN includes data sampling over the surface, and a data reshaping method for the convolution and pooling layers. We evaluated the performance of the gCNN in sex classification using cortical thickness maps of both hemispheres from the Human Connectome Project (HCP). The classification accuracy of the gCNN was significantly higher than those of a support vector machine (SVM) and a 2D CNN for thickness maps generated by a map projection. The gCNN also demonstrated position invariance of local features, which rendered reuse of its pre-trained model for applications other than that for which the model was trained without significant distortion in the final outcome. The superior performance of the gCNN is attributable to CNN properties stemming from its brain-like architecture, and its surface-based representation of cortical information. The gCNN provides much-needed access to surface-based machine learning, which can be used in both scientific investigations and clinical applications

Strong and Reversible Adhesion of Interlocked 3D-Microarchitectures

Diverse physical interlocking devices have recently been developed based on one-dimensional (1D), high-aspect-ratio inorganic and organic nanomaterials. Although these 1D nanomaterial-based interlocking devices can provide reliable and repeatable shear adhesion, their adhesion in the normal direction is typically very weak. In addition, the high-aspect-ratio, slender structures are mechanically less durable. In this study, we demonstrate a highly flexible and robust interlocking system that exhibits strong and reversible adhesion based on physical interlocking between three-dimensional (3D) microscale architectures. The 3D microstructures have protruding tips on their cylindrical stems, which enable tight mechanical binding between the microstructures. Based on the unique 3D architectures, the interlocking adhesives exhibit remarkable adhesion strengths in both the normal and shear directions. In addition, their adhesion is highly reversible due to the robust mechanical and structural stability of the microstructures. An analytical model is proposed to explain the measured adhesion behavior, which is in good agreement with the experimental results

Functional neural differentiation of human adipose tissue-derived stem cells using bFGF and forskolin

<p>Abstract</p> <p>Background</p> <p>Adult mesenchymal stem cells (MSCs) derived from adipose tissue have the capacity to differentiate into mesenchymal as well as endodermal and ectodermal cell lineage <it>in vitro</it>. We characterized the multipotent ability of human adipose tissue-derived stem cells (hADSCs) as MSCs and investigated the neural differentiation potential of these cells.</p> <p>Results</p> <p>Human ADSCs from earlobe fat maintained self-renewing capacity and differentiated into adipocytes, osteoblasts, or chondrocytes under specific culture conditions. Following neural induction with bFGF and forskolin, hADSCs were differentiated into various types of neural cells including neurons and glia <it>in vitro</it>. In neural differentiated-hADSCs (NI-hADSCs), the immunoreactivities for neural stem cell marker (nestin), neuronal markers (Tuj1, MAP2, NFL, NFM, NFH, NSE, and NeuN), astrocyte marker (GFAP), and oligodendrocyte marker (CNPase) were significantly increased than in the primary hADSCs. RT-PCR analysis demonstrated that the mRNA levels encoding for ABCG2, nestin, Tuj1, MAP2, NFL, NFM, NSE, GAP43, SNAP25, GFAP, and CNPase were also highly increased in NI-hADSCs. Moreover, NI-hADSCs acquired neuron-like functions characterized by the display of voltage-dependent tetrodotoxin (TTX)-sensitive sodium currents, outward potassium currents, and prominent negative resting membrane potentials under whole-cell patch clamp recordings. Further examination by RT-PCR showed that NI-hADSCs expressed high level of ionic channel genes for sodium (SCN5A), potassium (MaxiK, Kv4.2, and EAG2), and calcium channels (CACNA1C and CACNA1G), which were expressed constitutively in the primary hADSCs. In addition, we demonstrated that Kv4.3 and Eag1, potassium channel genes, and NE-Na, a TTX-sensitive sodium channel gene, were highly induced following neural differentiation.</p> <p>Conclusions</p> <p>These combined results indicate that hADSCs have the same self-renewing capacity and multipotency as stem cells, and can be differentiated into functional neurons using bFGF and forskolin.</p

Improvement of retinoids production in recombinant E. coli using glyoxylic acid

Isoprenoids are the most chemically diverse compounds found in nature. They are present in all organisms and have essential roles in membrane structure, redox chemistry, reproductive cycles, growth regulation, signal transduction and defense mechanisms. In spite of their diversity of functions and structures, all isoprenoids are derived from the common building blocks of isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP). Optimization of IPP synthesis pathway is of benefit to mass production of various isoprenoids. There are two pathways of 2-C-Methyl-D-erythritol-4-phosphate (MEP) and mevalonate (MVA) for IPP synthesis. Prokaryotes including E. coli generally use MEP pathway whereas MVA pathway is used in eukaryotes. To improve isoprenoid production, it was performed the deletion of genes in E. coli, which are involved in both formation of fermentation by-products such as organic acids and alcohols, and consumption of precursors of MEP and MVA pathways, pyruvate and acetyl-CoA. As a result, we were able to develop a strain with improved fermentation productivity and carbon source utilization efficiency, the mutant strain was called AceCo. Higher lycopene production was achieved in the AceCo strain compared to the wild type MG1655 strain due to no formation of the inhibitory by-products. However, retinoids production of AceCo strain decreased to a half of that of MG1655 strain. Please click Additional Files below to see the full abstract