Tight bound on coherent-state-based entanglement generation over lossy channels

The first stage of the hybrid quantum repeaters is entanglement generation based on transmission of pulses in coherent states over a lossy channel. Protocols to make entanglement with only one type of error are favorable for rendering subsequent entanglement distillation efficient. Here we provide the tight upper bound on performances of these protocols that is determined only by the channel loss. In addition, we show that this bound is achievable by utilizing a proposed protocol [arXiv:0811.3100] composed of a simple combination of linear optical elements and photon-number-resolving detectors.Comment: 12 pages, 3 figure

Knockout of all ErbB-family genes delineates their roles in proliferation, survival, and migration

The ErbB-family receptors play pivotal roles in the proliferation, migration, and survival of epithelial cells. Because our knowledge on the ErbB-family receptors was obtained largely by the exogenous application of their ligands, it remains unknown to which extent each of the ErbB contributes to these outputs. We here knocked out each ErbB gene, various combinations of ErbB genes, or all in Madin-Darby canine kidney cells to delineate the contribution of each gene. ERK activation waves during collective cell migration were mediated primarily by ErbB1 and secondarily by the ErbB2/ErbB3 heterodimer. Either ErbB1 or the ErbB2/ErbB3 complex was sufficient for the G1/S progression. The saturation cell density was markedly reduced in cells deficient in all ErbB-proteins, but not cells retaining only ErbB2, which cannot bind to ligands. Thus, the ligand-independent ErbB2 activity is sufficient for preventing apoptosis at high cell density. In short, systematic knockout of ErbB-family genes delineated the roles of each ErbB receptor

Performance improvement of automated melanoma diagnosis system by data augmentation

Color information is an important tool for diagnosing melanoma. In this study, we used a hyper-spectral imager (HSI), which can measure color information in detail, to develop an automated melanoma diagnosis system. In recent years, the effectiveness of deep learning has become more widely accepted in the field of image recognition. We therefore integrated the deep convolutional neural network with transfer learning into our system. We tried data augmentation to demonstrate how our system improves diagnostic performance. 283 melanoma lesions and 336 non-melanoma lesions were used for the analysis. The data measured by HSI, called the hyperspectral data (HSD), were converted to a single-wavelength image averaged over plus or minus 3 nm. We used GoogLeNet which was pre-trained by ImageNet and then was transferred to analyze the HSD. In the transfer learning, we used not only the original HSD but also artificial augmentation dataset to improve the melanoma classification performance of GoogLeNet. Since GoogLeNet requires three-channel images as input, three wavelengths were selected from those single-wavelength images and assigned to three channels in wavelength order from short to long. The sensitivity and specificity of our system were estimated by 5-fold cross-val-idation. The results of a combination of 530, 560, and 590 nm (combination A) and 500, 620, and 740 nm (com-bination B) were compared. We also compared the diagnostic performance with and without the data augmentation. All images were augmented by inverting the image vertically and/or horizontally. Without data augmentation, the respective sensitivity and specificity of our system were 77.4% and 75.6% for combination A and 73.1% and 80.6% for combination B. With data augmentation, these numbers improved to 79.9% and 82.4% for combination A and 76.7% and 82.2% for combination B. From these results, we conclude that the diagnostic performance of our system has been improved by data augmentation. Furthermore, our system suc-ceeds to differentiate melanoma with a sensitivity of almost 80%

Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet

Background: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing melanoma. This paper reports the development of such a diagnostic system using hyperspectral data (HSD) and a convolutional neural network, which is a type of machine learning. Materials and Methods: HSD were acquired using a hyperspectral imager, which is a type of spectrometer that can simultaneously capture information about wavelength and position. GoogLeNet pre-trained with Imagenet was used to model the convolutional neural network. As many CNNs (including GoogLeNet) have three input channels, the HSD (involving 84 channels) could not be input directly. For that reason, a “Mini Network” layer was added to reduce the number of channels from 84 to 3 just before the GoogLeNet input layer. In total, 619 lesions (including 278 melanoma lesions and 341 non-melanoma lesions) were used for training and evaluation of the network. Results and Conclusion: The system was evaluated by 5-fold cross-validation, and the results indicate sensitivity, specificity, and accuracy of 69.1%, 75.7%, and 72.7% without data augmentation, 72.3%, 81.2%, and 77.2% with data augmentation, respectively. In future work, it is intended to improve the Mini Network and to increase the number of lesions

Development of a restroom cleaning system for convenience stores

Toilet cleaning is an unhygienic task, and automation of this task is required, particularly in convenience stores. In this study, we propose a toilet cleaning system consisting of a toilet bowl for reducing urine scattering, a toilet bowl top cleaning mechanism, a toilet bowl lifting mechanism, and a toilet floor cleaning robot. The toilet bowl of the proposed system is a combination of the shapes of a typical toilet bowl and a urinal. It effectively reduces splashing when a human urinates in a standing position. At the beginning, the toilet bowl lifting mechanism lifts the toilet bowl to prevent the restroom contamination in advance, and the toilet bowl top cleaning mechanism and floor cleaning robot clean the toilet bowl and floor surface, respectively. The effectiveness of the proposed system was verified in ten experiments using simulated urine and garbage (three pieces of toilet paper, one toilet paper core, and one paper cup). The system was able to collect garbage in all experiments. Each cleaning task required approximately 17 s, and the average removal rate of simulated urine was 97.8%.</p

Side-chain engineering in a thermal precursor approach for efficient photocurrent generation

An ideal active-layer compound for bulk-heterojunction (BHJ) organic photovoltaic devices (OPVs) can assemble upon deposition to form the effective π-π stacking that facilitates exciton diffusion and charge-carrier transport. It is also expected to possess high-enough miscibility for forming sufficient heterojunctions to ensure efficient charge separation. However, these characteristics are often not compatible in organic small-molecule semiconductors: compounds endowed with rich self-π-π interaction capacity tend to be poor in miscibility, or maybe even insoluble in extreme cases. Herein, we postulate that a thermal precursor approach can serve as a way out of this dilemma, provided that molecules are properly engineered. This work evaluates a series of diketopyrrolopyrrole (DPP)-tetrabenzoporphyrin (BP) conjugates named Cn-DPP-BP (n = 4, 6, 8 or 10 depending on the length of alkyl groups on the DPP unit) as a p-type material in BHJ OPVs. These compounds are strongly aggregating and insoluble, thus processed via the thermal precursor approach in which the corresponding soluble derivatives (Cn-DPP-CP) are solution-processed into thin films and then converted to the target materials by in situ thermal reactions. The comparative study shows that the short-circuit current density largely depends on the length of alkyl substituents, ranging from 0.88 mA cm-2 with C10-DPP-BP to 15.2 mA cm-2 with C4-DPP-BP. Investigation into the structure of active layers through fluorescence-decay analysis, atomic-force microscopy, and two-dimensional grazing-incidence wide-angle X-ray diffractometry indicates that the introduction of shorter alkyl chains positively affects the miscibility and molecular orientation in BHJ layers. This trend is not fully parallel to those observed in the BHJ systems prepared through conventional solution techniques, and will provide a unique basis for devising a new class of high-performance OPV materials