Showing papers by "Waseda University published in 2017"

Globally and locally consistent image completion

Abstract: We present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitrary resolutions by filling-in missing regions of any shape. To train this image completion network to be consistent, we use global and local context discriminators that are trained to distinguish real images from completed ones. The global discriminator looks at the entire image to assess if it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. We show that our approach can be used to complete a wide variety of scenes. Furthermore, in contrast with the patch-based approaches such as PatchMatch, our approach can generate fragments that do not appear elsewhere in the image, which allows us to naturally complete the images of objects with familiar and highly specific structures, such as faces.

Nanoarchitectured Design of Porous Materials and Nanocomposites from Metal-Organic Frameworks.

Abstract: The emergence of metal-organic frameworks (MOFs) as a new class of crystalline porous materials is attracting considerable attention in many fields such as catalysis, energy storage and conversion, sensors, and environmental remediation due to their controllable composition, structure and pore size. MOFs are versatile precursors for the preparation of various forms of nanomaterials as well as new multifunctional nanocomposites/hybrids, which exhibit superior functional properties compared to the individual components assembling the composites. This review provides an overview of recent developments achieved in the fabrication of porous MOF-derived nanostructures including carbons, metal oxides, metal chalcogenides (metal sulfides and selenides), metal carbides, metal phosphides and their composites. Finally, the challenges and future trends and prospects associated with the development of MOF-derived nanomaterials are also examined.

Towards standards for human fecal sample processing in metagenomic studies

Abstract: Technical variation in metagenomic analysis must be minimized to confidently assess the contributions of microbiota to human health. Here we tested 21 representative DNA extraction protocols on the same fecal samples and quantified differences in observed microbial community composition. We compared them with differences due to library preparation and sample storage, which we contrasted with observed biological variation within the same specimen or within an individual over time. We found that DNA extraction had the largest effect on the outcome of metagenomic analysis. To rank DNA extraction protocols, we considered resulting DNA quantity and quality, and we ascertained biases in estimates of community diversity and the ratio between Gram-positive and Gram-negative bacteria. We recommend a standardized DNA extraction method for human fecal samples, for which transferability across labs was established and which was further benchmarked using a mock community of known composition. Its adoption will improve comparability of human gut microbiome studies and facilitate meta-analyses.

Ectopic colonization of oral bacteria in the intestine drives TH1 cell induction and inflammation

Abstract: Intestinal colonization by bacteria of oral origin has been correlated with several negative health outcomes, including inflammatory bowel disease. However, a causal role of oral bacteria ectopically colonizing the intestine remains unclear. Using gnotobiotic techniques, we show that strains of Klebsiella spp. isolated from the salivary microbiota are strong inducers of T helper 1 (TH1) cells when they colonize in the gut. These Klebsiella strains are resistant to multiple antibiotics, tend to colonize when the intestinal microbiota is dysbiotic, and elicit a severe gut inflammation in the context of a genetically susceptible host. Our findings suggest that the oral cavity may serve as a reservoir for potential intestinal pathobionts that can exacerbate intestinal disease.

Performance of the ATLAS trigger system in 2015

Abstract: During 2015 the ATLAS experiment recorded 3.8 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV. The ATLAS trigger system is a crucial component of the experiment, respons ...

One-Pot Synthesis of Zeolitic Imidazolate Framework 67-Derived Hollow Co3S4@MoS2 Heterostructures as Efficient Bifunctional Catalysts

Abstract: Herein, we present a facile metal–organic framework-engaged strategy to synthesize hollow Co3S4@MoS2 heterostructures as efficient bifunctional catalysts for both H2 and O2 generation. The well-known cobalt-based metal–organic zeolitic imidazolate frameworks (ZIF-67) are used not only as the morphological template but also as the cobalt precursor. During the two-step temperature-raising hydrothermal process, ZIF-67 polyhedrons are first transformed to hollow cobalt sulfide polyhedrons by sulfidation, and then molybdenum disulfide nanosheets further grow and deposit on the surface of hollow cobalt sulfide polyhedrons at the increased temperature. The crystalline hollow Co3S4@MoS2 heterostructures are finally obtained after subsequent thermal annealing under a N2 atmosphere. Due to the synergistic effects between the hydrogen evolution reaction active catalyst of MoS2 and the oxygen evolution reaction active catalyst of Co3S4, the obtained hollow Co3S4@MoS2 heterostructures exhibit outstanding bifunctional ...

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

Abstract: The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Cross-coupling of aromatic esters and amides

Abstract: Catalytic cross-coupling reactions of aromatic esters and amides have recently gained considerable attention from synthetic chemists as de novo and efficient synthetic methods to form C–C and C–heteroatom bonds. Esters and amides can be used as diversifiable groups in metal-catalyzed cross-coupling: in a decarbonylative manner, they can be utilized as leaving groups, whereas in a non-decarbonylative manner, they can form ketone derivatives. In this review, recent advances of this research topic are discussed.

Search for new high-mass phenomena in the dilepton final state using 36 fb−1 of proton-proton collision data at √s=13 TeV with the ATLAS detector

Abstract: A search is conducted for new resonant and non-resonant high-mass phenomena in dielectron and dimuon fi nal states. The search uses 36 : 1 fb(-1) of proton-proton collision data, collected at root ...

Hierarchical porous carbons with layer-by-layer motif architectures from confined soft-template self-assembly in layered materials.

Abstract: Although various two-dimensional (2D) nanomaterials have been explored as promising capacitive materials due to their unique layered structure, their natural restacking tendency impedes electrolyte transport and significantly restricts their practical applications. Herein, we synthesize all-carbon layer-by-layer motif architectures by introducing 2D ordered mesoporous carbons (OMC) within the interlayer space of 2D nanomaterials. As a proof of concept, MXenes are selected as 2D hosts to design 2D-2D heterostructures. Further removing the metal elements from MXenes leads to the formation of all-carbon 2D-2D heterostructures consisting of alternating layers of MXene-derived carbon (MDC) and OMC. The OMC layers intercalated with the MDC layers not only prevent restacking but also facilitate ion diffusion and electron transfer. The performance of the obtained hybrid carbons as supercapacitor electrodes demonstrates their potential for upcoming electronic devices. This method allows to overcome the restacking and blocking of 2D nanomaterials by constructing ion-accessible OMC within the 2D host material.

Search for new phenomena in dijet events using 37 fb-1 of pp collision data collected at √s=13 TeV with the ATLAS detector

Abstract: Dijet events are studied in the proton-proton collision dataset recorded at root s = 13 TeV with the ATLAS detector at the Large Hadron Collider in 2015 and 2016, corresponding to integrated lumino ...

Theoretical Analysis of Interactions between Potassium Ions and Organic Electrolyte Solvents: A Comparison with Lithium, Sodium, and Magnesium Ions

Abstract: Ion–solvent interactions play a crucial role in secondary battery systems: the desolvation of ions at an electrode/electrolyte interface can be the rate-determining step of a battery reaction, for instance. The present theoretical study investigates the interactions between K ions and organic electrolyte solvents for application in non-aqueous K-ion batteries, which have recently drawn interest as novel rechargeable batteries. Compared to Li, Na, and Mg ions, K ions display the lowest interaction energy, reflecting the large ionic radius and weak Lewis acidity of K. The weak interaction of K ions with solvents is consistent with the high rate capability exhibited by K-ion batteries and the relatively low solubility of K-ion salts observed experimentally.

Self-polymerized dopamine as an organic cathode for Li- and Na-ion batteries

Abstract: Self-polymerized dopamine is a versatile coating material that has various oxygen and nitrogen functional groups. Here, we demonstrate the redox-active properties of self-polymerized dopamine on the surface of few-walled carbon nanotubes (FWNTs), which can be used as organic cathode materials for both Li- and Na-ion batteries. We reveal the multiple redox reactions between self-polymerized dopamine and electrolyte ions in the high voltage region from 2.5 to 4.1 V vs. Li using both density functional theory (DFT) calculations and electrochemical measurements. Free-standing and flexible hybrid electrodes are assembled using a vacuum filtration method, which have a 3D porous network structure consisting of polydopamine coated FWNTs. The hybrid electrodes exhibit gravimetric capacities of ∼133 mA h g−1 in Li-cells and ∼109 mA h g−1 in Na-cells utilizing double layer capacitance from FWNTs and multiple redox-reactions from polydopamine. The polydopamine itself within the hybrid film can store high gravimetric capacities of ∼235 mA h g−1 in Li-cells and ∼213 mA h g−1 in Na-cells. In addition, the hybrid electrodes show a high rate-performance and excellent cycling stability, suggesting that self-polymerized dopamine is a promising cathode material for organic rechargeable batteries.

Mesoporous metallic rhodium nanoparticles

Abstract: Mesoporous noble metals are an emerging class of cutting-edge nanostructured catalysts due to their abundant exposed active sites and highly accessible surfaces Although various noble metal (eg Pt, Pd and Au) structures have been synthesized by hard- and soft-templating methods, mesoporous rhodium (Rh) nanoparticles have never been generated via chemical reduction, in part due to the relatively high surface energy of rhodium (Rh) metal Here we describe a simple, scalable route to generate mesoporous Rh by chemical reduction on polymeric micelle templates [poly(ethylene oxide)-b-poly(methyl methacrylate) (PEO-b-PMMA)] The mesoporous Rh nanoparticles exhibited a ∼26 times enhancement for the electrocatalytic oxidation of methanol compared to commercially available Rh catalyst Surprisingly, the high surface area mesoporous structure of the Rh catalyst was thermally stable up to 400 °C The combination of high surface area and thermal stability also enables superior catalytic activity for the remediation of nitric oxide (NO) in lean-burn exhaust containing high concentrations of O2 Mesoporous noble metal nanostructures offer great promise in catalytic applications Here, Yamauchi and co-workers synthesize mesoporous rhodium nanoparticles using polymeric micelle templates, and report appreciable activities for methanol oxidation and NO remediation

The status of DECIGO

Abstract: DECIGO (DECi-hertz Interferometer Gravitational wave Observatory) is the planned Japanese space gravitational wave antenna, aiming to detect gravitational waves from astrophysically and cosmologically significant sources mainly between 01 Hz and 10 Hz and thus to open a new window for gravitational wave astronomy and for the universe DECIGO will consists of three drag-free spacecraft arranged in an equilateral triangle with 1000 km arm lengths whose relative displacements are measured by a differential Fabry-Perot interferometer, and four units of triangular Fabry-Perot interferometers are arranged on heliocentric orbit around the sun DECIGO is vary ambitious mission, we plan to launch DECIGO in era of 2030s after precursor satellite mission, B-DECIGO B-DECIGO is essentially smaller version of DECIGO: B-DECIGO consists of three spacecraft arranged in an triangle with 100 km arm lengths orbiting 2000 km above the surface of the earth It is hoped that the launch date will be late 2020s for the present

Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning

Abstract: We propose a practical state-of-the-art method to develop a machine-learning-based humanoid robot that can work as a production line worker. The proposed approach provides an intuitive way to collect data and exhibits the following characteristics: task performing capability, task reiteration ability, generalizability, and easy applicability. The proposed approach utilizes a real-time user interface with a monitor and provides a first-person perspective using a head-mounted display. Through this interface, teleoperation is used for collecting task operating data, especially for tasks that are difficult to be applied with a conventional method. A two-phase deep learning model is also utilized in the proposed approach. A deep convolutional autoencoder extracts images features and reconstructs images, and a fully connected deep time delay neural network learns the dynamics of a robot task process from the extracted image features and motion angle signals. The “Nextage Open” humanoid robot is used as an experimental platform to evaluate the proposed model. The object folding task utilizing with 35 trained and 5 untrained sensory motor sequences for test. Testing the trained model with online generation demonstrates a 77.8% success rate for the object folding task.

Bifidobacterium-Rich Fecal Donor May Be a Positive Predictor for Successful Fecal Microbiota Transplantation in Patients with Irritable Bowel Syndrome.

Abstract: Background/Aims: Dysbiosis is associated with various systemic disorders including irritable bowel syndrome (IBS). Fecal microbiota transplantation (FMT) might re

Dysbiosis and compositional alterations with aging in the gut microbiota of patients with heart failure

Abstract: Emerging evidence has suggested a potential impact of gut microbiota on the pathophysiology of heart failure (HF). However, it is still unknown whether HF is associated with dysbiosis in gut microbiota. We investigated the composition of gut microbiota in patients with HF to elucidate whether gut microbial dysbiosis is associated with HF. We performed 16S ribosomal RNA gene sequencing of fecal samples obtained from 12 HF patients and 12 age-matched healthy control (HC) subjects, and analyzed the differences in gut microbiota. We further compared the composition of gut microbiota of 12 HF patients younger than 60 years of age with that of 10 HF patients 60 years of age or older. The composition of gut microbial communities of HF patients was distinct from that of HC subjects in both unweighted and weighted UniFrac analyses. Eubacterium rectale and Dorea longicatena were less abundant in the gut microbiota of HF patients than in that of HC subjects. Compared to younger HF patients, older HF patients had diminished proportions of Bacteroidetes and larger quantities of Proteobacteria. The genus Faecalibacterium was depleted, while Lactobacillus was enriched in the gut microbiota of older HF patients. These results suggest that patients with HF harbor significantly altered gut microbiota, which varies further according to age. New concept of heart-gut axis has a great potential for breakthroughs in the development of novel diagnostic and therapeutic approach for HF.

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

Abstract: Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

Design, management and control of demanufacturing and remanufacturing systems

Abstract: In the recent years, increasing attention has been posed towards enhancing the sustainability of manufacturing processes by reducing the consumption of resources and key materials, the energy consumption and the environmental footprint, while also increasing companies’ competitiveness in global market contexts. De- and remanufacturing includes the set of technologies/systems, tools and knowledge-based methods to recover and reuse functions and materials from industrial waste and post-consumer products, under a Circular Economy perspective. This new paradigm can potentially support the sustainability challenges in strategic manufacturing sectors, such as aeronautics, automotive, electronics, consumer goods, and mechatronics. A new generation of smart de- and remanufacturing systems showing higher levels of automation, flexibility and adaptability to changing material mixtures and values is emerging and there is a need for systematizing the existing approaches to support their operations. Such innovative de- and remanufacturing system design, management and control approaches as well as advanced technological enablers have a key role to support the Circular Economy paradigm. This paper revises system level problems, methods and tools to support this paradigm and highlights the main challenges and opportunities towards a new generation of advanced de- and remanufacturing systems.

Assembly of hollow mesoporous nanoarchitectures composed of ultrafine Mo2C nanoparticles on N-doped carbon nanosheets for efficient electrocatalytic reduction of oxygen

Abstract: A simple method is developed to assemble Mo2C nanocrystals on the surfaces of hollow, highly conductive mesoporous nanoparticles. Diblock copolymer (PS-b-PEO) micelles are used as templates to assist in the fast complexation of molybdate (MoO42−) and polydopamine (PDA) precursors to make hollow precursor MoO42−/PDA/PS-b-PEO particles. Then these particles are carbonized to generate mesoporous N-doped carbon nanosheets riddled with ultrafine molybdenum carbide (Mo2C) nanoparticles (MMo2C/NCS). An N-doped carbon matrix serves as an electron conductor and helps to prevent the aggregation of the Mo2C nanoparticles. The Mo2C nanoparticles in turn enhance the catalytic performance for the oxygen reduction reaction (ORR). The unique mesoporous 2D nanosheet and its derived 3D hollow structure expose numerous active catalytic sites while enabling free diffusion of the electrolyte and mass transfer. Based on these properties, MMo2C/NCS show enhanced catalytic activity for the ORR.

Highly Efficient and Stable Perovskite Solar Cells by Interfacial Engineering Using Solution-Processed Polymer Layer

Abstract: Solution-processed organo-lead halide perovskite solar cells with deep pinholes in the perovskite layer lead to shunt-current leakage in devices. Herein, we report a facile method for improving the performance of perovskite solar cells by inserting a solution-processed polymer layer between the perovskite layer and the hole-transporting layer. The photovoltaic conversion efficiency of the perovskite solar cell increased to 18.1% and the stability decreased by only about 5% during 20 days of exposure in moisture ambient conditions through the incorporation of a poly(methyl methacrylate) (PMMA) polymer layer. The improved photovoltaic performance of devices with a PMMA layer is attributed to the reduction of carrier recombination loss from pinholes, boundaries, and surface states of perovskite layer. The significant gain generated by this simple procedure supports the use of this strategy in further applications of thin-film optoelectronic devices.

Sedentary time in older adults: a critical review of measurement, associations with health, and interventions

Abstract: Sedentary time (ST) is an important risk factor for a variety of health outcomes in older adults. Consensus is needed on future research directions so that collaborative and timely efforts can be made globally to address this modifiable risk factor. In this review, we examined current literature to identify gaps and inform future research priorities on ST and healthy ageing. We reviewed three primary topics:(1) the validity/reliability of self-report measurement tools, (2) the consequences of prolonged ST on geriatric-relevant health outcomes (physical function, cognitive function, mental health, incontinence and quality of life) and(3) the effectiveness of interventions to reduce ST in older adults. Methods A trained librarian created a search strategy that was peer reviewed for completeness. Results Self-report assessment of the context and type of ST is important but the tools tend to underestimate total ST. There appears to be an association between ST and geriatric-relevant health outcomes, although there is insufficient longitudinal evidence to determine a dose-response relationship or a threshold for clinically relevant risk. The type of ST may also affect health; some cognitively engaging sedentary behaviours appear to benefit health, while time spent in more passive activities may be detrimental. Short-term feasibility studies of individual-level ST interventions have been conducted; however, few studies have appropriately assessed the impact of these interventions on geriatric-relevant health outcomes, nor have they addressed organisation or environment level changes. Research is specifically needed to inform evidence-based interventions that help maintain functional autonomy among older adults. This consensus statement has been endorsed by the following societies: Academy of Geriatric Physical Therapy, Exercise & Sports Science Australia, Canadian Centre for Activity and Aging, Society of Behavioral Medicine, and the National Centre for Sport and Exercise Medicine.

Software Defined Machine-to-Machine Communication for Smart Energy Management

Abstract: The successful realization of smart energy management relies on ubiquitous and reliable information exchange among millions of sensors and actuators deployed in the field with little or no human intervention. This motivates us to propose a unified communication framework for smart energy management by exploring the integration of software-defined networking with machine-to-machine communication. In this article, first we provide a comprehensive review of the state-of-the-art contributions from the perspective of software defined networking and machineto- machine integration. Second, the overall design of the proposed software-defined machine-to-machine (SD-M2M) framework is presented, with an emphasis on its technical contributions to cost reduction, fine granularity resource allocation, and end-to-end quality of service guarantee. Then a case study is conducted for an electric vehicle energy management system to validate the proposed SD-M2M framework. Finally, we identify several open issues and present key research opportunities.