Showing papers by "National Institute of Advanced Industrial Science and Technology published in 2018"

Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet

Abstract: The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs in the field of action recognition have improved significantly. However, to date, conventional research has only explored relatively shallow 3D architectures. We examine the architectures of various 3D CNNs from relatively shallow to very deep ones on current video datasets. Based on the results of those experiments, the following conclusions could be obtained: (i) ResNet-18 training resulted in significant overfitting for UCF-101, HMDB-51, and ActivityNet but not for Kinetics. (ii) The Kinetics dataset has sufficient data for training of deep 3D CNNs, and enables training of up to 152 ResNets layers, interestingly similar to 2D ResNets on ImageNet. ResNeXt-101 achieved 78.4% average accuracy on the Kinetics test set. (iii) Kinetics pretrained simple 3D architectures outperforms complex 2D architectures, and the pretrained ResNeXt-101 achieved 94.5% and 70.2% on UCF-101 and HMDB-51, respectively. The use of 2D CNNs trained on ImageNet has produced significant progress in various tasks in image. We believe that using deep 3D CNNs together with Kinetics will retrace the successful history of 2D CNNs and ImageNet, and stimulate advances in computer vision for videos. The codes and pretrained models used in this study are publicly available1.

A library of atomically thin metal chalcogenides

Abstract: Investigations of two-dimensional transition-metal chalcogenides (TMCs) have recently revealed interesting physical phenomena, including the quantum spin Hall effect1,2, valley polarization3,4 and two-dimensional superconductivity 5 , suggesting potential applications for functional devices6–10. However, of the numerous compounds available, only a handful, such as Mo- and W-based TMCs, have been synthesized, typically via sulfurization11–15, selenization16,17 and tellurization 18 of metals and metal compounds. Many TMCs are difficult to produce because of the high melting points of their metal and metal oxide precursors. Molten-salt-assisted methods have been used to produce ceramic powders at relatively low temperature 19 and this approach 20 was recently employed to facilitate the growth of monolayer WS2 and WSe2. Here we demonstrate that molten-salt-assisted chemical vapour deposition can be broadly applied for the synthesis of a wide variety of two-dimensional (atomically thin) TMCs. We synthesized 47 compounds, including 32 binary compounds (based on the transition metals Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re, Pt, Pd and Fe), 13 alloys (including 11 ternary, one quaternary and one quinary), and two heterostructured compounds. We elaborate how the salt decreases the melting point of the reactants and facilitates the formation of intermediate products, increasing the overall reaction rate. Most of the synthesized materials in our library are useful, as supported by evidence of superconductivity in our monolayer NbSe2 and MoTe2 samples21,22 and of high mobilities in MoS2 and ReS2. Although the quality of some of the materials still requires development, our work opens up opportunities for studying the properties and potential application of a wide variety of two-dimensional TMCs.

Metal-Organic Frameworks as Platforms for Catalytic Applications.

Abstract: Metal-organic frameworks (MOFs), also called porous coordination polymers, represent a class of crystalline porous materials built from organic linkers and metal ions/clusters. The unique features of MOFs, including structural diversity and tailorability as well as high surface area, etc., enable them to be a highly versatile platform for potential applications in many fields. Herein, an overview of recent developments achieved in MOF catalysis, including heterogeneous catalysis, photocatalysis, and eletrocatalysis over MOFs and MOF-based materials, is provided. The active sites involved in the catalysts are particularly emphasized. The challenges, future trends, and prospects associated with MOFs and their related materials for catalysis are also discussed.

Nanomaterials derived from metal–organic frameworks

Abstract: The thermal transformation of metal–organic frameworks (MOFs) generates a variety of nanostructured materials, including carbon-based materials, metal oxides, metal chalcogenides, metal phosphides and metal carbides. These derivatives of MOFs have characteristics such as high surface areas, permanent porosities and controllable functionalities that enable their good performance in sensing, gas storage, catalysis and energy-related applications. Although progress has been made to tune the morphologies of MOF-derived structures at the nanometre scale, it remains crucial to further our knowledge of the relationship between morphology and performance. In this Review, we summarize the synthetic strategies and optimized methods that enable control over the size, morphology, composition and structure of the derived nanomaterials. In addition, we compare the performance of materials prepared by the MOF-templated strategy and other synthetic methods. Our aim is to reveal the relationship between the morphology and the physico-chemical properties of MOF-derived nanostructures to optimize their performance for applications such as sensing, catalysis, and energy storage and conversion. Nanomaterials derived from metal–organic frameworks (MOFs) show good performance in sensing, gas storage, catalysis and energy-related applications. In this Review, the influence of the morphology of MOF-derived nanostructures on their performance is elucidated, and the opportunities in this field are discussed.

Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA

Abstract: We present possible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron star systems, which are the most promising targets for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and 90% credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5– 20 deg2 requires at least three detectors of sensitivity within a factor of ∼2 of each other and with a broad frequency bandwidth. When all detectors, including KAGRA and the third LIGO detector in India, reach design sensitivity, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

The 2018 GaN power electronics roadmap

Abstract: Gallium nitride (GaN) is a compound semiconductor that has tremendous potential to facilitate economic growth in a semiconductor industry that is silicon-based and currently faced with diminishing returns of performance versus cost of investment. At a material level, its high electric field strength and electron mobility have already shown tremendous potential for high frequency communications and photonic applications. Advances in growth on commercially viable large area substrates are now at the point where power conversion applications of GaN are at the cusp of commercialisation. The future for building on the work described here in ways driven by specific challenges emerging from entirely new markets and applications is very exciting. This collection of GaN technology developments is therefore not itself a road map but a valuable collection of global state-of-the-art GaN research that will inform the next phase of the technology as market driven requirements evolve. First generation production devices are igniting large new markets and applications that can only be achieved using the advantages of higher speed, low specific resistivity and low saturation switching transistors. Major investments are being made by industrial companies in a wide variety of markets exploring the use of the technology in new circuit topologies, packaging solutions and system architectures that are required to achieve and optimise the system advantages offered by GaN transistors. It is this momentum that will drive priorities for the next stages of device research gathered here.

HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis

Abstract: We present a major update of the HOCOMOCO collection that consists of patterns describing DNA binding specificities for human and mouse transcription factors. In this release, we profited from a nearly doubled volume of published in vivo experiments on transcription factor (TF) binding to expand the repertoire of binding models, replace low-quality models previously based on in vitro data only and cover more than a hundred TFs with previously unknown binding specificities. This was achieved by systematic motif discovery from more than five thousand ChIP-Seq experiments uniformly processed within the BioUML framework with several ChIP-Seq peak calling tools and aggregated in the GTRD database. HOCOMOCO v11 contains binding models for 453 mouse and 680 human transcription factors and includes 1302 mononucleotide and 576 dinucleotide position weight matrices, which describe primary binding preferences of each transcription factor and reliable alternative binding specificities. An interactive interface and bulk downloads are available on the web: http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco11. In this release, we complement HOCOMOCO by MoLoTool (Motif Location Toolbox, http://molotool.autosome.ru) that applies HOCOMOCO models for visualization of binding sites in short DNA sequences.

Parallelization of MAFFT for large-scale multiple sequence alignments.

Abstract: Summary We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences. Availability and implementation This feature is available in MAFFT versions 7.355 or later at https://mafft.cbrc.jp/alignment/software/mpi.html. Supplementary information Supplementary data are available at Bioinformatics online.

Pristine Metal–Organic Frameworks and their Composites for Energy Storage and Conversion

Abstract: Metal-organic frameworks (MOFs), a new class of crystalline porous organic-inorganic hybrid materials, have recently attracted increasing interest in the field of energy storage and conversion. Herein, recent progress of MOFs and MOF composites for energy storage and conversion applications, including photochemical and electrochemical fuel production (hydrogen production and CO2 reduction), water oxidation, supercapacitors, and Li-based batteries (Li-ion, Li-S, and Li-O2 batteries), is summarized. Typical development strategies (e.g., incorporation of active components, design of smart morphologies, and judicious selection of organic linkers and metal nodes) of MOFs and MOF composites for particular energy storage and conversion applications are highlighted. A broad overview of recent progress is provided, which will hopefully promote the future development of MOFs and MOF composites for advanced energy storage and conversion applications.

ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data

Abstract: We have fully integrated public chromatin chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq data (n > 70,000) derived from six representative model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast), and have devised a data-mining platform-designated ChIP-Atlas (http://chip-atlas.org). ChIP-Atlas is able to show alignment and peak-call results for all public ChIP-seq and DNase-seq data archived in the NCBI Sequence Read Archive (SRA), which encompasses data derived from GEO, ArrayExpress, DDBJ, ENCODE, Roadmap Epigenomics, and the scientific literature. All peak-call data are integrated to visualize multiple histone modifications and binding sites of transcriptional regulators (TRs) at given genomic loci. The integrated data can be further analyzed to show TR-gene and TR-TR interactions, as well as to examine enrichment of protein binding for given multiple genomic coordinates or gene names. ChIP-Atlas is superior to other platforms in terms of data number and functionality for data mining across thousands of ChIP-seq experiments, and it provides insight into gene regulatory networks and epigenetic mechanisms.

The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE)

Abstract: Life cycle assessment (LCA) has been used to assess freshwater-related impacts according to a new water footprint framework formalized in the ISO 14046 standard. To date, no consensus-based approach exists for applying this standard and results are not always comparable when different scarcity or stress indicators are used for characterization of impacts. This paper presents the outcome of a 2-year consensus building process by the Water Use in Life Cycle Assessment (WULCA), a working group of the UNEP-SETAC Life Cycle Initiative, on a water scarcity midpoint method for use in LCA and for water scarcity footprint assessments. In the previous work, the question to be answered was identified and different expert workshops around the world led to three different proposals. After eliminating one proposal showing low relevance for the question to be answered, the remaining two were evaluated against four criteria: stakeholder acceptance, robustness with closed basins, main normative choice, and physical meaning. The recommended method, AWARE, is based on the quantification of the relative available water remaining per area once the demand of humans and aquatic ecosystems has been met, answering the question “What is the potential to deprive another user (human or ecosystem) when consuming water in this area?” The resulting characterization factor (CF) ranges between 0.1 and 100 and can be used to calculate water scarcity footprints as defined in the ISO standard. After 8 years of development on water use impact assessment methods, and 2 years of consensus building, this method represents the state of the art of the current knowledge on how to assess potential impacts from water use in LCA, assessing both human and ecosystem users’ potential deprivation, at the midpoint level, and provides a consensus-based methodology for the calculation of a water scarcity footprint as per ISO 14046.

Vowel recognition with four coupled spin-torque nano-oscillators

Abstract: In recent years, artificial neural networks have become the flagship algorithm of artificial intelligence1. In these systems, neuron activation functions are static, and computing is achieved through standard arithmetic operations. By contrast, a prominent branch of neuroinspired computing embraces the dynamical nature of the brain and proposes to endow each component of a neural network with dynamical functionality, such as oscillations, and to rely on emergent physical phenomena, such as synchronization2–6, for solving complex problems with small networks7–11. This approach is especially interesting for hardware implementations, because emerging nanoelectronic devices can provide compact and energy-efficient nonlinear auto-oscillators that mimic the periodic spiking activity of biological neurons12–16. The dynamical couplings between oscillators can then be used to mediate the synaptic communication between the artificial neurons. One challenge for using nanodevices in this way is to achieve learning, which requires fine control and tuning of their coupled oscillations17; the dynamical features of nanodevices can be difficult to control and prone to noise and variability18. Here we show that the outstanding tunability of spintronic nano-oscillators—that is, the possibility of accurately controlling their frequency across a wide range, through electrical current and magnetic field—can be used to address this challenge. We successfully train a hardware network of four spin-torque nano-oscillators to recognize spoken vowels by tuning their frequencies according to an automatic real-time learning rule. We show that the high experimental recognition rates stem from the ability of these oscillators to synchronize. Our results demonstrate that non-trivial pattern classification tasks can be achieved with small hardware neural networks by endowing them with nonlinear dynamical features such as oscillations and synchronization. A network of four spin-torque nano-oscillators can be trained in real time to recognize spoken vowels, in a simple and scalable approach that could be exploited for large-scale neural networks.

Accurate and efficient data acquisition methods for high-resolution angle-resolved photoemission microscopy.

Abstract: Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique in materials science, as it can directly probe electronic states inside solids in energy (E) and momentum (k) space. As an advanced technique, spatially-resolved ARPES using a well-focused light source (high-resolution ARPES microscopy) has recently attracted growing interests because of its capability to obtain local electronic information at micro- or nano-metric length scales. However, there exist several technical challenges to guarantee high precision in determining translational and rotational positions in reasonable measurement time. Here we present two methods of obtaining k-space mapping and real-space imaging in high-resolution ARPES microscopy. One method is for k-space mapping measurements that enables us to keep a target position on a sample surface during sample rotation by compensating rotation-induced displacements (tracing acquisition method). Another method is for real-space imaging measurements that significantly reduces total acquisition time (scanning acquisition method). We provide several examples of these methods that clearly indicate higher accuracy in k-space mapping as well as higher efficiency in real-space imaging, and thus improved throughput of high-resolution APRES microscopy.

Carbon Nanotubes and Related Nanomaterials: Critical Advances and Challenges for Synthesis toward Mainstream Commercial Applications

Abstract: Advances in the synthesis and scalable manufacturing of single-walled carbon nanotubes (SWCNTs) remain critical to realizing many important commercial applications. Here we review recent breakthroughs in the synthesis of SWCNTs and highlight key ongoing research areas and challenges. A few key applications that capitalize on the properties of SWCNTs are also reviewed with respect to the recent synthesis breakthroughs and ways in which synthesis science can enable advances in these applications. While the primary focus of this review is on the science framework of SWCNT growth, we draw connections to mechanisms underlying the synthesis of other 1D and 2D materials such as boron nitride nanotubes and graphene.

Atomically thin noble metal dichalcogenide: a broadband mid-infrared semiconductor.

Abstract: The interest in mid-infrared technologies surrounds plenty of important optoelectronic applications ranging from optical communications, biomedical imaging to night vision cameras, and so on. Although narrow bandgap semiconductors, such as Mercury Cadmium Telluride and Indium Antimonide, and quantum superlattices based on inter-subband transitions in wide bandgap semiconductors, have been employed for mid-infrared applications, it remains a daunting challenge to search for other materials that possess suitable bandgaps in this wavelength range. Here, we demonstrate experimentally for the first time that two-dimensional (2D) atomically thin PtSe2 has a variable bandgap in the mid-infrared via layer and defect engineering. Here, we show that bilayer PtSe2 combined with defects modulation possesses strong light absorption in the mid-infrared region, and we realize a mid-infrared photoconductive detector operating in a broadband mid-infrared range. Our results pave the way for atomically thin 2D noble metal dichalcogenides to be employed in high-performance mid-infrared optoelectronic devices.

RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

Abstract: We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset.

Encapsulating highly catalytically active metal nanoclusters inside porous organic cages

Abstract: The creation of metal nanoclusters with dimensions ranging from subnanometre to ~2 nm for heterogeneous catalysis has received substantial attention. However, synthesizing these structures while retaining surface activity and avoiding aggregation is challenging. Here, we report a reverse double-solvents approach that enables encapsulation of highly catalytically active Pd nanoclusters inside the newly formed discrete organic molecular cage, RCC3. By encapsulating within the open cavities of soluble RCC3 cages, the obtained Pd nanocluster cores are produced with precisely controlled size (~0.72 nm) and show high solubility, excellent dispersibility and accessibility in solution, presenting significantly enhanced catalytic activities towards various liquid-phase catalytic reactions. Moreover, owing to the effective confinement of cage cavities, the as-prepared Pd nanoclusters possess excellent stability and durability. The strategy of encapsulation of metal nanoclusters within soluble porous organic cages is promising for developing stable and active catalysts.

Recent progress in the phase-transition mechanism and modulation of vanadium dioxide materials

Abstract: Metal-to-insulator transition (MIT) behaviors accompanied by a rapid reversible phase transition in vanadium dioxide (VO2) have gained substantial attention for investigations into various potential applications and obtaining good materials to study strongly correlated electronic behaviors in transition metal oxides (TMOs). Although its phase-transition mechanism is still controversial, during the past few decades, people have made great efforts in understanding the MIT mechanism, which could also benefit the investigation of MIT modulation. This review summarizes the recent progress in the phase-transition mechanism and modulation of VO2 materials. A representative understanding on the phase-transition mechanism, such as the lattice distortion and electron correlations, are discussed. Based on the research of the phase-transition mechanism, modulation methods, such as element doping, electric field (current and gating), and tensile/compression strain, as well as employing lasers, are summarized for comparison. Finally, discussions on future trends and perspectives are also provided. This review gives a comprehensive understanding of the mechanism of MIT behaviors and the phase-transition modulations. Smart coatings that alter their electrical properties on demand can be created from shape-shifting vanadium dioxide (VO2) crystals. Xun Cao from Shanghai Institute of Ceramics, Chinese Academy of Sciences in Shanghai and co-workers review efforts to understand the mechanisms that enable VO2 to rapidly switch between two crystal states — one with metallic conductivity, the other insulating—at near-room temperature conditions. Theoretical calculations and nanoscale experiments reveal that VO2 transitions are triggered by a combination of interactions between electrons and atoms in the crystal lattice, and through forces that cause electrons to avoid each other. Several innovative methods of manipulating the VO2 switching temperature have emerged including foreign element additions, laser irradiation, and controlled substrate bending. The sensitivity of VO2 transitions to mechanical stress has inspired proposals for ultrafast response breath sensors and artificial skin. In this article, we review the prototypical phase-transition material-VO2, which undergoes structure and conductivity changes simultaneously. The recent progresses in the transition mechanism are also discussed. Besides, this work gives a comprehensive understanding of the phase-transition modulations, such as element doping, electric field (current and gating) and tensile/compression strain, as well as employing laser.

Vapour–liquid–solid growth of monolayer MoS 2 nanoribbons

Abstract: Chemical vapour deposition of two-dimensional materials typically involves the conversion of vapour precursors to solid products in a vapour-solid-solid mode Here, we report the vapour-liquid-solid growth of monolayer MoS2, yielding highly crystalline ribbons with a width of few tens to thousands of nanometres This vapour-liquid-solid growth is triggered by the reaction between MoO3 and NaCl, which results in the formation of molten Na-Mo-O droplets These droplets mediate the growth of MoS2 ribbons in the 'crawling mode' when saturated with sulfur The locally well-defined orientations of the ribbons reveal the regular horizontal motion of the droplets during growth Using atomic-resolution scanning transmission electron microscopy and second harmonic generation microscopy, we show that the ribbons are grown homoepitaxially on monolayer MoS2 with predominantly 2H- or 3R-type stacking Our findings highlight the prospects for the controlled growth of atomically thin nanostructure arrays for nanoelectronic devices and the development of unique mixed-dimensional structures

Direct Visualization of the Reversible O2- /O- Redox Process in Li-Rich Cathode Materials.

Abstract: Conventional cathodes of Li-ion batteries mainly operate through an insertion-extraction process involving transition metal redox. These cathodes will not be able to meet the increasing requirements until lithium-rich layered oxides emerge with beyond-capacity performance. Nevertheless, in-depth understanding of the evolution of crystal and excess capacity delivered by Li-rich layered oxides is insufficient. Herein, various in situ technologies such as X-ray diffraction and Raman spectroscopy are employed for a typical material Li1.2 Ni0.2 Mn0.6 O2 , directly visualizing O- O- (peroxo oxygen dimers) bonding mostly along the c-axis and demonstrating the reversible O2- /O- redox process. Additionally, the formation of the peroxo OO bond is calculated via density functional theory, and the corresponding OO bond length of ≈1.3 A matches well with the in situ Raman results. These findings enrich the oxygen chemistry in layered oxides and open opportunities to design high-performance positive electrodes for lithium-ion batteries.

Rechargeable Solid-State Li–Air and Li–S Batteries: Materials, Construction, and Challenges

Abstract: Nonaqueous Li–air and Li–S batteries are attracting considerable interest because of their outstanding theoretical capacities and energy densities. However, despite the substantial progress in their development, safety remains an issue because of the flammability of their organic electrolytes. Moreover, the electrolyte volatilization of Li–air batteries and “shuttle effect” in Li–S batteries seriously hinder their development. The use of solid-state Li–air and Li–S batteries is one of the best solutions. Nevertheless, many challenges remain in solid electrolytes, electrodes, and interfaces. In this review, a comprehensive discussion on the development of solid-state Li–air and Li–S batteries is provided. The discussion begins with introduction of the progress in solid electrolytes, including their ionic conductivities and chemical stabilities. It then moves on to the cathodes of both batteries and the interface between electrolytes and electrodes. The reaction process inside the cathode is also presented. Suggestions for the optimization of composite cathodes and modification of the electrode–electrolyte interface are provided in the end. Intensive effort is required for the development of solid-state Li–air and Li–S batteries in the future.

Coherent control of single electrons: a review of current progress.

Abstract: In this report we review the present state of the art of the control of propagating quantum states at the single-electron level and its potential application to quantum information processing. We give an overview of the different approaches that have been developed over the last few years in order to gain full control over a propagating single-electron in a solid-state system. After a brief introduction of the basic concepts, we present experiments on flying qubit circuits for ensemble of electrons measured in the low frequency (DC) limit. We then present the basic ingredients necessary to realise such experiments at the single-electron level. This includes a review of the various single-electron sources that have been developed over the last years and which are compatible with integrated single-electron circuits. This is followed by a review of recent key experiments on electron quantum optics with single electrons. Finally we will present recent developments in the new physics that has emerged using ultrashort voltage pulses. We conclude our review with an outlook and future challenges in the field.

Wafer-scale, layer-controlled organic single crystals for high-speed circuit operation.

Abstract: Two-dimensional (2D) layered semiconductors are a novel class of functional materials that are an ideal platform for electronic applications, where the whole electronic states are directly modified by external stimuli adjacent to their electronic channels. Scale-up of the areal coverage while maintaining homogeneous single crystals has been the relevant challenge. We demonstrate that wafer-size single crystals composed of an organic semiconductor bimolecular layer with an excellent mobility of 10 cm2 V-1 s-1 can be successfully formed via a simple one-shot solution process. The well-controlled process to achieve organic single crystals composed of minimum molecular units realizes unprecedented low contact resistance and results in high-speed transistor operation of 20 MHz, which is twice as high as the common frequency used in near-field wireless communication. The capability of the solution process for scale-up coverage of high-mobility organic semiconductors opens up the way for novel 2D nanomaterials to realize products with large-scale integrated circuits on film-based devices.

Deriving Validity Time in Knowledge Graph

Abstract: Knowledge Graphs (KGs) are a popular means to represent knowledge on the Web, typically in the form of node/edge labelled directed graphs. We consider temporal KGs, in which edges are further annotated with time intervals, reflecting when the relationship between entities held in time. In this paper, we focus on the task of predicting time validity for unannotated edges. We introduce the problem as a variation of relational embedding. We adapt existing approaches, and explore the importance example selection and the incorporation of side information in the learning process. We present our experimental evaluation in details.

Superlong Single-Crystal Metal-Organic Framework Nanotubes.

Abstract: Nanotubes have attracted great attention. Here, we report the fabrication of the first single-crystal metal–organic framework (MOF) nanotubes. Superlong single-crystal cobalt–organic framework (Co-MOF) nanotubes, which have a diameter of ∼70 nm and length of 20–35 μm with parallel multichannels (window size: 1.1 nm), have been successfully synthesized via an amorphous MOF-mediated recrystallization approach. The synthesized MOF nanotubes can be used as a nanocolumn for separation of large molecules. Carbonization of the Co-MOF nanotubes in an argon atmosphere preserves the 1D morphology, affording long carbon nanofibers. A hierarchical architecture composed of carbon nanofibers wrapped by carbon nanotubes (20–30 nm in diameter and 200–300 nm in length) with cobalt nanoparticles on the top is formed by the carbonization of the Co-MOF nanotubes along with dicyandiamide as a nitrogen and a secondary carbon source. The resulting hierarchical dendrites with carbon nanofiber trunks and carbon nanotube branches ...