Showing papers by "University of Electro-Communications published in 2019"

Engineering bunched Pt-Ni alloy nanocages for efficient oxygen reduction in practical fuel cells

Abstract: Development of efficient and robust electrocatalysts is critical for practical fuel cells. We report one-dimensional bunched platinum-nickel (Pt-Ni) alloy nanocages with a Pt-skin structure for the oxygen reduction reaction that display high mass activity (3.52 amperes per milligram platinum) and specific activity (5.16 milliamperes per square centimeter platinum), or nearly 17 and 14 times higher as compared with a commercial platinum on carbon (Pt/C) catalyst. The catalyst exhibits high stability with negligible activity decay after 50,000 cycles. Both the experimental results and theoretical calculations reveal the existence of fewer strongly bonded platinum-oxygen (Pt-O) sites induced by the strain and ligand effects. Moreover, the fuel cell assembled by this catalyst delivers a current density of 1.5 amperes per square centimeter at 0.6 volts and can operate steadily for at least 180 hours.

Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning

Abstract: To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep ${Q}$ -network (DQN)-based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a ${Q}$ -function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

Climbing the Apex of the ORR Volcano Plot via Binuclear Site Construction: Electronic and Geometric Engineering.

Abstract: Great enthusiasm in single-atom catalysts (SACs) for the oxygen reduction reaction (ORR) has been aroused by the discovery of M-NX as a promising ORR catalysis center. However, the performance of SACs lags far behind that of state-of-the-art Pt due to the unsatisfactory adsorption-desorption behaviors of the reported catalytic centers. To address this issue, rational manipulation of the active site configuration toward a well-managed energy level and geometric structure is urgently desired, yet still remains a challenge. Herein, we report a novel strategy to accomplish this task through the construction of an Fe-Co dual-atom centered site. A spontaneously absorbed electron-withdrawing OH ligand was proposed to act proactively as an energy level modifier to empower easy intermediate desorption, while the triangular Fe-Co-OH coordination facilitates O-O bond scission. Benefiting from these attributes, the as-constructed FeCoN5-OH site enables an ORR onset potential and half-wave potential of up to 1.02 and 0.86 V (vs RHE), respectively, with an intrinsic activity over 20 times higher than the single-atom FeN4 site. Our finding not only opens up a novel strategy to tailor the electronic structure of an atomic site toward boosted activity but also provides new insights into the fundamental understanding of diatomic sites for ORR electrocatalysis.

KAGRA: 2.5 Generation Interferometric Gravitational Wave Detector

Abstract: The recent detections of gravitational waves (GWs) reported by the LIGO and Virgo collaborations have made a significant impact on physics and astronomy. A global network of GW detectors will play a key role in uncovering the unknown nature of the sources in coordinated observations with astronomical telescopes and detectors. Here we introduce KAGRA, a new GW detector with two 3 km baseline arms arranged in an ‘L’ shape. KAGRA’s design is similar to the second generations of Advanced LIGO and Advanced Virgo, but it will be operating at cryogenic temperatures with sapphire mirrors. This low-temperature feature is advantageous for improving the sensitivity around 100 Hz and is considered to be an important feature for the third-generation GW detector concept (for example, the Einstein Telescope of Europe or the Cosmic Explorer of the United States). Hence, KAGRA is often called a 2.5-generation GW detector based on laser interferometry. KAGRA’s first observation run is scheduled in late 2019, aiming to join the third observation run of the advanced LIGO–Virgo network. When operating along with the existing GW detectors, KAGRA will be helpful in locating GW sources more accurately and determining the source parameters with higher precision, providing information for follow-up observations of GW trigger candidates.

Stretchable pumps for soft machines

Abstract: Machines made of soft materials bridge life sciences and engineering1. Advances in soft materials have led to skin-like sensors and muscle-like actuators for soft robots and wearable devices1-3. Flexible or stretchable counterparts of most key mechatronic components have been developed4,5, principally using fluidically driven systems6-8; other reported mechanisms include electrostatic9-12, stimuli-responsive gels13,14 and thermally responsive materials such as liquid metals15-17 and shape-memory polymers18. Despite the widespread use of fluidic actuation, there have been few soft counterparts of pumps or compressors, limiting the portability and autonomy of soft machines4,8. Here we describe a class of soft-matter bidirectional pumps based on charge-injection electrohydrodynamics19. These solid-state pumps are flexible, stretchable, modular, scalable, quiet and rapid. By integrating the pump into a glove, we demonstrate wearable active thermal management. Embedding the pump in an inflatable structure produces a self-contained fluidic 'muscle'. The stretchable pumps have potential uses in wearable laboratory-on-a-chip and microfluidic sensors, thermally active clothing and autonomous soft robots.

Edge Computing in 5G: A Review

Abstract: 5G is the next generation cellular network that aspires to achieve substantial improvement on quality of service, such as higher throughput and lower latency. Edge computing is an emerging technology that enables the evolution to 5G by bringing cloud capabilities near to the end users (or user equipment, UEs) in order to overcome the intrinsic problems of the traditional cloud, such as high latency and the lack of security. In this paper, we establish a taxonomy of edge computing in 5G, which gives an overview of existing state-of-the-art solutions of edge computing in 5G on the basis of objectives, computational platforms, attributes, 5G functions, performance measures, and roles. We also present other important aspects, including the key requirements for its successful deployment in 5G and the applications of edge computing in 5G. Then, we explore, highlight, and categorize recent advancements in edge computing for 5G. By doing so, we reveal the salient features of different edge computing paradigms for 5G. Finally, open research issues are outlined.

Suppression of Charge Carrier Recombination in Lead-Free Tin Halide Perovskite via Lewis Base Post-treatment.

Abstract: Lead-free tin perovskite solar cells (PSCs) show the most promise to replace the more toxic lead-based perovskite solar cells. However, the efficiency is significantly less than that of lead-based PSCs as a result of low open-circuit voltage. This is due to the tendency of Sn2+ to oxidize into Sn4+ in the presence of air together with the formation of defects and traps caused by the fast crystallization of tin perovskite materials. Here, post-treatment of the tin perovskite layer with edamine Lewis base to suppress the recombination reaction in tin halide PSCs results in efficiencies higher than 10%, which is the highest reported efficiency to date for pure tin halide PSCs. The X-ray photoelectron spectroscopy data suggest that the recombination reaction originates from the nonstoichiometric Sn:I ratio rather than the Sn4+:Sn2+ ratio. The amine group in edamine bonded the undercoordinated tin, passivating the dangling bonds and defects, resulting in suppressed charge carrier recombination.

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Abstract: With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

The ALMA Survey of 70 μm Dark High-mass Clumps in Early Stages (ASHES). I. Pilot Survey: Clump Fragmentation

Abstract: © 2019. The American Astronomical Society. All rights reserved. The ALMA Survey of 70 μm dark High-mass clumps in Early Stages (ASHES) is designed to systematically characterize the earliest stages and constrain theories of high-mass star formation. Twelve massive (>500 M⊙ ), cold (≤15 K), 3.6-70 μm dark prestellar clump candidates, embedded in infrared dark clouds, were carefully selected in the pilot survey to be observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We have mosaicked each clump (∼1 arcmin2) in continuum and line emission with the 12 m, 7 m, and Total Power (TP) arrays at 224 GHz (1.34 mm), resulting in ∼1.″2 resolution (∼4800 au, at the average source distance). As the first paper in the series, we concentrate on the continuum emission to reveal clump fragmentation. We detect 294 cores, from which 84 (29%) are categorized as protostellar based on outflow activity or "warm core" line emission. The remaining 210 (71%) are considered prestellar core candidates. The number of detected cores is independent of the mass sensitivity range of the observations and, on average, more massive clumps tend to form more cores. We find a large population of low-mass ( 30 M⊙) prestellar cores (maximum mass 11 M⊙). From the prestellar core mass function, we derive a power-law index of 1.17 ± 0.10, which is slightly shallower than Salpeter. We used the minimum spanning tree (MST) technique to characterize the separation between cores and their spatial distribution, and to derive mass segregation ratios. While there is a range of core masses and separations detected in the sample, the mean separation and mass per clump are well explained by thermal Jeans fragmentation and are inconsistent with turbulent Jeans fragmentation. Core spatial distribution is well described by hierarchical subclustering rather than centrally peaked clustering. There is no conclusive evidence of mass segregation. We test several theoretical conditions and conclude that overall, competitive accretion and global hierarchical collapse scenarios are favored over the turbulent core accretion scenario.

A multi-objective optimization-based layer-by-layer blade-coating approach for organic solar cells: rational control of vertical stratification for high performance

Abstract: A major breakthrough in organic solar cells (OSCs) in the last thirty years was the development of the bulk heterojunction (BHJ) solution processing strategy, which effectively provided a nanoscale phase-separated morphology, aiding in the separation of Coulombically bound excitons and facilitating charge transport and extraction. Compared with the application of the layer-by-layer (LbL) approach proposed in the same period, the BHJ spin-coating technology shows overwhelming advantages for evaluating the performance of photovoltaic materials and achieving more-efficient photoelectric conversion. Thus, in this study, we have further compared the BHJ and LbL processing strategies via the doctor-blade coating technology because it is a roll-to-roll compatible high-throughput thin film fabrication route. We systematically evaluated multiple target parameters, including morphological characteristics, optical simulation, physical kinetics, device efficiency, and blend stability issues. It is worth emphasizing that our findings disprove the old stereotypes such as the BHJ processing method is superior to the LbL technology for the preparation of high-performance OSCs and the LbL approach requires an orthogonal solvent and donor/acceptor materials with special solubility. Our studies demonstrate that the LbL blade-coating approach is a promising strategy to effectively reduce the efficiency-stability gap of OSCs and even a superior alternative to the BHJ method in commercial applications.

CsPb(IxBr1-x)(3) solar cells

Abstract: Owing to its nice performance, low cost, and simple solution-processing, organic-inorganic hybrid perovskite solar cell (PSC) becomes a promising candidate for next-generation high-efficiency solar cells. The power conversion efficiency (PCE) has boosted from 3.8% to 25.2% over the past ten years. Despite the rapid progress in PCE, the device stability is a key issue that impedes the commercialization of PSCs. Recently, all-inorganic cesium lead halide perovskites have attracted much attention due to their better stability compared with their organic-inorganic counterpart. In this progress report, we summarize the properties of CsPb(IxBr1−x)3 and their applications in solar cells. The current challenges and corresponding solutions are discussed. Finally, we share our perspectives on CsPb(IxBr1−x)3 solar cells and outline possible directions to further improve the device performance.

Strain Relaxation and Light Management in Tin–Lead Perovskite Solar Cells to Achieve High Efficiencies

Abstract: Tin–lead (Sn–Pb)-based perovskite solar cells (PSCs) still exhibit inferior power conversion efficiency (PCE) compared to their pure Pb counterparts because of high voltage loss (VL) and high photo...

GeI2 Additive for High Optoelectronic Quality CsPbI3 Quantum Dots and Their Application in Photovoltaic Devices

Abstract: Trioctylphosphine (TOP)-based syntheses of CsPbI3 perovskite quantum dots (QDs) yield unprecedented high photoluminescence quantum yield (PL QY), lower Stokes shifts, and longer carrier lifetimes d...

A Reliable Energy Efficient Dynamic Spectrum Sensing for Cognitive Radio IoT Networks

Abstract: The Internet of Things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded. By applying cognitive radio (CR) capabilities to IoT, a novel cognitive radio IoT (CR-IoT) network arises as a promising solution to tackle the spectrum scarcity problem in conventional IoT network. CR is a form of wireless communication whereby a radio is dynamically programmed and configured to detect available spectrum channels. This enhances the spectrum utilization efficiency of radio frequency while avoiding interference and overcrowding to other users. Energy efficiency in CR-IoT network must be carefully formulated since the sensor nodes consume significant energy to support CR operations, such as in dynamic spectrum sensing and switching. In this paper, we study channel spectrum sensing to boost energy efficiency in clustered CR-IoT networks. We propose a two-way information exchange dynamic spectrum sensing algorithms to improve energy efficiency for data transmission in licensed channels. In addition, the concern of the energy consumption in dynamic spectrum sensing and switching, we propose an energy efficient optimal transmit power allocation technique to enhance the dynamic spectrum sensing and data throughput. Simulation results validate that the proposed dynamic spectrum sensing technique can significantly reduce the energy consumption in CR-IoT networks.

Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation

Abstract: To minimize the annotation costs associated with the training of semantic segmentation models, researchers have extensively investigated weakly-supervised segmentation approaches. In the current weakly-supervised segmentation methods, the most widely adopted approach is based on visualization. However, the visualization results are not generally equal to semantic segmentation. Therefore, to perform accurate semantic segmentation under the weakly supervised condition, it is necessary to consider the mapping functions that convert the visualization results into semantic segmentation. For such mapping functions, the conditional random field and iterative re-training using the outputs of a segmentation model are usually used. However, these methods do not always guarantee improvements in accuracy; therefore, if we apply these mapping functions iteratively multiple times, eventually the accuracy will not improve or will decrease. In this paper, to make the most of such mapping functions, we assume that the results of the mapping function include noise, and we improve the accuracy by removing noise. To achieve our aim, we propose the self-supervised difference detection module, which estimates noise from the results of the mapping functions by predicting the difference between the segmentation masks before and after the mapping. We verified the effectiveness of the proposed method by performing experiments on the PASCAL Visual Object Classes 2012 dataset, and we achieved 64.9% in the val set and 65.5% in the test set. Both of the results become new state-of-the-art under the same setting of weakly supervised semantic segmentation.

Relationship between Lattice Strain and Efficiency for Sn-Perovskite Solar Cells

Abstract: In the composition of Q0.1(FA0.75MA0.25)0.9SnI3, Q is replaced with Na+, K+, Cs+, ethylammonium+ (EA+), and butylammonium+ (BA+), respectively, and the relationship between actually measured lattice strain and photovoltaic performances is discussed. The lattice strain evaluated by the Williamson-hall plot of X-ray diffraction data decreased as the tolerance factor was close to one. The efficiency of the Sn-perovskite solar cell was enhanced as the lattice strain decreased. Among them, EA0.1(FA0.75MA0.25)0.9SnI3 having lowest lattice strain gave the best result of 5.41%. Because the carrier mobility increased with a decrease in the lattice strain, these lattice strains would disturb carrier mobility and decrease the solar cell efficiency. Finally, the results that the efficiency of the SnGe-perovskite solar cells was gradually enhanced from 6.42 to 7.60% during storage, was explained by the lattice strain relaxation during the storage.

Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval

Abstract: Deep cross-modal learning has successfully demonstrated excellent performance in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities, such as audio and lyrics, should be taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Data in different modalities are converted to the same canonical space where intermodal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study that uses deep architectures for learning the temporal correlation between audio and lyrics. A pretrained Doc2Vec model followed by fully connected layers is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: (i) We propose an end-to-end network to learn cross-modal correlation between audio and lyrics, where feature extraction and correlation learning are simultaneously performed and joint representation is learned by considering temporal structures. (ii) And, as for feature extraction, we further represent an audio signal by a short sequence of local summaries (VGG16 features) and apply a recurrent neural network to compute a compact feature that better learns the temporal structures of music audio. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval.

Additive Engineering to Grow Micron-Sized Grains for Stable High Efficiency Perovskite Solar Cells

Abstract: A high-quality perovskite photoactive layer plays a crucial role in determining the device performance. An additive engineering strategy is introduced by utilizing different concentrations of N,1-diiodoformamidine (DIFA) in the perovskite precursor solution to essentially achieve high-quality monolayer-like perovskite films with enhanced crystallinity, hydrophobic property, smooth surface, and grain size up to nearly 3 µm, leading to significantly reduced grain boundaries, trap densities, and thus diminished hysteresis in the resultant perovskite solar cells (PSCs). The optimized devices with 2% DIFA additive show the best device performance with a significantly enhanced power conversion efficiency (PCE) of 21.22%, as compared to the control devices with the highest PCE of 19.07%. 2% DIFA modified devices show better stability than the control ones. Overall, the introduction of DIFA additive is demonstrated to be a facile approach to obtain high-efficiency, hysteresis-less, and simultaneously stable PSCs.

Suppressed Triplet Exciton Diffusion Due to Small Orbital Overlap as a Key Design Factor for Ultralong-Lived Room-Temperature Phosphorescence in Molecular Crystals.

Abstract: Persistent room-temperature phosphorescence (RTP) under ambient conditions is attracting attention due to its strong potential for applications in bioimaging, sensing, or optical recording. Molecular packing leading to a rigid crystalline structure that minimizes nonradiative pathways from triplet state is often investigated for efficient RTP. However, for complex conjugated systems a key strategy to suppress the nonradiative deactivation is not found yet. Here, the origin of small rates of a nonradiative decay process from triplet states of conjugated molecular crystals showing RTP is reported. Optical microscopy analysis showed that, despite a favorable molecular stacking, an aromatic crystal with strong RTP is characterized by small diffusion length and small values of the diffusion coefficient of triplet excitons. Quantum chemical calculations reveal a large overlap between the lowest unoccupied molecular orbitals but very small overlap between the highest occupied molecular orbitals (HOMOs). Inefficient electron exchange caused by the small overlap of HOMOs prevents triplet excitons from diffusing over long distances and consequently from quenching at defect sites inside the crystal or at the crystal surface. These results will allow design of comprehensive molecular structures to obtain molecular solids with more efficient RTP.

Category-Based Deep CCA for Fine-Grained Venue Discovery From Multimodal Data

Abstract: In this work, travel destinations and business locations are taken as venues. Discovering a venue by a photograph is very important for visual context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photographs generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a novel deep learning model, category-based deep canonical correlation analysis. Given a photograph as input, this model performs: 1) exact venue search (find the venue where the photograph was taken) and 2) group venue search (find relevant venues that have the same category as the photograph), by the cross-modal correlation between the input photograph and textual description of venues. In this model, data in different modalities are projected to a same space via deep networks. Pairwise correlation (between different modality data from the same venue) for exact venue search and category-based correlation (between different modality data from different venues with the same category) for group venue search are jointly optimized. Because a photograph cannot fully reflect rich text description of a venue, the number of photographs per venue in the training phase is increased to capture more aspects of a venue. We build a new venue-aware multimodal data set by integrating Wikipedia featured articles and Foursquare venue photographs. Experimental results on this data set confirm the feasibility of the proposed method. Moreover, the evaluation over another publicly available data set confirms that the proposed method outperforms state of the arts for cross-modal retrieval between image and text.

Space gravitational-wave antennas DECIGO and B-DECIGO

Abstract: DECi-hertz Interferometer Gravitational-wave Observatory (DECIGO) is a future Japanese space gravitational-wave antenna. The most important objective of DECIGO, among various sciences to be aimed at, is to detect gravitational waves coming from the inflation of the universe. DECIGO consists of four clusters of spacecraft, and each cluster consists of three spacecraft with three Fabry–Perot Michelson interferometers. As a pathfinder mission of DECIGO, B-DECIGO will be launched, hopefully in the 2020s, to demonstrate technologies necessary for DECIGO as well as to lead to fruitful multimessenger astronomy. B-DECIGO is a small-scale or simpler version of DECIGO with the sensitivity slightly worse than that of DECIGO, yet good enough to provide frequent detection of gravitational waves.

Decentralized Trust Evaluation in Vehicular Internet of Things

Abstract: Trust management in a decentralized vehicular network, such as vehicular ad hoc network, is particularly challenging due to the lack of centralized communication infrastructure and a fast varying feature of the vehicular environment. In this paper, we propose a decentralized trust management scheme for vehicular networks. The proposed scheme uses a fuzzy logic-based trust calculation approach to evaluate the direct trust where trustee nodes are located within the transmission range of a trustor node. A reinforcement learning-based approach is also employed to estimate the indirect trust where the behaviors of trustee cannot be observed directly. The extensive simulations are conducted to show the advantage of the proposed scheme over other baseline approaches.

Anonymization of Sensitive Quasi-Identifiers for l-Diversity and t-Closeness

Abstract: A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, …, lq)-diversity and (t1, …, tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: An anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer's objective. Our proposed method was experimentally evaluated using real data sets.

High-coherence ultra-broadband bidirectional dual-comb fiber laser

Abstract: Dual-comb spectroscopy has emerged as an attractive spectroscopic tool for high-speed, high-resolution, and high-sensitivity broadband spectroscopy. It exhibits certain advantages when compared to the conventional Fourier-transform spectroscopy. However, the high cost of the conventional system, which is based on two mode-locked lasers and a complex servo system with a common single-frequency laser, limits the applicability of the dual-comb spectroscopy system. In this study, we overcame this problem with a bidirectional dual-comb fiber laser that generates two high-coherence ultra-broadband frequency combs with slightly different repetition rates (frep). The two direct outputs from the single-laser cavity displayed broad spectra of > 50 nm; moreover, an excessively small difference in the repetition rate (< 1.5 Hz) was achieved with high relative stability, owing to passive common-mode noise cancellation. With this slight difference in the repetition rate, the applicable optical spectral bandwidth in dual-comb spectroscopy could attain ~479 THz (~3,888 nm). In addition, we successfully generated high-coherence ultra-broadband frequency combs via nonlinear spectral broadening and detected high signal-to-noise-ratio carrier–envelope offset frequency (fCEO) beat signals using the self-referencing technique. We also demonstrated the high relative stability between the two fCEO beat signals and tunability. To our knowledge, this is the first demonstration of fCEO detection and frequency measurement using a self-referencing technique for a dual-comb fiber laser. The developed high-coherence ultra-broadband dual-comb fiber laser with capability of fCEO detection is likely to be a highly effective tool in practical, high-sensitivity, ultra-broadband applications.

Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study

Abstract: A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic findings and blood test results), accumulated daily as data in hospitals, could improve the accuracy of screening to classify patients at high or low risk of developing gastric cancer. We used XGBoost, a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. Longitudinal and comprehensive medical check-up data were collected from 25,942 participants who underwent multiple endoscopies from 2006 to 2017 at a single facility in Japan. The participants were classified into a case group (y = 1) or a control group (y = 0) if gastric cancer was or was not detected, respectively, during a 122-month period. Among 1,431 total participants (89 cases and 1,342 controls), 1,144 (80%) were randomly selected for use in training 10 classification models; the remaining 287 (20%) were used to evaluate the models. The results showed that XGBoost outperformed logistic regression and showed the highest area under the curve value (0.899). Accumulating more data in the facility and performing further analyses including other input variables may help expand the clinical utility.

Symbol Emergence in Cognitive Developmental Systems: A Survey

Abstract: Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to symbols . However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, second, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems.

All-polarization-maintaining, polarization-multiplexed, dual-comb fiber laser with a nonlinear amplifying loop mirror

Abstract: We developed an all-polarization-maintaining, polarization-multiplexed, dual-comb fiber laser with a nonlinear amplifying loop mirror (NALM) mode-locking mechanism. Owing to the use of the slow and fast axes of a polarization-maintaining fiber (PMF), the dual-frequency combs with slightly different repetition rates from the single-laser cavity are generated at the same center wavelength without extra-cavity nonlinear spectral broadening. The narrow relative beat note between the two frequency combs is obtained with a full-width-at-half-maximum of ~1 kHz in the optical frequency domain. The two frequency combs have high relative stability and mutual coherence owing to passive common-mode noise cancellation.

Deep reinforcement learning for coherent beam combining applications.

Abstract: Coherent beam combining is a method to scale the peak and average power levels of laser systems beyond the limit of a single emitter system. This is achieved by stabilizing the relative optical phase of multiple lasers and combining them. We investigated the use of reinforcement learning (RL) and neural networks (NN) in this domain. Starting from a randomly initialized neural network, the system converged to a phase stabilization policy, which was comparable to a software implemented proportional-integral-derivative (PID) controller. Furthermore, we demonstrate the capability of neural networks to predict relative phase noise, which is one potential advantage of this method.