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http://hdl.handle.net/2115/145
2021-04-16T03:39:58ZFilamentary switching of ReRAM investigated by in-situ TEM
http://hdl.handle.net/2115/80771
Title: Filamentary switching of ReRAM investigated by in-situ TEM
Authors: Arita, Masashi; Tsurumaki-Fukuchi, Atsushi; Takahashi, Yasuo
Abstract: The filament operation of resistive random-access memory was studied via in-situ transmission electron microscopy, and the contribution of the conductive filament to the resistance switching was experimentally confirmed. In addition to the operation principles the device degradation mechanism was studied through repeated write/erase operations. The importance of controlling Cu movement in the switching layer was confirmed for stable CBRAM (conductive bridge random access memory) operations. A device structure with double switching layers and device miniaturization was effective in restricting over accumulation of Cu in the switching layer and localizing the filament. This may improve the robustness of the device against performance degradation. (C) 2020 The Japan Society of Applied Physics2020-03-31T15:00:00ZArita, MasashiTsurumaki-Fukuchi, AtsushiTakahashi, YasuoThe filament operation of resistive random-access memory was studied via in-situ transmission electron microscopy, and the contribution of the conductive filament to the resistance switching was experimentally confirmed. In addition to the operation principles the device degradation mechanism was studied through repeated write/erase operations. The importance of controlling Cu movement in the switching layer was confirmed for stable CBRAM (conductive bridge random access memory) operations. A device structure with double switching layers and device miniaturization was effective in restricting over accumulation of Cu in the switching layer and localizing the filament. This may improve the robustness of the device against performance degradation. (C) 2020 The Japan Society of Applied PhysicsStochastic modeling and scalable predictive control for automated demand response
http://hdl.handle.net/2115/80597
Title: Stochastic modeling and scalable predictive control for automated demand response
Authors: Kobayashi, Koichi; Hiraishi, Kunihiko
Abstract: Automated demand response (ADR) is a utility program that is designed to achieve electricity conservation. An ADR program is regarded as the problem of controlling the power consumption of a set of consumers. In this article, we propose a control-theoretic approach for an ADR program. First, a mathematical model of the power consumption is proposed. This model can express complex behavior by switching a Markov chain. Its effectiveness is illustrated by modeling the power consumption of an air-conditioner. Next, a new method of model predictive control for a set of consumers is developed using the proposed model. The control strategy at each time is chosen from a given finite set by solving a mixed integer linear programming (MILP) problem. The advantage of the proposed method is that the MILP problem is scalable with respect to the number of consumers. To show its effectiveness, we present a numerical example.2021-03-31T15:00:00ZKobayashi, KoichiHiraishi, KunihikoAutomated demand response (ADR) is a utility program that is designed to achieve electricity conservation. An ADR program is regarded as the problem of controlling the power consumption of a set of consumers. In this article, we propose a control-theoretic approach for an ADR program. First, a mathematical model of the power consumption is proposed. This model can express complex behavior by switching a Markov chain. Its effectiveness is illustrated by modeling the power consumption of an air-conditioner. Next, a new method of model predictive control for a set of consumers is developed using the proposed model. The control strategy at each time is chosen from a given finite set by solving a mixed integer linear programming (MILP) problem. The advantage of the proposed method is that the MILP problem is scalable with respect to the number of consumers. To show its effectiveness, we present a numerical example.Channel prediction of wideband OFDM systems in a millimeter-wave band based on multipath delay estimation
http://hdl.handle.net/2115/80557
Title: Channel prediction of wideband OFDM systems in a millimeter-wave band based on multipath delay estimation
Authors: Takano, Yuta; Nishimura, Toshihiko; Ohgane, Takeo; Ogawa, Yasutaka; Hagiwara, Junichiro
Abstract: Multi-user MIMO systems enable high capacity transmission. A base station, however, needs accurate channel state information (CSI). In time-varying environments, the CSI may be outdated at the actual transmission time. One of the solutions to this issue is channel prediction. The authors have proposed the prediction method using FISTA, a compressive sensing technique, for OFDM systems in a millimeter-wave band. Unfortunately, in realistic multipath environments, the prediction performance of the proposed technique degrades. In this letter, we examine the prediction performance in wider OFDM systems. It will be shown that FISTA reveals excellent performance in a sufficiently wide band case.2020-11-30T15:00:00ZTakano, YutaNishimura, ToshihikoOhgane, TakeoOgawa, YasutakaHagiwara, JunichiroMulti-user MIMO systems enable high capacity transmission. A base station, however, needs accurate channel state information (CSI). In time-varying environments, the CSI may be outdated at the actual transmission time. One of the solutions to this issue is channel prediction. The authors have proposed the prediction method using FISTA, a compressive sensing technique, for OFDM systems in a millimeter-wave band. Unfortunately, in realistic multipath environments, the prediction performance of the proposed technique degrades. In this letter, we examine the prediction performance in wider OFDM systems. It will be shown that FISTA reveals excellent performance in a sufficiently wide band case.Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses
http://hdl.handle.net/2115/80554
Title: Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses
Authors: Hagiwara, Naruki; Sekizaki, Shoma; Kuwahara, Yuji; Asai, Tetsuya; Akai-Kasaya, Megumi
Abstract: Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.2020-12-31T15:00:00ZHagiwara, NarukiSekizaki, ShomaKuwahara, YujiAsai, TetsuyaAkai-Kasaya, MegumiNetworks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.Stochastic electron energy gain under sheath electric field near sidewall of chamber to drive inductively coupled magnetized plasmas
http://hdl.handle.net/2115/80455
Title: Stochastic electron energy gain under sheath electric field near sidewall of chamber to drive inductively coupled magnetized plasmas
Authors: Takahashi, Hironori; Sugawara, Hirotake
Abstract: Single-electron motions near the sidewall of a chamber to drive a type of inductively coupled magnetized plasmas are analyzed using a Monte Carlo method in a simulation model with sheath electric field (E-sh) to understand the mechanism of electron energy gain (EEG). The analysis reveals that the E x B drift caused by E-sh, along the rf electric field induced in parallel to the sidewall, makes the EEG high and asymmetric between the first and second halves of an rf period. We observe spatial distributions of the EEG as a function of the distance from the wall under several sheath conditions. The EEG is higher under higher Esh and high EEG values are more concentrated in the sheath whose potential gradient is larger. Finally, observation of the phase-resolved EEG distributions demonstrates statistically that the EEG mechanism found in stochastic individual electron behaviors also works for a majority of electrons. (C) 2020 The Japan Society of Applied Physics.2020-02-29T15:00:00ZTakahashi, HironoriSugawara, HirotakeSingle-electron motions near the sidewall of a chamber to drive a type of inductively coupled magnetized plasmas are analyzed using a Monte Carlo method in a simulation model with sheath electric field (E-sh) to understand the mechanism of electron energy gain (EEG). The analysis reveals that the E x B drift caused by E-sh, along the rf electric field induced in parallel to the sidewall, makes the EEG high and asymmetric between the first and second halves of an rf period. We observe spatial distributions of the EEG as a function of the distance from the wall under several sheath conditions. The EEG is higher under higher Esh and high EEG values are more concentrated in the sheath whose potential gradient is larger. Finally, observation of the phase-resolved EEG distributions demonstrates statistically that the EEG mechanism found in stochastic individual electron behaviors also works for a majority of electrons. (C) 2020 The Japan Society of Applied Physics.AdaLSH: Adaptive LSH for Solving c-Approximate Maximum Inner Product Search Problem
http://hdl.handle.net/2115/80413
Title: AdaLSH: Adaptive LSH for Solving c-Approximate Maximum Inner Product Search Problem
Authors: Lu, Kejing; Kudo, Mineichi
Abstract: Maximum inner product search (MIPS) problem has gained much attention in a wide range of applications. In order to overcome the curse of dimensionality in high-dimensional spaces, most of existing methods first transform the MIPS problem into another approximate nearest neighbor search (ANNS) problem and then solve it by Locality Sensitive Hashing (LSH). However, due to the error incurred by the transmission and incomprehensive search strategies, these methods suffer from low precision and have loose probability guarantees. In this paper, we propose a novel search method named Adaptive-LSH (AdaLSH) to solve MIPS problem more efficiently and more precisely. AdaLSH examines objects in the descending order of both norms and (the probably correctly estimated) cosine angles with a query object in support of LSH with extendable windows. Such extendable windows bring not only efficiency in searching but also the probability guarantee of finding exact or approximate MIP objects. AdaLSH gives a better probability guarantee of success than those in conventional algorithms, bringing less running times on various datasets compared with them. In addition, AdaLSH can even support exact MIPS with probability guarantee.2020-12-31T15:00:00ZLu, KejingKudo, MineichiMaximum inner product search (MIPS) problem has gained much attention in a wide range of applications. In order to overcome the curse of dimensionality in high-dimensional spaces, most of existing methods first transform the MIPS problem into another approximate nearest neighbor search (ANNS) problem and then solve it by Locality Sensitive Hashing (LSH). However, due to the error incurred by the transmission and incomprehensive search strategies, these methods suffer from low precision and have loose probability guarantees. In this paper, we propose a novel search method named Adaptive-LSH (AdaLSH) to solve MIPS problem more efficiently and more precisely. AdaLSH examines objects in the descending order of both norms and (the probably correctly estimated) cosine angles with a query object in support of LSH with extendable windows. Such extendable windows bring not only efficiency in searching but also the probability guarantee of finding exact or approximate MIP objects. AdaLSH gives a better probability guarantee of success than those in conventional algorithms, bringing less running times on various datasets compared with them. In addition, AdaLSH can even support exact MIPS with probability guarantee.Adapting the Learning Rate of the Learning Rate in Hypergradient Descent
http://hdl.handle.net/2115/80339
Title: Adapting the Learning Rate of the Learning Rate in Hypergradient Descent
Authors: Itakura, Kazuma; Atarashi, Kyohei; Oyama, Satoshi; Kurihara, Masahito
Abstract: Gradient descent is a widely used optimization method. The adjustment of the learning rate is an important factor in improving its performance, and many researchers have investigated methods for automatically adjusting the learning rate. One such method, hypergradient descent, automatically adjusts the learning rate by using gradient descent. However, it introduces the “learning rate of the learning rate,” and an appropriate value for the learning rate of the learning rate must be chosen in order to effectively adjust the learning rate. We investigated the use of two datasets and two optimization methods for doing this and achieved an effective adjustment of the learning rate when the objective function was convex and L -smooth.2020-11-30T15:00:00ZItakura, KazumaAtarashi, KyoheiOyama, SatoshiKurihara, MasahitoGradient descent is a widely used optimization method. The adjustment of the learning rate is an important factor in improving its performance, and many researchers have investigated methods for automatically adjusting the learning rate. One such method, hypergradient descent, automatically adjusts the learning rate by using gradient descent. However, it introduces the “learning rate of the learning rate,” and an appropriate value for the learning rate of the learning rate must be chosen in order to effectively adjust the learning rate. We investigated the use of two datasets and two optimization methods for doing this and achieved an effective adjustment of the learning rate when the objective function was convex and L -smooth.Unsupervised Feature Learning for Output Control of Generative Models
http://hdl.handle.net/2115/80338
Title: Unsupervised Feature Learning for Output Control of Generative Models
Authors: Toda, Kazuki; Atarashi, Kyohei; Oyama, Satoshi; Kurihara, Masahito
Abstract: Deep generative models are being actively studied, particularly variational autoencoders (VAEs) because they can generate high-quality images. The M2 model supports semi-supervised learning from both labeled and unlabeled data, which enables the generated images to be easily controlled by changing the class label values. However, generative models must be learned from only unlabeled data when class labels are not available. A model is presented that incorporates a deep clustering method into the M2 model, which enables clusters to be identified among unlabeled data so that each data point can be assigned to one of the clusters. The generated images in unlabeled datasets can easily be controlled by changing the cluster assignment of each data point.2020-11-30T15:00:00ZToda, KazukiAtarashi, KyoheiOyama, SatoshiKurihara, MasahitoDeep generative models are being actively studied, particularly variational autoencoders (VAEs) because they can generate high-quality images. The M2 model supports semi-supervised learning from both labeled and unlabeled data, which enables the generated images to be easily controlled by changing the class label values. However, generative models must be learned from only unlabeled data when class labels are not available. A model is presented that incorporates a deep clustering method into the M2 model, which enables clusters to be identified among unlabeled data so that each data point can be assigned to one of the clusters. The generated images in unlabeled datasets can easily be controlled by changing the cluster assignment of each data point.A bad arm existence checking problem: How to utilize asymmetric problem structure?
http://hdl.handle.net/2115/80332
Title: A bad arm existence checking problem: How to utilize asymmetric problem structure?
Authors: Tabata, Koji; Nakamura, Atsuyoshi; Honda, Junya; Komatsuzaki, Tamiki
Abstract: We study a bad arm existence checking problem in a stochastic K-armed bandit setting, in which a player's task is to judge whether a positive arm exists or all the arms are negative among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold theta(U), and it is negative if that is less than another given threshold theta(L) (<= theta(U)). This problem is a formalization of diagnosis of disease or machine failure. An interesting structure of this problem is the asymmetry of positive and negative arms' roles; finding one positive arm is enough to judge positive existence while all the arms must be discriminated as negative to judge whole negativity. In the case with Delta = theta(U) - theta(L) > 0, we propose elimination algorithms with arm selection policy (policy to determine the next arm to draw) and decision condition (condition to conclude positive arm's existence or the drawn arm's negativity) utilizing this asymmetric problem structure and prove its effectiveness theoretically and empirically.2020-01-31T15:00:00ZTabata, KojiNakamura, AtsuyoshiHonda, JunyaKomatsuzaki, TamikiWe study a bad arm existence checking problem in a stochastic K-armed bandit setting, in which a player's task is to judge whether a positive arm exists or all the arms are negative among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold theta(U), and it is negative if that is less than another given threshold theta(L) (<= theta(U)). This problem is a formalization of diagnosis of disease or machine failure. An interesting structure of this problem is the asymmetry of positive and negative arms' roles; finding one positive arm is enough to judge positive existence while all the arms must be discriminated as negative to judge whole negativity. In the case with Delta = theta(U) - theta(L) > 0, we propose elimination algorithms with arm selection policy (policy to determine the next arm to draw) and decision condition (condition to conclude positive arm's existence or the drawn arm's negativity) utilizing this asymmetric problem structure and prove its effectiveness theoretically and empirically.Complexity of bird song caused by adversarial imitation learning
http://hdl.handle.net/2115/80329
Title: Complexity of bird song caused by adversarial imitation learning
Authors: Yamazaki, Seiya; Iizuka, Hiroyuki; Yamamoto, Masahito
Abstract: Biological evolution produces complexity through genetic variations based on randomness. In conventional communication or language simulation models, genetic variations based on randomness and fitness function rewarding task achievements play an important role in evolving communication signals to complex ones. However, it is known that not only genetic variations evolve communication but also imitative learning during developmental processes contributes to the evolution of communication. What we investigated here was to find a different principle of generating complexity which does not rely on the randomness or external environmental complexity but only on the learning processes in communication. Our hypothesis is that the contradictory learning mechanism we call the adversarial imitation learning can work to increase the complexity without relying on the random processes. To investigate our hypothesis, we implemented the adversarial imitation learning on a simulation where two agents interact with and imitate each other. Our results showed that the adversarial imitation learning causes chaotic dynamics and investigating the learning results in different types of interaction between the two; it was clarified that the adversarial imitation learning is necessary for the emergence of the chaotic time series.2020-01-31T15:00:00ZYamazaki, SeiyaIizuka, HiroyukiYamamoto, MasahitoBiological evolution produces complexity through genetic variations based on randomness. In conventional communication or language simulation models, genetic variations based on randomness and fitness function rewarding task achievements play an important role in evolving communication signals to complex ones. However, it is known that not only genetic variations evolve communication but also imitative learning during developmental processes contributes to the evolution of communication. What we investigated here was to find a different principle of generating complexity which does not rely on the randomness or external environmental complexity but only on the learning processes in communication. Our hypothesis is that the contradictory learning mechanism we call the adversarial imitation learning can work to increase the complexity without relying on the random processes. To investigate our hypothesis, we implemented the adversarial imitation learning on a simulation where two agents interact with and imitate each other. Our results showed that the adversarial imitation learning causes chaotic dynamics and investigating the learning results in different types of interaction between the two; it was clarified that the adversarial imitation learning is necessary for the emergence of the chaotic time series.