scholarly journals Time-varying data processing with nonvolatile memristor-based temporal kernel

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yoon Ho Jang ◽  
Woohyun Kim ◽  
Jihun Kim ◽  
Kyung Seok Woo ◽  
Hyun Jae Lee ◽  
...  
Keyword(s):  
Data Processing ◽  
Time Constant ◽  
Sequential Data ◽  
Reservoir Computing ◽  
Time Varying ◽  
Linear Classifier ◽  
Recent Advances ◽  
Limited Application ◽  
Application Fields

Abstract Recent advances in physical reservoir computing, which is a type of temporal kernel, have made it possible to perform complicated timing-related tasks using a linear classifier. However, the fixed reservoir dynamics in previous studies have limited application fields. In this study, temporal kernel computing was implemented with a physical kernel that consisted of a W/HfO2/TiN memristor, a capacitor, and a resistor, in which the kernel dynamics could be arbitrarily controlled by changing the circuit parameters. After the capability of the temporal kernel to identify the static MNIST data was proven, the system was adopted to recognize the sequential data, ultrasound (malignancy of lesions) and electrocardiogram (arrhythmia), that had a significantly different time constant (10−7 vs. 1 s). The suggested system feasibly performed the tasks by simply varying the capacitance and resistance. These functionalities demonstrate the high adaptability of the present temporal kernel compared to the previous ones.

scholarly journals Nonvolatile Memristor-Based Reservoir Computing System with Reservoir Controllability

2021 ◽  
Author(s):  
Yoonho Jang ◽  
Ji Hun Kim ◽  
Gyung Seok Woo ◽  
Hyun Jae Lee ◽  
Woohyun Kim ◽  
...  
Keyword(s):  
Time Constant ◽  
Computing System ◽  
Sequential Data ◽  
Reservoir Computing ◽  
Data Set ◽  
Recent Advances ◽  
Limited Application ◽  
Application Fields

Abstract Recent advances in reservoir computing (RC) using memristors have made it possible to perform complicated timing-related recognition tasks using simple hardware. However, the fixed reservoir dynamics in previous studies have severely limited application fields. In this study, RC was implemented with a reservoir that consisted of a W/HfO2/TiN memristor (M), a capacitor (C), and a resistor (R), in which the reservoir dynamics could be arbitrarily controlled by changing their parameters. After the capability of the RC to identify the static MNIST data set was proven, the system was adopted to recognize the sequential data set [ultrasound (malignancy of lesions) and electrocardiogram (arrhythmia)] that had a significantly different time constant (107 vs. 1 s). The suggested RC system feasibly performed the tasks by simply varying the C and R, while the M remained unvaried. These functionalities demonstrate the high adaptability of the present RC system compared to the previous ones.

Ligand-Driven Advances in Iridium Catalyzed sp3 C-H Borylation: 2,2’-Dipyridylarylmethane

Synlett ◽  
2020 ◽  
Author(s):  
Margaret R Jones ◽  
Nathan D. Schley
Keyword(s):  
Recent Work ◽  
Organic Synthesis ◽  
Functional Group ◽  
Reaction Conditions ◽  
Recent Advances ◽  
Additional Ligand ◽  
Limited Application ◽  
Hydrocarbon Substrates ◽  
Phenanthroline Ligands

The field of catalytic C-H borylation has grown considerably since its founding, providing a means for the preparation of synthetically versatile organoborane products. While sp2 C-H borylation methods have found widespread and practical use in organic synthesis, the analogous sp3 C-H borylation reaction remains challenging and has seen limited application. Existing catalysts are often hindered by incomplete consumption of the diboron reagent, poor functional group tolerance, harsh reaction conditions, and the need for excess or neat substrate. These challenges acutely affect C-H borylation chemistry of unactivated hydrocarbon substrates, which has lagged in comparison to methods for the C-H borylation of activated compounds. Herein we discuss recent advances in sp3 C-H borylation of undirected substrates in the context of two particular challenges: (1) utilization of the diboron reagent and (2) the need for excess or neat substrate. Our recent work on the application of dipyridylarylmethane ligands in sp3 C-H borylation has allowed us to make contributions in this space and has presented an additional ligand scaffold to supplement traditional phenanthroline ligands.

Identification of Time-Varying Time Constants of Thermocouple Sensors and Its Application to Temperature Measurement

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Kenneth Kar ◽  
Akshya K. Swain ◽  
Robert Raine
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Time Constant ◽  
Identification Problem ◽  
Basis Functions ◽  
New Technique ◽  
Ratio Test ◽  
Time Varying ◽  
Time Constants ◽  
Orthogonal Least Squares

The present study addresses the problem of estimating time-varying time constants associated with thermocouple sensors by a set of basis functions. By expanding each time-varying time constant onto a finite set of basis sequences, the time-varying identification problem reduces to a parameter estimation problem of a time-invariant system. The proposed algorithm, to be called as orthogonal least-squares with basis function expansion algorithm, combines the orthogonal least-squares algorithm with an error reduction ratio test to include significant basis functions into the model, which results in a parsimonious model structure. The performance of the method was compared with a linear Kalman filter. Simulations on engine data have demonstrated that the proposed method performs satisfactorily and is better than the Kalman filter. The new technique has been applied in a Stirling cycle compressor. The sinusoidal variations in time constant are tracked properly using the new technique, but the linear Kalman filter fails to do so. Both model validation and thermodynamic laws confirm that the new technique gives unbiased estimates and that the assumed thermocouple model is adequate.

scholarly journals Organic Cocrystals: Recent Advances and Perspectives for Electronic and Magnetic Applications

2021 ◽  
Vol 9 ◽  
Author(s):  
Mengjia Jiang ◽  
Chun Zhen ◽  
Shuyu Li ◽  
Xiaotao Zhang ◽  
Wenping Hu
Keyword(s):  
Magnetic Properties ◽  
Magnetic Fields ◽  
Research Interest ◽  
Comprehensive Understanding ◽  
Electronic And Magnetic Properties ◽  
Recent Advances ◽  
Magnetic Applications ◽  
Application Fields ◽  
Growth Methods

Cocrystal engineering is an advanced supramolecular strategy that has attracted a lot of research interest. Many studies on cocrystals in various application fields have been reported, with a particular focus on the optoelectronics field. However, few articles have combined and summarized the electronic and magnetic properties of cocrystals. In this review, we first introduce the growth methods that serve as the basis for realizing the different properties of cocrystals. Thereafter, we present an overview of cocrystal applications in electronic and magnetic fields. Some functional devices based on cocrystals are also introduced. We hope that this review will provide researchers with a more comprehensive understanding of the latest progress and prospects of cocrystals in electronic and magnetic fields.

scholarly journals On Learning Interpreted Languages with Recurrent Models

2022 ◽  
pp. 1-13
Author(s):  
Denis Paperno
Keyword(s):  
Natural Language ◽  
Data Processing ◽  
Syntactic Structure ◽  
Neural Nets ◽  
Training Data ◽  
Sequential Data ◽  
Extensive Training ◽  
Formal Syntax ◽  
Compositional Interpretation

Abstract Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.

Recent Advances in Data Processing from Caustics

1991 ◽  
pp. 260-272 ◽  
Author(s):  
H. P. Rossmanith ◽  
R. E. Knasmillner
Keyword(s):  
Data Processing ◽  
Recent Advances

scholarly journals Interactions in Fixed Effects Regression Models

2020 ◽  
pp. 004912412091493
Author(s):  
Marco Giesselmann ◽  
Alexander W. Schmidt-Catran
Keyword(s):  
Monte Carlo ◽  
Time Constant ◽  
Fixed Effects ◽  
Regression Models ◽  
Time Varying ◽  
Unit Level ◽  
Monte Carlo Experiments ◽  
Product Term ◽  
Fixed Effects Regression ◽  
Effect Heterogeneity

An interaction in a fixed effects (FE) regression is usually specified by demeaning the product term. However, algebraic transformations reveal that this strategy does not yield a within-unit estimator. Instead, the standard FE interaction estimator reflects unit-level differences of the interacted variables. This property allows interactions of a time-constant variable and a time-varying variable in FE to be estimated but may yield unwanted results if both variables vary within units. In such cases, Monte Carlo experiments confirm that the standard FE estimator of x ⋅ z is biased if x is correlated with an unobserved unit-specific moderator of z (or vice versa). A within estimator of an interaction can be obtained by first demeaning each variable and then demeaning their product. This “double-demeaned” estimator is not subject to bias caused by unobserved effect heterogeneity. It is, however, less efficient than standard FE and only works with T > 2.

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