scholarly journals Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation

2021 ◽  
Vol 15 ◽  
Author(s):  
Avi Hazan ◽  
Elishai Ezra Tsur
Keyword(s):  
Large Scale ◽  
Data Representation ◽  
Spiking Neurons ◽  
High Dimensional ◽  
Neural Engineering ◽  
Dimensional Representation ◽  
Response Curves ◽  
High Performing ◽  
Analog Implementation ◽  
Hardware Designs

Brain-inspired hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for artificial intelligence. The Neural Engineering Framework (NEF) brings forth a theoretical framework for representing high-dimensional mathematical constructs with spiking neurons to implement functional large-scale neural networks. Here, we present OZ, a programable analog implementation of NEF-inspired spiking neurons. OZ neurons can be dynamically programmed to feature varying high-dimensional response curves with positive and negative encoders for a neuromorphic distributed representation of normalized input data. Our hardware design demonstrates full correspondence with NEF across firing rates, encoding vectors, and intercepts. OZ neurons can be independently configured in real-time to allow efficient spanning of a representation space, thus using fewer neurons and therefore less power for neuromorphic data representation.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

Rethinking the notion of the high-performing education system: A Daoist response

2021 ◽  
pp. 174549992098369
Author(s):  
Charlene Tan
Keyword(s):  
Education System ◽  
Large Scale ◽  
Alternative Interpretation ◽  
Educational Systems ◽  
Education Systems ◽  
High Performing ◽  
Mutual Learning ◽  
System A ◽  
High Performers ◽  
Major Implication

This article challenges the dominant notion of the ‘high-performing education system’ and offers an alternative interpretation from a Daoist perspective. The paper highlights two salient characteristics of such a system: its ability to outperform other education systems in international large-scale assessments; and its status as a positive or negative ‘reference society’. It is contended that external standards are applied and imposed on educational systems across the globe, judging a system to be high- or low- performing, and consequently worthy of emulation or deserving of criticism. Three cardinal Daoist principles that are drawn from the Zhuangzi are expounded: a rejection of an external and oppressive dao (way); the emptying of one’s heart-mind; and an ethics of difference. A major implication is a celebration of a plurality of high performers and reference societies, each unique in its own dao but converging on mutual learning and appreciation.

scholarly journals Electrocatalysis for Oxygen Reduction Reaction on EDTAFeNa and Melamine co-Derived Self-Supported Fe-N-C Materials

Catalysts ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 623
Author(s):  
Mengfan Shen ◽  
Ziwei Meng ◽  
Tong Xue ◽  
Hongfang Shen ◽  
Xiang-Hui Yan
Keyword(s):  
Oxygen Reduction Reaction ◽  
Oxygen Reduction ◽  
Large Scale ◽  
Acid Leaching ◽  
Reduction Reaction ◽  
Scale Production ◽  
Final Annealing ◽  
High Performing ◽  
Half Wave ◽  
Mesoscopic Structure

To explore high-performing alternatives to platinum-based catalysts is highly desirable for lowering costs and thus promoting fuel cell commercialization. Herein, self-supported Fe-N-C materials were prepared by the pyrolysis of dual precursors including EDTA ferric sodium (EDTAFeNa) and melamine (MA), followed by acid-leaching and final annealing. Towards an oxygen reduction reaction (ORR) in 0.1 M KOH, the as-prepared MA/EDTAFeNa-HT2 delivered onset (Eonset) and half-wave (E1/2) potentials of 0.97 and 0.84 V vs. RHE, respectively, identical with that of a state-of-the-art Pt/C catalyst, accompanied with predominantly a four-electron pathway. The introduction of MA and extension of acid-leaching promoted a positive shift of 50 mV for E1/2 relative to that of only the EDTAFeNa-derived counterpart. It was revealed that the enhancement of ORR activity is attributed to a decrease in magnetic Fe species and increase in pyridinic/quanternary nitrogen content whilst nearly excluding effects of the graphitization degree, variety of crystalline iron species, and mesoscopic structure. The usage of dual precursors exhibited great potential for the large-scale production of inexpensive and efficient Fe-N-C materials.

scholarly journals School Choice During a Period of Radical School Reform. Evidence from Academy Conversion in England

2020 ◽  
Author(s):  
Marco Bertoni ◽  
Stephen Gibbons ◽  
Olmo Silva
Keyword(s):  
School Choice ◽  
Large Scale ◽  
English Education ◽  
Limited Information ◽  
State Control ◽  
High Performing ◽  
Opt Out ◽  
Improved Performance ◽  
Expected Benefits ◽  
State Schools

Abstract We study how demand responds to the rebranding of existing state schools as autonomous ‘academies’ in the context of a radical and large-scale reform to the English education system. The academy programme encouraged schools to opt out of local state control and funding, but provided parents and students with limited information on the expected benefits. We use administrative data on school applications for three cohorts of students to estimate whether this rebranding changes schools’ relative popularity. We find that families – particularly higher-income, White British – are more likely to rank converted schools above non-converted schools on their applications. We also find that it is mainly schools that are high-performing, popular and proximate to families’ homes that attract extra demand after conversion. Overall, the patterns we document suggest that families read academy conversion as a signal of future quality gains – although this signal is in part misleading as we find limited evidence that conversion causes improved performance.

scholarly journals Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

2021 ◽  
Vol 11 (2) ◽  
pp. 472
Author(s):  
Hyeongmin Cho ◽  
Sangkyun Lee
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Large Scale ◽  
High Dimensional Data ◽  
Quality Measures ◽  
Training Data ◽  
Measure Data ◽  
High Dimensional ◽  
Small Scale ◽  
Class Separability

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

scholarly journals Fundamental Dynamical Modes Underlying Human Brain Synchronization

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Catalina Alvarado-Rojas ◽  
Michel Le Van Quyen
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Electrode Placement ◽  
High Dimensional ◽  
Sleep Cycle ◽  
Epileptic Patients ◽  
Brain States ◽  
Intracranial Recordings ◽  
Characteristic Dynamic

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

scholarly journals Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sai Kiranmayee Samudrala ◽  
Jaroslaw Zola ◽  
Srinivas Aluru ◽  
Baskar Ganapathysubramanian
Keyword(s):  
Dimensionality Reduction ◽  
Organic Solar Cells ◽  
Large Scale ◽  
Parallel Implementation ◽  
High Dimensional Data ◽  
Real Life ◽  
Processing Parameters ◽  
High Dimensional ◽  
Morphology Evolution ◽  
Reduction Techniques

Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

scholarly journals Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments

Technometrics ◽  
2021 ◽  
pp. 1-36
Author(s):  
Pulong Ma ◽  
Anirban Mondal ◽  
Bledar A. Konomi ◽  
Jonathan Hobbs ◽  
Joon Jin Song ◽  
...  
Keyword(s):  
Computer Model ◽  
Large Scale ◽  
High Dimensional ◽  
System Uncertainty ◽  
Functional Output ◽  
Computer Model Emulation
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