Trajectories of Infants’ Biobehavioral Development: Timing and Rate of A-Not-B Performance Gains and EEG Maturation

We present a method to improve the performance of a reservoir computer by keeping the reservoir fixed and increasing the number of output neurons. The additional neurons are nonlinear functions, typically chosen randomly, of the reservoir neurons. We demonstrate the interest of this expanded output layer on an experimental opto-electronic system subject to slow parameter drift which results in loss of performance. We can partially recover the lost performance by using the output layer expansion. The proposed scheme allows for a trade-off between performance gains and system complexity.

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The Evolution of Resource-Based Inquiry: A Review and Meta-Analytic Integration of the Strategic Resources–Actions–Performance Pathway

Journal of Management ◽

10.1177/0149206321994182 ◽

2021 ◽

pp. 014920632199418

Author(s):

Laura D’Oria ◽

T. Russell Crook ◽

David J. Ketchen ◽

David G. Sirmon ◽

Mike Wright

Keyword(s):

Structural Equation ◽

Equation Modeling ◽

Competitive Advantages ◽

Strategy Research ◽

Resource Based View ◽

Strategic Resources ◽

Analytic Integration ◽

Resource Orchestration ◽

Performance Gains ◽

Conceptual Evolution

Understanding why some firms outperform others is central to strategy research. The resource-based view (RBV) suggests that competitive advantages arise due to possessing strategic resources (i.e., assets that are valuable, rare, nonsubstitutable, and inimitable), and researchers have extended this logic to explain performance differences. However, RBV is relatively silent about the actions managers could use to create or capitalize on a resource-based advantage. Enriching RBV, the resource orchestration framework describes specific managerial actions that use such resources to realize performance gains. After reviewing the conceptual evolution of these two literature streams as well as related streams, we use meta-analytic structural equation modeling to aggregate evidence from 255 samples involving 111,614 observations to answer outstanding research questions regarding the strategic resources–actions–performance pathway. The results show strong complementarity and interdependence between their logics. Additional inquiry drawing on their complementarity is a clear path toward enhancing scholars’ understanding of how and why some firms outperform others. We build on our findings to lay a foundation for such inquiry, including a call for theorizing centered on the interdependence of resources and actions, as well as new theoretical terrain that can help resource-based inquiry continue to evolve.

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Performance gains prompt leach expansion at Pinto Valley

Focus on Catalysts ◽

10.1016/j.focat.2020.11.016 ◽

2020 ◽

Vol 2020 (12) ◽

pp. 4

Keyword(s):

Performance Gains

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Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080523 ◽

2021 ◽

Vol 10 (8) ◽

pp. 523

Author(s):

Nicholus Mboga ◽

Stefano D’Aronco ◽

Tais Grippa ◽

Charlotte Pelletier ◽

Stefanos Georganos ◽

...

Keyword(s):

Land Cover ◽

Domain Adaptation ◽

Central Africa ◽

Semantic Segmentation ◽

Target Domain ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

The Cost ◽

Performance Gains

Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform long-term studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for land-cover generation for two focus cities Goma-Gisenyi and Bukavu. Our experimental evaluation shows that the domain adaptation methods can reach an overall accuracy between 60% and 70% for different regions. If we add a small amount of labelled data from the target domain, too, further performance gains can be achieved.

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Caffeine may disrupt the impact of real-time drowsiness on cognitive performance: a double-blind, placebo-controlled small-sample study

Scientific Reports ◽

10.1038/s41598-021-83504-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

E. Aidman ◽

M. Balin ◽

K. Johnson ◽

S. Jackson ◽

G. M. Paech ◽

...

Keyword(s):

Real Time ◽

Cognitive Performance ◽

Small Sample ◽

Night Time ◽

Double Blind ◽

Infra Red ◽

Procedural Decisions ◽

Performance Gains ◽

The Impact ◽

Modest Reduction

AbstractCaffeine is widely used to promote alertness and cognitive performance under challenging conditions, such as sleep loss. Non-digestive modes of delivery typically reduce variability of its effect. In a placebo-controlled, 50-h total sleep deprivation (TSD) protocol we administered four 200 mg doses of caffeine-infused chewing-gum during night-time circadian trough and monitored participants' drowsiness during task performance with infra-red oculography. In addition to the expected reduction of sleepiness, caffeine was found to disrupt its degrading impact on performance errors in tasks ranging from standard cognitive tests to simulated driving. Real-time drowsiness data showed that caffeine produced only a modest reduction in sleepiness (compared to our placebo group) but substantial performance gains in vigilance and procedural decisions, that were largely independent of the actual alertness dynamics achieved. The magnitude of this disrupting effect was greater for more complex cognitive tasks.

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Recovery from memory failure when recalling a memorized performance: The role of musical structure and performance cues

Musicae Scientiae ◽

10.1177/10298649211025491 ◽

2021 ◽

pp. 102986492110254

Author(s):

Roger Chaffin ◽

Jane Ginsborg ◽

James Dixon ◽

Alexander P. Demos

Keyword(s):

Vocal Music ◽

Musical Structure ◽

Public Performance ◽

Memory Failure ◽

And Performance ◽

Written Recall ◽

Performance Gains ◽

Over Time

To perform reliably and confidently from memory, musicians must able to recover from mistakes and memory failures. We describe how an experienced singer (the second author) recovered from mistakes and gaps in recall as she periodically recalled the score of a piece of vocal music that she had memorized for public performance, writing out the music six times over a five-year period following the performance. Five years after the performance, the singer was still able to recall two-thirds of the piece. When she made mistakes, she recovered and went on, leaving gaps in her written recall that lengthened over time. We determined where in the piece gaps started ( losses) and ended ( gains), and compared them with the locations of structural beats (starts of sections and phrases) and performance cues ( PCs) that the singer reported using as mental landmarks to keep track of her progress through the piece during the sung, public performance. Gains occurred on structural beats where there was a PC; losses occurred on structural beats without a PC. As the singer’s memory faded over time, she increasingly forgot phrases that did not start with a PC and recovered at the starts of phrases that did. Our study shows how PCs enable musicians to recover from memory failures.

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The Romano–Wolf multiple-hypothesis correction in Stata

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x20976314 ◽

2020 ◽

Vol 20 (4) ◽

pp. 812-843

Author(s):

Damian Clarke ◽

Joseph P. Romano ◽

Michael Wolf

Keyword(s):

Error Rate ◽

Multiple Testing ◽

Original Data ◽

Dependence Structure ◽

Familywise Error Rate ◽

Testing Procedures ◽

Multiple Testing Procedures ◽

Multiple Hypothesis ◽

Wide Range ◽

Performance Gains

When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this article, we discuss the Romano–Wolf multiple-hypothesis correction and document its implementation in Stata. The Romano–Wolf correction (asymptotically) controls the familywise error rate, that is, the probability of rejecting at least one true null hypothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from using rwolf over other multiple-testing procedures that control the familywise error rate.

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Bayesian cumulative shrinkage for infinite factorizations

Biometrika ◽

10.1093/biomet/asaa008 ◽

2020 ◽

Vol 107 (3) ◽

pp. 745-752 ◽

Cited By ~ 1

Author(s):

Sirio Legramanti ◽

Daniele Durante ◽

David B Dunson

Keyword(s):

Monte Carlo ◽

Factor Analysis ◽

Monte Carlo Algorithm ◽

Model Complexity ◽

Latent Factors ◽

Shrinkage Process ◽

Shrinkage Prior ◽

Performance Gains ◽

Shrinkage Priors ◽

Model Dimension

Summary The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumulative shrinkage process, for the parameters that control the dimension in overcomplete formulations. Our construction has broad applicability and is based on an interpretable sequence of spike-and-slab distributions which assign increasing mass to the spike as the model complexity grows. Using factor analysis as an illustrative example, we show that this formulation has theoretical and practical advantages relative to current competitors, including an improved ability to recover the model dimension. An adaptive Markov chain Monte Carlo algorithm is proposed, and the performance gains are outlined in simulations and in an application to personality data.

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