scholarly journals Sequential Detection of Regime Changes in Neural Data

Author(s):  
Taposh Banerjee ◽  
Stephen Allsop ◽  
Kay M. Tye ◽  
Demba Ba ◽  
Vahid Tarokh
Author(s):  
Stephan Haggard ◽  
Robert R. Kaufman

From the 1980s through the first decade of the twenty-first century, the spread of democracy across the developing and postcommunist worlds transformed the global political landscape. What drove these changes and what determined whether the emerging democracies would stabilize or revert to authoritarian rule? This book takes a comprehensive look at the transitions to and from democracy in recent decades. Deploying both statistical and qualitative analysis, the book engages with theories of democratic change and advocates approaches that emphasize political and institutional factors. While inequality has been a prominent explanation for democratic transitions, the book argues that its role has been limited, and elites as well as masses can drive regime change. Examining seventy-eight cases of democratic transition and twenty-five cases of reversion to autocracy since 1980, the book shows how differences in authoritarian regimes and organizational capabilities shape popular protest and elite initiatives in transitions to democracy, and how institutional weaknesses cause some democracies to fail. The determinants of democracy lie in the strength of existing institutions and the public's capacity to engage in collective action. There are multiple routes to democracy, but those growing out of mass mobilization may provide more checks on incumbents than those emerging from intra-elite bargains. Moving beyond well-known beliefs regarding regime changes, this book explores the conditions under which transitions to democracy are likely to arise.


2002 ◽  
Vol 66 (1) ◽  
pp. 202 ◽  
Author(s):  
M. Tejedor ◽  
C. C. Jiménez ◽  
F. Díaz

RSC Advances ◽  
2021 ◽  
Vol 11 (20) ◽  
pp. 12361-12373
Author(s):  
A. Arunjegan ◽  
P. Rajaji ◽  
S. Sivanesan ◽  
P. Panneerselvam

In this paper, we propose a fluorescent biosensor for the sequential detection of Pb2+ ions and the cancer drug epirubicin (Epn) using the interactions between label-free guanine-rich ssDNA (LFGr-ssDNA), acridine orange (AO), and a metal–phenolic nanomaterial.


Author(s):  
Amerigo Caruso ◽  
Linda Hammann

AbstractProspero Balbo, the head of a leading Piedmontese noble family, followed a career path similar to that of the versatile French statesman Talleyrand. In the aftermath of 1789, Balbo served under four different regimes: the Old Regime monarchy, the Russian provisional administration of Piedmont in 1799, the Napoleonic empire, and the restored Savoy monarchy. After the short-lived revolutionary movement of 1821 in Sardinia-Piedmont, Prospero lost his job as interior minister and his son, Cesare, was forced into exile. The revolutionary waves of 1820–1821 were the most recent of numerous disruptive events and regime changes that jeopardized Europe and the Atlantic world between the late 1770 s and the early 1820 s. These five decades of revolutionary upheavals, wars, and persistent insecurity forced the traditional elites to mobilize their material, cultural, and social resources to preserve their prestige and power. Based on extensive archival research, this article examines the resilience-strengthening resources and strategies implemented by members of the Balbo family during periods of political turmoil. In doing so, the article aims to develop an analytical and conceptual framework to describe historical processes in terms of resilience and vulnerability. This new approach enables us to look afresh at elite transformations and at the dynamics of political change and continuity in early nineteenth-century Europe.


2021 ◽  
Author(s):  
Parikshat Sirpal ◽  
Rafat Damseh ◽  
Ke Peng ◽  
Dang Khoa Nguyen ◽  
Frédéric Lesage

AbstractIn this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model’s fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.


Energy Policy ◽  
2021 ◽  
Vol 154 ◽  
pp. 112257
Author(s):  
Faraz Farhidi ◽  
Vahid Khiabani

2020 ◽  
Vol 172 ◽  
pp. 107872 ◽  
Author(s):  
Qing Guo ◽  
Yang Zhang ◽  
Zhi-Hong Lin ◽  
Qian-Yong Cao ◽  
Yong Chen
Keyword(s):  

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