scholarly journals Long-term Evolution of the Short-term X-Ray Variability of the Jetted TDE Swift J1644+57

2021 ◽  
Vol 920 (1) ◽  
pp. 60
Author(s):  
Chichuan Jin
Keyword(s):  
Long Term Evolution ◽  
Short Term ◽  
X Ray ◽  
Term Evolution

scholarly journals Growth and business cycle in Argentina. A long-run approach, 1870–2015

2020 ◽  
Vol 28 (84) ◽  
pp. 197-220
Author(s):  
María Dolores Gadea ◽  
Isabel Sanz-Villarroya
Keyword(s):  
Long Term Evolution ◽  
Growth Potential ◽  
Short Term ◽  
Content Type ◽  
Developed Economies ◽  
Term Evolution ◽  
Long Run ◽  
High Volatility ◽  
The Moment

Purpose The purpose of this study is to focus deeply on the short term to explain the relative long-term evolution of the Argentinian economy in the long and the short term. Design/methodology/approach The study of the long-term evolution of the Argentine economy and identifying the moment in which it began to lose ground compared to other developed economies, such as Australia and Canada, constitutes the central axis of the historiography of this country. However, an additional problem presented by the Argentine economy is its high volatility. For this reason, the long term should be influenced by the short term, an issue that requires a more detailed study of the cyclical behavior and a deep analysis of the relationship between the long and the short term. Findings The results obtained point to a cyclical development that influences the long-term evolution and, therefore, explains Argentina’s convergence process with Australia and Canada. Frequent deep busts and short booms characterize the Argentine cycle, offsetting its long-term growth potential. Originality/value Although the long term has been profusely studied in Argentina, the short term has not been analyzed to the same extent, which is surprising given the extreme volatility of this economy (Prebisch, 1950). The studies performed on economic cycles have always been partial, disconnected from the long term and carried out without much technical rigor.

scholarly journals The long-term evolution of the accreting millisecond X-ray pulsar Swift J1756.9−2508

2010 ◽  
Vol 403 (3) ◽  
pp. 1426-1432 ◽  
Author(s):  
Alessandro Patruno ◽  
Diego Altamirano ◽  
Chris Messenger
Keyword(s):  
Long Term Evolution ◽  
X Ray ◽  
Term Evolution

scholarly journals On the long-term evolution of rotating radio transients

2020 ◽  
Vol 500 (3) ◽  
pp. 3281-3289
Author(s):  
A A Gençali ◽  
Ü Ertan
Keyword(s):  
Long Term Evolution ◽  
Compact Objects ◽  
Dipole Field ◽  
Death Valley ◽  
X Ray ◽  
Term Evolution ◽  
Radio Transients ◽  
Rotating Radio Transients ◽  
Field Strengths

ABSTRACT Investigation of the long-term evolution of rotating radio transients (RRATs) is important to understand the evolutionary connections between the isolated neutron star populations in a single picture. The X-ray luminosities of RRATs (except one source) are not known. In the fallback disc model, we have developed a method to estimate the dipole field strengths of RRATs without X-ray information. We have found that RRATs could have dipole field strengths, B0, at the poles ranging from ∼7 × 109 to ∼6 × 1011 G which fill the gap between the B0 ranges of central compact objects (CCOs) and dim isolated neutron stars (XDINs) estimated in the same model. In our model, most of RRATs are evolving at ages (∼2–6) × 105 yr, much smaller than their characteristic ages, such that, cooling luminosities of a large fraction of relatively nearby RRATs could be detected by the eROSITA all-sky survey. Many RRATs are located above the upper border of the pulsar death valley with the fields inferred from the dipole-torque formula, while they do not show strong, continuous radio pulses. The B0 values estimated in our model, place all RRATs either into the death valley or below the death line. We have tentatively proposed that RRATs could be the sources below their individual death points, and their short radio bursts could be ignited by the disc-field interaction occasionally enhancing the flux of open field lines through the magnetic poles. We have also discussed the evolutionary links between CCOs, RRATs, and XDINs.

Long-term Evolution, Short-term Evolution, and Population Genetic Theory

1998 ◽  
Vol 191 (4) ◽  
pp. 391-396 ◽  
Author(s):  
Ilan Eshel ◽  
Marcus W. Feldman ◽  
Aviv Bergman
Keyword(s):  
Population Genetic ◽  
Long Term Evolution ◽  
Short Term ◽  
Genetic Theory ◽  
Term Evolution

scholarly journals X-RAY ENHANCEMENT AND LONG-TERM EVOLUTION OF SWIFT J1822.3–1606

2013 ◽  
Vol 778 (2) ◽  
pp. 119 ◽  
Author(s):  
Onur Benli ◽  
Ş. Çalışkan ◽  
Ü. Ertan ◽  
M. A. Alpar ◽  
J. E. Trümper ◽  
...  
Keyword(s):  
Long Term Evolution ◽  
X Ray ◽  
Term Evolution

Few-Shot Learning in Spiking Neural Networks by Multi-Timescale Optimization

2021 ◽  
pp. 1-34
Author(s):  
Runhao Jiang ◽  
Jie Zhang ◽  
Rui Yan ◽  
Huajin Tang
Keyword(s):  
Adaptive Learning ◽  
Temporal Dynamics ◽  
Long Term Evolution ◽  
Collaborative Optimization ◽  
Neural Dynamics ◽  
Short Term ◽  
Weight Decay ◽  
Term Evolution ◽  
Priori Knowledge

Learning new concepts rapidly from a few examples is an open issue in spike-based machine learning. This few-shot learning imposes substantial challenges to the current learning methodologies of spiking neuron networks (SNNs) due to the lack of task-related priori knowledge. The recent learning-to-learn (L2L) approach allows SNNs to acquire priori knowledge through example-level learning and task-level optimization. However, an existing L2L-based framework does not target the neural dynamics (i.e., neuronal and synaptic parameter changes) on different timescales. This diversity of temporal dynamics is an important attribute in spike-based learning, which facilitates the networks to rapidly acquire knowledge from very few examples and gradually integrate this knowledge. In this work, we consider the neural dynamics on various timescales and provide a multi-timescale optimization (MTSO) framework for SNNs. This framework introduces an adaptive-gated LSTM to accommodate two different timescales of neural dynamics: short-term learning and long-term evolution. Short-term learning is a fast knowledge acquisition process achieved by a novel surrogate gradient online learning (SGOL) algorithm, where the LSTM guides gradient updating of SNN on a short timescale through an adaptive learning rate and weight decay gating. The long-term evolution aims to slowly integrate acquired knowledge and form, which can be achieved by optimizing the LSTM guidance process to tune SNN parameters on a long timescale. Experimental results demonstrate that the collaborative optimization of multi-timescale neural dynamics can make SNNs achieve promising performance for the few-shot learning tasks.

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