Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task

Earthquake forecasting during the complex Amatrice-Norcia seismic sequence

Earthquake forecasting is the ultimate challenge for seismologists, because it condenses the scientific knowledge about the earthquake occurrence process, and it is an essential component of any sound risk mitigation planning. It is commonly assumed that, in the short term, trustworthy earthquake forecasts are possible only for typical aftershock sequences, where the largest shock is followed b...

The role of sleep on children's motor memory consolidation in a motor sequence task

Sleep has been shown to affect the consolidation of motor memory in adults. However, sleep-dependent consolidation is not well-specified in children as like as adults. So, the purpose of this study is to investigate the effect of sleep on childrenchr('39')s motor memory consolidation. The participants were 32 voluntary boys aging from 10 to 12 years old from city of Kerman, which had the necess...

Constrained Seismic Sequence Stratigraphy of Asmari - Kajhdumi interval with well-log Data

Sequence stratigraphy is a key step in interpretation of the seismic reflection data. It was originally developed by seismic specialists, and then the usage of high-resolution well logs and core data was taken into consideration in its implementation. The current paper aims in performing sequence stratigraphy using three-dimensional seismic data, well logs (gamma ray, sonic, porosity, density, ...

A MODEL FOR THE BASIC HELIX- LOOPHELIX MOTIF AND ITS SEQUENCE SPECIFIC RECOGNITION OF DNA

A three dimensional model of the basic Helix-Loop-Helix motif and its sequence specific recognition of DNA is described. The basic-helix I is modeled as a continuous ?-helix because no ?-helix breaking residue is found between the basic region and the first helix. When the basic region of the two peptide monomers are aligned in the successive major groove of the cognate DNA, the hydrophobi...

Task Loss Estimation for Sequence Prediction

Often, the performance on a supervised machine learning task is evaluated with a task loss function that cannot be optimized directly. Examples of such loss functions include the classification error, the edit distance and the BLEU score. A common workaround for this problem is to instead optimize a surrogate loss function, such as for instance cross-entropy or hinge loss. In order for this rem...

A New Class of Banach Sequence Spaces