108 research outputs found
Inferring short-term volatility indicators from Bitcoin blockchain
In this paper, we study the possibility of inferring early warning indicators
(EWIs) for periods of extreme bitcoin price volatility using features obtained
from Bitcoin daily transaction graphs. We infer the low-dimensional
representations of transaction graphs in the time period from 2012 to 2017
using Bitcoin blockchain, and demonstrate how these representations can be used
to predict extreme price volatility events. Our EWI, which is obtained with a
non-negative decomposition, contains more predictive information than those
obtained with singular value decomposition or scalar value of the total Bitcoin
transaction volume
Limiting Behaviour of the Mean Residual Life
In survival or reliability studies, the mean residual life or life expectancy
is an important characteristic of the model. Here, we study the limiting
behaviour of the mean residual life, and derive an asymptotic expansion which
can be used to obtain a good approximation for large values of the time
variable. The asymptotic expansion is valid for a quite general class of
failure rate distributions--perhaps the largest class that can be expected
given that the terms depend only on the failure rate and its derivatives.Comment: 19 page
Synapse efficiency diverges due to synaptic pruning following over-growth
In the development of the brain, it is known that synapses are pruned
following over-growth. This pruning following over-growth seems to be a
universal phenomenon that occurs in almost all areas -- visual cortex, motor
area, association area, and so on. It has been shown numerically that the
synapse efficiency is increased by systematic deletion. We discuss the synapse
efficiency to evaluate the effect of pruning following over-growth, and
analytically show that the synapse efficiency diverges as O(log c) at the limit
where connecting rate c is extremely small. Under a fixed synapse number
criterion, the optimal connecting rate, which maximize memory performance,
exists.Comment: 15 pages, 16 figure
On the exchange of intersection and supremum of sigma-fields in filtering theory
We construct a stationary Markov process with trivial tail sigma-field and a
nondegenerate observation process such that the corresponding nonlinear
filtering process is not uniquely ergodic. This settles in the negative a
conjecture of the author in the ergodic theory of nonlinear filters arising
from an erroneous proof in the classic paper of H. Kunita (1971), wherein an
exchange of intersection and supremum of sigma-fields is taken for granted.Comment: 20 page
Criticality Analysis of Activity Networks under Interval Uncertainty
Dedicated to the memory of Professor Stefan Chanas - The extended abstract version of this paper has appeared in Proceedings of 11th International Conference on Principles and Practice of Constraint Programming (CP2005) ("Interval Analysis in Scheduling", Fortin et al. 2005)International audienceThis paper reconsiders the Project Evaluation and Review Technique (PERT) scheduling problem when information about task duration is incomplete. We model uncertainty on task durations by intervals. With this problem formulation, our goal is to assert possible and necessary criticality of the different tasks and to compute their possible earliest starting dates, latest starting dates, and floats. This paper combines various results and provides a complete solution to the problem. We present the complexity results of all considered subproblems and efficient algorithms to solve them
Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model
We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative (‘non-physical’) approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host
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