9,925 research outputs found
Recommended from our members
Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data
A new common mortality modeling structure is presented for analyzing mortality dynamics for a pool of countries, under the framework of generalized linear models (GLM). The countries are first classified by fuzzy c-means cluster analysis in order to construct the common sparse age-period model structure for the mortality experience. Next, we propose a method to create the common sex difference age-period model structure and then use this to produce the residual age-periodmodel structure for each country and sex. The time related principal components are extrapolated using dynamic linear regression (DLR) models and coherent mortality forecasts are investigated. We make use of mortality data from the “Human Mortality Database”
The Importance of Forgetting: Limiting Memory Improves Recovery of Topological Characteristics from Neural Data
We develop of a line of work initiated by Curto and Itskov towards
understanding the amount of information contained in the spike trains of
hippocampal place cells via topology considerations. Previously, it was
established that simply knowing which groups of place cells fire together in an
animal's hippocampus is sufficient to extract the global topology of the
animal's physical environment. We model a system where collections of place
cells group and ungroup according to short-term plasticity rules. In
particular, we obtain the surprising result that in experiments with spurious
firing, the accuracy of the extracted topological information decreases with
the persistence (beyond a certain regime) of the cell groups. This suggests
that synaptic transience, or forgetting, is a mechanism by which the brain
counteracts the effects of spurious place cell activity
Functional estimation and hypothesis testing in nonparametric boundary models
Consider a Poisson point process with unknown support boundary curve ,
which forms a prototype of an irregular statistical model. We address the
problem of estimating non-linear functionals of the form .
Following a nonparametric maximum-likelihood approach, we construct an
estimator which is UMVU over H\"older balls and achieves the (local) minimax
rate of convergence. These results hold under weak assumptions on which
are satisfied for , . As an application, we consider the
problem of estimating the -norm and derive the minimax separation rates in
the corresponding nonparametric hypothesis testing problem. Structural
differences to results for regular nonparametric models are discussed.Comment: 21 pages, 1 figur
A Multivariate Fit Luminosity Function And World Model For Long Gamma-Ray Bursts
It is proposed that the luminosity function, the rest-frame spectral correlations, and distributions of cosmological long-duration (Type-II) gamma-ray bursts (LGRBs) may be very well described as a multivariate log-normal distribution. This result is based on careful selection, analysis, and modeling of LGRBs' temporal and spectral variables in the largest catalog of GRBs available to date: 2130 BATSE GRBs, while taking into account the detection threshold and possible selection effects. Constraints on the joint rest-frame distribution of the isotropic peak luminosity (L-iso), total isotropic emission (E-iso), the time-integrated spectral peak energy (E-p,E-z), and duration (T-90,T-z) of LGRBs are derived. The presented analysis provides evidence for a relatively large fraction of LGRBs that have been missed by the BATSE detector with E-iso extending down to similar to 10(49) erg and observed spectral peak energies (Ep) as low as similar to 5 keV. LGRBs with rest-frame duration T-90,T-z less than or similar to 1 s or observer-frame duration T-90 less than or similar to 2 s appear to be rare events (less than or similar to 0.1% chance of occurrence). The model predicts a fairly strong but highly significant correlation (rho = 0.58 +/- 0.04) between E-iso and E-p,E-z of LGRBs. Also predicted are strong correlations of L-iso and E-iso with T-90,T-z and moderate correlation between L-iso and E-p,E-z. The strength and significance of the correlations found encourage the search for underlying mechanisms, though undermine their capabilities as probes of dark energy's equation of Stateat high redshifts. The presented analysis favors-but does not necessitate-a cosmic rate for BATSE LGRBs tracing metallicity evolution consistent with a cutoff Z/Z(circle dot) similar to 0.2-0.5, assuming no luminosity-redshift evolution.Institute for Fusion Studie
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
- …