Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification

We introduce a family of unsupervised, domain-free, and (asymptotically) model-independent algorithms based on the principles of algorithmic probability and information theory designed to minimize the loss of algorithmic information, including a lossless-compression-based lossy compression algorithm. The methods can select and coarse-grain data in an algorithmic-complexity fashion (without the use of popular compression algorithms) by collapsing regions that may procedurally be regenerated from a computable candidate model. We show that the method can preserve the salient properties of objects and perform dimension reduction, denoising, feature selection, and network sparsification. As validation case, we demonstrate that the method preserves all the graph-theoretic indices measured on a well-known set of synthetic and real-world networks of very different nature, ranging from degree distribution and clustering coefficient to edge betweenness and degree and eigenvector centralities, achieving equal or significantly better results than other data reduction and some of the leading network sparsification methods. The methods (InfoRank, MILS) can also be applied to applications such as image segmentation based on algorithmic probability.Comment: 23 pages in double column including Appendix, online implementation at http://complexitycalculator.com/MILS

Presynaptic self-depression at developing neocortical synapses

A central tenet of most theories of synaptic modification during cortical development is that correlated activity drives plasticity in synaptically connected neurons. Unexpectedly, however, using sensory-evoked activity patterns recorded from the developing mouse cortex in vivo, the synaptic learning rule that we uncover here relies solely on the presynaptic neuron. A burst of three presynaptic spikes followed, within a restricted time window, by a single presynaptic spike induces robust long-term depression (LTD) at developing layer 4 to layer 2/3 synapses. This presynaptic spike pattern-dependent LTD (p-LTD) can be induced by individual presynaptic layer 4 cells, requires presynaptic NMDA receptors and calcineurin, and is expressed presynaptically. However, in contrast to spike timing-dependent LTD, p-LTD is independent of postsynaptic and astroglial signaling. This spike pattern-dependent learning rule complements timing-based rules and is likely to play a role in the pruning of synaptic input during cortical development

A 2.5MHz bandpass active complex filter With 2.4MHz bandwidth for wireless communications

Trabajo presentado al 23rd DCIS celebrado en grenoble del 12 al 14 de noviembre de 2008.This paper presents a fully differential 8th order transconductor-based active complex filter with 2.4MHz bandwidth and centered at 2.5MHz, designed in a 90nm 2.5V 7M and MIM capacitors CMOS process technology. The filter compliants with the requirements of the IEEE802.15.4 standard. Simulation results including mismatching and process variations over the extracted view of the circuit are shown. The filter has a nominal gain of 12dB, good selectivity (20dB@2MHz offset), high image rejection (51dB nominal) and low power consumption (3.6mA @2.5V).This work has been founded in part by the EC through the project A109-Medea+ WITNESS (Wireless technologies for small area networks with embedded security and safety), the Spanish Regional Government of Junta de Andalucía under project TIC-927 and the Spanish Government under project TEC2007-68072.Peer reviewe

Controls of Multimodal Wave Conditions in a Complex Coastal Setting

Coastal hazards emerge from the combined effect of wave conditions and sea level anomalies associated with storms or low-frequency atmosphere-ocean oscillations. Rigorous characterization of wave climate is limited by the availability of spectral wave observations, the computational cost of dynamical simulations, and the ability to link wave-generating atmospheric patterns with coastal conditions. We present a hybrid statistical-dynamical approach to simulating nearshore wave climate in complex coastal settings, demonstrated in the Southern California Bight, where waves arriving from distant, disparate locations are refracted over complex bathymetry and shadowed by offshore islands. Contributions of wave families and large-scale atmospheric drivers to nearshore wave energy flux are analyzed. Results highlight the variability of influences controlling wave conditions along neighboring coastlines. The universal method demonstrated here can be applied to complex coastal settings worldwide, facilitating analysis of the effects of climate change on nearshore wave climate.This work was funded by the U.S. Geological Survey (USGS) Coastal and Marine Geology Program. The authors thank Jorge Perez, IH Cantabria, for providing the GOW wave hindcast and for assistance with wave spectra, and Sean Vitousek, University of Chicago, for a helpful review. This material is based upon work supported by the U.S. Geological Survey under grant/cooperative agreement GI5AC00426. A. R., J. A. A. A., and F. J. M. acknowledge the support of the Spanish “Ministerio de Economía y Competitividad” under grant BIA2014-59643-R. J. A. A. A. was funded by the Spanish “Ministerio de Educación, Cultura y Deporte” FPU (Formación del Profesorado Universitario) studentship BOE-A-2013-12235. Reanalyses of ocean data are available for research purposes through IH Cantabria (contact [email protected]). Southern California Bight look-up table data are available at https://doi.org/10.1594/PANGAEA.880314. Related Southern California nearshore wave data can be found at http://dx.doi.org/10.5066/F7N29V2V