Is It Possible to Predict Strong Earthquakes?

The possibility of earthquake prediction is one of the key open questions in modern geophysics. We propose an approach based on the analysis of common short-term candidate precursors (2 weeks to 3 months prior to strong earthquake) with the subsequent processing of brain activity signals generated in specific types of rats (kept in laboratory settings) who reportedly sense an impending earthquake few days prior to the event. We illustrate the identification of short-term precursors using the groundwater sodium-ion concentration data in the time frame from 2010 to 2014 (a major earthquake occurred on February 28, 2013), recorded at two different sites in the south-eastern part of the Kamchatka peninsula, Russia. The candidate precursors are observed as synchronized peaks in the nonstationarity factors, introduced within the flicker-noise spectroscopy framework for signal processing, for the high-frequency component of both time series. These peaks correspond to the local reorganizations of the underlying geophysical system that are believed to precede strong earthquakes. The rodent brain activity signals are selected as potential "immediate" (up to 2 weeks) deterministic precursors due to the recent scientific reports confirming that rodents sense imminent earthquakes and the population-genetic model of Kirshvink (2000) showing how a reliable genetic seismic escape response system may have developed over the period of several hundred million years in certain animals. The use of brain activity signals, such as electroencephalograms, in contrast to conventional abnormal animal behavior observations, enables one to apply the standard "input-sensor-response" approach to determine what input signals trigger specific seismic escape brain activity responsesComment: 28 pages, 3 figures; accepted by Pure and Applied Geophysics. arXiv admin note: text overlap with arXiv:1202.0096, arXiv:1101.147

Reply to ``Comment on ``Lateral Casimir Force beyond the Proximity Force Approximation'' ''

We reply to the comment arXiv:quant-ph/0702060 on our letter arXiv:quant-ph/0603120 [Phys. Rev. Lett. 96, 100402 (2006)]Comment: 1 pag

Cocaine Addiction as a Homeostatic Reinforcement Learning Disorder

Drug addiction implicates both reward learning and homeostatic regulation mechanisms of the brain. This has stimulated 2 partially successful theoretical perspectives on addiction. Many important aspects of addiction, however, remain to be explained within a single, unified framework that integrates the 2 mechanisms. Building upon a recently developed homeostatic reinforcement learning theory, the authors focus on a key transition stage of addiction that is well modeled in animals, escalation of drug use, and propose a computational theory of cocaine addiction where cocaine reinforces behavior due to its rapid homeostatic corrective effect, whereas its chronic use induces slow and long-lasting changes in homeostatic setpoint. Simulations show that our new theory accounts for key behavioral and neurobiological features of addiction, most notably, escalation of cocaine use, drug-primed craving and relapse, individual differences underlying dose-response curves, and dopamine D2-receptor downregulation in addicts. The theory also generates unique predictions about cocaine self-administration behavior in rats that are confirmed by new experimental results. Viewing addiction as a homeostatic reinforcement learning disorder coherently explains many behavioral and neurobiological aspects of the transition to cocaine addiction, and suggests a new perspective toward understanding addiction

Separate, measure and conquer: faster polynomial-space algorithms for Max 2-CSP and counting dominating sets

We show a method resulting in the improvement of several polynomial-space, exponential-time algorithms. The method capitalizes on the existence of small balanced separators for sparse graphs, which can be exploited for branching to disconnect an instance into independent components. For this algorithm design paradigm, the challenge to date has been to obtain improvements in worst-case analyses of algorithms, compared with algorithms that are analyzed with advanced methods, notably Measure and Conquer. Our contribution is the design of a general method to integrate the advantage from the separator-branching into Measure and Conquer, for a more precise and improved running time analysi

Casimir effect with rough metallic mirrors

We calculate the second order roughness correction to the Casimir energy for two parallel metallic mirrors. Our results may also be applied to the plane-sphere geometry used in most experiments. The metallic mirrors are described by the plasma model, with arbitrary values for the plasma wavelength, the mirror separation and the roughness correlation length, with the roughness amplitude remaining the smallest length scale for perturbation theory to hold. From the analysis of the intracavity field fluctuations, we obtain the Casimir energy correction in terms of generalized reflection operators, which account for diffraction and polarization coupling in the scattering by the rough surfaces. We present simple analytical expressions for several limiting cases, as well as numerical results that allow for a reliable calculation of the roughness correction in real experiments. The correction is larger than the result of the Proximity Force Approximation, which is obtained from our theory as a limiting case (very smooth surfaces).Comment: 16 page