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Register a new userThe Proof of Innocence
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Dmitri Krioukov
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A way to fight your traffic tickets. The paper was awarded a special prize of $400 that the author did not have to pay to the state of California. In view of enormous, extremely surprising and completely unexpected public interest to this work, we have added an appendix answering the two most common questions.
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The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: For to all those who have, more will be given. Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew rich get richer effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his/her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect, and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals, and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience.
We employ simulated annealing to identify the global solution of a dynamical model, to make a favorable impression upon colleagues at the departmental holiday party and then exit undetected as soon as possible. The procedure, Gradual Freeze-out of an Optimal Estimation via Optimization of Parameter Quantification - GFOOEOPQ, is designed for the socially awkward. The socially awkward among us possess little instinct for pulling off such a maneuver, and may benefit from a machine to do it for us. The method rests upon Bayes Theorem, where the probability of a future model state depends on current knowledge of the model. Here, model state vectors are party attendees, and the future event of interest is their disposition toward us at times following the party. We want these dispositions to be favorable. To this end, we first interact so as to make favorable impressions, or at least ensure that these people remember having seen us there. Then we identify the exit that minimizes the chance that anyone notes how early we high-tailed it. Now, poorly-resolved estimates will correspond to degenerate solutions. As noted, we possess no instinct to identify a global optimum by ourselves. This can have disastrous consequences. For this reason, GFOOEOPQ employs annealing to iteratively home in on this optimum. The method is illustrated via a simulated event hosted by someone in the physics department (I am not sure who), in a two-bedroom apartment on the fifth floor of an elevator building in Manhattan, with viable Exit parameters: front door, side door to a stairwell, fire escape, and a bathroom window that opens onto the fire escape. Preliminary tests are reported at two real social celebrations. The procedure is generalizable to corporate events and family gatherings. Readers are encouraged to report novel applications of GFOOEOPQ, to expand the algorithm.
Despite a previous description of his state as a stable fixed point, just past midnight this morning Mr. Boddy was murdered again. In fact, over 70 years Mr. Boddy has been reported murdered $10^6$ times, while there exist no documented attempts at intervention. Using variational data assimilation, we train a model of Mr. Boddys dynamics on the time series of observed murders, to forecast future murders. The parameters to be estimated include instrument, location, and murderer. We find that a successful estimation requires three additional elements. First, to minimize the effects of selection bias, generous ranges are placed on parameter searches, permitting values such as the Cliff, the Poisoned Apple, and the Wife. Second, motive, which was not considered relevant to previous murders, is added as a parameter. Third, Mr. Boddys little-known asthmatic condition is considered as an alternative cause of death. Following this mornings event, the next local murder is forecast for 17:19:03 EDT this afternoon, with a standard deviation of seven hours, at The Kitchen at 4330 Katonah Avenue, Bronx, NY, 10470, with either the Lead Pipe or the Lead Bust of Washington Irving. The motive is: Case of Mistaken Identity, and there was no convergence upon a murderer. Testing of the procedures predictive power will involve catching the D train to 205th Street and a few transfers over to Katonah Avenue, and sitting around waiting with our eyes peeled. We discuss the problem of identifying a global solution - that is, the best reason for murder on a landscape riddled with pretty-decent reasons. We also discuss the procedures assumption of Gaussian-distributed errors, which will under-predict rare events. This under-representation of highly improbable events may be offset by the fact that the training data, after all, consists of multiple murders of a single person.
Different definitions of links in climate networks may lead to considerably different network topologies. We construct a network from climate records of surface level atmospheric temperature in different geographical sites around the globe using two commonly used definitions of links. Utilizing detrended fluctuation analysis, shuffled surrogates and separation analysis of maritime and continental records, we find that one of the major influences on the structure of climate networks is due to the auto-correlation in the records, that may introduce spurious links. This may explain why different methods could lead to different climate network topologies.
In-situ observations of magnetic field fluctuations in the solar wind show a broad continuum in the power spectral density (PSD) with a power-law range of scaling often identified as an inertial range of magnetohydrodynamic turbulence. However, both turbulence and discontinuities are present in the solar wind on these inertial range of scales. We identify and remove these discontinuities using a method which for the first time does not impose a characteristic scale on the resultant time-series. The PSD of vector field fluctuations obtained from at-a point observations is a tensor that can in principle be anisotropic with scaling exponents that depend on background field and flow direction. This provides a key test of theories of turbulence. We find that the removal of discontinuities from the observed time-series can significantly alter the PSD trace anisotropy. It becomes quasi-isotropic, in that the observed exponent does not vary with the background field angle once the discontinuities are removed. This is consistent with the predictions of the Iroshnikov-Kraichnan model of turbulence. As a consistency check we construct a surrogate time-series from the observations that is composed solely of discontinuities. The surrogate provides an estimate of the PSD due solely to discontinuities and this provides the effective noise-floor produced by discontinuities for all scales greater than a few ion-cyclotron scales.
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