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Policing efforts to thwart urban crime often rely on detailed reports of criminal infractions. However, crime rates do not document the distribution of crime in isolation, but rather its complex relationship with policing and society. Several results attempting to predict future crime now exist, with varying degrees of predictive efficacy. However, the very idea of predictive policing has stirred controversy, with the algorithms being largely black boxes producing little to no insight into the social system of crime, and its rules of organization. The issue of how enforcement interacts with, modulates, and reinforces crime has been rarely addressed in the context of precise event predictions. In this study, we demonstrate that predictive tools are not purely an instrument enhacing state power, but may be effectively used to seek out systemic biases in urban enforcement. We introduce a novel stochastic inference algorithm as a new forecasting approach that learns spatio-temporal dependencies from individual event reports with demonstrated performance far surpassing past results (e.g., average AUC of ~90% in the City of Chicago for property and violent crimes predicted a week in advance within spatial tiles ~1000ft across). These precise predictions enable equally precise evaluation of inequities in law enforcement, discovering that response to increased crime rates is biased by the socio-economic status of neighborhoods, draining policy resources to wealthy areas with disproportionately negative impacts for the inner city, as demonstrated in Chicago and six other major U.S. metropolitan areas. While the emergence of powerful predictive tools raise concerns regarding the unprecedented power they place in the hands of over-zealous states in the name of civilian protection, our approach demonstrates how sophisticated algorithms enable us to audit enforcement biases, and hold states accountable in ways previously inconceivable.

With the popularization of intravenous thrombolysis, more and more people use intravenous thrombolysis to treat related diseases, but problems also arise. There are still a considerable number of patients with early disease after thrombolytic therapy not only not significantly improving, but also progressing, that is, early neurological deterioration (END). In view of this problem, the prediction of END after intravenous thrombolysis becomes very important. With the development of medical technology, research on the prediction of END after intravenous thrombolysis has gradually been carried out. Effective prediction is of great significance for the prevention and treatment of END after intravenous thrombolysis. This article aimed to carry out a meta-analysis of the predictive role of END after intravenous thrombolysis. Through an informed analysis of all studies of this type in this field, this article determines a method for predicting END after intravenous thrombolysis. The actual effect of its role is revealed in this paper, and its purpose is to promote the development of this field. This article addresses the same type of study on the predictive role of neurological deterioration after intravenous thrombolysis. The article performs test and meta-analysis of its role by conditionally searching for literature studies. It is explained using the relevant theoretical formulas. The analysis results show that the prediction of END after intravenous thrombolysis in this paper can effectively help make a preliminary judgment on the possible later neurological deterioration. Although there is an error between the predicted curve and the actual curve, the difference between the two is between 1% and 5%. It can basically effectively predict the occurrence of END. Therefore, the prediction of END after intravenous thrombolysis has a very large preventive effect on the END after intravenous thrombolysis.

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