We used a single RTX 2080 Ti GPU. While T5-small underperforms its larger version, T5-11B, the latter has 11 billion parameters. Gabon and South Africa, are ranked 119th and 121st, respectively . ANSWER: 119th HUMAN: What is Gabon ' s ranking? ColdGAN: What is Gabon ' s rank on the HDI?

Swanton Sector NBPC President Sean Walsh joined'Fox & Friends First' to discuss Mayorkas' testimony before Congress as the crisis continues to spiral. Facial recognition technology makes unlocking your smartphone a breeze. But with the convenience, comes a disturbing new crime trend for bandits. It involves "drug-facilitated robbery" schemers who knock their victims out with date rape drugs, unlock the victims' phones with their unconscious faces and drain their bank accounts of tens of thousands of dollars. While robberies involving incapacitated victims are nothing new, the technology offers thieves quick and easy access to incapacitated victims.

Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Accountability Project. The most accurate algorithm among all (XGBoost) is then used for predicting clearance outcomes state-wise. Second, SHAP, a framework for Explainable Artificial Intelligence, is employed to capture the most important features in explaining clearance patterns both at the national and state levels. Results: At the national level, XGBoost demonstrates to achieve the best performance overall. Substantial predictive variability is detected state-wise. In terms of explainability, SHAP highlights the relevance of several features in consistently predicting investigation outcomes. These include homicide circumstances, weapons, victims' sex and race, as well as number of involved offenders and victims. Conclusions: Explainable Machine Learning demonstrates to be a helpful framework for predicting homicide clearance. SHAP outcomes suggest a more organic integration of the two theoretical perspectives emerged in the literature. Furthermore, jurisdictional heterogeneity highlights the importance of developing ad hoc state-level strategies to improve police performance in clearing homicides.

"But to measure cause and effect, you must ensure that simple correlation, however tempting it may be, is not mistaken for a cause. In the 1990s, the stork population in Germany increased and the German at-home birth rates rose as well. Shall we credit storks for airlifting the babies?" One of the basic tenets of statistics is: correlation is not causation. Correlation between variables shows a pattern in the data and that these variables tend to'move together'.

Daily crime rates before and during pandemic-related stay-at-home orders in 27 major metropolitan areas were studied by experts led from the Universities of Cambridge and Utrecht.

Relevant research has been standing out in the computing community aiming to develop computational models capable of predicting occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crimes, and analyzing crimes according to time. This, due to the social impact and also the complex origin of the data, thus showing itself as an interesting computational challenge. This research presents a computational model for the prediction of homicide crimes, based on tabular data of crimes registered in the city of Bel\'em - Par\'a, Brazil. Statistical tests were performed with 8 different classification methods, both Random Forest, Logistic Regression, and Neural Network presented best results, AUC ~ 0.8. Results considered as a baseline for the proposed problem.

More than 50 million smart speakers have been installed in American households. For police, that means 50 million potential virtual witnesses to crimes that occur in the privacy of one's home. But the legal protections for this type of privacy-invading, Internet of Things–enabled evidence are still very unclear. The question matters because one of those smart speakers was just called to be a witness in a brutal double homicide in New Hampshire. Timothy Verrill stands accused of stabbing Christine Sullivan and Jenna Pellegrini to death over suspicion that one of them was a police informant.

Multivariate Bernoulli autoregressive (BAR) processes model time series of events in which the likelihood of current events is determined by the times and locations of past events. These processes can be used to model nonlinear dynamical systems corresponding to criminal activity, responses of patients to different medical treatment plans, opinion dynamics across social networks, epidemic spread, and more. Past work examines this problem under the assumption that the event data is complete, but in many cases only a fraction of events are observed. Incomplete observations pose a significant challenge in this setting because the unobserved events still govern the underlying dynamical system. In this work, we develop a novel approach to estimating the parameters of a BAR process in the presence of unobserved events via an unbiased estimator of the complete data log-likelihood function. We propose a computationally efficient estimation algorithm which approximates this estimator via Taylor series truncation and establish theoretical results for both the statistical error and optimization error of our algorithm. We further justify our approach by testing our method on both simulated data and a real data set consisting of crimes recorded by the city of Chicago.

The rise of big data policing rests in part on the belief that data- based decisions can be more objective, fair, and accurate than traditional policing. Data is data and thus, the thinking goes, not subject to the same subjective errors as human decision making. As David Vladeck, the former director of the Bureau of Consumer Protection at the Federal Trade Commission (who was, thus, in charge of much of the law surrounding big data consumer protection), once warned, "Algorithms may also be imperfect decisional tools. Algorithms themselves are designed by humans, leaving open the possibility that unrecognized human bias may taint the process. And algorithms are no better than the data they process, and we know that much of that data may be unreliable, outdated, or reflect bias."