Officers from the Los Angeles Police Department and California Highway Patrol make arrests as rioters continue to create havoc in LA. (Derek Shook for Fox News Digital) LOS ANGELES – As the protests against Los Angeles' immigration raids spread, state law enforcement leaders are sounding the alarm on the dangers facing officers on the front lines of the riots. "I've been around a very long time, and I have seen similar to what we're facing now," Jake Johnson, president of the California Association of Highway Patrolmen (CAHP), told Fox News Digital. "But I've never seen the amount of onslaught." Thousands of protesters descended on Los Angeles in the last two weeks after U.S. Immigration and Customs Enforcement (ICE) officers began conducting raids throughout the sanctuary city. The violence included rioters hurling projectiles at law enforcement officers and lighting numerous self-driving electric vehicles on fire.

Fox News' Jonathan Hunt reports the latest on the anti-ICE riots in Los Angeles. Correspondent Rich Edson details Dems' response to Trump deploying the National Guard and'Outnumbered' co-host Kayleigh McEnany weighs in on the escalation. Dozens of protesters have been arrested following a weekend of violence across Los Angeles as tensions hit a boiling point over immigration raids throughout the city. On Sunday, law enforcement officials from multiple agencies arrested 41 protesters as anti-Immigration and Customs Enforcement (ICE) demonstrations spiraled out of control. Of the nearly four-dozen arrests, 21 were made by the Los Angeles Police Department (LAPD), 19 by California Highway Patrol and one by the Los Angeles Sheriff's Department.

Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer. Understanding the patient and provider level heterogeneity in FN hospital admissions has potential to inform personalized interventions focused on increasing survival of individuals with FN. We leverage machine learning techniques to disentangling the complex interactions among multi domain risk factors in a population with FN. Data from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample and Nationwide Inpatient Sample (NIS) were used to build machine learning based models of mortality for adult cancer patients who were diagnosed with FN during a hospital admission. In particular, the importance of risk factors from different domains (including demographic, clinical, and hospital associated information) was studied. A set of more interpretable (decision tree, logistic regression) as well as more black box (random forest, gradient boosting, neural networks) models were analyzed and compared via multiple cross validation. Our results demonstrate that a linear prediction score of FN mortality among adults with cancer, based on admission information is effective in classifying high risk patients; clinical diagnoses is the domain with the highest predictive power. A number of the risk variables (e.g. sepsis, kidney failure, etc.) identified in this study are clinically actionable and may inform future studies looking at the patients prior medical history are warranted.

We study the problem of learning choice functions, which play an important role in various domains of application, most notably in the field of economics. Formally, a choice function is a mapping from sets to sets: Given a set of choice alternatives as input, a choice function identifies a subset of most preferred elements. Learning choice functions from suitable training data comes with a number of challenges. For example, the sets provided as input and the subsets produced as output can be of any size. Moreover, since the order in which alternatives are presented is irrelevant, a choice function should be symmetric. Perhaps most importantly, choice functions are naturally context-dependent, in the sense that the preference in favor of an alternative may depend on what other options are available. We formalize the problem of learning choice functions and present two general approaches based on two representations of context-dependent utility functions. Both approaches are instantiated by means of appropriate neural network architectures, and their performance is demonstrated on suitable benchmark tasks.

Object ranking is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects, which are typically represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. Current approaches commonly focus on ranking by scoring, i.e., on learning an underlying latent utility function that seeks to capture the inherent utility of each object. These approaches, however, are not able to take possible effects of context-dependence into account, where context-dependence means that the utility or usefulness of an object may also depend on what other objects are available as alternatives. In this paper, we formalize the problem of context-dependent ranking and present two general approaches based on two natural representations of context-dependent ranking functions. Both approaches are instantiated by means of appropriate neural network architectures. We demonstrate empirically that our methods outperform traditional approaches on benchmark tasks, for which context-dependence is playing a relevant role.

Since the Turing test was first proposed by Alan Turing in 1950, the primary goal of artificial intelligence has been predicated on the ability for computers to imitate human behavior. However, the majority of uses for the computer can be said to fall outside the domain of human abilities and it is exactly outside of this domain where computers have demonstrated their greatest contribution to intelligence. Another goal for artificial intelligence is one that is not predicated on human mimicry, but instead, on human amplification. This article surveys various systems that contribute to the advancement of human and social intelligence. The alleged shortcut to knowledge, which is faith, is only a short-circuit destroying the mind.