An event sequence generated by a temporal point process is often associated with a hidden and structured event branching process that captures the triggering relations between its historical and current events. In this study, we design a new plug-and-play module based on the Bregman ADMM (BADMM) algorithm, which infers event branches associated with event sequences in the maximum likelihood estimation framework of temporal point processes (TPPs). Specifically, we formulate the inference of event branches as an optimization problem for the event transition matrix under sparse and low-rank constraints, which is embedded in existing TPP models or their learning paradigms. We can implement this optimization problem based on subspace clustering and sparse group-lasso, respectively, and solve it using the Bregman ADMM algorithm, whose unrolling leads to the proposed BADMM module. When learning a classic TPP (e.g., Hawkes process) by the expectation-maximization algorithm, the BADMM module helps derive structured responsibility matrices in the E-step. Similarly, the BADMM module helps derive low-rank and sparse attention maps for the neural TPPs with self-attention layers. The structured responsibility matrices and attention maps, which work as learned event transition matrices, indicate event branches, e.g., inferring isolated events and those key events triggering many subsequent events. Experiments on both synthetic and real-world data show that plugging our BADMM module into existing TPP models and learning paradigms can improve model performance and provide us with interpretable structured event branches.

As former President Donald Trump awaits a Manhattan jury's verdict, he can be forgiven for feeling that his criminal trial resembles a surreal "Alice in Wonderland" farce. He is left to peer through a "Looking-Glass" where everything is backward. The culprit for this hallucinatory nightmare is District Attorney Alvin Bragg who brought a bizarre case based on warped interpretations of law and distorted facts. It is now up to twelve jurors to wade through the lunacy in search of the illusive truth. Bragg's fractured case requires the jury to reach several distinct conclusions on issues that make little sense to begin with.

Without a body to show in the 1996 San Luis Obispo cold case, the prosecution concluded its presentation in the Kristin Smart murder trial with a sexually explicit screenshot of another woman with a red ball gag in her mouth on the accused killer Paul Flores' San Pedro bed. The image came from Paul Flores' computer, experts testified. Judge Jennifer O'Keefe on Tuesday instructed Monterey County jurors that it was only to be considered as corroborating evidence to a single detail from the testimonies of two women who told the panel they were raped by Flores and that he owned a red ball gag. One warm Friday night in late spring 10 years ago, Kristin Denise Smart and three other young women started walking from their dorms at Cal Poly San Luis Obispo. Jurors had already heard those women testify that the man last seen walking with Kristin Smart on the Cal Poly San Luis Obispo campus on May 25, 1996, before she vanished, sexually assaulted them in Los Angeles separately in 2008 and 2011.

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups' labels. We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier's prediction. For example, a jury learning model for online toxicity might centrally feature women and Black jurors, who are commonly targets of online harassment. To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators' models to populate the jury, then runs inference to classify. Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent.

Who goes to a demonstration with a high-powered assault weapon? He planned on bringing his high-powered assault weapon to that demonstration with every intention of using it. He claims it was self-defense -- it was not. He was out for blood, his goal was to shoot and kill as many as possible and claim it was self-defense. Absolutely relieved that the jury in this case based their deliberations and ultimate not guilty verdict on the facts and didn't cower to the obvious intended intimidation of BLM, Antifa, the left-leaning liberal loonies, and last but not least, the lying fake media.

'Tucker Carlson Tonight' host makes the case for why Kyle Rittenhouse is not receiving a fair trial The judge in the Kyle Rittenhouse trial has just sent the jurors home for the night to think about the trial for yet another day. So far, deliberations, in this case, have lasted about 20 hours. In a normal proceeding, we'd have the jury's decision in about 20 minutes. The essential question, in this case, is really clear did Kyle Rittenhouse have good reason to believe dangerous men were trying to murder him? And the answer is also clear and unequivocal?

The #GirlsInAI2021 international hackathon by Teens in AI was attended by 83 teams composed of over 950 participants from 23 different countries. The first prize was won by a French team, composed of three teenagers aged 16 to 17. They designed Hear-Me, an application based on artificial intelligence for people with hearing loss. The Hear-Me app is an AI-based tool combined with a device that aims to improve communication and interaction for people with hearing loss in face-to-face situations. The platform aims to help hearing impaired people in particular contexts, such as the one that requires wearing a mask during the Covid-19 crisis.

An attorney's computer program offers to screen potential jurors based on their ethnicity, political views and occupation to find a jury most favorable to a defense lawyer's case. Momus Analytics, the company was founded by attorney Alex Alvarez, trawls potential jurors' social media accounts and uses the findings to predict whether or not they should be chosen. The program includes a racially-biased algorithm that suggests Asian, Central American, and South American people are more likely to be leaders - a quality the program appears to prize. People who described their race as'other' were found to be likely to be leaders. Alvarez, who worked with Texas-based software designer Frogslayer to develop the program, has a pending patent application for the program.

Some say allowing artificial intelligence (AI) to determine guilt or innocence in a courtroom is a step too far. But for those who are sceptical about the neutrality of human judgment, or have witnessed an unfair justice system in action, AI and legal robots could be the answer to providing a fair and impartial jury. We already automate so much else in society, so why not extend this smart automation to juries? After all, lawyers rely on technology to scan documents for keywords or evaluate collected data. And people can now use legal chatbots to determine if it's worthwhile to pursue their case in court.

It's likely that most people locked in our jails believe that with a better lawyer, a more lenient judge or a more understanding jury things might have been very different for them. Human error, they will say, is to blame for them being banged up. But can the human element be removed? Law firms are already using computer algorithms to perform background research other tasks traditionally performed by human staff. As computer researchers get closer to creating true Artificial Intelligence, it's predicted to eliminate most paralegal and legal research positions within the next decade.