Legal experts analyze the challenging defense strategy for Tyler Robinson, who allegedly shot Charlie Kirk at Utah Valley University, as prosecutors prepare evidence for trial.

BBC Verify has been to the house in Washington, Utah which has been linked to Tyler Robinson - the suspect in the killing of Charlie Kirk. Sitting in the driveway was a grey car, similar to the model detectives said the suspect had driven to Utah Valley University where Kirk was fatally shot. BBC Verify's Nick Beake has been searching for answers at the location and on social media. Angola: The notorious prison being used in Trump's immigration crackdown The new detention facility inside the prison is designed to hold more than 400 undocumented immigrants convicted of serious crimes. Jackson Denio, a 13-year-old from New Hampshire, might have set the world record for the largest catch of a halibut fish.

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

The Web site Stack Overflow was created in 2008 as a place for programmers to answer one another's questions. At the time, the Web was thin on high-quality technical information; if you got stuck while coding and needed a hand, your best bet was old, scattered forum threads that often led nowhere. Jeff Atwood and Joel Spolsky, a pair of prominent software developers, sought to solve this problem by turning programming Q. & A. into a kind of multiplayer game. On Stack Overflow--the name refers to a common way that programs crash--people could earn points for posting popular questions and leaving helpful answers. Points earned badges and special privileges; users would be motivated by a mix of altruism and glory.

Zero-shot learning (ZSL) aims to recognize the novel classes which cannot be collected for training a prediction model. Accordingly, generative models (e.g., generative adversarial network (GAN)) are typically used to synthesize the visual samples conditioned by the class semantic vectors and achieve remarkable progress for ZSL. However, existing GAN-based generative ZSL methods are based on hand-crafted models, which cannot adapt to various datasets/scenarios and fails to model instability. To alleviate these challenges, we propose evolutionary generative adversarial network search (termed EGANS) to automatically design the generative network with good adaptation and stability, enabling reliable visual feature sample synthesis for advancing ZSL. Specifically, we adopt cooperative dual evolution to conduct a neural architecture search for both generator and discriminator under a unified evolutionary adversarial framework. EGANS is learned by two stages: evolution generator architecture search and evolution discriminator architecture search. During the evolution generator architecture search, we adopt a many-to-one adversarial training strategy to evolutionarily search for the optimal generator. Then the optimal generator is further applied to search for the optimal discriminator in the evolution discriminator architecture search with a similar evolution search algorithm. Once the optimal generator and discriminator are searched, we entail them into various generative ZSL baselines for ZSL classification. Extensive experiments show that EGANS consistently improve existing generative ZSL methods on the standard CUB, SUN, AWA2 and FLO datasets. The significant performance gains indicate that the evolutionary neural architecture search explores a virgin field in ZSL.

In zero-shot learning (ZSL), generative methods synthesize class-related sample features based on predefined semantic prototypes. They advance the ZSL performance by synthesizing unseen class sample features for better training the classifier. We observe that each class's predefined semantic prototype (also referred to as semantic embedding or condition) does not accurately match its real semantic prototype. So the synthesized visual sample features do not faithfully represent the real sample features, limiting the classifier training and existing ZSL performance. In this paper, we formulate this mismatch phenomenon as the visual-semantic domain shift problem. We propose a dynamic semantic prototype evolving (DSP) method to align the empirically predefined semantic prototypes and the real prototypes for class-related feature synthesis. The alignment is learned by refining sample features and semantic prototypes in a unified framework and making the synthesized visual sample features approach real sample features. After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8.5\%, 8.0\%, and 9.7\% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.

In many cases, federal agents can request access to state DMV records by filling out a form. This is an example of a Homeland Security request that was made to the Vermont Department of Motor Vehicles in 2017. In many cases, federal agents can request access to state DMV records by filling out a form. This is an example of a Homeland Security request that was made to the Vermont Department of Motor Vehicles in 2017. Immigration and Customs Enforcement agents mine millions of driver's license photos for possible facial recognition matches -- and some of those efforts target undocumented immigrants who have legally obtained driver's licenses, according to researchers at Georgetown University Law Center, which obtained documents related to the searches.

Now we're going to look more broadly at what's been revealed today about ICE turning to DMV offices for help with facial recognition - that is, using driver's license photographs and algorithms to identify people suspected of being in the country illegally. Now, this collaboration was unearthed by a team at Georgetown University, and here to brief us is NPR's Aarti Shahani. CORNISH: I understand that in the past, ICE has gone to DMV offices and just asked for records on immigrants. We just heard about the case in Vermont that alleges that much. What exactly is new here?

Many convex problems in machine learning and computer science share the same form: \begin{align*} \min_{x} \sum_{i} f_i( A_i x + b_i), \end{align*} where $f_i$ are convex functions on $\mathbb{R}^{n_i}$ with constant $n_i$, $A_i \in \mathbb{R}^{n_i \times d}$, $b_i \in \mathbb{R}^{n_i}$ and $\sum_i n_i = n$. This problem generalizes linear programming and includes many problems in empirical risk minimization. In this paper, we give an algorithm that runs in time \begin{align*} O^* ( ( n^{\omega} + n^{2.5 - \alpha/2} + n^{2+ 1/6} ) \log (n / \delta) ) \end{align*} where $\omega$ is the exponent of matrix multiplication, $\alpha$ is the dual exponent of matrix multiplication, and $\delta$ is the relative accuracy. Note that the runtime has only a log dependence on the condition numbers or other data dependent parameters and these are captured in $\delta$. For the current bound $\omega \sim 2.38$ [Vassilevska Williams'12, Le Gall'14] and $\alpha \sim 0.31$ [Le Gall, Urrutia'18], our runtime $O^* ( n^{\omega} \log (n / \delta))$ matches the current best for solving a dense least squares regression problem, a special case of the problem we consider. Very recently, [Alman'18] proved that all the current known techniques can not give a better $\omega$ below $2.168$ which is larger than our $2+1/6$. Our result generalizes the very recent result of solving linear programs in the current matrix multiplication time [Cohen, Lee, Song'19] to a more broad class of problems. Our algorithm proposes two concepts which are different from [Cohen, Lee, Song'19] : $\bullet$ We give a robust deterministic central path method, whereas the previous one is a stochastic central path which updates weights by a random sparse vector. $\bullet$ We propose an efficient data-structure to maintain the central path of interior point methods even when the weights update vector is dense.

Business person Brian Beaumont has overcome challenges brought on by dyslexia. Brian Beaumont was a below average student prior to entering graduate school in the early 1980s. So Beaumont, now 60, asked his professors if he could tape their lectures to make better use of his 60- to 90-minute commute time in and around Los Angeles. "I did not realize at the time I was making an accommodation for my dyslexia," Beaumont says. "I had problems listening and taking notes at the same time. Now, I could sit back and just listen to the lecture. I could focus on the main points the professor was making."