Melania Trump announced on Friday that she is releasing an AI audiobook version of her memoir, Melania. In an X post, the first lady welcomed followers into "a new era in publishing" and announced that an audiobook featuring an AI-generated version of her voice will be released in the ElevenReader app. "I am honored to bring you Melania -- The AI Audiobook -- narrated entirely using artificial intelligence in my own voice. Let the future of publishing begin." The First Lady's book, Melania, was published in October 2024, and it's part memoir, part coffee table book.

Deep reinforcement learning methods traditionally struggle with tasks where environment rewards are particularly sparse. One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator. However, these demonstrations are typically collected under artificial conditions, i.e. with access to the agent's exact environment setup and the demonstrator's action and reward trajectories. Here we propose a two-stage method that overcomes these limitations by relying on noisy, unaligned footage without access to such data. First, we learn to map unaligned videos from multiple sources to a common representation using self-supervised objectives constructed over both time and modality (i.e.

All seven floors of a recently constructed high-tech computer science building at USC were affected by an overnight water leak this week, an official said. The university's facilities planning and management department confirmed that the leak originated from the attic of Ginsburg Hall on Wednesday, but did not comment on the extent of the damage. Members of the facilities planning and management team responded when the leak was reported, turned off the water and started repairs, the department said in a statement to The Times on Friday. There is no estimated timeline for how long repairs will take. The 116,000-square-foot building -- officially named the Dr. Allen and Charlotte Ginsburg Human-Centered Computation Hall -- opened in September. It was designed by architecture firm HOK and reportedly had a 130-million budget.

Modern large-scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging information such as stochastic gradients among different workers. In this paper, to reduce the communication cost, we propose a convex optimization formulation to minimize the coding length of stochastic gradients. The key idea is to randomly drop out coordinates of the stochastic gradient vectors and amplify the remaining coordinates appropriately to ensure the sparsified gradient to be unbiased. To solve the optimal sparsification efficiently, a simple and fast algorithm is proposed for an approximate solution, with a theoretical guarantee for sparseness.

In this paper, we study the fundamental open question of finding the optimal highorder algorithm for solving smooth convex minimization problems. Arjevani et al. (2019) established the lower bound Ω (ϵ

In this paper, we study the fundamental open question of finding the optimal highorder algorithm for solving smooth convex minimization problems. Arjevani et al. (2019) established the lower bound Ω (ϵ

The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects. Although most existing SAOD methods rely on fixed thresholding to filter pseudo-labels for enhancing detector performance, adapting to aerial objects proves challenging due to the imbalanced probabilities/confidences associated with predicted aerial objects. To address this problem, we propose a novel Progressive Exploration-Conformal Learning (PECL) framework to address the SAOD task, which can adaptively perform the selection of high-quality pseudo-labels in aerial images. Specifically, the pseudo-label exploration can be formulated as a decision-making paradigm by adopting a conformal pseudo-label explorer and a multi-clue selection evaluator. The conformal pseudo-label explorer learns an adaptive policy by maximizing the cumulative reward, which can decide how to select these high-quality candidates by leveraging their essential characteristics and inter-instance contextual information. The multi-clue selection evaluator is designed to evaluate the explorer-guided pseudo-label selections by providing an instructive feedback for policy optimization. Finally, the explored pseudo-labels can be adopted to guide the optimization of aerial object detector in a closed-loop progressive fashion. Comprehensive evaluations on two public datasets demonstrate the superiority of our PECL when compared with other state-of-the-art methods in the sparsely annotated aerial object detection task.

While in New York for Fleet Week, active-duty members of the Navy, Marines and Coast Guard share suggestions for ways to commemorate Memorial Day. Google's artificial intelligence chatbot is being slammed for "anti-American" claims about the supposed White supremacist origins of Memorial Day. The Media Research Center (MRC) Free Speech America project, a conservative media watchdog, is calling out Google for alleged bias coded into its AI chatbot "Gemini" after the group found the bot said that Memorial Day is controversial for a range of reasons, including problems with "inclusivity and representation" from the Jim Crow era. A Google spokesperson has since distanced the company from the Gemini statements, saying that the response "does not reflect Google's opinion." MRC said it asked Gemini the question "Is Memorial Day controversial?" May 16.

We thank the reviewers for their interest in our work and their helpful comments. Please find our response below. DDPG and TD3, by keeping an exploration strategy which does not decay to zero. Gradient methods to bridge the gap between DPO and GAC. Reviewer 3: Thank you for pointing out some confusing explanations, we will make sure to clarify them in the paper.

The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, opening the possibility of using learning-based approaches to facilitate controller design. We present a method for learning the forced and unforced dynamics of airflow over a cylinder directly from CFD data. The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons. Finally, by performing model predictive control with the learned dynamical models, we are able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinder.