In'watershed moment', Tesla board to vote on Musk's $1 trillion package Tesla's board is set to vote on CEO Elon Musk's $1 trillion pay package as major proxy adviser firms urge shareholders to reject the deal. The vote is scheduled for Thursday and will determine whether Musk secures what is the largest compensation package in corporate history. These firms often influence large passive funds that hold significant stakes in the electric carmaker. Tesla has faced mounting challenges this year, with global sales declining and investor confidence wavering. In July, Tesla reported a 13.5 percent decline in sales in the United States. They jumped 7.4 percent in the third quarter ending in September compared with the same period the year before, as US consumers scrambled to take advantage of a $7,500 EV tax credit that was set to expire that month.

ABSTRACT Despite their impressive realism, modern text-to-image models still struggle with compositionality, often failing to render accurate object counts, attributes, and spatial relations. To address this challenge, we present a training-free framework that combines an object-centric approach with self-refinement to improve layout faithfulness while preserving aesthetic quality. Specifically, we leverage large language models (LLMs) to synthesize explicit layouts from input prompts, and we inject these layouts into the image generation process, where a object-centric vision-language model (VLM) judge re-ranks multiple candidates to select the most prompt-aligned outcome iteratively. By unifying explicit layout-grounding with self-refine-based inference-time scaling, our framework achieves stronger scene alignment with prompts compared to recent text-to-image models. Index T erms-- text-to-image synthesis, inference-time-scaling, object-centric 1. INTRODUCTION Text-to-image (T2I) diffusion models now deliver striking realism and diversity from textual prompts [1, 2, 3, 4], yet they still struggle with compositionality: the precise rendering of object counts, attributes, and spatial relations [5].

Recent progress in Large Multi-modal Models (LMMs) has enabled effective vision-language reasoning, yet the ability to understand video content remains constrained by suboptimal frame selection strategies. Existing approaches often rely on static heuristics or external retrieval modules to feed frame information into video-LLMs, which may fail to provide the query-relevant information. In this work, we introduce ReFoCUS (Reinforcement-guided Frame Optimization for Contextual UnderStanding), a novel frame-level policy optimization framework that shifts the optimization target from textual responses to visual input selection. ReFoCUS learns a frame selection policy via reinforcement learning, using reward signals derived from a reference LMM to reflect the model's intrinsic preferences for frames that best support temporally grounded responses. To efficiently explore the large combinatorial frame space, we employ an autoregressive, conditional selection architecture that ensures temporal coherence while reducing complexity. Our approach does not require explicit supervision at the frame-level and consistently improves reasoning performance across multiple video QA benchmarks, highlighting the benefits of aligning frame selection with model-internal utility.

Recent advancements have progressively incorporated frequency-based techniques into deep learning models, leading to notable improvements in accuracy and efficiency for time series analysis tasks. However, the Mid-Frequency Spectrum Gap in the real-world time series, where the energy is concentrated at the low-frequency region while the middle-frequency band is negligible, hinders the ability of existing deep learning models to extract the crucial frequency information. Additionally, the shared Key-Frequency in multivariate time series, where different time series share indistinguishable frequency patterns, is rarely exploited by existing literature. This work introduces a novel module, Adaptive Mid-Frequency Energy Optimizer, based on convolution and residual learning, to emphasize the significance of mid-frequency bands. We also propose an Energy-based Key-Frequency Picking Block to capture shared Key-Frequency, which achieves superior inter-series modeling performance with fewer parameters. A novel Key-Frequency Enhanced Training strategy is employed to further enhance Key-Frequency modeling, where spectral information from other channels is randomly introduced into each channel. Our approach advanced multivariate time series forecasting on the challenging Traffic, ECL, and Solar benchmarks, reducing MSE by 4%, 6%, and 5% compared to the previous SOTA iTransformer. Code is available at this GitHub Repository: https://github.com/Levi-Ackman/ReFocus.

Marisa Johnson's six-year-old daughter was just learning to read independently when her Alameda, California, school shut down last year. Without solid literacy skills and lots of time stuck at home, the tot is spending much more time playing video games and watching shows than reading books. "She's definitely reading less," Johnson says. "The only way we can be alone among ourselves is with screens." As many parents know, screen time has ballooned during the pandemic.

In each of its annual budget requests, the Trump administration made deep funding cuts to federal research spending, in spite of Congress' consistent refusals. However, the administration's 2021 proposal actually sought to promote AI and quantum computing research. It asked for double funding to those departments in the National Science Foundation, the National Institutes of Health, the Department of Energy, Darpa, and the Joint AI Center to $2 billion annually. While decried as wholly inadequate to address the field's rate of technical advance, that funding bump would come at the expense of funding other basic sciences in those same agencies, as well as an overall reduction in research and development spending by 9 percent over 2020, to $142.2 billion. "I find it disappointing and concerning that funding for basic research is down," Martijn Rasser, a senior fellow at the Center for a New American Security, told Wired in 2020.

I have been reading and researching what has been written about the future of work and how we can prepare and be ready to adapt. I am going to use a gross over-simplified definition of business as demand meeting supply to explain what I have learned. In this simple definition, I look back at talent shortages or surpluses I have lived through and see them all as moderate adjustments of talent demand and supply. For example, we needed fewer transcriptionists and couldn't hire enough customer service representatives. What is different in this latest shift is that the scale of innovation is spectacular in terms of what technology can perform, and the pace of this shift is so much faster than what we have experienced before.

IBM is probably the lower hanging fruit in this case, but Google is now investing resources into a new Cloud Machine Learning product that it hopes will make it competitive in the Cloud industry against the likes of Amazon, Microsoft, and IBM. The company has slowly lost pace with the rest of the industry in the last few years. There's no good reason for losing ground other than focus. Now, that the company has reorganized under Alphabet, it appears Google is back to being interested in innovating and cutting off the slough that has been weighing it down. If Google can minimize distractions and retain focus long enough, it could still have a shot somewhere in the Cloud industry.