DSN primarily seeks to transfer knowledge to the new coming task from the learned tasks by selecting the affiliated weights of a small set of neurons to be activated, including the reused neurons from prior tasks via neuron-wise masks. And it also transfers possibly valuable knowledge to the earlier tasks via data-free replay.

Experiments demonstrate that our approach yields substantial improvements in high-quality and stable speech generation, consistency with the condition factor, and inference efficiency.

Despite these successes, the highly technical nature of financial texts requires domain-specific LLMs to understand complex financial language and concepts effectively.

Bregman divergence for greater adaptability in personalized scenarios.

For all parameter settings, see Appendix B.2. The code of our CRCL will be released on GitHub.

Gestala is the latest company to emerge from China's burgeoning brain-computer interface industry. It plans to access the brain with noninvasive ultrasound technology. China's brain-computer interface industry is growing fast, and the newest company to emerge from the country is aiming to access the brain without the use of invasive implants . Gestala, newly founded in Chengdu with offices in Shanghai and Hong Kong, plans to use ultrasound technology to stimulate--and eventually read from--the brain, according to CEO and cofounder Phoenix Peng. It's the second company to launch in recent weeks with the aim of tapping into the brain with ultrasound.

Wireframe parsing aims to recover line segments and their junctions to form a structured geometric representation useful for downstream tasks such as Simultaneous Localization and Mapping (SLAM). Existing methods predict lines and junctions separately and reconcile them post-hoc, causing mismatches and reduced robustness. We present Co-PLNet, a point-line collaborative framework that exchanges spatial cues between the two tasks, where early detections are converted into spatial prompts via a Point-Line Prompt Encoder (PLP-Encoder), which encodes geometric attributes into compact and spatially aligned maps. A Cross-Guidance Line Decoder (CGL-Decoder) then refines predictions with sparse attention conditioned on complementary prompts, enforcing point-line consistency and efficiency. Experiments on Wireframe and YorkUrban show consistent improvements in accuracy and robustness, together with favorable real-time efficiency, demonstrating our effectiveness for structured geometry perception.