Dakich: sports media has created an'industry' out of complaining about white athletes like Caitlin Clark Greg Sankey insists SEC is'strongest league' despite Big Ten winning three straight national championships Phillies look to upset Dodgers behind Zack Wheeler as Philadelphia's turnaround continues in LA Joey McGuire calls Steve Sarkisian's bluff, dares Texas to play Texas Tech in Week 1 Rams troublemaker WR Puka Nacua says he's a changed man after biting incident and stint in rehab Chiefs have no plans to release Rashee Rice and see jail time as a'life lesson' opportunity Diamondbacks fans catch same player's home run on back-to-back nights after showing up on the wrong date Purple Heart recipient speaks out after Graham Platner's controversial remarks'Chipotle Karen' caught hurling burrito bowl at worker's face US and Iranian negotiators reach tentative agreement awaiting Trump's approval Jill Biden dismisses concerns of Joe Biden's cognitive decline OutKick-Sports Umpire Dan Bellino's baffling foul tip call on Seiya Suzuki renews calls for robot review in MLB Dan Bellino insisted on the call despite replay showing Suzuki's bat was a foot above the ball It is probably time to bring in the robots to police MLB umpires missing foul tips. Late in Thursday night's Cubs-Pirates game with home plate umpire Dan Bellino, who has 15 years of MLB service, made one of the most baffling foul tip calls you're ever going to see. With the count 2-1 on Seiya Suzuki, Pirates pitcher Yohan Ramirez delivered an off-speed pitch off the inside of the plate that had Suzuki nearly offering at it before pulling the bat back. Bellino says, to which Suzuki instantly pleads that he didn't tip the ball. Bellino will hear nothing of the sort.

Deep learning has exhibited superior performance for various tasks, especially for high-dimensional datasets, such as images. To understand this property, we investigate the approximation and estimation ability of deep learning on anisotropic Besov spaces. The anisotropic Besov space is characterized by direction-dependent smoothness and includes several function classes that have been investigated thus far. We demonstrate that the approximation error and estimation error of deep learning only depend on the average value of the smoothness parameters in all directions. Consequently, the curse of dimensionality can be avoided if the smoothness of the target function is highly anisotropic. Unlike existing studies, our analysis does not require a low-dimensional structure of the input data. We also investigate the minimax optimality of deep learning and compare its performance with that of the kernel method (more generally, linear estimators). The results show that deep learning has better dependence on the input dimensionality if the target function possesses anisotropic smoothness, and it achieves an adaptive rate for functions with spatially inhomogeneous smoothness.

Deep learning has exhibited superior performance for various tasks, especially for high-dimensional datasets, such as images. To understand this property, we investigate the approximation and estimation ability of deep learning on anisotropic Besov spaces. The anisotropic Besov space is characterized by direction-dependent smoothness and includes several function classes that have been investigated thus far. We demonstrate that the approximation error and estimation error of deep learning only depend on the average value of the smoothness parameters in all directions. Consequently, the curse of dimensionality can be avoided if the smoothness of the target function is highly anisotropic. Unlike existing studies, our analysis does not require a low-dimensional structure of the input data. We also investigate the minimax optimality of deep learning and compare its performance with that of the kernel method (more generally, linear estimators). The results show that deep learning has better dependence on the input dimensionality if the target function possesses anisotropic smoothness, and it achieves an adaptive rate for functions with spatially inhomogeneous smoothness.

Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating natural language data.

We show that the proposed algorithms converge to the (regularized) global optimal solution, andmoreover,theirratesofconvergence areofpolynomial orderinthe online setting and exponential order inthe finite sample setting, respectively.

The excess risk bound achieves the so-called fast learning rate.

Deep learning has exhibited superior performance for various tasks, especially for high-dimensional datasets, such as images. To understand this property, we investigate the approximation and estimation ability of deep learning on anisotropic Besov spaces. The anisotropic Besov space is characterized by direction-dependent smoothness and includes several function classes that have been investigated thus far. We demonstrate that the approximation error and estimation error of deep learning only depend on the average value of the smoothness parameters in all directions. Consequently, the curse of dimensionality can be avoided if the smoothness of the target function is highly anisotropic.

Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating natural language data.

We comment on Cai and Wang (2020, arXiv:2006.13318), who analyze over-smoothing in GNNs via Dirichlet energy. We show that under mild spectral conditions (including with Leaky-ReLU), the Dirichlet energy of node embeddings decreases exponentially with depth; we further extend the result to spectral polynomial filters and provide a short proof for the Leaky-ReLU case. Experiments on edge deletion and weight amplification illustrate when Dirichlet energy increases, hinting at practical ways to relieve over-smoothing.

Labor shortage due to the declining birth rate has become a serious problem in the construction industry, and automation of construction work is attracting attention as a solution to this problem. This paper proposes a method to realize state estimation of dump truck position, orientation and articulation angle using multiple GNSS for automatic operation of dump trucks. RTK-GNSS is commonly used for automation of construction equipment, but in mountainous areas, mobile networks often unstable, and RTK-GNSS using GNSS reference stations cannot be used. Therefore, this paper develops a state estimation method for dump trucks that does not require a GNSS reference station by using the Centimeter Level Augmentation Service (CLAS) of the Japanese Quasi-Zenith Satellite System (QZSS). Although CLAS is capable of centimeter-level position estimation, its positioning accuracy and ambiguity fix rate are lower than those of RTK-GNSS. To solve this problem, we construct a state estimation method by factor graph optimization that combines CLAS positioning and moving-base RTK-GNSS between multiple GNSS antennas. Evaluation tests under real-world environments have shown that the proposed method can estimate the state of dump trucks with the same accuracy as conventional RTK-GNSS, but does not require a GNSS reference station.