Tootfinder

Sources: WBD would like the NBA to consider a "fourth package", which would carve out a small package of games rights, alongside Disney's, Amazon's, and NBCU's (Brian Steinberg/Variety)
https://variety.com/2024/tv/news/warne

Warner Bros. Discovery's NBA Rights Hopes Hinge on 'Fourth Package'
Warner Bros. Discovery is trying to cajole the NBA to carve out a small fourth package of NBA games, but such efforts may be tough to accomplish

This https://arxiv.org/abs/2308.14915 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Uncertainty-driven Affordance Discovery for Efficient Robotics Manipulation
Robotics affordances, providing information about what actions can be taken in a given situation, can aid robotics manipulation. However, learning about affordances requires expensive large annotated datasets of interactions or demonstrations. In this work, we show active learning can mitigate this problem and propose the use of uncertainty to drive an interactive affordance discovery process. We show that our method enables the efficient discovery of visual affordances for several action primi…

“Warner Bros. Discovery has a 10-year, $650 million deal in place to be the new home of the French Open in the United States beginning in 2025, executives briefed on the agreement told The Athletic.”
#tennis #FrenchOpen #WBD

French Open, TNT Sports agree to 10-year, $650 million deal: Sources
NBC has been the longtime home of the Open, which it shares currently with Tennis Channel.

This https://arxiv.org/abs/2308.14915 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Uncertainty-driven Affordance Discovery for Efficient Robotics Manipulation
Robotics affordances, providing information about what actions can be taken in a given situation, can aid robotics manipulation. However, learning about affordances requires expensive large annotated datasets of interactions or demonstrations. In this work, we show active learning can mitigate this problem and propose the use of uncertainty to drive an interactive affordance discovery process. We show that our method enables the efficient discovery of visual affordances for several action primi…

The test of investors' behavioral bias through the price discovery process in cryptoasset exchange" Transactional-level evidence from Thailand
Roongkiat Ratanabanchuen, Kanis Saengchote, Voraprapa Nakavachara, Thitiphong Amonthumniyom, Pongsathon Parinyavuttichai, Polpatt Vinaibodee
https://arxiv.org/abs/2406.02878

The test of investors' behavioral bias through the price discovery process in cryptoasset exchange" Transactional-level evidence from Thailand
Analyzing investors' trading behavior in cryptoasset markets provides new evidence supporting the theory that retail investors likely exhibit behavioral biases. We investigate the price discovery process between Thailand's most highly liquid exchange and the global exchange. Under the no-arbitrage assumption, bid-offer quotes in the local exchange should quickly move to match the new price levels in the global exchange, as the price process of cryptoassets in the local exchange does not contain…

Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations
Kai L. Polsterer, Bernd Doser, Andreas Fehlner, Sebastian Trujillo-Gomez
https://arxiv.org/abs/2406.03810

Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations
Simulations are the best approximation to experimental laboratories in astrophysics and cosmology. However, the complexity, richness, and large size of their outputs severely limit the interpretability of their predictions. We describe a new, unbiased, and machine learning based approach to obtaining useful scientific insights from a broad range of simulations. The method can be used on today's largest simulations and will be essential to solve the extreme data exploration and analysis challeng…

Data-driven discovery of self-similarity using neural networks
Ryota Watanabe, Takanori Ishii, Yuji Hirono, Hirokazu Maruoka
https://arxiv.org/abs/2406.03896

Data-driven discovery of self-similarity using neural networks
Finding self-similarity is a key step for understanding the governing law behind complex physical phenomena. Traditional methods for identifying self-similarity often rely on specific models, which can introduce significant bias. In this paper, we present a novel neural network-based approach that discovers self-similarity directly from observed data, without presupposing any models. The presence of self-similar solutions in a physical problem signals that the governing law contains a function …

This https://arxiv.org/abs/2308.08841 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Machine Learning-Assisted Discovery of Flow Reactor Designs
Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generatio…

This https://arxiv.org/abs/2405.07066 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Unveiling the Structure of Hidden-Bottom Strange Pentaquarks via Magnetic Moments
Motivated by the discovery of hidden-charm strange pentaquarks, we conduct a systematic study of the magnetic moments of the hidden-bottom strange pentaquarks in molecular picture. We calculate magnetic moments of hidden-bottom strange pentaquarks with strangeness-1 and 2. Magnetic moment gives valuable information about the inner structure and shape of the hadron. The obtained results may be helpful to determine the inner structure of these new yet hypothetical states.

Item-Language Model for Conversational Recommendation
Li Yang, Anushya Subbiah, Hardik Patel, Judith Yue Li, Yanwei Song, Reza Mirghaderi, Vikram Aggarwal
https://arxiv.org/abs/2406.02844

Item-Language Model for Conversational Recommendation
Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modality to include image, audio, and video capabilities. Recommender systems, on the other hand, have been critical for information seeking and item discovery needs. Recently, there have been attempts to apply LLMs for recommendations. One difficulty of current attempts is that the …