Tootfinder

The players I can't stop drafting in 2025 fantasy football... so far https://www.nytimes.com/athletic/6523703/2025/07/31/fantasy-football-2025-most-drafted-players-jake-ciely/

Nuclear Matter and Finite Nuclei: Relativistic Thomas-Fermi Approximation Versus Relativistic Mean-Field Approach
Shuying Li, Hong Shen, Jinniu Hu
https://arxiv.org/abs/2507.23587

Nuclear Matter and Finite Nuclei: Relativistic Thomas-Fermi Approximation Versus Relativistic Mean-Field Approach
The Thomas-Fermi approximation is a powerful method that has been widely used to describe atomic structures, finite nuclei, and nonuniform matter in supernovae and neutron-star crusts. Nonuniform nuclear matter at subnuclear density is assumed to be composed of a lattice of heavy nuclei surrounded by dripped nucleons, and the Wigner-Seitz cell is commonly introduced to simplify the calculations. The self-consistent Thomas--Fermi approximation can be employed to study both a nucleus surrounded b…

Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization
Anton Pembek, Artem Fatkulin, Anton Klenitskiy, Alexey Vasilev
https://arxiv.org/abs/2507.19473

Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization
Many sequential recommender systems suffer from the cold start problem, where items with few or no interactions cannot be effectively used by the model due to the absence of a trained embedding. Content-based approaches, which leverage item metadata, are commonly used in such scenarios. One possible way is to use embeddings derived from content features such as textual descriptions as initialization for the model embeddings. However, directly using frozen content embeddings often results in sub…

Measures of Overlapping Multivariate Gaussian Clusters in Unsupervised Online Learning
Miha O\v{z}bot, Igor \v{S}krjanc
https://arxiv.org/abs/2508.15444 https://

Measures of Overlapping Multivariate Gaussian Clusters in Unsupervised Online Learning
In this paper, we propose a new measure for detecting overlap in multivariate Gaussian clusters. The aim of online learning from data streams is to create clustering, classification, or regression models that can adapt over time based on the conceptual drift of streaming data. In the case of clustering, this can result in a large number of clusters that may overlap and should be merged. Commonly used distribution dissimilarity measures are not adequate for determining overlapping clusters in th…

The JWST/AURORA Survey: Multiple Balmer and Paschen Emission Lines for Individual Star-forming Galaxies at z=1.5-4.4. I. A Diversity of Nebular Attenuation Curves and Evidence for Non-Unity Dust Covering Fractions
Naveen A. Reddy, Alice E. Shapley, Ryan L. Sanders, Michael W. Topping, Richard S. Ellis, Max Pettini, Gabriel Brammer, Fergus Cullen, Natascha M. Forster Schreiber, Ali A. Khostovan, Derek J. McLeod, Ross J. McLure, Desika Narayanan, Pascal A. Oesch, Anthony J. Pahl, Charles…

The JWST/AURORA Survey: Multiple Balmer and Paschen Emission Lines for Individual Star-forming Galaxies at z=1.5-4.4. I. A Diversity of Nebular Attenuation Curves and Evidence for Non-Unity Dust Covering Fractions
We present the nebular attenuation curves and dust covering fractions for 24 redshift z=1.5-4.4 star-forming galaxies using multiple Balmer and Paschen lines from the JWST/AURORA survey. Nebular reddening derived from Paschen lines exceeds that from Balmer lines for at least half the galaxies in the sample when assuming the commonly-adopted Galactic extinction curve, implying the presence of optically-thick star formation. The nebular attenuation curves exhibit a broad range of normalizations (…

Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy
Yiting Yang, Hao Luo, Yuan Sun, Qingsen Yan, Haokui Zhang, Wei Dong, Guoqing Wang, Peng Wang, Yang Yang, Hengtao Shen
https://arxiv.org/abs/2507.13260

Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy
A prevalent approach in Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViT) involves freezing the majority of the backbone parameters and solely learning low-rank adaptation weight matrices to accommodate downstream tasks. These low-rank matrices are commonly derived through the multiplication structure of down-projection and up-projection matrices, exemplified by methods such as LoRA and Adapter. In this work, we observe an approximate orthogonality among any two ro…

A Stable and Principled Loss Function for Direct Language Model Alignment
Yuandong Tan
https://arxiv.org/abs/2508.07137 https://arxiv.org/pdf/2508.07137

A Stable and Principled Loss Function for Direct Language Model Alignment
The alignment of large language models (LLMs) with human preferences is commonly achieved through Reinforcement Learning from Human Feedback (RLHF). Direct Preference Optimization (DPO) simplified this paradigm by establishing a direct mapping between the optimal policy and a reward function, eliminating the need for an explicit reward model. However, we argue that the DPO loss function is theoretically misaligned with its own derivation, as it promotes the indefinite maximization of a logits d…

Sulphur abundances in star-forming regions from optical emission lines: A new approach based on photoionization models consistent with the direct method
Enrique P\'erez-Montero, Borja P\'erez-D\'iaz, Jos\'e M. V\'ilchez, Igor A. Zinchenko, Asier Castrillo, Marta Gavil\'an, Sandra Zamora, \'Angeles I. D\'iaz
https://

Sulphur abundances in star-forming regions from optical emission lines: A new approach based on photoionization models consistent with the direct method
The derivation of sulphur chemical abundances in the gas-phase of star-forming galaxies is explored in this work, using the emission lines produced in these regions in the optical part of the spectrum and by means of photoionization models. We adapted the code HII-CHI-mistry to account for these abundances by implementing additional grids of models that assume a variable sulphur-to-oxygen abundance ratio, beyond the commonly assumed solar ratio. The addition of these models, and their use in a …

Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models
Dipam Goswami, Liying Wang, Bart{\l}omiej Twardowski, Joost van de Weijer
https://arxiv.org/abs/2506.00037

Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models
Text embedding models enable semantic search, powering several NLP applications like Retrieval Augmented Generation by efficient information retrieval (IR). However, text embedding models are commonly studied in scenarios where the training data is static, thus limiting its applications to dynamic scenarios where new training data emerges over time. IR methods generally encode a huge corpus of documents to low-dimensional embeddings and store them in a database index. During retrieval, a semant…

Digital gazetteers: review and prospects for place name knowledge bases
Kalana Wijegunarathna, Kristin Stock, Christopher B. Jones
https://arxiv.org/abs/2507.08553

Digital gazetteers: review and prospects for place name knowledge bases
Gazetteers typically store data on place names, place types and the associated coordinates. They play an essential role in disambiguating place names in online geographical information retrieval systems for navigation and mapping, detecting and disambiguating place names in text, and providing coordinates. Currently there are many gazetteers in use derived from many sources, with no commonly accepted standard for encoding the data. Most gazetteers are also very limited in the extent to which th…