Tootfinder

A High Geometric Albedo for LTT9779b Points Towards a Metal-rich Atmosphere and Silicate Clouds
Suman Saha, James S. Jenkins, Vivien Parmentier, Sergio Hoyer, Magali Deleuil, Ian J. M. Crossfield, Pablo A. Pe\~na R., Jose I. Vines, R. Ram\'irez Reyes, Mat\'ias R. D\'iaz
https://arxiv.org/abs/2506.17550

A High Geometric Albedo for LTT9779b Points Towards a Metal-rich Atmosphere and Silicate Clouds
Aims: In this work, we aim to confirm the high albedo of the benchmark ultrahot Neptune LTT9779b using 20 secondary eclipse measurements of the planet observed with CHEOPS. In addition, we perform a search for variability in the reflected light intensity of the planet as a function of time. Methods: First, we used the TESS follow-up data of LTT9779b from three sectors (2, 29, and 69) to remodel the transit signature and estimate an updated set of transit and ephemeris parameters, which were dir…

Rethinking VAE: From Continuous to Discrete Representations Without Probabilistic Assumptions
Songxuan Shi
https://arxiv.org/abs/2507.17255 https://…

Rethinking VAE: From Continuous to Discrete Representations Without Probabilistic Assumptions
This paper explores the generative capabilities of Autoencoders (AEs) and establishes connections between Variational Autoencoders (VAEs) and Vector Quantized-Variational Autoencoders (VQ-VAEs) through a reformulated training framework. We demonstrate that AEs exhibit generative potential via latent space interpolation and perturbation, albeit limited by undefined regions in the encoding space. To address this, we propose a new VAE-like training method that introduces clustering centers to enha…

Overfitting in Histopathology Model Training: The Need for Customized Architectures
Saghir Alfasly, Ghazal Alabtah, H. R. Tizhoosh
https://arxiv.org/abs/2506.16631

Overfitting in Histopathology Model Training: The Need for Customized Architectures
This study investigates the critical problem of overfitting in deep learning models applied to histopathology image analysis. We show that simply adopting and fine-tuning large-scale models designed for natural image analysis often leads to suboptimal performance and significant overfitting when applied to histopathology tasks. Through extensive experiments with various model architectures, including ResNet variants and Vision Transformers (ViT), we show that increasing model capacity does not …

Debiased maximum-likelihood estimators for hazard ratios under machine-learning adjustment
Takashi Hayakawa, Satoshi Asai
https://arxiv.org/abs/2507.17686 …

Debiased maximum-likelihood estimators for hazard ratios under machine-learning adjustment
Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the indefinite baseline hazard of the model fails to identify temporal change in the risk set composition due to treatment assignment and unobserved factors among multiple, contradictory scenarios. To alleviate this problem, especially in studies based on observational data with uncontrolled dynamic treatment and real-time measurement of many covariates, we propose ab…

It appears federal funding cuts to MS-ISAC , albeit to an outsider of the program, is going to exact some significant changes to the org, including the introduction of some new membership fees for some services.
https://www.cisecurity.org/ms-isac/defending-ameri…

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Yisu Wang, Xinjiao Li, Ruilong Wu, Huangxun Chen, Dirk Kutscher
https://arxiv.org/abs/2506.16235

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
Training large-scale distributed machine learning models imposes considerable demands on network infrastructure, often resulting in sudden traffic spikes that lead to congestion, increased latency, and reduced throughput, which would ultimately affect convergence times and overall training performance. While gradient compression techniques are commonly employed to alleviate network load, they frequently compromise model accuracy due to the loss of gradient information. This paper introduces N…

PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
Haotong Du, Yaqing Wang, Fei Xiong, Lei Shao, Ming Liu, Hao Gu, Quanming Yao, Zhen Wang
https://arxiv.org/abs/2506.18382

PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
With the expansion of business scales and scopes on online platforms, multi-scenario matching has become a mainstream solution to reduce maintenance costs and alleviate data sparsity. The key to effective multi-scenario recommendation lies in capturing both user preferences shared across all scenarios and scenario-aware preferences specific to each scenario. However, existing methods often overlook user-specific modeling, limiting the generation of personalized user representations. To address …

Model Predictive Control for Unlocking Energy Flexibility of Heat Pump and Thermal Energy Storage Systems: Experimental Results
Weihong Tang, Yun Li, Shalika Walker, Tamas Keviczky
https://arxiv.org/abs/2507.17552

Model Predictive Control for Unlocking Energy Flexibility of Heat Pump and Thermal Energy Storage Systems: Experimental Results
Increasing penetration of renewable energy sources (RES) and electrification of energy systems necessitates the engagement of demand-side management (DSM) to help alleviate congestion in electricity grid. Heat pump and thermal energy storage (HPTES) systems, being energy efficient solutions, are becoming popular in modern buildings and are promising to contribute to demand-side management (DSM) due to their significant share in household electricity consumption. For typical HPTES systems, this …

PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training
Anirudh Satheesh, Anant Khandelwal, Mucong Ding, Radu Balan
https://arxiv.org/abs/2507.17151

PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training
Neural operators offer a powerful paradigm for solving partial differential equations (PDEs) that cannot be solved analytically by learning mappings between function spaces. However, there are two main bottlenecks in training neural operators: they require a significant amount of training data to learn these mappings, and this data needs to be labeled, which can only be accessed via expensive simulations with numerical solvers. To alleviate both of these issues simultaneously, we propose PICore…

DeCo-SGD: Joint Optimization of Delay Staleness and Gradient Compression Ratio for Distributed SGD
Rongwei Lu, Jingyan Jiang, Chunyang Li, Haotian Dong, Xingguang Wei, Delin Cai, Zhi Wang
https://arxiv.org/abs/2507.17346

DeCo-SGD: Joint Optimization of Delay Staleness and Gradient Compression Ratio for Distributed SGD
Distributed machine learning in high end-to-end latency and low, varying bandwidth network environments undergoes severe throughput degradation. Due to its low communication requirements, distributed SGD (D-SGD) remains the mainstream optimizer in such challenging networks, but it still suffers from significant throughput reduction. To mitigate these limitations, existing approaches typically employ gradient compression and delayed aggregation to alleviate low bandwidth and high latency, respec…