Tootfinder

Okay, time to continue bashing. Perhaps this one project is just that bad, but Conan sounds like a complete antithesis of what a package manager is all about.
Well, this package insists to build its dependencies via Conan. Except it insists on really old versions that don't work with my glibc. So I need to start swapping dependencies.
Except it turns out Conan doesn't care much about resolving dependencies. So I actually need to start adjust versions of the dependencies of packages that it wants to build. And then it starts rebuilding other stuff and again everything fails because of incompatible versions.
And when I finally manage to find a working set, the actual project fails over protobuf version. After a long WTF-ing, I finally realize that it's complaining, because it somehow managed to mix the version of protobuf built by Conan and the external protobuf installed by Conda.
So yeah, great job. A package manager that doesn't really resolve dependencies but instead forces a dependency hell on you, and on top of that ends up mixing system packages with its own packages.

little known fediverse tip: you can follow @PeerTube@<peertube instance> to get a feed of all videos on that instance! for instance, @…'s PeerTube instance is @…

Multi-objective Portfolio Optimization Via Gradient Descent
Christian Oliva, Pedro R. Ventura, Luis F. Lago-Fern\'andez
https://arxiv.org/abs/2507.16717 https://

Multi-objective Portfolio Optimization Via Gradient Descent
Traditional approaches to portfolio optimization, often rooted in Modern Portfolio Theory and solved via quadratic programming or evolutionary algorithms, struggle with scalability or flexibility, especially in scenarios involving complex constraints, large datasets and/or multiple conflicting objectives. To address these challenges, we introduce a benchmark framework for multi-objective portfolio optimization (MPO) using gradient descent with automatic differentiation. Our method supports any …

Stop Killing Games: A History
#StopKillingGames

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
Maciej Wysocki, Pawe{\l} Sakowski
https://arxiv.org/abs/2508.14999 https://

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use …

Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
Xinyue Zhu, Binghao Huang, Yunzhu Li
https://arxiv.org/abs/2507.15062

Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
Handheld grippers are increasingly used to collect human demonstrations due to their ease of deployment and versatility. However, most existing designs lack tactile sensing, despite the critical role of tactile feedback in precise manipulation. We present a portable, lightweight gripper with integrated tactile sensors that enables synchronized collection of visual and tactile data in diverse, real-world, and in-the-wild settings. Building on this hardware, we propose a cross-modal representatio…

Eigen Portfolios: From Single Component Models to Ensemble Approaches
ZhengXiang Zhou, Yuqi Luan
https://arxiv.org/abs/2508.15586 https://arxiv.org/pdf/250…

Eigen Portfolios: From Single Component Models to Ensemble Approaches
The increasing integration of data science techniques into quantitative finance has enabled more systematic and data-driven approaches to portfolio construction. This paper investigates the use of Principal Component Analysis (PCA) in constructing eigen-portfolios - portfolios derived from the principal components of the asset return correlation matrix. We begin by formalizing the mathematical underpinnings of eigen-portfolios and demonstrate how PCA can reveal latent orthogonal factors driving…

ProTip: Sleep with one paw in your bowl to never miss mealtime.

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
Qingliang Fan, Ruike Wu, Yanrong Yang
https://arxiv.org/abs/2507.16433 https://…

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
Accurate transfer of information across multiple sectors to enhance model estimation is both significant and challenging in multi-sector portfolio optimization involving a large number of assets in different classes. Within the framework of factor modeling, we propose a novel data-adaptive multi-task learning methodology that quantifies and learns the relatedness among the principal temporal subspaces (spanned by factors) across multiple sectors under study. This approach not only improves the …

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#PurdueFortWayne

Purdue Fort Wayne professor weaves math into everyday life | Purdue University Fort Wayne
Hanan Alyami, M.S. ’14, likes to use uncommon ways to look at and teach math. Last semester, she taught three classes at Purdue University Fort Wayne, primarily to elementary education majors learning to be math instructors.