Tootfinder

I recommend visiting, or at least thinking about, /r/spicypillows every so often as a reminder to check on and keep track of all your lithium batteries. 🔥⁠🪫⁠🔥

The table's final dry fit. It's almost perfectly level which was one thing I was worried about.
Also, the glue up with shims.
Tomorrow, the finishing goes on and it should be ready to attach the top
#woodworking
#inprogress

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Shabazz Palaces:
🎵 A Treatease Dedicated to the Avian Airess From North East Nubis (1000 Questions, 1 Answer)
#ShabazzPalaces
https://shabazzpalaces.bandcamp.com/track/a-treatease-dedicated-to-the-avian-airess-from-north-east-nubis-1000-questions-1-answer
https://open.spotify.com/track/2evTqwZ8d0mh4KigJar6c8

Subspace Variational Quantum Simulation: Fidelity Lower Bounds as Measures of Training Success
Seung Park, Dongkeun Lee, Jeongho Bang, Hoon Ryu, Kyunghyun Baek
https://arxiv.org/abs/2509.06360

Subspace Variational Quantum Simulation: Fidelity Lower Bounds as Measures of Training Success
We propose an iterative variational quantum algorithm to simulate the time evolution of arbitrary initial states within a given subspace. The algorithm compresses the Trotter circuit into a shorter-depth parameterized circuit, which is optimized simultaneously over multiple initial states in a single training process using fidelity-based cost functions. After the whole training procedure, we provide an efficiently computable lower bound on the fidelities for arbitrary states within the subspace…

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
Jiaojiao Zhang, Yuqi Xu, Kun Yuan
https://arxiv.org/abs/2509.05213 https://arx…

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
This work addresses the key challenges of applying federated learning to large-scale deep neural networks, particularly the issue of client drift due to data heterogeneity across clients and the high costs of communication, computation, and memory. We propose FedSub, an efficient subspace algorithm for federated learning on heterogeneous data. Specifically, FedSub utilizes subspace projection to guarantee local updates of each client within low-dimensional subspaces, thereby reducing communicat…

LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection
Jian Wu, Hang Yu, Bingchang Liu, Wenjie Yang, Peng Di, Jianguo Li, Yue Zhang
https://arxiv.org/abs/2509.06524

LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection
Adapting large language models (LLMs) to specific domains often faces a critical bottleneck: the scarcity of high-quality, human-curated data. While large volumes of unchecked data are readily available, indiscriminately using them for fine-tuning risks introducing noise and degrading performance. Strategic data selection is thus crucial, requiring a method that is both accurate and efficient. Existing approaches, categorized as similarity-based and direct optimization methods, struggle to simu…

Some relationships with subnormal operators and existence of hyperinvariant subspaces
Maria F. Gamal'
https://arxiv.org/abs/2509.05453 https://arxiv.or…

Some relationships with subnormal operators and existence of hyperinvariant subspaces
If $T$ is a polynomially bounded operator, $\mathcal M$ is an invariant subspace of $T$, $T|_{\mathcal M}$ is a unilateral shift and $T^*|_{\mathcal M^\perp}$ is subnormal, then $T$ has a nontrivial hyperinvariant subspace. If an operator $T$ is intertwined from both sides with two operators, one of which is hyponormal and other is the adjoint to hyponormal, then $T$ has a nontrivial hyperinvariant subspace. The existence of nontrivial hyperinvariant subspaces for subnormal operators …

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
Hiromi Ishii (Jij, Inc), Taro Shimizu (Jij, Inc), Toshiki Teramura (Jij, Inc)
https://arxiv.org/abs/2506.06495

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
In solving mathematical optimization problems efficiently, it is crucial to make use of information about specific types of constraints, such as the one-hot or Special-Ordered Set (SOS) constraints. In many cases, exploiting such information gives asymptotically better execution time. JijModeling, an industrial-strength mathematical optimization modeller, achieves this by separating the symbolic representation of an optimization problem from the input data. In this paper, we will report a real-…

Pilzsammeln kann eine wunderbare Erfahrung sein, wenn man einige wichtige Regeln beachtet. 🌳🍄 Könnt Ihr #Speisepilze sicher identifizieren? Kennt Ihr die giftigen „Doppelgänger“? Was sollte beim #Schwammerlsuchen noch alles beachtet werden? Do's und Don'ts für ein sicheres und n…

Die Do's und Don'ts des Pilzsammelns: Expertentipps für sicheres Sammeln
Hol dir wichtige Tipps für Pilzsammler: Wie man Pilze sicher sammelt, lagert und zubereitet, ohne das natürliche Ökosystem zu schädigen.

Design and Implementation of a Domain-specific Language for Modelling Evacuation Scenarios Using Eclipse EMG/GMF Tool
Heerok Banerjee
https://arxiv.org/abs/2509.06688 https://…

Design and Implementation of a Domain-specific Language for Modelling Evacuation Scenarios Using Eclipse EMG/GMF Tool
Domain-specific languages (DSLs) play a crucial role in resolving internal dependencies across enterprises and boosts their upfront business management processes. Yet, a lot of development is needed to build modelling frameworks which support graphical interfaces (canvas, pallettes etc.), hierarchical structures and easy implementation to shorten the gap for novice users. In this paper, a DSL namely, Bmod is introduced, which can be used to model evacuation scenarios. The language is built usin…