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. 🔥⁠🪫⁠🔥

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…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Wu-Tang Clan:
🎵 Da Mystery of Chessboxin’
#WuTangClan
https://sivickbeats.bandcamp.com/track/wu-tang-clan-da-mystery-of-chessboxin-sivickbeats-remix
https://open.spotify.com/track/4xOTVtbCPcfxMs2hSt9D6i

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…

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 …

LSMTCR: A Scalable Multi-Architecture Model for Epitope-Specific T Cell Receptor de novo Design
Ruihao Zhang, Xiao Liu
https://arxiv.org/abs/2509.07627 https://

LSMTCR: A Scalable Multi-Architecture Model for Epitope-Specific T Cell Receptor de novo Design
Designing full-length, epitope-specific TCR α\b{eta} remains challenging due to vast sequence space, data biases and incomplete modeling of immunogenetic constraints. We present LSMTCR, a scalable multi-architecture framework that separates specificity from constraint learning to enable de novo, epitope-conditioned generation of paired, full-length TCRs. A diffusion-enhanced BERT encoder learns time-conditioned epitope representations; conditional GPT decoders, pretrained on CDR3\b{eta} and tr…

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…

Dynamic Scheduling in Fiber and Spaceborne Quantum Repeater Networks
Paolo Fittipaldi
https://arxiv.org/abs/2510.05854 https://arxiv.org/pdf/2510.05854

Dynamic Scheduling in Fiber and Spaceborne Quantum Repeater Networks
The problem of scheduling in quantum networks amounts to choosing which entanglement swapping operations to perform to better serve user demand. The choice can be carried out following a variety of criteria (e.g. ensuring all users are served equally vs. prioritizing specific critical applications, adopting heuristic or optimization-based algorithms...), requiring a method to compare different solutions and choose the most appropriate. We present a framework to mathematically formulate the sche…