Tootfinder

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

Get 9-30x speed doing areal-weighted interpolation with my new {𝐝𝐮𝐜𝐤𝐬𝐟} #rstats package compared to {sf}/{areal}. Experimental, but tested against both {areal} and {sf}. https://github.com/e-kotov/ducksf . Despite…

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. 🔥⁠🪫⁠🔥

SpecifyUI: Supporting Iterative UI Design Intent Expression through Structured Specifications and Generative AI
Yunnong Chen, Chengwei Shi, Liuqing Chen
https://arxiv.org/abs/2509.07334

SpecifyUI: Supporting Iterative UI Design Intent Expression through Structured Specifications and Generative AI
Large language models (LLMs) promise to accelerate UI design, yet current tools struggle with two fundamentals: externalizing designers' intent and controlling iterative change. We introduce SPEC, a structured, parameterized, hierarchical intermediate representation that exposes UI elements as controllable parameters. Building on SPEC, we present SpecifyUI, an interactive system that extracts SPEC from UI references via region segmentation and vision-language models, composes UIs across multipl…

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…

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Decheng Duan, Yingyi Zhang, Jitong Peng, Chengzhi Zhang
https://arxiv.org/abs/2509.07801

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Structured information extraction from scientific literature is crucial for capturing core concepts and emerging trends in specialized fields. While existing datasets aid model development, most focus on specific publication sections due to domain complexity and the high cost of annotating scientific texts. To address this limitation, we introduce SciNLP - a specialized benchmark for full-text entity and relation extraction in the Natural Language Processing (NLP) domain. The dataset comprises …

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…

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 …

Table is finished and installed. Mac LC III has FINALLY been moved out of my office.
#woodworking
#retrocomputing

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…