Tootfinder

Generalization Error Estimation for Primal--Dual Algorithms in Non-Smooth Regression
Kai Tan, Pierre C Bellec
https://arxiv.org/abs/2608.13870 https://arxiv.org/pdf/2608.13870 https://arxiv.org/html/2608.13870
arXiv:2608.13870v1 Announce Type: new
Abstract: This paper studies trajectory-wise estimation of generalization error for primal--dual algorithms in non-smooth regression. Motivating examples include \(\ell_1\)-penalized least absolute deviations regression and square-root Lasso regression, where the data-fitting loss is non-differentiable and existing risk estimators for gradient-type optimization paths do not apply directly. We develop a general recursive framework that includes the Chambolle--Pock algorithm and related primal--dual splitting methods. We estimate risk by correcting each in-sample fitted value with a weighted combination of past dual iterates. The ideal weights are Stein derivative contractions and depend on the design covariance. We construct replacement weights from observable derivative contractions of the fitted-signal trajectory, yielding a covariance-free, data-driven correction. For high-dimensional Gaussian designs and fixed finite iteration horizon, we prove finite-sample guarantees for both estimators. For square-root ridge, we further establish a matched-Gaussian universality result beyond Gaussian designs. Numerical experiments show that the proposed estimators accurately track the out-of-sample risk along finite optimization paths.
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07:16 AM
What a morning and long day it was last week! Was already out & about for 3 hours when I got to this place, earlier mostly taking hundreds of images of the nearby (~500m away) Breslauer Hut and vicinity during sunrise... The first large (and loud) group of people already got up at 4AM, getting ready for climbing Wildspitze, another group left an hour later, as did I. Maintenance/repair works on the hut continued at 08:30, taking lots more pictures to document, interviewin…

There is more to #SolarPunk than solar panels and bikes. What we are talking about is being part of a living world. The hegemonic culture is one of hierarchical domination.
But many of the the cultures that hegemony suppresses bring a vibrancy to life. I don't buy yeast from the store, sealed in a plastic packet, a coffin for a living thing. I feed my sourdough. I am obligated to it, in a relationship with it, as the culture is reliant on me for it's survival. It speaks to me, with smell and color. I must pay attention. I have to learn it's nature, how it grows and eats at different temperatures informed by different times of the year. Mushrooms, yeast, kiefer, plants, independent things, things I can't control, life that is intertwined with my own.
But even my #bicycle speaks to me, which is something I hadn't considered until reading this and thinking about that framing.
It is not simply that "we need a different economic system." What we are talking about is a fundamentally different interpretation and experience of the world, from the hierarchical structure of death to a web of life, where even "inanimate" things become imbued with agency.
https://substack.com/app-link/post?publication_id=2936873&post_id=151655646

The {esquisse} package makes it easy to plot your data in different ways with a drag and drop interface: #rstats

Explore and Visualize Your Data Interactively
A shiny gadget to create ggplot2 figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.

Automatic Field-of-View Adjustment for a View-Expansive Microscope via LSTM-Based Gaze and Pipette Motion Interpretation
Kenta Yokoe, Takuya Hara, Tadayoshi Aoyama
https://arxiv.org/abs/2608.10401 https://arxiv.org/pdf/2608.10401 https://arxiv.org/html/2608.10401
arXiv:2608.10401v1 Announce Type: new
Abstract: Intracytoplasmic sperm injection (ICSI) operators frequently adjust the field-of-view (FOV) during procedures, which interrupts workflow and increases procedure time. Conventional microscopes require manual objective lens switching and illumination adjustments to achieve different FOV sizes. We propose an AI-based automatic FOV adjustment method integrated with a view-expansive microscope. This microscope enables the simultaneous acquisition of a large FOV and high-resolution images using a single objective lens through multiview imaging with galvanometer mirrors and high-speed vision, thereby eliminating the need for physical lens exchanges. Our method utilizes a long short-term memory (LSTM) model to predict the appropriate FOV size based on real-time analysis of the pipette's position and velocity, combined with the operator's gaze position. The AI model is trained using ICSI procedure data from an expert with over five years of micromanipulation experience. Experimental evaluation with novice operators reveals that the proposed automatic FOV adjustment system significantly improves the ICSI procedure speed, reducing the average task completion time from 60.5 to 48.0 s (p < 0.001). The experiments also demonstrate that this improvement enables novice operators to achieve ICSI working speeds equivalent to those of expert operators.
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Hierarchical Bayesian Calibration with Bayesian Committee Machine
Sebastian Heinekamp, David M. Higdon, Andreas Adelmann
https://arxiv.org/abs/2608.12603 https://arxiv.org/pdf/2608.12603 https://arxiv.org/html/2608.12603
arXiv:2608.12603v1 Announce Type: new
Abstract: Calibrating computational models to experimental data is a core task in applied statistics, especially in scientific domains, where physical experiments are costly and simulations play a central role in design and inference. Motivated by uncertainty quantification challenges in particle accelerator experiments, we develop and evaluate a Hierarchical Bayesian Calibration framework. In contrast to standard Bayesian calibration, certain inputs - such as beam injection amplitude - must be estimated separately for each experiment. We adopt the Kennedy-O'Hagan formulation and extend it with a hierarchical prior structure to model the distribution of experiment-specific calibration parameters, thus borrowing strength and improving generalisation across repeated experiments. A key methodological challenge arises from the need to evaluate a large number of forward simulations, which renders conventional Markov chain Monte Carlo approaches computationally prohibitive. To address this, we leverage the Bayesian Committee Machine as a scalable modelling strategy for Gaussian Process emulators. The BCM provides a principled divide-and-conquer approach, enabling parallel inference and reducing computational cost without requiring problem-specific tuning of the emulator approximation. Posterior sampling is performed using the No-U-Turn Sampler, supported by automatic differentiation in Julia, which removes the need for analytic gradient derivation and facilitates flexible model specification. We assess the proposed framework using established benchmark problems and simulated data from the Argonne Wakefield Accelerator. The results demonstrate substantial computational savings and robust calibration performance, highlighting the applicability of the method to large-scale scientific modelling problems.
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Empirical likelihood confidence regions for ordered bivariate means
Naresh Garg
https://arxiv.org/abs/2608.12174 https://arxiv.org/pdf/2608.12174 https://arxiv.org/html/2608.12174
arXiv:2608.12174v1 Announce Type: new
Abstract: Let $\boldsymbol{X}_i=(X_{1i},X_{2i})^\top$ be independent and identically distributed observations with mean $\boldsymbol{\mu}=(\mu_1,\mu_2)^\top$ constrained by $\mu_1\leq\mu_2$. We study empirical-likelihood inference for a fixed mean vector and distinguish it from the previously known test of equality against an ordered alternative. At a fixed interior point, the constrained empirical likelihood ratio has the usual $\chi^2_2$ limit. At a fixed boundary point $(m,m)^\top$, its limit is the chi-bar-square distribution $\tfrac12\chi^2_1 \tfrac12\chi^2_2$. By contrast, profiling the unknown common mean in the equality-versus-order test yields $\tfrac12\chi^2_0 \tfrac12\chi^2_1$, the $k=2$ ordered-mean case of El Barmi (1996). We give an exact reduction of the latter statistic to the empirical likelihood of the paired differences, establish the localization step needed for the fixed-boundary expansion, and derive a local-to-boundary limit showing that interior calibration is not uniform over $n^{-1/2}$-neighborhoods of the boundary. Monte Carlo experiments under Gaussian, Student $t_5$, and shifted log-normal sampling examine fixed, boundary, and local regimes with explicit numerical-failure accounting. Illustrative paired-data analyses show the practical distinction between fixed-candidate confidence regions, directional equality tests, and ordinary scalar empirical-likelihood intervals truncated to the nonnegative parameter space.
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ProtoGIB-Workload: Learning Workload-Specific Neural Topology Prototypes across Subjects
Yuzhe Zhang, Yixi Zhang, Shengdian Jiang, Chengxi Xie, Jihong Wang, Huan Liu, Man Yao, Minnan Luo, Chao Shen
https://arxiv.org/abs/2608.10647 https://arxiv.org/pdf/2608.10647 https://arxiv.org/html/2608.10647
arXiv:2608.10647v1 Announce Type: new
Abstract: Reliable electroencephalography (EEG)-based mental workload recognition is crucial for adaptive human-centered systems, yet practical deployment requires models to generalize to users unseen during training. Although functional connectivity graphs are widely adopted to capture workload-related neural interactions, they inherently entangle task-relevant structures with subject-specific physiological traits and sample-level noise. This entanglement often leads models to learn structural shortcuts, severely degrading cross-subject generalization. To address this, we propose ProtoGIB-Workload, a novel framework that explicitly regularizes and aligns graph structures for subject-independent workload recognition. Our approach introduces a Stochastic Graph Information Bottleneck (SGIB) to compress dense correlation priors into compact, task-relevant subgraphs, filtering out input-related redundancy. Crucially, to prevent the retention of subject-specific spurious edges, we propose a Class-Conditional Topology Stabilizer (CTS). Leveraging the fixed electrode coordinates of EEG data, CTS operates directly on graph-generation probabilities to encourage consistent edge-generation statistics across different subjects sharing the same workload class. Extensive experiments on two public EEG workload datasets and one in-house EEG cognitive load dataset of air traffic controllers under strict leave-one-subject-out (LOSO) protocols demonstrate that ProtoGIB-Workload significantly outperforms state-of-the-art temporal and graph-based baselines, improving the cross-subject Macro-F1 score by an average of 5.15% (up to 6.34%). Further analyses confirm that our method successfully extracts stable, cross-subject consistent neural connectivity patterns.
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Price Responses of Rwandan Tungsten Exports under Conflict Minerals Regulation
Haruka Nagamori, Kazuhiko Nishimura
https://arxiv.org/abs/2608.08690 https://arxiv.org/pdf/2608.08690 https://arxiv.org/html/2608.08690
arXiv:2608.08690v1 Announce Type: new
Abstract: Section 1502 of the Dodd--Frank Act, enacted in 2010, requires U.S.-listed companies using tin, tantalum, tungsten, and gold (3TG) from the Democratic Republic of the Congo and adjoining countries to disclose information on the minerals' origins. Concerns have been raised that the regulation may have induced a de facto embargo through avoidance of sourcing from the covered region. However, how the price responsiveness of mineral exports evolved under changing institutional and market conditions remains insufficiently understood. Since tungsten production in the covered region is concentrated almost entirely in Rwanda, this study examines the price responsiveness of Rwandan tungsten exports from January 2009 to December 2023.
Because missing export quantity data prevent continuous observation of export unit values, we apply the identification approach of Nakano and Nishimura (2025), combining monthly mirror trade data from UN Comtrade with exchange rates and a world average price. An importer fixed-effects model is estimated using export value as the dependent variable, with the sample divided into four periods according to changes in the institutional and market environment.
The results reveal substantial temporal variation in price responsiveness. A statistically significant negative price response is observed in Period 1 ($\eta=-20.814$, $p<0.01$), disappears in Period 2 ($\eta=1.814$, $p>0.10$), reappears in Period 3 ($\eta=-5.277$, $p<0.01$), and disappears again in Period 4 ($\eta=0.440$, $p>0.10$). Coefficient-difference tests confirm significant changes between Periods 1 and 2 ($p=0.0021$) and between Periods 3 and 4 ($p=0.0006$). These findings suggest that the price responsiveness of Rwandan tungsten exports varied substantially over time rather than following a uniform trajectory after the regulation.
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