Tootfinder

Adaptive Beam Selection for Efficient Scanning Probe Tomography
San Dinh, Zichao Wendy Di, Matt Menickelly
https://arxiv.org/abs/2606.21713 https://arxiv.org/pdf/2606.21713 https://arxiv.org/html/2606.21713
arXiv:2606.21713v1 Announce Type: new
Abstract: In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more projections increase experiment costs, acquisition time and the radiation dose imparted to the sample. One mitigation to these trade-offs is to adopt a sequential design of experiments, in which each subsequent measurement is determined as a function of previously acquired data in order to maximize information gain. In practice, a widely used heuristic to maximize information is to align beams with the edges of the sample. A key challenge, however, is that the true sample is unknown, so identifying edge-aligned beams typically requires reconstructing the sample based on available measurements. This work proposes a novel sequential design method that identifies edge-aligned measurements directly from the sinogram, bypassing any reconstruction, thereby improving computational efficiency and reducing the experimental design's susceptibility to reconstruction errors. Our method dynamically selects the next set of measurement beams by maximizing an acquisition function that balances exploration and exploitation over the domain of all possible measurements, improving reconstruction quality while reducing measurement redundancy.
toXiv_bot_toot

MORSE-PI -- Flexible and artefact-free image reconstruction for structural and functional QSM and other phase-critical imaging applications
Barbara Dymerska, Oliver Josephs, Benjamin James, Vahid Malekian, Nadine N. Graedel, Martina F. Callaghan
https://arxiv.org/abs/2606.21336 https://arxiv.org/pdf/2606.21336 https://arxiv.org/html/2606.21336
arXiv:2606.21336v1 Announce Type: new
Abstract: Phase imaging applications such as QSM are highly sensitive to noise amplifications, phase singularities, and other artefacts, particularly in challenging scenarios such as ultra-high field (7T), under-sampled or single-echo acquisitions. We present a novel image reconstruction method, MORSE-PI, designed to produce high-SNR, artefact-free, and singularity-free phase images for both structural and functional phase-based brain imaging. MORSE-PI extends our previous approach, MORSE, by introducing a Virtual Reference Coil (VRC). The VRC is constructed as a linear combination of coil sensitivity maps, with correlations enhanced between coil elements using the noise covariance matrix. Such a VRC ensures robust signal support across the entire brain and is used to correct phase offsets in the MORSE-derived coil sensitivity estimates, resulting in artefact-free, high SNR phase. Compared to GRAPPA with ASPIRE phase correction, MORSE-PI demonstrates greater robustness to artefacts such as noise amplification and aliasing, and shows improved reproducibility in structural imaging at both 3T and 7T. Unlike ESPIRiT and GRAPPA combined with adaptive coil combination methods, MORSE-PI yields singularity-free phase maps. MORSE-PI enables high-SNR reconstructions even for the most challenging scenarios, such as single-echo EPI at 7T. Its efficient, containerised implementation using the Gadgetron framework supports deployment on the MRI scanner console during measurements. MORSE-PI offers a flexible and computationally efficient solution for generating high-SNR, artefact- and singularity-free phase images in both single- and multi-echo GRE and EPI acquisitions. This makes it particularly well-suited for structural and functional QSM, as well as other phase-based MRI applications. Its robustness and rapid computational time facilitate efficient deployment on scanners across field strengths.
toXiv_bot_toot

Converse bounds for multiple graph alignment and correlation detection based on last matching
Taha Ameen, Bruce Hajek
https://arxiv.org/abs/2608.14450 https://arxiv.org/pdf/2608.14450 https://arxiv.org/html/2608.14450
arXiv:2608.14450v1 Announce Type: new
Abstract: The paper focuses on information theoretic converse bounds for the alignment of $m$ correlated graphs and for the detection of correlation among $m$ graphs. A simple idea for $m\geq 3$ is that if the alignment of $m-1$ of the graphs is revealed as extra information (by a genie for example) then it is still necessary to produce the alignment between the one remaining graph and the others, i.e. the last matching must be accomplished. For both Gaussian and Erdos-Renyi models, the last-matching problem is equivalent to one with two observed graphs, providing a path to extend converse bounds for $m=2$ to larger $m$. While the method is rather obvious for alignment, we show that the method can also be used to derive converse bounds for weak detection of correlation.
toXiv_bot_toot

Since it was relevant to a discussion I just had on here and is something most people probably haven't thought about much (unless you've taken one of a handful of philosophy classes), I thought I'd try to lay out a key piece of Descartes' Meditations (#philosophy

A simplified reconstruction of Positron Emission Tomography image using Time of Flight simulated data in Gate 10
Marcin Balcerzyk, Santiago Jimenez-Serrano, \'Oscar Pietrzyk, Edwing Ulin-Brise\~no, Marta Freire
https://arxiv.org/abs/2607.20169 https://arxiv.org/pdf/2607.20169 https://arxiv.org/html/2607.20169
arXiv:2607.20169v1 Announce Type: new
Abstract: Background: Ultrafast Time-of-Flight (TOF) information in Gate 10 simulations enables direct 3D PET reconstruction without scanner-specific modeling. Although such picosecond timing is not achievable in current detectors, simulated data allow exploration of idealized TOF regimes and rapid evaluation of prototype PET designs. Methods: The TOF-driven reconstruction assigns each coincidence to its annihilation position using sub-picosecond timestamps, producing voxelized 3D histograms with flexible voxel size and field-of-view selection. The approach operates directly on Gate-sorted coincidences, requires no corrections, and outputs MHD/DICOM images. Timestamp precision (~10^-13 s) enables localization on the millimeter scale. Results: Full 3D images of the simulated INSPIRE PET scanner are generated in under one second. The method resolves 0.25 mm features in point-source studies and 1.2 mm rods in the Derenzo phantom, reproduces ground-truth activity distributions in image-quality tests, and maintains quantitative stability across geometries. Performance reflects the theoretical benefits of ultrafast TOF rather than current detector capabilities. Significance: This fast, geometry-agnostic reconstruction tool supports early-stage PET prototyping, allowing rapid assessment of spatial resolution, sensitivity, and uniformity without implementing complex reconstruction software. It is broadly applicable to any simulated PET system with sorted coincidences and enables systematic exploration of ultrafast TOF performance in a controlled environment.
toXiv_bot_toot

À medida que envelheço, presto menos atenção ao que as pessoas dizem;
simplesmente observo o que fazem.
-- Andrew Carnegie

{dtrack} makes documentation of data wrangling part of the analysis and creates pretty flow charts: #rstats

Replaced article(s) found for math.AT. https://arxiv.org/list/math.AT/new
[1/1]:
- Representation characterization of equivariant geometric bordism
Hao Li, Bo Chen, Zhi L\"u, Qifan Shen
https://arxiv.org/abs/2501.06565 https://mastoxiv.page/@arXiv_mathAT_bot/113825435888761016
- On the integral cohomology of real toric manifolds
Feifei Fan
https://arxiv.org/abs/2509.06059 https://mastoxiv.page/@arXiv_mathAT_bot/115173630437550865
- Categorified Koszul duality of algebras
Isamu Iwanari
https://arxiv.org/abs/2512.12263 https://mastoxiv.page/@arXiv_mathAG_bot/115728435911956655
- $A_3$-formality for pro-2 Demushkin groups
Ambrus P\'al, Gereon Quick
https://arxiv.org/abs/2607.01028 https://mastoxiv.page/@arXiv_mathGR_bot/116849287251959106
- The Geometry of Cochains on Sampled Vietoris-Rips Complexes
Darrick Lee, Kelly Maggs
https://arxiv.org/abs/2608.11137 https://mastoxiv.page/@arXiv_mathDG_bot/117081537320356423
toXiv_bot_toot

Navigating committor landscape of biomolecules with a general pairwise interaction model
Jintu Zhang, Zichang Jin, Huifeng Zhao, Kai Zhu, Bowei Zhao, Xujun Zhang, Peilin Kang, Tingjun Hou
https://arxiv.org/abs/2606.31832 https://arxiv.org/pdf/2606.31832 https://arxiv.org/html/2606.31832
arXiv:2606.31832v1 Announce Type: new
Abstract: Sampling rare conformation transitions between metastable states is a central challenge in atomistic simulations. While the committor function serve as an ideal reaction coordinate for driving enhanced sampling, their high-dimensional inputs and complex functional forms limit the efficacy of standard feedforward neural networks in modeling them. Inspired by recent breakthroughs in biomolecular structure prediction, we propose a novel committor learning framework grounded in the AlphaFold 3 paradigm. By integrating a lightweight, differentiable atom-level embedding with a simplified Pairformer architecture, our method inherently captures intricate dynamical features of diverse biosystems without requiring specialized prior knowledge. We demonstrate the superior expressiveness and accuracy of the proposed framework across multiple atomistic processes. For the folding of the chignolin mini-protein, our model reveals the finer-grained structure of its transition state ensemble (TSE) and a detailed bifurcated reaction mechanism. Furthermore, for calixarene host-guest systems, we develop a unified committor model that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.
toXiv_bot_toot

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.
toXiv_bot_toot