Finite-size effects for anisotropic bootstrap percolation: logarithmic corrections

In this note we analyze an anisotropic, two-dimensional bootstrap percolation model introduced by Gravner and Griffeath. We present upper and lower bounds on the finite-size effects. We discuss the similarities with the semi-oriented model introduced by Duarte.Comment: Key words: Bootstrap percolation, anisotropy, finite-size effect

Mid-infrared VIPA Spectrometer for Rapid and Broadband Trace Gas Detection

We present and characterize a 2-D imaging spectrometer based on a virtually-imaged phased array (VIPA) disperser for rapid, high-resolution molecular detection using mid-infrared (MIR) frequency combs at 3.1 and 3.8 \mu m. We demonstrate detection of CH4 at 3.1 \mu m with >3750 resolution elements spanning >80 nm with ~600 MHz resolution in a <10 \mu s acquisition time. In addition to broadband detection, rapid, time-resolved single-image detection is demonstrated by capturing dynamic concentration changes of CH4 at a rate of ~375 frames per second. Changes in absorption above the noise floor of 5\times 10-4 are readily detected on the millisecond time scale, leading to important future applications such as real time monitoring of trace gas concentrations and detection of reactive intermediates

Out of distribution detection for intra-operative functional imaging

Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room.Comment: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-32689-0_

Fermion family recurrences in the Dyson-Schwinger formalism

We study the multiple solutions of the truncated propagator Dyson-Schwinger equation for a simple fermion theory with Yukawa coupling to a scalar field. Upon increasing the coupling constant $g$, other parameters being fixed, more than one non-perturbative solution breaking chiral symmetry becomes possible and we find these numerically. These ``recurrences'' appear as a mechanism to generate different fermion generations as quanta of the same fundamental field in an interacting field theory, without assuming any composite structure. The number of recurrences or flavors is reduced to a question about the value of the Yukawa coupling, and has no special profound significance in the Standard Model. The resulting mass function can have one or more nodes and the measurement that potentially detects them can be thought of as a collider-based test of the virtual dispersion relation $E=\sqrt{p^2+M(p^2)^2}$ for the charged lepton member of each family. This requires three independent measurements of the charged lepton's energy, three-momentum and off-shellness. We illustrate how this can be achieved for the (more difficult) case of the tau lepton

Unsupervised Domain Transfer with Conditional Invertible Neural Networks

Synthetic medical image generation has evolved as a key technique for neural network training and validation. A core challenge, however, remains in the domain gap between simulations and real data. While deep learning-based domain transfer using Cycle Generative Adversarial Networks and similar architectures has led to substantial progress in the field, there are use cases in which state-of-the-art approaches still fail to generate training images that produce convincing results on relevant downstream tasks. Here, we address this issue with a domain transfer approach based on conditional invertible neural networks (cINNs). As a particular advantage, our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood training. To showcase our method's generic applicability, we apply it to two spectral imaging modalities at different scales, namely hyperspectral imaging (pixel-level) and photoacoustic tomography (image-level). According to comprehensive experiments, our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks (binary and multi-class). cINN-based domain transfer could thus evolve as an important method for realistic synthetic data generation in the field of spectral imaging and beyond

Why is the winner the best?

International benchmarking competitions have becomefundamental for the comparative performance assessmentof image analysis methods. However, little attention hasbeen given to investigating what can be learnt from thesecompetitions. Do they really generate scientific progress?What are common and successful participation strategies?What makes a solution superior to a competing method?To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted inthe scope of IEEE ISBI 2021 and MICCAI 2021. Statisticalanalyses performed based on comprehensive descriptions ofthe submitted algorithms linked to their rank as well as theunderlying participation strategies revealed common char-acteristics of winning solutions. These typically includethe use of multi-task learning (63%) and/or multi-stagepipelines (61%), and a focus on augmentation (100%), im-age preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning teamis a computer scientist with a doctoral degree, five years ofexperience in biomedical image analysis, and four years ofexperience in deep learning. Two core general developmentstrategies stood out for highly-ranked teams: the reflectionof the metrics in the method design and the focus on analyz-ing and handling failure cases. According to the organizers,43% of the winning algorithms exceeded the state of the artbut only 11% completely solved the respective domain prob-lem. The insights of our study could help researchers (1)improve algorithm development strategies when approach-ing new problems, and (2) focus on open research questionsrevealed by this work

Learned spectral decoloring enables photoacoustic oximetry.

Funder: Projekt DEALThe ability of photoacoustic imaging to measure functional tissue properties, such as blood oxygenation sO[Formula: see text], enables a wide variety of possible applications. sO[Formula: see text] can be computed from the ratio of oxyhemoglobin HbO[Formula: see text] and deoxyhemoglobin Hb, which can be distuinguished by multispectral photoacoustic imaging due to their distinct wavelength-dependent absorption. However, current methods for estimating sO[Formula: see text] yield inaccurate results in realistic settings, due to the unknown and wavelength-dependent influence of the light fluence on the signal. In this work, we propose learned spectral decoloring to enable blood oxygenation measurements to be inferred from multispectral photoacoustic imaging. The method computes sO[Formula: see text] pixel-wise, directly from initial pressure spectra [Formula: see text], which represent initial pressure values at a fixed spatial location [Formula: see text] over all recorded wavelengths [Formula: see text]. The method is compared to linear unmixing approaches, as well as pO[Formula: see text] and blood gas analysis reference measurements. Experimental results suggest that the proposed method is able to obtain sO[Formula: see text] estimates from multispectral photoacoustic measurements in silico, in vitro, and in vivo

Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality

Induced pseudoscalar coupling of the proton weak interaction

The induced pseudoscalar coupling $g_p$ is the least well known of the weak coupling constants of the proton's charged--current interaction. Its size is dictated by chiral symmetry arguments, and its measurement represents an important test of quantum chromodynamics at low energies. During the past decade a large body of new data relevant to the coupling $g_p$ has been accumulated. This data includes measurements of radiative and non radiative muon capture on targets ranging from hydrogen and few--nucleon systems to complex nuclei. Herein the authors review the theoretical underpinnings of $g_p$, the experimental studies of $g_p$, and the procedures and uncertainties in extracting the coupling from data. Current puzzles are highlighted and future opportunities are discussed.Comment: 58 pages, Latex, Revtex4, prepared for Reviews of Modern Physic