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In this paper, we present some new (in-)tractability results related to the integration problem in subspaces of the Wiener algebra over the $d$-dimensional unit cube. We show that intractability holds for multivariate integration in the standard Wiener algebra in the deterministic setting, in contrast to polynomial tractability in an unweighted subspace of the Wiener algebra recently shown by Goda (2023). Moreover, we prove that multivariate integration in the subspace of the Wiener algebra introduced by Goda is strongly polynomially tractable if we switch to the randomized setting. We also identify subspaces in which multivariate integration in the deterministic setting are (strongly) polynomially tractable and we compare these results with the bound which can be obtained via Hoeffding's inequality., Comment: 16 pages

We propose a randomized lattice algorithm for approximating multivariate periodic functions over the $d$-dimensional unit cube from the weighted Korobov space with mixed smoothness $\alpha > 1/2$ and product weights $\gamma_1,\gamma_2,\ldots\in [0,1]$. Building upon the deterministic lattice algorithm by Kuo, Sloan, and Wo\'{z}niakowski (2006), we incorporate a randomized quadrature rule by Dick, Goda, and Suzuki (2022) to accelerate the convergence rate. This randomization involves drawing the number of points for function evaluations randomly, and selecting a good generating vector for rank-1 lattice points using the randomized component-by-component algorithm. We prove that our randomized algorithm achieves a worst-case root mean squared $L_2$-approximation error of order $M^{-\alpha/2 - 1/8 + \varepsilon}$ for an arbitrarily small $\varepsilon > 0$, where $M$ denotes the maximum number of function evaluations, and that the error bound is independent of the dimension $d$ if the weights satisfy $\sum_{j=1}^\infty \gamma_j^{1/\alpha} < \infty$. Our upper bound converges faster than a lower bound on the worst-case $L_2$-approximation error for deterministic rank-1 lattice-based approximation proved by Byrenheid, K\"{a}mmerer, Ullrich, and Volkmer (2017). We also show a lower error bound of order $M^{-\alpha/2-1/2}$ for our randomized algorithm, leaving a slight gap between the upper and lower bounds open for future research., Comment: 22 pages

Matching users with mutual preferences is a critical aspect of services driven by reciprocal recommendations, such as job search. To produce recommendations in such scenarios, one can predict match probabilities and construct rankings based on these predictions. However, this direct match prediction approach often underperforms due to the extreme sparsity of match labels. Therefore, most existing methods predict preferences separately for each direction (e.g., job seeker to employer and employer to job seeker) and then aggregate the predictions to generate overall matching scores and produce recommendations. However, this typical approach often leads to practical issues, such as biased error propagation between the two models. This paper introduces and demonstrates a novel and practical solution to improve reciprocal recommendations in production by leveraging pseudo-match scores. Specifically, our approach generates dense and more directly relevant pseudo-match scores by combining the true match labels, which are accurate but sparse, with relatively inaccurate but dense match predictions. We then train a meta-model to output the final match predictions by minimizing the prediction loss against the pseudo-match scores. Our method can be seen as a best-of-both (BoB) approach, as it combines the high-level ideas of both direct match prediction and the two separate models approach. It also allows for user-specific weights to construct personalized pseudo-match scores, achieving even better matching performance through appropriate tuning of the weights. Offline experiments on real-world job search data demonstrate the superior performance of our BoB method, particularly with personalized pseudo-match scores, compared to existing approaches in terms of finding potential matches.

Cutting mechanics in soft solids present a complex mechanical challenge due to the intricate behavior of soft ductile materials as they undergo crack nucleation and propagation. Recent research has explored the relationship between the cutting force needed to continuously cut a soft material and the radius of the wire (blade). A typical simplifying assumption is that of material incompressibility, albeit no material in nature is really incompressible. In this study, we relax this assumption and examine how material (in)compressibility influences the correlation between cutting forces and material properties like toughness and modulus. The ratio {\mu}/\k{appa}, where {\mu} and \k{appa} are the shear and bulk moduli, indicates the material's degree of compressibility, where incompressible materials have {\mu}/\k{appa}=0, and larger {\mu}/\k{appa} provide higher volumetric compressibility. Following previous observations, we obtain two cutting regimes: (i) high toughness or small wire radius, and (ii) low toughness or large wire radius. Regime (i) is dominated by frictional dissipation, while regime (ii) is dominated by adhesive debonding and/or the wear resistance of the material. These regimes are controlled by the ratio between the wire radius and the elasto-cohesive length of the material: the critical crack opening displacement at fast fracture under uniaxial tension. In the large radius, regime (ii), our theoretical findings reveal that incompressible materials require larger forces. Notably, however, the elasto-cohesive length of the material, defining the transition wire radius between regimes (i) and (ii), is larger for compressible materials, which are therefore more likely to be cut in regime (i), and thus requiring larger cutting forces.

Phonocardiography has recently gained popularity in low-cost and remote monitoring, including passive fetal heart monitoring. Development for methods which analyse phonocardiographical data try to capitalize on this opportunity, and in recent years a multitude of such algorithms and models have been published. Although there is little to no standardization in these published algorithms and multiple parts of these models have to be reimplemented on a case-by-case basis. Datasets containing heart sound recordings also lack standardization in both data storage and labeling, especially in fetal phonocardiography. We are presenting a toolbox that can serve as a basis for a future standard framework for heart sound analysis. This toolbox contains some of the most widely used processing steps, and with these, complex analysis processes can be created. These functions can be individually tested. Due to the interdependence of the steps, we validated the current segmentation stage using a manually labeled fetal phonocardiogram dataset comprising 50 one-minute abdominal PCG recordings, which include 6,758 S1 and 6,729 S2 labels. Our results were compared to other common and available segmentation methods, peak detection with the Neurokit2 library, and the Hidden Semi-Markov Model by Springer et al. With a 30 ms tolerance our best model achieved a 97.1% F1 score and 10.8 +/- 7.9 ms mean absolute error for S1 detection. This detection accuracy outperformed all tested methods. With this a more accurate S2 detection method can be created as a multi-step process. After an accurate segmentation the extracted features should be representative of the selected segments, which allows for more accurate statistics or classification models. The toolbox contains functions for both feature extraction and statistics creation which are compatible with the previous steps., Comment: 25 pages, 11 figures, submitted to Open Source and Validated Computational Tools for Physiological Time Series Analysis, for associated program documentation, see https://pypcg-toolbox.readthedocs.io/en/latest/

In this short note, we prove that the one-dimensional Kronecker sequence $i\alpha \bmod 1, i=0,1,2,\ldots,$ is quasi-uniform if and only if $\alpha$ is a badly approximable number. Our elementary proof relies on a result on the three-gap theorem for Kronecker sequences due to Halton (1965)., Comment: minor revision, 5 pages

We study the multivariate integration problem for periodic functions from the weighted Korobov space in the randomized setting. We introduce a new randomized rank-1 lattice rule with a randomly chosen number of points, which avoids the need for component-by-component construction in the search for good generating vectors while still achieving nearly the optimal rate of the randomized error. Our idea is to exploit the fact that at least half of the possible generating vectors yield nearly the optimal rate of the worst-case error in the deterministic setting. By randomly choosing generating vectors $r$ times and comparing their corresponding worst-case errors, one can find one generating vector with a desired worst-case error bound with a very high probability, and the (small) failure probability can be controlled by increasing $r$ logarithmically as a function of the number of points. Numerical experiments are conducted to support our theoretical findings., Comment: revision, 22 pages, 3 figures

Numerical integration over the real line for analytic functions is studied. Our main focus is on the sharpness of the error bounds. We first derive two general lower estimates for the worst-case integration error, and then apply these to establish lower bounds for various quadrature rules. These bounds turn out to be either novel or improve upon existing results, leading to lower bounds that closely match upper bounds for various formulas. Specifically, for the suitably truncated trapezoidal rule, we improve upon general lower bounds on the worst-case error obtained by Sugihara [\textit{Numer. Math.}, 75 (1997), pp.~379--395] and provide exceptionally sharp lower bounds apart from a polynomial factor, and in particular show that the worst-case error for the trapezoidal rule by Sugihara is not improvable by more than a polynomial factor. Additionally, our research reveals a discrepancy between the error decay of the trapezoidal rule and Sugihara's lower bound for general numerical integration rules, introducing a new open problem. Moreover, Gauss--Hermite quadrature is proven sub-optimal under the decay conditions on integrands we consider, a result not deducible from upper-bound arguments alone. Furthermore, to establish the near-optimality of the suitably scaled Gauss--Legendre and Clenshaw--Curtis quadratures, we generalize a recent result of Trefethen [\textit{SIAM Rev.}, 64 (2022), pp.~132--150] for the upper error bounds in terms of the decay conditions., Comment: 23 pages, no figure

Are common quasi-Monte Carlo sequences quasi-uniform? While this question remains widely open, in this short note, we prove that the two-dimensional Sobol' sequence is not quasi-uniform. This result partially answers an unsolved problem of Sobol' and Shukhman (2007) in a negative manner., Comment: 5 pages

Cutting mechanics in soft solids have been a subject of study for several decades, an interest fuelled by the multitude of its applications, including material testing, manufacturing, and biomedical technology. Wire cutting is the simplest model system to analyze the cutting resistance of a soft material. However, even for this simple system, the complex failure mechanisms that underpin cutting are still not completely understood. Several models that connect the critical cutting force to the radius of the wire and the key mechanical properties of the cut material have been proposed. An almost ubiquitous simplifying assumption is a state of plane (and anti-plane) strain in the material. In this paper, we show that this assumption can lead to erroneous conclusions because even such a simple cutting problem is essentially three-dimensional. A planar approximation restricts the analysis to the stress distribution in the mid-plane. However, through finite element modeling, we reveal that the maximal tensile stress - and thus the likely location of cut initiation - is in fact located in the front plane. Friction reduces the magnitude of this stress, but this detrimental effect can be counteracted by large slice-to-push (shear-to-indentation) ratios. The introduction of these end effects helps reconcile a recent controversy around the role of friction in wire cutting, for it implies that slicing can indeed reduce required cutting forces, but only if the slice-push ratio and the friction coefficient are sufficiently large. Material strain-stiffening reduces the critical indentation depth required to initiate the cut further and thus needs to be considered when cutting non-linearly elastic materials.

This paper presents a series of feminist epistemological concepts as tools for developing critical, more accountable, and contextualised approaches to machine learning systems design. Namely, we suggest that the methods of situated knowledges or situating, figurations or figuring, diffraction or diffracting, and critical fabulation or speculation can be productively actualised in the field of machine learning systems design. We also suggest that the meta-method for doing this actualisation requires not so much translation but transposition - a creative and critical adaptation to speak to machine learning contexts., Comment: Workshop A Toolbox of Feminist Wonder - Theories and methods that can make a difference, Conference on Computer-Supported Cooperative Work and Social Computing CSCW 23, October 14-18, 2023, Minneapolis, MN, USA, 5 pages

Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and increasingly used for in a variety of research and clinical application to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time series analysis recorded using a standard finger-based transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. This algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineers a set of 74 PPG biomarkers. Studying the PPG time series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.org, Comment: The manuscript was submitted to "Physiological Measurement" on September 5, 2023

PhysioZoo is a collaborative platform designed for the analysis of continuous physiological time series. The platform currently comprises four modules, each consisting of a library, a user interface, and a set of tutorials: (1) PhysioZoo HRV, dedicated to studying heart rate variability (HRV) in humans and other mammals; (2) PhysioZoo SPO2, which focuses on the analysis of digital oximetry biomarkers (OBM) using continuous oximetry (SpO2) measurements from humans; (3) PhysioZoo ECG, dedicated to the analysis of electrocardiogram (ECG) time series; (4) PhysioZoo PPG, designed to study photoplethysmography (PPG) time series. In this proceeding, we introduce the PhysioZoo platform as an open resource for digital physiological biomarkers engineering, facilitating streamlined analysis and data visualization of physiological time series while ensuring the reproducibility of published experiments. We welcome researchers to contribute new libraries for the analysis of various physiological time series, such as electroencephalography, blood pressure, and phonocardiography. You can access the resource at physiozoo.com. We encourage researchers to explore and utilize this platform to advance their studies in the field of continuous physiological time-series analysis., Comment: 4 pages, 2 figure, 50th Computing in Cardiology conference in Atlanta, Georgia, USA on 1st - 4th October 2023

Efficient and accurate evaluation of long-term photoplethysmography (PPG) recordings is essential for both clinical assessments and consumer products. In 2021, the top opensource peak detectors were benchmarked on the Multi-Ethnic Study of Atherosclerosis (MESA) database consisting of polysomnography (PSG) recordings and continuous sleep PPG data, where the Automatic Beat Detector (Aboy) had the best accuracy. This work presents Aboy++, an improved version of the original Aboy beat detector. The algorithm was evaluated on 100 adult PPG recordings from the MESA database, which contains more than 4.25 million reference beats. Aboy++ achieved an F1-score of 85.5%, compared to 80.99% for the original Aboy peak detector. On average, Aboy++ processed a 1 hour-long recording in less than 2 seconds. This is compared to 115 seconds (i.e., over 57-times longer) for the open-source implementation of the original Aboy peak detector. This study demonstrated the importance of developing robust algorithms like Aboy++ to improve PPG data analysis and clinical outcomes. Overall, Aboy++ is a reliable tool for evaluating long-term wearable PPG measurements in clinical and consumer contexts., Comment: 4 pages, 1 figure, 50th Computing in Cardiology conference in Atlanta, Georgia, USA on 1st - 4th October 2023

Premature births can lead to complications, with fetal lung immaturity being a primary concern. Currently, fetal lung maturity (FLM) requires an invasive surfactant extraction procedure between the 32nd and 39th weeks of pregnancy. Unfortunately, there is no non-invasive method for FLM assessment. This work hypothesized that fetal breathing movement (FBM) and surfactant levels are inversely coupled and that FBM can serve as a proxy for FLM estimation. To investigate the correlation between FBM and FLM, antenatal corticosteroid (ACS) was administered to increase fetal pulmonary surfactant levels in a high-risk 35th-week pregnant woman showing intrauterine growth restriction. Synchronous sonographic and phonographic measurements were continuously recorded for 25 minutes before and after the ASC treatments. Before the ACS injection, 268 continuous movements FBM episodes were recorded. The number of continuous FBM episodes significantly decreased to 3, 43, and 79 within 24, 48, and 72 hours, respectively, of the first injection of ACS, suggesting an inversely coupled connection between FBM and surfactant level s. Therefore, FBM may serve as a proxy for FLM estimation. Quantitative confirmation of these findings would suggest that FBM measurements could be used as a non-invasive and widely accessible FLM-assessment tool for high-risk pregnancies and routine examinations., Comment: 4 pages, 3 figures, 50th Computing in Cardiology conference in Atlanta, Georgia, USA on 1st - 4th October 2023

Motivated by various computational applications, we investigate the problem of estimating nested expectations. Building upon recent work by the authors, we propose a novel Monte Carlo estimator for nested expectations, inspired by sparse grid quadrature, that does not require sampling from inner conditional distributions. Theoretical analysis establishes an upper bound on the mean squared error of our estimator under mild assumptions on the problem, demonstrating its efficiency for cases with low-dimensional outer variables. We illustrate the effectiveness of our estimator through its application to problems related to value of information analysis, with moderate dimensionality. Overall, our method presents a promising approach to efficiently estimate nested expectations in practical computational settings.

Building upon recent work by the author, we prove that multivariate integration in the following subspace of the Wiener algebra over $[0,1)^d$ is strongly polynomially tractable: \[ F_d:=\left\{ f\in C([0,1)^d)\:\middle| \: \|f\|:=\sum_{\boldsymbol{k}\in \mathbb{Z}^{d}}|\hat{f}(\boldsymbol{k})|\max\left(\mathrm{width}(\mathrm{supp}(\boldsymbol{k})),\min_{j\in \mathrm{supp}(\boldsymbol{k})}\log |k_j|\right)<\infty \right\},\] with $\hat{f}(\boldsymbol{k})$ being the $\boldsymbol{k}$-th Fourier coefficient of $f$, $\mathrm{supp}(\boldsymbol{k}):=\{j\in \{1,\ldots,d\}\mid k_j\neq 0\}$, and $\mathrm{width}: 2^{\{1,\ldots,d\}}\to \{1,\ldots,d\}$ being defined by \[ \mathrm{width}(u):=\max_{j\in u}j-\min_{j\in u}j+1,\] for non-empty subset $u\subseteq \{1,\ldots,d\}$ and $\mathrm{width}(\emptyset):=1$. Strong polynomial tractability is achieved by an explicit quasi-Monte Carlo rule using a multiset union of Korobov's $p$-sets. We also show that, if we replace $\mathrm{width}(\mathrm{supp}(\boldsymbol{k}))$ with 1 for all $\boldsymbol{k}\in \mathbb{Z}^d$ in the above definition of norm, multivariate integration is polynomially tractable but not strongly polynomially tractable., Comment: 8 pages