Hasil untuk "The Bible"

The Tien Mai

Multiple binary responses arise in many modern data-analytic problems. Although fitting separate logistic regressions for each response is computationally attractive, it ignores shared structure and can be statistically inefficient, especially in high-dimensional and class-imbalanced regimes. Low-rank models offer a natural way to encode latent dependence across tasks, but existing methods for binary data are largely likelihood-based and focus on pointwise classification rather than ranking performance. In this work, we propose a unified framework for learning with multiple binary responses that directly targets discrimination by minimizing a surrogate loss for the area under the ROC curve (AUC). The method aggregates pairwise AUC surrogate losses across responses while imposing a low-rank constraint on the coefficient matrix to exploit shared structure. We develop a scalable projected gradient descent algorithm based on truncated singular value decomposition. Exploiting the fact that the pairwise loss depends only on differences of linear predictors, we simplify computation and analysis. We establish non-asymptotic convergence guarantees, showing that under suitable regularity conditions, leading to linear convergence up to the minimax-optimal statistical precision. Extensive simulation studies demonstrate that the proposed method is robust in challenging settings such as label switching and data contamination and consistently outperforms likelihood-based approaches.

The Anh Bui

Let $ν= (ν_1, \ldots, ν_n) \in (-1/2, \infty)^n$, with $n \ge 1$, and let $Δ_ν$ be the multivariate Bessel operator defined by \[ Δ_ν = -\sum_{j=1}^n\left( \frac{\partial^2}{\partial x_j^2} - \frac{ν_j^2 - 1/4}{x_j^2} \right). \] In this paper, we develop the theory of Hardy spaces and BMO-type spaces associated with the Bessel operator $Δ_ν$. We then study the higher-order Riesz transforms associated with $Δ_ν$. First, we show that these transforms are Calderón-Zygmund operators. We further prove that they are bounded on the Hardy spaces and BMO-type spaces associated with $Δ_ν$.

The Tien Mai

Aggregation methods have emerged as a powerful and flexible framework in statistical learning, providing unified solutions across diverse problems such as regression, classification, and density estimation. In the context of generalized linear models (GLMs), where responses follow exponential family distributions, aggregation offers an attractive alternative to classical parametric modeling. This paper investigates the problem of sparse aggregation in GLMs, aiming to approximate the true parameter vector by a sparse linear combination of predictors. We prove that an exponential weighted aggregation scheme yields a sharp oracle inequality for the Kullback-Leibler risk with leading constant equal to one, while also attaining the minimax-optimal rate of aggregation. These results are further enhanced by establishing high-probability bounds on the excess risk.

The Tien Mai

High-dimensional linear regression is a fundamental tool in modern statistics, particularly when the number of predictors exceeds the sample size. The classical Lasso, which relies on the squared loss, performs well under Gaussian noise assumptions but often deteriorates in the presence of heavy-tailed errors or outliers commonly encountered in real data applications such as genomics, finance, and signal processing. To address these challenges, we propose a novel robust regression method, termed Heavy Lasso, which incorporates a loss function inspired by the Student's t-distribution within a Lasso penalization framework. This loss retains the desirable quadratic behavior for small residuals while adaptively downweighting large deviations, thus enhancing robustness to heavy-tailed noise and outliers. Heavy Lasso enjoys computationally efficient by leveraging a data augmentation scheme and a soft-thresholding algorithm, which integrate seamlessly with classical Lasso solvers. Theoretically, we establish non-asymptotic bounds under both $\ell_1$ and $\ell_2 $ norms, by employing the framework of localized convexity, showing that the Heavy Lasso estimator achieves rates comparable to those of the Huber loss. Extensive numerical studies demonstrate Heavy Lasso's superior performance over classical Lasso and other robust variants, highlighting its effectiveness in challenging noisy settings. Our method is implemented in the R package heavylasso available on Github.

The Tien Mai

This paper addresses the problem of sparse phase retrieval, a fundamental inverse problem in applied mathematics, physics, and engineering, where a signal need to be reconstructed using only the magnitude of its transformation while phase information remains inaccessible. Leveraging the inherent sparsity of many real-world signals, we introduce a novel sparse quasi-Bayesian approach and provide the first theoretical guarantees for such an approach. Specifically, we employ a scaled Student distribution as a continuous shrinkage prior to enforce sparsity and analyze the method using the PAC-Bayesian inequality framework. Our results establish that the proposed Bayesian estimator achieves minimax-optimal convergence rates under sub-exponential noise, matching those of state-of-the-art frequentist methods. To ensure computational feasibility, we develop an efficient Langevin Monte Carlo sampling algorithm. Through numerical experiments, we demonstrate that our method performs comparably to existing frequentist techniques, highlighting its potential as a principled alternative for sparse phase retrieval in noisy settings.

The Anh Bui

Let $ν\in [-1/2,\infty)^n$, $n\ge 1$, and let $\mathcal{L}_ν$ be a self-adjoint extension of the differential operator \[ L_ν:= \sum_{i=1}^n \left[-\frac{\partial^2}{\partial x_i^2} + x_i^2 + \frac{1}{x_i^2}(ν_i^2 - \frac{1}{4})\right] \] on $C_c^\infty(\mathbb{R}_+^n)$ as the natural domain. In this paper, we first prove that the Riesz transform associated with $\mathcal L_ν$ is a Calderón-Zygmund operator, answering the open problem in [JFA, 244 (2007), 399-443]. In addition, we develop the theory of Hardy spaces and Campanato spaces associated with $\mathcal{L}_ν$. As applications, we prove that the Riesz transform related to $\mathcal{L}_ν$ is bounded on these Hardy spaces and Campanato spaces, completing the description of the boundedness of the Riesz transform in the Laguerre expansion setting.

The ATLAS Collaboration

The inclusive cross-section for the production of a single top quark in association with a $W$ boson is measured using 140 fb$^{-1}$ of proton-proton collision data collected with the ATLAS detector at $\sqrt{s} = 13$ TeV. Events containing two charged leptons and at least one jet identified as originating from a $b$-quark are selected. A multivariate discriminant is constructed to separate the $tW$ signal from the $t\bar{t}$ background. The cross-section is extracted using a profile likelihood fit to the signal and control regions and it is measured to be $σ_{tW} = 75^{+15}_{-14}$ pb, in good agreement with the Standard Model prediction. The measured cross-section is used to extract a value for the left-handed form factor at the $Wtb$ vertex times the CKM matrix element $|f_\text{LV}V_{tb}|$ of $0.97 \pm 0.10$.

The Tien Mai

This work addresses the problem of high-dimensional classification by exploring the generalized Bayesian logistic regression method under a sparsity-inducing prior distribution. The method involves utilizing a fractional power of the likelihood resulting the fractional posterior. Our study yields concentration results for the fractional posterior, not only on the joint distribution of the predictor and response variable but also for the regression coefficients. Significantly, we derive novel findings concerning misclassification excess risk bounds using sparse generalized Bayesian logistic regression. These results parallel recent findings for penalized methods in the frequentist literature. Furthermore, we extend our results to the scenario of model misspecification, which is of critical importance.

The ATLAS Collaboration

Measurements of inclusive and differential production cross-sections of a top-quark-top-antiquark pair in association with a $W$ boson ($t\bar{t}W$) are presented. They are performed by targeting final states with two same-sign or three isolated leptons (electrons or muons) and are based on $\sqrt{s}=13$ TeV proton-proton collision data with an integrated luminosity of 140 fb$^{-1}$, recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider. The inclusive $t\bar{t}W$ production cross-section is measured to be $880 \pm 80$ fb, compared to a reference theoretical prediction of $745 \pm 50\,\textrm{(scale)} \pm 13\,\textrm{(2-loop approx.)} \pm 19\,\textrm{(PDF,} α_{\textrm{S}})$ fb. Differential cross-section measurements characterise this process in detail for the first time. Several particle-level observables are compared with a variety of theoretical predictions, which generally agree well with the normalised differential cross-section results. Additionally, the relative charge asymmetry of $t\bar{t}W^{+}$ and $t\bar{t}W^{-}$ is measured inclusively to be ${A_{\mathrm{C}}^{\mathrm{rel}}} = 0.33 \pm 0.05$, in very good agreement with the theoretical prediction of $0.322 \pm 0.003\,\mathrm{(scale)} \pm 0.007\,\mathrm{(PDF)}$, as well as differentially.

The Tien Mai

This paper investigates sparse high-dimensional linear regression, particularly examining the properties of the posterior under conditions of random design and unknown error variance. We provide consistency results for the posterior and analyze its concentration rates, demonstrating adaptiveness to the unknown sparsity level of the regression coefficient vector. Furthermore, we extend our investigation to establish concentration outcomes for parameter estimation using specific distance measures. These findings are in line with recent discoveries in frequentist studies. Additionally, by employing techniques to address model misspecification through a fractional posterior, we broaden our analysis through oracle inequalities to encompass the critical aspect of model misspecification for the regular posterior. Our novel findings are demonstrated using two different types of sparsity priors: a shrinkage prior and a spike-and-slab prior.

The Tien Mai

Count data is prevalent in various fields like ecology, medical research, and genomics. In high-dimensional settings, where the number of features exceeds the sample size, feature selection becomes essential. While frequentist methods like Lasso have advanced in handling high-dimensional count data, Bayesian approaches remain under-explored with no theoretical results on prediction performance. This paper introduces a novel probabilistic machine learning framework for high-dimensional count data prediction. We propose a pseudo-Bayesian method that integrates a scaled Student prior to promote sparsity and uses an exponential weight aggregation procedure. A key contribution is a novel risk measure tailored to count data prediction, with theoretical guarantees for prediction risk using PAC-Bayesian bounds. Our results include non-asymptotic oracle inequalities, demonstrating rate-optimal prediction error without prior knowledge of sparsity. We implement this approach efficiently using Langevin Monte Carlo method. Simulations and a real data application highlight the strong performance of our method compared to the Lasso in various settings.

The Tien Mai

This study investigates the misclassification excess risk bound in the context of 1-bit matrix completion, a significant problem in machine learning involving the recovery of an unknown matrix from a limited subset of its entries. Matrix completion has garnered considerable attention in the last two decades due to its diverse applications across various fields. Unlike conventional approaches that deal with real-valued samples, 1-bit matrix completion is concerned with binary observations. While prior research has predominantly focused on the estimation error of proposed estimators, our study shifts attention to the prediction error. This paper offers theoretical analysis regarding the prediction errors of two previous works utilizing the logistic regression model: one employing a max-norm constrained minimization and the other employing nuclear-norm penalization. Significantly, our findings demonstrate that the latter achieves the minimax-optimal rate without the need for an additional logarithmic term. These novel results contribute to a deeper understanding of 1-bit matrix completion by shedding light on the predictive performance of specific methodologies.

The Anh Han

This brief discusses evolutionary game theory as a powerful and unified mathematical tool to study evolution of collective behaviours. It summarises some of my recent research directions using evolutionary game theory methods, which include i) the analysis of statistical properties of the number of (stable) equilibria in a random evolutionary game, and ii) the modelling of safety behaviours' evolution and the risk posed by advanced Artificial Intelligence technologies in a technology development race. Finally, it includes an outlook and some suggestions for future researchers.

The ATLAS Collaboration

A search for leptoquarks decaying into the $bτ$ final state is performed using Run 2 proton-proton collision data from the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb$^{-1}$ at $\sqrt{s} = 13$ TeV recorded by the ATLAS detector. The benchmark models considered in this search are vector leptoquarks with electric charge of 2/3e and scalar leptoquarks with an electric charge of 4/3e. No significant excess above the Standard Model prediction is observed, and 95% confidence level upper limits are set on the cross-section times branching fraction of leptoquarks decaying into $bτ$. For the vector leptoquark production two models are considered: the Yang-Mills and Minimal coupling models. In the Yang-Mills (Minimal coupling) scenario, vector leptoquarks with a mass below 1.58 (1.35) TeV are excluded for a gauge coupling of 1.0 and below 2.05 (1.99) TeV for a gauge coupling of 2.5. In the case of scalar leptoquarks, masses below 1.28 TeV (1.53 TeV) are excluded for a Yukawa coupling of 1.0 (2.5). Finally, an interpretation of the results with minimal model dependence is performed for each of the signal region categories, and limits on the visible cross-section for beyond the Standard Model processes are provided.

Tobias J. Houston

Within translation studies, functionalist translations and even more specifically, translations guided by Skopos theory are very much purposeful activities. Skopos theory applied to Bible translation, however, is sometimes met with resistance by practitioners who believe that Skopos theory betrays too much of the source text. This article began by outlining Skopos theory and the additional notion of loyalty as introduced by Christiane Nord. Even with loyalty applied to the theory, many Bible translation practitioners continue to fear it. After the initial presentation of Skopos theory plus loyalty, the Hebrew lexeme חֶסֶד (ḥesed) was introduced in this article and discussed in relation to its biblical use. The discussion of ḥesed was then widened to functionalist Bible translation to redeem the loyalty notion as applied to Bible translation. This article claimed that ḥesed as loyalty further helps to protect both the source text and target audiences from radical or unacceptable translations. Contribution: Bible translation is a purposeful activity. This article attempted to allay the fears that some Bible translation practitioners have about the possibility of radical translations undertaken using Skopos theory. Loyalty as understood through the lens of the Hebrew חֶסֶד (ḥesed) was utilised afresh to allay these fears.

Mikael Tellbe

G. Nandakumar, N. Ryde, M. Montelius et al.

Phosphorus (P) is considered to be one of the key elements for life, making it an important element to look for in the abundance analysis of spectra of stellar systems. Yet, there exists only a handful of spectroscopic studies to estimate the P abundances and investigate its trend across a range of metallicities. We have observed full HK band spectra at a spectral resolving power of R=45,000 with IGRINS instrument. Abundances are determined using SME in combination with 1D MARCS stellar atmosphere models. The investigated sample of stars have reliable stellar parameters estimated using optical FIES spectra (GILD; Jönsson et al. in prep.). In order to determine the P abundances from the 16482.92 Angstrom P line, we take special care of the CO($ν=7-4$) blend. We determine the C, N, O abundances from atomic carbon and a range of non-blended molecular lines (CO, CN, OH) which are aplenty in the H band region of K giant stars, assuring an appropriate modelling of the blending CO($ν=7-4$) line. We present [P/Fe] vs [Fe/H] trend for 38 K giant stars in the metallicity range of -1.2 dex $<$ [Fe/H] $<$ 0.4 dex. We find that our trend matches well with the compiled literature sample of prominently dwarf stars and limited number of giant stars. Our trend is found to be higher by $\sim$ 0.05 - 0.1 dex compared to the theoretical chemical evolution trend in Cescutti et al. 2012 resulting from core collapse supernova (type II) of massive stars with the P yields from Kobayashi et al. (2006) arbitrarily increased by a factor of 2.75. Thus the enhancement factor might need to be $\sim$ 0.05 - 0.1 dex higher to match our trend. We also find an empirically determined primary behaviour for phosphorus. Furthermore, the phosphorus abundance is found to be elevated by $\sim$ 0.6 - 0.9 dex in two metal poor s-enriched stars compared to the theoretical chemical evolution trend.

The ATLAS Collaboration

Cross-section measurements for a $Z$ boson produced in association with high-transverse-momentum jets ($p_{\mathrm{T}} \geq 100$ GeV) and decaying into a charged-lepton pair ($e^+e^-,μ^+μ^-$) are presented. The measurements are performed using proton-proton collisions at $\sqrt{s}=13$ TeV corresponding to an integrated luminosity of $139$ fb$^{-1}$ collected by the ATLAS experiment at the LHC. Measurements of angular correlations between the $Z$ boson and the closest jet are performed in events with at least one jet with $p_{\mathrm{T}} \geq 500$ GeV. Event topologies of particular interest are the collinear emission of a $Z$ boson in dijet events and a boosted $Z$ boson recoiling against a jet. Fiducial cross sections are compared with state-of-the-art theoretical predictions. The data are found to agree with next-to-next-to-leading-order predictions by NNLOjet and with the next-to-leading-order multi-leg generators MadGraph5_aMC@NLO and Sherpa.

The Tien Mai

In this paper, we present a theoretical study of a low-rank matrix single index model. This model is recently introduced in biostatistics however its theoretical properties on estimating together the link function and the coefficient matrix are not yet carried out. Here, we advance on using PAC-Bayesian bounds technique to provide a rigorous theoretical understanding for jointly estimation of the link function and the coefficient matrix.

The CMS Collaboration

The collective behavior of K$^0_\mathrm{S}$ and $Λ/\barΛ$ strange hadrons is studied by measuring the elliptic azimuthal anisotropy ($v_2$) using the scalar-product and multiparticle correlation methods. Proton-lead (pPb) collisions at a nucleon-nucleon center-of-mass energy $\sqrt{s_\mathrm{NN}}$ = 8.16 TeV and lead-lead (PbPb) collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV collected by the CMS experiment at the LHC are investigated. Nonflow effects in the pPb collisions are studied by using a subevent cumulant analysis and by excluding events where a jet with transverse momentum greater than 20\GeV is present. The strange hadron $v_2$ values extracted in \pPb collisions via the four- and six-particle correlation method are found to be nearly identical, suggesting the collective behavior. Comparisons of the pPb and PbPb results for both strange hadrons and charged particles illustrate how event-by-event flow fluctuations depend on the system size.