相关论文: The Four-Year COBE Normalization and Large-Scale S…

Distributionally robust optimization (DRO) has attracted attention in machine learning due to its connections to regularization, generalization, and robustness. Existing work has considered uncertainty sets based on phi-divergences and…

A precise determination of the primordial spectrum of matter density fluctuations at super-horizon scales is essential in understanding large scale structure in the universe. Attempts to constrain or obtain the primordial spectrum using…

We study linear power spectra and formation of large scale structures in flat cosmological models with $\Lambda \ge 0$ and cold plus hot dark matter components (MLM). The hot component consists of massive neutrinos with cosmological density…

Covariate-adaptive randomization (CAR) procedures are frequently used in comparative studies to increase the covariate balance across treatment groups. However, because randomization inevitably uses the covariate information when forming…

A new technique of Fourier analysis on a cut sky (Gorski, 1994) has been applied to the two year COBE DMR sky maps. The Bayesian power spectrum estimation results are consistent with the Harrison-Zel'dovich n=1 model. The maximum likelihood…

We employ the machinery of smooth scaling and coarse-graining of observables, developed recently by us in the context of so-called fluctuation operators (inspired by prior work of Verbeure et al) to make a rigorous renormalisation group…

We introduce a set of four new publicly available N-body simulations, the most recent additions to the Texas P^3M Database. Our models probe the less studied parameter space region of moderate volume (100/h Mpc box) combined with fine mass…

A finite-size scaling theory for the $\phi^4_4$ model is derived using renormalization group methods. Particular attention is paid to the partition function zeroes, in terms of which all thermodynamic observables can be expressed. While the…

We numerically reexamine the scaling behavior of period doublings in four-dimensional volume-preserving maps in order to resolve a discrepancy between numerical results on scaling of the coupling parameter and the approximate…

The X-Ray Background (XRB) probes structure on scales intermediate between those explored by local galaxy redshift surveys and by the COBE Microwave Background measurements. We predict the large scale angular fluctuations in the XRB,…

The (large angle) COBE DMR data can be used to probe the global topology of our universe on scales comparable to and just beyond the present ``horizon''. For compact topologies, the two main effects on the CMB are: [1] the breaking of…

Univariate and multivariate normal probability distributions are widely used when modeling decisions under uncertainty. Computing the performance of such models requires integrating these distributions over specific domains, which can vary…

We prove a convergence result for a large class of random models that encompasses the case of the BPHZ models used in the study of singular stochastic PDEs. We introduce for that purpose a useful variation on the notion of regularity…

We present a bifidelity Karhunen-Lo\`eve expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs. The approach combines the spectral efficiency of the KLE with polynomial chaos expansions (PCEs)…

The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three folds: firstly, fully sampled multi-coil k-space data from the…

The cosine router in Mixture of Experts (MoE) has recently emerged as an attractive alternative to the conventional linear router. Indeed, the cosine router demonstrates favorable performance in image and language tasks and exhibits better…

We propose a new methodology for denoising, variance-stabilizing and normalizing signals whose both mean and variance are parameterized by a single unknown varying parameter, such as Poisson or scaled chi-squared. Key to our methodology is…

We introduce a modified version of the disordered Klein-Gordon lattice model, having two parameters for controlling the disorder strength: $D$, which determines the range of the coefficients of the on-site potentials, and $W$, which defines…

We study inference for censored survival data where some covariates are distorted by some unknown functions of an observable confounding variable in a multiplicative form. Example of this kind of data in medical studies is the common…

Analyzing large samples of high-dimensional data under dependence is a challenging statistical problem as long time series may have change points, most importantly in the mean and the marginal covariances, for which one needs valid tests.…