Miracle-dz/UniFAR-FTUs huggingface dataset:
Recursive Least Square: RLS Method-Based Time Series Data Prediction for Many Missing Data

Prediction methods for time series data with many missing data based on Recursive Least Square (RLS) method are proposed. There are two parameter tuning algorithms, time update and measurement update algorithms for parameter estimation of Kalman filter. Two learning methods for parameter estimation of Kalman filter are proposed based on RLS method. One is the method without measurement update algorithm (RLS1). The other one is the method without both time and measurement update algorithms (RLS-2). The methods are applied to the time series data of Defense Meteorological Satellite Program (DMSP) / Special Sensor Microwave/Imager (SSM/I) data with a plenty of missing data. It is found that the proposed RLS-2 method shows smooth and fast convergence in learning process in comparison to the RLS-1. Keywords—Special Sensor Microwave/Imager (SSM/I); Defense Meteorological Satellite Program (DMSP); Kalman filter; Recursive Least Square (RLS) method; missing data; parameter estimation
HIV protease inhibitor A-792611 is a novel functional inhibitor of human PXR

Drug-drug interactions involving induction of cytochrome P450 enzymes (CYPs) can lead to loss of drug efficacy. Certain drugs, particularly those used to treat mycobacterial and human immunodeficiency virus (HIV) infections, are especially prone to induce CYPs. During studies to examine drug-interaction potential of compounds in cultured human hepatocytes, exposure with A-792611, a novel HIV protease inhibitor (PI) previously under investigation for the treatment of HIV infection, resulted in significant downregulation of constitutive CYP3A4 expression. Furthermore, coadministration of A-792611 was found to attenuate CYP3A4 induction mediated by known inducers rifampin and efavirenz. A-792611 also attenuated the rifampin and ritonavir-mediated activation of the human pregnane X receptor (PXR) in luciferase reporter assays. Microarray analysis on cultured human hepatocytes revealed that A792611 treatment downregulated the expression of PXR target genes CYP3A4, CYP2B6, CYP2C8, and CYP2C9, whereas there was a lack of inductive effect observed in treated rat hepatocytes. A-792611 did not interact with other ligand-activated nuclear receptors that regulate CYP expression such as constitutive androstane receptor, farnesoid X receptor, vitamin D receptor and peroxisome proliferator-activated receptor α. These data suggest that A-792611 is a functional and effective human PXR inhibitor. Among the class of HIV-PIs, which are typically PXR activators, A-792611 appears to have a unique property for PXR antagonism and could be a useful tool for studying nuclear receptor pathway regulation. This article has not been copyedited and formatted. The final version may differ from this version. DMD Fast Forward. Published on December 20, 2007 as DOI: 10.1124/dmd.107. 019547 at A PE T Jornals on Feruary 1, 2021 dm d.aspurnals. org D ow nladed from
On the Density and Structure Formation in Gels and Clusters of Colloidal Rods and Fibers

Structures of gels and clusters of disordered rods and fibers are described by scaling relations for a homogeneous random fiber network and a heterogeneous (that is, fractal) fiber structure. Both models relate the gel density to the particle shape. It is shown that gel densities for attractive colloid rods (iron hydroxide, clay minerals) are often too low to fill space with a homogeneous microstructure. Space filling with heterogeneous rod clusters allows for a very low solid content due to a combination of high particle aspect ratios and low fractal dimensions. This view is supported by reanalysis of early simulations of Vold (J. Phys. Chem. 1959, 63, 1608) of ballistic rod deposition, and yield stress measurements on boehmite-rod suspensions in an accompanying paper (Wierenga et al. Langmuir 1998, 14, 0000). Attention is also given to rapid rod coagulation and retarding factors, such as polydispersity in attraction strength, which may strongly affect gelation kinetics.
Tracking Resistance of Epoxy/Al2O3 Nanocomposites Under DC Voltage

Based on the viewpoint that the properties of polymer could be changed by nano particles, epoxy/Al2O3 nanocomposites were prepared to study the effects of nano particles on tracking failure. Nano-Al2O3 particles were mixed into neat epoxy with filler content of 1, 3, 5 and 7 wt%. Tracking experiments were carried out by applying positive dc voltage. The effects of the filler content on tracking resistance were studied by recording the tracking phenomena and discharge currents during the tracking process. Obtained results showed that when the filler content increased from 0 to 5 wt%, the duration of the whole tracking process increased, the weight loss of nanocomposites and the area of tracking pattern decreased. The opposite tendency was observed when the content increased from 5 to 7 wt%. The discharge currents were studied using the recurrence plot technique to investigate the nonlinear characteristics of discharge currents and reflect the effects of nano particles on discharge. The fractal dimension method was used to study the structures of scintillation discharge distribution and tracking pattern to analyze the effects of nano particles on the occurrence of tracking pattern. The results showed that the nano-Al2O3 particles had very significant effects on the tracking resistance of epoxy. The best restriction effects were obtained with the filler content of 5 wt%. Index Terms epoxy, nano-Al2O3 particles, dc voltage, tracking failure, tracking pattern.
Light-Induced Director Reorientation in Nematic Liquid Crystals Doped with Azobenzene-Containing Macromolecules of Different Architecture

The results of the studies of the orientational optical nonlinearity of a nematic host doped with high-molar-mass compounds of different molecular architecture such as a comb-like polymer and a dendrimer containing azobenzene chromophores are presented. The nonlinearity induced by these compounds was found to be higher than that induced by the low-molar-mass dyes similar in structure to the light-absorbing constituents of the polymer and dendrimer. Possible reasons for the nonlinearity enhancement for macromolecular dopants are discussed.
Early-Universe-Physics Independent and Uncalibrated Cosmic Standards: Constraint on Ωm and Implications for the Hubble Tension

To further shed light on whether pre-recombination models can resolve the Hubble tension, we explore constraints on the cosmic background evolution that are insensitive to early-universe physics. The analysis of the cosmic microwave background (CMB) anisotropy has been thought to highly rely on early-universe physics. However, we show that the difference between the sound horizon at recombination and that at the end of the drag epoch is small and insensitive to early-universe physics. This allows us to link the absolute sizes of the two horizons and treat them as free parameters. Jointly, the CMB peak angular size, Baryon Acoustic Oscillations (BAO), and Type Ia supernovae can be used as “early-universe-physics independent and uncalibrated cosmic standards”, which measure the cosmic history from recombination to today. They can set strong and robust constraints on the post-recombination cosmic background, especially the matter density parameter with Ωm = 0.302 ± 0.008 (68% C.L.). When combined with other non-local observations that are independent of or insensitive to early-universe physics, we obtain several constraints on H0 that are currently more consistent with the Planck 2018 result than the local measurement results such as those based on Cepheids. These results suggest a tension between the post-recombination, but non-local, observations and the local measurements. This tension cannot be resolved by modifying the pre-recombination early universe physics.
Fairness Through Regularization for Learning to Rank

Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-users. Previous work on fair ranking has mostly focused on application-specific fairness notions, often tailored to online advertising, and it rarely considers learning as part of the process. In this work, we show how to transfer numerous fairness notions from binary classification to a learning to rank setting. Our formalism allows us to design methods for incorporating fairness objectives with provable generalization guarantees. An extensive experimental evaluation shows that our method can improve ranking fairness substantially with no or only little loss of model quality.
Full waveform inversion using extended and simultaneous sources

PDE-constrained optimization problems are often treated using the reduced formulation where the PDE constraints are eliminated. This approach is known to be more computationally feasible than other alternatives at large scales. However, the elimination of the constraints forces the optimization process to fulfill the constraints at all times. In some problems this may lead to a highly non-linear objective, which is hard to solve. An example to such a problem, which we focus on in this work, is Full Waveform Inversion (FWI), which appears in seismic exploration of oil and gas reservoirs, and medical imaging. In an attempt to relieve the non-linearity of FWI, several approaches suggested to expand the optimization search space and relax the PDE constraints. This comes, however, with severe memory and computational costs, which we aim to reduce. In this work we adopt the expanded search space approach, and suggest a new formulation of FWI using extended source functions. To make the source-extended problem more feasible in memory and computations, we couple the source extensions in the form of a low-rank matrix. This way, we have a large-but-manageable additional parameter space, which has a rather low memory footprint, and is much more suitable for solving large scale instances of the problem than the full rank additional space. In addition, we show how our source-extended approach is applied together with the popular simultaneous sources technique -- a stochastic optimization technique that significantly reduces the computations needed for FWI inversions. We demonstrate our approaches for solving FWI problems using 2D and 3D models with high frequency data only.
FIRST J153350.8+272729: The Radio Afterglow of a Decades-old Tidal Disruption Event

We present the discovery of the fading radio transient FIRST J153350.8+272729. The source had a maximum observed 5 GHz radio luminosity of 8 × 1039 erg s−1 in 1986, but by 2019 had faded by a factor of nearly 400. It is located at the center of a galaxy (SDSS J153350.89+272729) at 147 Mpc, which shows weak Type II Seyfert activity. We show that a tidal disruption event (TDE) is the preferred scenario for FIRST J153350.8+272729, although it could plausibly be interpreted as the afterglow of a long-duration γ-ray burst. This is only the second TDE candidate to be first discovered at radio wavelengths. Its luminosity fills a gap between the radio afterglows of subrelativistic TDEs in the local universe, and relativistic TDEs at high redshifts. The unusual properties of FIRST J153350.8+272729 (ongoing nuclear activity in the host galaxy, high radio luminosity) motivate more extensive TDE searches in untargeted radio surveys.
Baking Properties of Bread and Cookies Incorporating Distillers' or Brewer's Grain from Wheat or Barley

The effect of granulation, level of soluble solids, and drying method on the mixing properties and baking performance of distillers’grain products from wheat in yeast-raised breads and cookies were evaluated and compared with products containing barley spent grains (BSG). Grinding had a variable effect on baking properties. Breads containing 8% wheat distillers' grains (WDG) (no solubles) had the lowest loaf volume. Breads made with WDG and BSG had the poorest crumb grain. There were few differences observed in the mixing and baking properties of wheat distillers’grains (DDGS or DDG) regardless of whether the materials were dried by harsh (steam tube, tunnel) or milder (atmospheric drum) drying treatments.
Disentangled Representations from Non-Disentangled Models

Constructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario. The dominating paradigm of unsupervised disentanglement is currently to train a generative model that separates different factors of variation in its latent space. This separation is typically enforced by training with specific regularization terms in the model's objective function. These terms, however, introduce additional hyperparameters responsible for the trade-off between disentanglement and generation quality. While tuning these hyperparameters is crucial for proper disentanglement, it is often unclear how to tune them without external supervision. This paper investigates an alternative route to disentangled representations. Namely, we propose to extract such representations from the state-of-the-art generative models trained without disentangling terms in their objectives. This paradigm of post hoc disentanglement employs little or no hyperparameters when learning representations while achieving results on par with existing state-of-the-art, as shown by comparison in terms of established disentanglement metrics, fairness, and the abstract reasoning task. All our code and models are publicly available.
Global multivariate model learning from hierarchically correlated data

Inverse statistical physics aims at inferring models compatible with a set of empirical averages estimated from a high-dimensional dataset of independently distributed equilibrium configurations of a given system. However, in several applications, such as biology, data result from stochastic evolutionary processes, and configurations are related through a hierarchical structure, typically represented by a tree, and therefore not independent. In turn, empirical averages of observables superpose intrinsic signals related to the equilibrium distribution of the studied system, and spurious historical (or phylogenetic) signals resulting from the structure underlying the data-generating process. The naive application of inverse statistical physics techniques therefore leads to systematic biases and an effective reduction of the sample size. To advance on the currently open task of extracting intrinsic signals from correlated data, we study a system described by a multivariate Ornstein–Uhlenbeck process defined on a finite tree. Using a Bayesian framework, we can disentangle covariances in the data corresponding to their multivariate Gaussian equilibrium distribution from those resulting from the historical correlations. Our approach leads to a clear gain in accuracy in the inferred equilibrium distribution, which corresponds to an effective two to fourfold increase in sample size.
Measuring topological order

The topological order of a (2+1)D topological phase of matter is characterized by its chiral central charge and a unitary modular tensor category that describes the universal fusion and braiding properties of its anyonic quasiparticles. I discuss the topologically invariant quantities associated with these and identify ones that are useful for determining the topological order. I propose a variety of physical experiments that probe these quantities and detail the relation of the measured data to the topological invariants.
DIELECTRIC CHARACTERISTICS OF POLYMER-CERAMIC-METAL COMPOSITES FOR THE APPLICATION OF EMBEDDED PASSIVE DEVICES

ABSTRACT The dielectric behavior of the composite of PMMA (polymethyl methacylate)-BT (BaTiO3), PMMA-Ni, and PMMA-BT-Ni were investigated as a function of BT and Ni volume fractions. In the case of the PMMA-BT composite, the dielectric constant increased gradually from ϵr = 3 to 15, as the amount of BT increased from 0 to 40%. In the PMMA-Ni composite, the percolation threshold that leads to a sudden increment of conductivity was observed when 4.5% of Ni was added and the dielectric constant also increased. In the PMMA-BT-Ni composite, the percolation threshold volume fraction of Ni, the percolation smearing region, and the dielectric constant increased as the amount of BT increased. The dielectric characteristics of the composites were explained on the basis of connectivity and mixing rules.
Rethinking Representations in P&C Actuarial Science with Deep Neural Networks

Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them. In particular, many emerging data sources (text, images, sensors) may complement traditional data to provide better insights to predict the future losses in an insurance contract. This paper presents some of these emerging data sources and presents a unified framework for actuaries to incorporate these in existing ratemaking models. Our approach stems from representation learning, whose goal is to create representations of raw data. A useful representation will transform the original data into a dense vector space where the ultimate predictive task is simpler to model. Our paper presents methods to transform non-vectorial data into vectorial representations and provides examples for actuarial science.
Self-Supervised VQ-VAE for One-Shot Music Style Transfer

Neural style transfer, allowing to apply the artistic style of one image to another, has become one of the most widely showcased computer vision applications shortly after its introduction. In contrast, related tasks in the music audio domain remained, until recently, largely untackled. While several style conversion methods tailored to musical signals have been proposed, most lack the ‘one-shot’ capability of classical image style transfer algorithms. On the other hand, the results of existing one-shot audio style transfer methods on musical inputs are not as compelling. In this work, we are specifically interested in the problem of one-shot timbre transfer. We present a novel method for this task, based on an extension of the vector-quantized variational autoencoder (VQ-VAE), along with a simple self-supervised learning strategy designed to obtain disentangled representations of timbre and pitch. We evaluate the method using a set of objective metrics and show that it is able to outperform selected baselines.
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy

We typically compute aggregate statistics on held-out test data to assess the generalization of machine learning models. However, statistics on test data often overstate model generalization, and thus, the performance of deployed machine learning models can be variable and untrustworthy. Motivated by these concerns, we develop methods to automatically discover and correct model errors beyond those available in the data. We propose Defuse, a method that generates novel model misclassifications, categorizes these errors into high-level model bugs, and efficiently labels and fine-tunes on the errors to correct them. To generate misclassified data, we propose an algorithm inspired by adversarial machine learning techniques that uses a generative model to find naturally occurring instances misclassified by a model. Further, we observe that the generative models have regions in their latent space with higher concentrations of misclassifications. We call these regions misclassification regions and find they have several useful properties. Each region contains a specific type of model bug; for instance, a misclassification region for an MNIST classifier contains a style of skinny 6 that the model mistakes as a 1. We can also assign a single label to each region, facilitating low-cost labeling. We propose a method to learn the misclassification regions and use this insight to both categorize errors and correct them. In practice, Defuse finds and corrects novel errors in classifiers. For example, Defuse shows that a high-performance traffic sign classifier mistakes certain 50km/h signs as 80km/h. Defuse corrects the error after fine-tuning while maintaining generalization on the test set.
Decomposition numbers for the principal $\Phi_{2n}$-block of $\mathrm{Sp}_{4n}(q)$ and $\mathrm{SO}_{4n+1}(q)$

We compute the decomposition numbers of the unipotent characters lying in the principal l-block of a finite group of Lie type B2n(q) or C2n(q) when q is an odd prime power and l is an odd prime number such that the order of q mod l is 2n. Along the way, we extend to these finite groups the results of [12] on the branching graph for Harish-Chandra induction and restriction.
Cryo-ePDF: Overcoming Electron Beam Damage to Study the Local Atomic Structure of Amorphous ALD Aluminum Oxide Thin Films within a TEM

Atomic layer deposition (ALD) provides uniform and conformal thin films that are of interest for a range of applications. To better understand the properties of amorphous ALD films, we need an improved understanding of their local atomic structure. Previous work demonstrated measurement of how the local atomic structure of ALD-grown aluminum oxide (AlOx) evolves in operando during growth by employing synchrotron high-energy X-ray diffraction (HE-XRD). In this work, we report on efforts to employ electron diffraction pair distribution function (ePDF) measurements using more broadly available transmission electron microscope (TEM) instrumentation to study the atomic structure of amorphous ALD-AlOx. We observe electron beam damage in the ALD-coated samples during ePDF at ambient temperature and successfully mitigate this beam damage using ePDF at cryogenic temperatures (cryo-ePDF). We employ cryo-ePDF and reverse Monte Carlo (RMC) modeling to obtain structural models of ALD-AlOx coatings formed at a range of deposition temperatures from 150 to 332 °C. From these model structures, we derive structural metrics including stoichiometry, pair distances, and coordination environments in the ALD-AlOx films as a function of deposition temperature. The structural variations we observe with growth temperature are consistent with temperature-dependent changes in the surface hydroxyl density on the growth surface. The sample preparation and cryo-ePDF procedures we report here can be used for the routine measurement of ALD-grown amorphous thin films to improve our understanding of the atomic structure of these materials, establish structure–property relationships, and help accelerate the timescale for the application of ALD to address technological needs.