Adaptive Estimation of Autoregressive Models with Time-Varying Variances

Stable autoregressive models of known finite order are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution or combinations of the two. This paper develops kernel-based estimators of the residual variances and associated adaptive least squares (ALS) estimators of the autoregressive coefficients. These are shown to be asymptotically efficient, having the same limit distribution as the infeasible generalized least squares (GLS). Comparisons of the efficient procedure and the ordinary least squares (OLS) reveal that least squares can be extremely inefficient in some cases while nearly optimal in others. Simulations show that, when least squares work well, the adaptive estimators perform comparably well, whereas when least squares work poorly, major efficiency gains are achieved by the new estimators.Adaptive estimation, Autoregression, Heterogeneity, Weighted regression

Adaptive Estimation of Autoregressive Models with Time-Varying Variances

Stable autoregressive models of known finite order are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution or combinations of the two. This paper develops kernel-based estimators of the residual variances and associated adaptive least squares (ALS) estimators of the autoregressive coefficients. These are shown to be asymptotically efficient, having the same limit distribution as the infeasible generalized least squares (GLS). Comparisons of the efficient procedure and ordinary least squares (OLS) reveal that least squares can be extremely inefficient in some cases while nearly optimal in others. Simulations show that, when least squares work well, the adaptive estimators perform comparably well, whereas when least squares work poorly, major efficiency gains are achieved by the new estimators.Adaptive estimation, Autoregression, Heterogeneity, Weighted regression

Hepatoma cell density promotes claudin-1 and scavenger receptor BI expression and hepatitis C virus internalization.

Hepatitis C virus (HCV) entry occurs via a pH- and clathrin-dependent endocytic pathway and requires a number of cellular factors, including CD81, the tight-junction proteins claudin 1 (CLDN1) and occludin, and scavenger receptor class B member I (SR-BI). HCV tropism is restricted to the liver, where hepatocytes are tightly packed. Here, we demonstrate that SR-BI and CLDN1 expression is modulated in confluent human hepatoma cells, with both receptors being enriched at cell-cell junctions. Cellular contact increased HCV pseudoparticle (HCVpp) and HCV particle (HCVcc) infection and accelerated the internalization of cell-bound HCVcc, suggesting that the cell contact modulation of receptor levels may facilitate the assembly of receptor complexes required for virus internalization. CLDN1 overexpression in subconfluent cells was unable to recapitulate this effect, whereas increased SR-BI expression enhanced HCVpp entry and HCVcc internalization, demonstrating a rate-limiting role for SR-BI in HCV internalization

Ethical Challenges in Data-Driven Dialogue Systems

The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.Comment: In Submission to the AAAI/ACM conference on Artificial Intelligence, Ethics, and Societ

Tilted Nonparametric Estimation of Volatility Functions with Empirical Applications

This paper proposes a novel positive nonparametric estimator of the conditional variance function without reliance on logarithmic or other transformations. The estimator is based on an empirical likelihood modification of conventional local level nonparametric regression applied to squared mean regression residuals. The estimator is shown to be asymptotically equivalent to the local linear estimator in the case of unbounded support but, unlike that estimator, is restricted to be non-negative in finite samples. It is fully adaptive to the unknown conditional mean function. Simulations are conducted to evaluate the finite sample performance of the estimator. Two empirical applications are reported. One uses cross section data and studies the relationship between occupational prestige and income. The other uses time series data on Treasury bill rates to fit the total volatility function in a continuous-time jump diffusion model

Diverse variability of surface chlorophyll during the evolution of Gulf Stream rings

Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 48(5), (2021): e2020GL091461, https://doi.org/10.1029/2020GL091461.We investigate how the near-surface chlorophyll (CHL)-a evolves in Gulf Stream (GS) warm-core rings (WCRs) and cold-core rings (CCRs) using multi-platform satellite observations. Averaged CHL anomaly (CHLA) within the rings exhibits both positive and negative linear trends during the evolution of the WCRs while negative trends dominate in CCRs. This difference is associated with a variety of physical processes occurring during the evolution process. Meanwhile, eddy-centric analysis reveals four spatial patterns of CHLA long-term trends, some of which highlights the importance of rings in shaping surface CHL. Short-term fluctuations of CHLA in WCRs and CCRs are closely correlated with mixed layer depth and sea surface temperature anomaly and highlight the complex interplay between multiple mechanisms. In addition, we find higher concentration CHL in some WCRs than that in CCRs during the same season, providing an alternative view of the characteristics of the surface ecosystem in Gulf Stream rings.This work was supported by the National Science Foundation Ocean Science Division under grant OCE-1558960. JN was supported by the Fundamental Research Funds for the Central Universities (Hohai University) under grant B200203005 and the China Scholarship Council.2021-08-1

A temperature independent driver for Mach-Zehnder modulators

This paper describes a fully differential inductorlesstemperature independent laser driver for a Mach-Zehndermodulator (MZM). The laser driver in this work exhibits robustnessin bandwidth and gain across the temperature range27oC to 135oC. This driver (fabricated using a 130nm CMOSprocess) can achieve a differential output voltage swing of up to4Vpp when driving a 50 Ohm Load at speeds of up to 12.5Gb/sacross a temperature range 27oC to 135oC

GeneFishing to reconstruct context specific portraits of biological processes.

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications