Search for: All records

Modern statistical learning problems often involve multiple related data sets, where learning efficiency on a target set can be improved by utilizing related source sets, while heterogeneity among the source sets may introduce bias. Existing approaches are limited by suboptimal performance in multi-source settings, insufficient use of covariance information, or the computational burden of tuning procedures. We propose a tuning-free and covariance-aware shrinkage framework that constructs shrinkage directions using covariance information to improve efficiency. We establish finite-sample risk bounds that yield an explicit risk-improving interval for the shrinkage size, making the procedure fully data-driven and tuning-free. When multiple source sets are available, we further propose a novel sequential algorithm that shrinks the estimator toward the sources one at a time according to their estimated risk reduction. The proposed algorithm asymptotically attains the oracle risk under mild conditions and is guaranteed to improve over the single-step shrinkage method in the literature. The framework is further extended to general smooth -estimation problems via a local quadratic approximation. Numerical studies show substantial gains over competing methods, especially when the source data sets are highly heterogeneous. 

From Big Data to Real-Time Decisions: Online Tensor Inference Modern digital platforms, from e-commerce and online advertising to mobile health, generate massive streams of high-dimensional data that must be analyzed in real time. In their paper “Online Tensor Inference,” Xin Wen, Will Wei Sun, and Yichen Zhang develop a new statistical framework that enables both efficient learning and rigorous inference for streaming tensor data. The authors propose an online low-rank tensor estimation method based on stochastic gradient descent that processes observations sequentially without storing historical data, overcoming the memory and scalability limitations of traditional offline approaches. Beyond estimation, the paper introduces a novel online debiasing technique that delivers valid confidence intervals and hypothesis tests on the fly without data splitting. Theoretical results establish near-minimax-optimal convergence rates and asymptotic normality for general linear functionals of tensors. Together, these advances provide a principled foundation for real-time, statistically grounded decision making in fast-changing, data-rich environments. 

Privacy policies are often complex. An exception is the two-page standardized notice that U.S. financial institutions must provide under the Gramm-Leach-Bliley Act (GLBA). However, banks now operate websites, mobile apps, and other services that involve complex data sharing practices that require additional privacy notices and do-not-sell opt-outs. We conducted a large-scale analysis of how U.S. banks implement privacy policies and controls in response to GLBA; other federal privacy policy requirements; and the California Consumer Privacy Act (CCPA), a key example for U.S. state privacy laws. We focused on the disclosure and control of a set of especially privacy-invasive practices: third-party data sharing for marketing-related purposes. We collected privacy policies for the 2,067 largest U.S. banks, 45.2% of which provided multiple policies. Across disclosures and controls for the same bank, we identified frequent, concerning inconsistencies---53.8% of banks with multiple privacy policies indicated in GLBA notices that they do not share with third parties but disclosed sharing in other policies. This multiplicity of policies, with the inconsistencies it causes, may create consumer confusion and undermine the transparency goals of the very laws that require them. Our findings call into question whether current policy requirements, such as the GLBA notice, are achieving their intended goals in today's online banking landscape. We discuss potential avenues for reforming and harmonizing privacy policies and control requirements across federal and state laws. 

Reconfigurable devices are gaining increasing attention as a viable alternative and supplementary solution to the traditional CMOS technology. In this article, we develop a more efficient field-programmable gate array (FPGA) based on the reconfigurable field-effective transistor (RFET). We use the multi-gate characteristics of RFET to redesign the key components of FPGAs, namely SRAM-controlled multiplexer and look-up tables. The compact structure of the proposed design requires fewer transistors and leads to reduced delay of the overall FPGA system. In addition, we develop a comprehensive technology/system co-design and co-optimization framework to thoroughly investigate the design space, including various device- and system-level design parameters. A series of benchmark tests show that under the optimal design, up to 32% and 13% reduction can be achieved in delay and energy-delay product (EDP), respectively, compared to the traditional CMOS FPGAs. 

Abstract Star formation is a fundamental, yet poorly understood, process of the Universe. It is important to study how star formation occurs in different galactic environments. Thus, here, in the first of a series of papers, we introduce the Low-metallicity Star Formation (LZ-STAR) survey of the Sh2-284 (hereafter S284) region, which, atZ ∼  0.3–0.5Z_⊙, is one of the lowest-metallicity star-forming regions of our Galaxy. LZ-STAR is a multifacility survey, including observations with JWST, the Atacama Large Millimeter/submillimeter Array (ALMA), Hubble Space Telescope, Chandra, and Gemini. As a starting point, we report JWST and ALMA observations of one of the most massive protostars in the region, S284p1. The observations of shock-excited molecular hydrogen reveal a symmetric, bipolar outflow originating from the protostar, spanning several parsecs, and fully covered by the JWST field of view and ALMA observations of CO(2–1) emission. These allow us to infer that the protostar has maintained a relatively stable orientation of disk accretion over its formation history. The JWST near-infrared continuum observations detect a centrally illuminated bipolar outflow cavity around the protostar, as well as a surrounding cluster of low-mass young stars. We develop new radiative transfer models of massive protostars designed for the low metallicity of S284. Fitting these models to the protostar’s spectral energy distribution implies a current protostellar mass of ∼10M_⊙has formed from an initial ∼100M_⊙core over the last ∼3 × 10⁵yr. Overall, these results indicate that massive stars can form in an ordered manner in low-metallicity, protocluster environments.