A Knowledge Transfer Framework for Differentially Private Sparse Learning

We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially private high-dimensional sparse learning framework using the idea of knowledge transfer. More specifically, we propose to distill the knowledge from a "teacher" estimator trained on a private dataset, by creating a new dataset from auxiliary features, and then train a differentially private "student" estimator using this new dataset. In addition, we establish the linear convergence rate as well as the utility guarantee for our proposed method. For sparse linear regression and sparse logistic regression, our method achieves improved utility guarantees compared with the best known results (Kifer et al., 2012; Wang and Gu, 2019). We further demonstrate the superiority of our framework through both synthetic and real-world data experiments.Comment: 24 pages, 2 figures, 3 table

Correlation of Circulating Glucocorticoid-Induced TNFR-Related Protein Ligand Levels with Disease Activity in Patients with Systemic Lupus Erythematosus

The aim of this paper is to investigate the correlation of glucocorticoid-induced tumor necrosis factor receptor- (TNFR-) related protein ligand (GITRL) with disease activity and organ involvement in patients with systemic lupus erythematosus (SLE). Serum GITRL levels were measured in 58 patients with SLE and 30 healthy controls matched for age and sex. Patients were assessed for clinical and laboratory variables. Correlations of serum GITRL levels with SLEDAI, laboratory values, and clinical manifestations were assessed. Serum GITRL levels were determined by ELISA. Serum GITRL levels were markedly increased in patients with SLE compared with healthy controls (mean 401.3 ng/mL and 36.59 ng/mL, resp.; P<0.0001). SLE patients with active disease showed higher serum GITRL levels compared to those with inactive disease (mean 403.3 ng/mL and 136.3 ng/mL, resp; P=0.0043) as well as normal controls (36.59 ng/mL; P<0.0001). Serum GITRL levels were positively correlated with SLEDAI, titers of anti-dsDNA antibody, erythrocyte sedimentation rate (ESR), and IgM and negatively correlated with complement3 (C3). Serum GITRL levels were higher in SLE patients with renal involvement and vasculitis compared with patients without the above-mentioned manifestations

A sample-position-autocorrection system with precision better than 1 \um~in angle-resolved photoemission experiments

We present the development of a high-precision sample-position-autocorrection system for photoemission experiments. A binocular vision method based on image pattern matching calculations was realized to track the sample position with an accuracy better than 1 \um, which was much smaller than the spot size of the incident laser. We illustrate the performance of the sample-position-autocorrection system with representative photoemission data on the topological insulator Bi$_2$Se$_3$ and an optimally-doped cuprate superconductor \Bi. Our method provides new possibilities for studying the temperature-dependent electronic structures in quantum materials by laser-based or spatially resolved photoemission systems with high precision and efficiency.Comment: 6 pages, 4 figure

Ultrafast Switching from the Charge Density Wave Phase to a Metastable Metallic State in 1T-TiSe2_2

The ultrafast electronic structures of the charge density wave material 1T-TiSe$_2$ were investigated by high-resolution time- and angle-resolved photoemission spectroscopy. We found that the quasiparticle populations drove ultrafast electronic phase transitions in 1T-TiSe$_2$ within 100 fs after photoexcitation, and a metastable metallic state, which was significantly different from the equilibrium normal phase, was evidenced far below the charge density wave transition temperature. Detailed time- and pump-fluence-dependent experiments revealed that the photoinduced metastable metallic state was a result of the halted motion of the atoms through the coherent electron-phonon coupling process, and the lifetime of this state was prolonged to picoseconds with the highest pump fluence used in this study. Ultrafast electronic dynamics were well captured by the time-dependent Ginzburg-Landau model. Our work demonstrates a mechanism for realizing novel electronic states by photoinducing coherent motion of atoms in the lattice.Comment: 13 Pages, 10 figure

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks