Leveraging vague prior information in general models via iteratively constructed Gamma-minimax estimators

Gamma-minimax estimation is an approach to incorporate prior information into an estimation procedure when it is implausible to specify one particular prior distribution. In this approach, we aim for an estimator that minimizes the worst-case Bayes risk over a set $\Gamma$ of prior distributions. Traditionally, Gamma-minimax estimation is defined for parametric models. In this paper, we define Gamma-minimaxity for general models and propose iterative algorithms with convergence guarantees to compute Gamma-minimax estimators for a general model space and a set of prior distributions constrained by generalized moments. We also propose encoding the space of candidate estimators by neural networks to enable flexible estimation. We illustrate our method in two settings, namely entropy estimation and a problem that arises in biodiversity studies

Individualized treatment rules under stochastic treatment cost constraints

Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened upon based on covariates to optimize the mean counterfactual outcome under treatment cost constraints when the treatment cost is random. In a particularly interesting special case, an instrumental variable corresponding to encouragement to treatment is intervened upon with constraints on the proportion receiving treatment. For such settings, we first develop a method to estimate optimal individualized treatment rules. We further construct an asymptotically efficient plug-in estimator of the corresponding average treatment effect relative to a given reference rule

Mobilization and Role of Starch, Protein, and Fat Reserves During Seed Germination of Six Wild Grassland Species

Since seed reserves can influence seed germination, the quantitative and qualitative differences in seed reserves may relate to the germination characteristics of species. The purpose of our study was to evaluate the correlation between germination and seed reserves, as well as their mobilization during germination of six grassland species (Chloris virgata, Kochia scoparia, Lespedeza hedysaroides, Astragalus adsurgens, Leonurus artemisia, and Dracocephalum moldavica) and compare the results with domesticated species. We measured starch, protein, and fat content in dry seeds and the initial absorption of water during imbibition. Starch, soluble protein, fat, and soluble sugar content also were determined at five stages during germination. Starch, protein, and fat reserves in dry seeds were not significantly correlated with germination percentage and rate (speed), but soluble sugar and soluble protein contents at different germination stages were positively significantly correlated with germination rate for the six species. Starch was mainly used during seed imbibition, and soluble protein was used from the imbibition stage to the highest germination stage. Fat content for all species remained relatively constant throughout germination for six species, regardless of the proportion of other seed reserves in the seeds. Our results for fat utilization differ from those obtained for cultivated grasses and legumes. These results provide new insight on the role of seed reserves as energy resources in germination for wild species

Doubly Robust Proximal Synthetic Controls

To infer the treatment effect for a single treated unit using panel data, synthetic control methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing synthetic control methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to synthetic controls to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and the generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a Pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil

Integrated photonics modular arithmetic processor

Integrated photonics computing has emerged as a promising approach to overcome the limitations of electronic processors in the post-Moore era, capitalizing on the superiority of photonic systems. However, present integrated photonics computing systems face challenges in achieving high-precision calculations, consequently limiting their potential applications, and their heavy reliance on analog-to-digital (AD) and digital-to-analog (DA) conversion interfaces undermines their performance. Here we propose an innovative photonic computing architecture featuring scalable calculation precision and a novel photonic conversion interface. By leveraging Residue Number System (RNS) theory, the high-precision calculation is decomposed into multiple low-precision modular arithmetic operations executed through optical phase manipulation. Those operations directly interact with the digital system via our proposed optical digital-to-phase converter (ODPC) and phase-to-digital converter (OPDC). Through experimental demonstrations, we showcase a calculation precision of 9 bits and verify the feasibility of the ODPC/OPDC photonic interface. This approach paves the path towards liberating photonic computing from the constraints imposed by limited precision and AD/DA converters.Comment: 23 pages, 9 figure

Surface plasmon polaritons assisted diffraction in periodic subwavelength holes of metal films with reduced interplane coupling

Metal films grown on Si wafer perforated with a periodic array of subwavelength holes have been fabricated and anomalous enhanced transmission in the mid-infrared regime has been observed. High order transmission peaks up to Si(2,2) are clearly revealed due to the large dielectric constant contrast of the dielectrics at the opposite interfaces. Si(1,1) peak splits at oblique incidence both in TE and TM polarization, which confirms that anomalous enhanced transmission is a surface plasmon polaritons (SPPs) assisted diffraction phenomenon. Theoretical transmission spectra agree excellently with the experimental results and confirm the role of SPPs diffraction by the lattice.Comment: 4 pages, 5 figures, 26 reference

Comparative Studies on Microbial Community Structure and Production Performance of Jiang-Flavor Daqu in Different Areas of Maotai Town

The microbial community structure and diversity of Jiang-flavor Daqu (TS, WS, WM, MH and DJ) from different areas of Maotai town were analyzed by using the third-generation nanopore sequencing platform, and its physicochemical indexes and characteristic flavor substances were measured. The results showed that there were some similarities and differences between Daqu in different areas of Maotai town. In terms of microbial community structure, Bacillus, Saccharopolyspora, Weissella, Staphylococcus and Streptomyces were the common dominant bacterial genera in the five Daqu samples. Among them, Bacillus was the absolute dominant bacteria in MH and DJ. Aspergillus and Penicillium were the common dominant fungal genera, and the proportions of Lichtheimia and Saccharomycopsis in TS, WM and MH were significantly higher than those in DJ and WS. Compared with TS and WM, network correlation analysis showed that MH, DJ and WS had stronger interactions among dominant bacteria. In addition, redundancy analysis (RDA) showed that Weissella was positively correlated with esterification power, liquefaction power, saccharification power, acetic acid, ethyl acetate, ethyl lactate and n-pentanol. Lichtheimia was positively correlated with liquefaction power, saccharification power, acetic acid, isovaleric acid, 2,3-butanediol, phenylacetaldehyde and dibutyl phthalate. Saccharomycopsis was positively correlated with esterification power and ethyl acetate. Bacillus was positively correlated with 2,3,5,6-tetramethylpyrazine, propionic acid, isovaleric acid, dibutyl phthalate, 2,3-butanediol and phenacetaldehyde

Baseline representativeness of patients in clinics enrolled in the PRimary care Opioid Use Disorders treatment (PROUD) trial: comparison of trial and non-trial clinics in the same health systems

BACKGROUND: Pragmatic primary care trials aim to test interventions in real world health care settings, but clinics willing and able to participate in trials may not be representative of typical clinics. This analysis compared patients in participating and non-participating clinics from the same health systems at baseline in the PRimary care Opioid Use Disorders treatment (PROUD) trial. METHODS: This observational analysis relied on secondary electronic health record and administrative claims data in 5 of 6 health systems in the PROUD trial. The sample included patients 16-90 years at an eligible primary care visit in the 3 years before randomization. Each system contributed 2 randomized PROUD trial clinics and 4 similarly sized non-trial clinics. We summarized patient characteristics in trial and non-trial clinics in the 2 years before randomization ( baseline ). Using mixed-effect regression models, we compared trial and non-trial clinics on a baseline measure of the primary trial outcome (clinic-level patient-years of opioid use disorder (OUD) treatment, scaled per 10,000 primary care patients seen) and a baseline measure of the secondary trial outcome (patient-level days of acute care utilization among patients with OUD). RESULTS: Patients were generally similar between the 10 trial clinics (n = 248,436) and 20 non-trial clinics (n = 341,130), although trial clinics\u27 patients were slightly younger, more likely to be Hispanic/Latinx, less likely to be white, more likely to have Medicaid/subsidized insurance, and lived in less wealthy neighborhoods. Baseline outcomes did not differ between trial and non-trial clinics: trial clinics had 1.0 more patient-year of OUD treatment per 10,000 patients (95% CI: - 2.9, 5.0) and a 4% higher rate of days of acute care utilization than non-trial clinics (rate ratio: 1.04; 95% CI: 0.76, 1.42). CONCLUSIONS: trial clinics and non-trial clinics were similar regarding most measured patient characteristics, and no differences were observed in baseline measures of trial primary and secondary outcomes. These findings suggest trial clinics were representative of comparably sized clinics within the same health systems. Although results do not reflect generalizability more broadly, this study illustrates an approach to assess representativeness of clinics in future pragmatic primary care trials