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  1. Published research highlights the presence of demographic bias in automated facial attribute classification. The proposed bias mitigation techniques are mostly based on supervised learning, which requires a large amount of labeled training data for generalizability and scalability. However, labeled data is limited, requires laborious annotation, poses privacy risks, and can perpetuate human bias. In contrast, self-supervised learning (SSL) capitalizes on freely available unlabeled data, rendering trained models more scalable and generalizable. However, these label-free SSL models may also introduce biases by sampling false negative pairs, especially at low-data regimes (< 200K images) under low compute settings. Further, SSL-based models may suffer from performance degradation due to a lack of quality assurance of the unlabeled data sourced from the web. This paper proposes a fully self-supervised pipeline for demographically fair facial attribute classifiers. Leveraging completely unlabeled data pseudolabeled via pre-trained encoders, diverse data curation techniques, and meta-learning-based weighted contrastive learning, our method significantly outperforms existing SSL approaches proposed for downstream image classification tasks. Extensive evaluations on the FairFace and CelebA datasets demonstrate the efficacy of our pipeline in obtaining fair performance over existing baselines. Thus, setting a new benchmark for SSL in the fairness of facial attribute classification. 
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    Free, publicly-accessible full text available September 3, 2025
  2. We experimentally demonstrate a pump-pulse-induced conversion of noise into solitons in multimode optical fibers. The process is based on the recently discovered phenomenon of soliton self-mode conversion, where a pump soliton in a higher-order spatial mode crafts another well-defined soliton, originating purely from noise, in a lower-order mode at a longer wavelength through intermodal Raman scattering. The lack of the need for any seed or cavity feedback demonstrates that soliton self-mode conversion is a fundamentally unavoidable, but nevertheless tailorable and hence useful, self-organizing nonlinear optical effect capable of turning noise into transform limited solitons.

     
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  3. Abstract We present details of a high-accuracy absolute scalar magnetometer based on pulsed proton NMR. The B-field magnitude is determined from the precession frequency of proton spins in a cylindrical sample of water after accounting for field perturbations from probe materials, sample shape, and other corrections. Features of the design, testing procedures, and corrections necessary for qualification as an absolute scalar magnetometer are described. The device was tested at B = 1.45 T but can be modified for a range exceeding 1–3 T. The magnetometer was used to calibrate other NMR magnetometers and measure absolute magnetic field magnitudes to an accuracy of 19 parts per billion as part of a measurement of the muon magnetic moment anomaly at Fermilab. 
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  4. We show that a soliton in a high-order spatial mode of a multi-mode fiber can completely lose its shot-to-shot coherence due to a noise seed with energy orders of magnitude below that of the soliton. The total degradation of shot-to-shot coherence is caused by a very strong recently demonstrated intermodal nonlinear effect, soliton self-mode conversion. The results indicate that the robustness of solitons against perturbations is not entirely applicable in the presence of intermodal nonlinearities, and, more generally, that certain single-mode results cannot be trivially extrapolated to multi-mode fibers.

     
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  5. Abstract Rationale: Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear. Objectives: We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program. Methods: Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants. Measurements and Main Results: In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability. Conclusions: Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates. 
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  6. We present details on a new measurement of the muon magnetic anomaly,aμ=(gμ2)/2. The result is based on positive muon data taken at Fermilab’s Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses3.1GeV/cpolarized muons stored in a 7.1-m-radius storage ring with a 1.45 T uniform magnetic field. The value ofaμis determined from the measured difference between the muon spin precession frequency and its cyclotron frequency. This difference is normalized to the strength of the magnetic field, measured using nuclear magnetic resonance. The ratio is then corrected for small contributions from beam motion, beam dispersion, and transient magnetic fields. We measureaμ=116592057(25)×1011(0.21 ppm). This is the world’s most precise measurement of this quantity and represents a factor of 2.2 improvement over our previous result based on the 2018 dataset. In combination, the two datasets yieldaμ(FNAL)=116592055(24)×1011(0.20 ppm). Combining this with the measurements from Brookhaven National Laboratory for both positive and negative muons, the new world average isaμ(exp)=116592059(22)×1011(0.19 ppm).

    Published by the American Physical Society2024 
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    Free, publicly-accessible full text available August 1, 2025
  7. Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas. 
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