The Representation of Immigrants in Federal, State and Local Government Work Forces

Public sector employment of immigrants can increase their economic assimilation and potentially improve their treatment by government. Yet, as we show using Census data from 1990, 2000, and 2009-11, immigrants are substantially under-represented in federal, state, and local governments. To understand why, we use logit analysis for federal and for state and local government employment in each time period to test whether immigrants’ weaker educational attainment and English proficiency, lower probabilities of being citizens and military veterans, and different age, gender, and race/ethnicity distributions can explain that under-representation. Disparities in education and preferential government treatment of veterans are factors, but citizenship requirements appear to be the major obstacle to immigrant employment in the public sector

Quantitative analysis of B-lymphocyte migration directed by CXCL13

B-lymphocyte migration, directed by chemokine gradients, is essential for homing to sites of antigen presentation

Band Gap Engineering with Ultralarge Biaxial Strains in Suspended Monolayer MoS2

We demonstrate the continuous and reversible tuning of the optical band gap of suspended monolayer MoS2 membranes by as much as 500 meV by applying very large biaxial strains. By using chemical vapor deposition (CVD) to grow crystals that are highly impermeable to gas, we are able to apply a pressure difference across suspended membranes to induce biaxial strains. We observe the effect of strain on the energy and intensity of the peaks in the photoluminescence (PL) spectrum, and find a linear tuning rate of the optical band gap of 99 meV/%. This method is then used to study the PL spectra of bilayer and trilayer devices under strain, and to find the shift rates and Gr\"uneisen parameters of two Raman modes in monolayer MoS2. Finally, we use this result to show that we can apply biaxial strains as large as 5.6% across micron sized areas, and report evidence for the strain tuning of higher level optical transitions.Comment: Nano Lett., Article ASA

Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression

Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nested set of sub-networks that are optimal for different configurations of resources. However, the dropout rate is fixed as a hyper-parameter over different layers during the whole training process. Therefore, when nodes are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. Another drawback is the generated sub-networks are deterministic networks without well-calibrated uncertainty. To address these two problems, we develop a Bayesian approach to nested neural networks. We propose a variational ordering unit that draws samples for nested dropout at a low cost, from a proposed Downhill distribution, which provides useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the node distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related approach on uncertainty-critical tasks in computer vision.Comment: 16 pages, 10 figure

Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report

Purpose: To introduce the concept of using large language models (LLMs) to re-label structure names in accordance with the American Association of Physicists in Medicine (AAPM) Task Group (TG)-263 standard, and to establish a benchmark for future studies to reference. Methods and Materials: The Generative Pre-trained Transformer (GPT)-4 application programming interface (API) was implemented as a Digital Imaging and Communications in Medicine (DICOM) storage server, which upon receiving a structure set DICOM file, prompts GPT-4 to re-label the structure names of both target volumes and normal tissues according to the AAPM TG-263. Three disease sites, prostate, head and neck, and thorax were selected for evaluation. For each disease site category, 150 patients were randomly selected for manually tuning the instructions prompt (in batches of 50) and 50 patients were randomly selected for evaluation. Structure names that were considered were those that were most likely to be relevant for studies utilizing structure contours for many patients. Results: The overall re-labeling accuracy of both target volumes and normal tissues for prostate, head and neck, and thorax cases was 96.0%, 98.5%, and 96.9% respectively. Re-labeling of target volumes was less accurate on average except for prostate - 100%, 93.1%, and 91.1% respectively. Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of both target volumes and normal tissues as presented in this work, LLMs are poised to be the preferred method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.Comment: 20 pages, 5 figures, 1 tabl

A single transcription factor is sufficient to induce and maintain secretory cell architecture

We hypothesized that basic helix–loop–helix (bHLH) MIST1 (BHLHA15) is a “scaling factor” that universally establishes secretory morphology in cells that perform regulated secretion. Here, we show that targeted deletion of MIST1 caused dismantling of the secretory apparatus of diverse exocrine cells. Parietal cells (PCs), whose function is to pump acid into the stomach, normally lack MIST1 and do not perform regulated secretion. Forced expression of MIST1 in PCs caused them to expand their apical cytoplasm, rearrange mitochondrial/lysosome trafficking, and generate large secretory granules. Mist1 induced a cohort of genes regulated by MIST1 in multiple organs but did not affect PC function. MIST1 bound CATATG/CAGCTG E boxes in the first intron of genes that regulate autophagosome/lysosomal degradation, mitochondrial trafficking, and amino acid metabolism. Similar alterations in cell architecture and gene expression were also caused by ectopically inducing MIST1 in vivo in hepatocytes. Thus, MIST1 is a scaling factor necessary and sufficient by itself to induce and maintain secretory cell architecture. Our results indicate that, whereas mature cell types in each organ may have unique developmental origins, cells performing similar physiological functions throughout the body share similar transcription factor-mediated architectural “blueprints.

Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics

We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs. This paper proposes evaluating LLMs on a highly-specialized topic, radiation oncology physics, which may be more pertinent to scientific and medical communities in addition to being a valuable benchmark of LLMs. We developed an exam consisting of 100 radiation oncology physics questions based on our expertise at Mayo Clinic. Four LLMs, ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and non-experts. ChatGPT (GPT-4) outperformed all other LLMs as well as medical physicists, on average. The performance of ChatGPT (GPT-4) was further improved when prompted to explain first, then answer. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices across a number of trials, whether correct or incorrect, a characteristic that was not observed in the human test groups. In evaluating ChatGPTs (GPT-4) deductive reasoning ability using a novel approach (substituting the correct answer with "None of the above choices is the correct answer."), ChatGPT (GPT-4) demonstrated surprising accuracy, suggesting the potential presence of an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow for further improvement when scoring based on a majority vote across trials. In contrast, a team of medical physicists were able to greatly outperform ChatGPT (GPT-4) using a majority vote. This study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants