cssi/SciSciGPT-SciSciNet huggingface dataset:
Fat-enlarged axillary lymph nodes are associated with node-positive breast cancer in obese patients.

Obesity associated fat infiltration of organ systems is accompanied by organ dysfunction and poor cancer outcomes. Obese women demonstrate variable degrees of fat infiltration of axillary lymph nodes (LNs), and they are at increased risk for node-positive breast cancer. However, the relationship between enlarged axillary nodes and axillary metastases has not been investigated. The purpose of this study is to evaluate the association between axillary metastases and fat-enlarged axillary nodes visualized on mammograms and breast MRI in obese women with a diagnosis of invasive breast cancer. This retrospective case–control study included 431 patients with histologically confirmed invasive breast cancer. The primary analysis of this study included 306 patients with pre-treatment and pre-operative breast MRI and body mass index (BMI) > 30 (201 node-positive cases and 105 randomly selected node-negative controls) diagnosed with invasive breast cancer between April 1, 2011, and March 1, 2020. The largest visible LN was measured in the axilla contralateral to the known breast cancer on breast MRI. Multivariate logistic regression models were used to assess the association between node-positive status and LN size adjusting for age, BMI, tumor size, tumor grade, tumor subtype, and lymphovascular invasion. A strong likelihood of node-positive breast cancer was observed among obese women with fat-expanded lymph nodes (adjusted OR for the 4th vs. 1st quartile for contralateral LN size on MRI: 9.70; 95% CI 4.26, 23.50; p < 0.001). The receiver operating characteristic curve for size of fat-enlarged nodes in the contralateral axilla identified on breast MRI had an area under the curve of 0.72 for predicting axillary metastasis, and this increased to 0.77 when combined with patient and tumor characteristics. Fat expansion of axillary lymph nodes was associated with a high likelihood of axillary metastases in obese women with invasive breast cancer independent of BMI and tumor characteristics.
Cryptocurrencies and Cagan’s model of hyperinflation

Abstract The drivers of the prices of Bitcoin and Ethereum are studied within a framework based on Cagan’s model of hyperinflation. In the model, the prices of the cryptocurrencies are driven by stochastic adoption and velocity shocks as well as endogenous expectations of future prices. The model is estimated with data for prices, transaction volumes, and money supplies. A majority of price fluctuations in both currencies can be attributed to shocks in adoption, velocity shocks are much less important. The money demand sensitivity to expected price changes is estimated to be larger for Bitcoin than for Ethereum, and both have higher sensitivity than fiat currencies during episodes of hyperinflation.
slim shady is a novel allele of PHYTOCHROME B present in the T-DNA line SALK_015201

Auxin is a hormone that is required for hypocotyl elongation during seedling development. In response to auxin, rapid changes in transcript and protein abundance occur in hypocotyls, and some auxin responsive gene expression is linked to hypocotyl growth. To functionally validate proteomic studies, a reverse genetics screen was performed on mutants in auxin-regulated proteins to identify novel regulators of plant growth. This uncovered a long hypocotyl mutant, which we called slim shady, in an annotated insertion line in IMMUNOREGULATORY RNA-BINDING PROTEIN (IRR). Overexpression of the IRR gene failed to rescue the slim shady phenotype and characterization of a second T-DNA allele of IRR found that it had a wild-type (WT) hypocotyl length. The slim shady mutant has an elevated expression of numerous genes associated with the brassinosteroid-auxin-phytochrome (BAP) regulatory module compared to WT, including transcription factors that regulate brassinosteroid, auxin, and phytochrome pathways. Additionally, slim shady seedlings fail to exhibit a strong transcriptional response to auxin. Using whole genome sequence data and genetic complementation analysis with SALK_015201C, we determined that a novel single nucleotide polymorphism in PHYTOCHROME B was responsible for the slim shady phenotype. This is predicted to induce a frameshift and premature stop codon at leucine 1125, within the histidine kinase-related domain of the carboxy terminus of PHYB, which is required for phytochrome signaling and function. Genetic complementation analyses with phyb-9 confirmed that slim shady is a mutant allele of PHYB. This study advances our understanding of the molecular mechanisms in seedling development, by furthering our understanding of how light signaling is linked to auxin-dependent cell elongation. Furthermore, this study highlights the importance of confirming the genetic identity of research material before attributing phenotypes to known mutations sourced from T-DNA stocks.
Accuracy study of a 2-component Point Doppler Velocimeter (PDV)

A two-component Point Doppler Velocimeter (PDV) which has recently been developed is described, and a series of velocity measurements which have been obtained to quantify the accuracy of the PDV system are summarized. This PDV system uses molecular iodine vapor cells as frequency discriminating filters to determine the Doppler shift of laser light which is scattered off of seed particles in a flow. The majority of results which have been obtained to date are for the mean velocity of a rotating wheel, although preliminary data are described for fully-developed turbulent pipe flow. Accuracy of the present wheel velocity data is approximately +/- 1 % of full scale, while linearity of a single channel is on the order of +/- 0.5 % (i.e., +/- 0.6 m/sec and +/- 0.3 m/sec, out of 57 m/sec, respectively). The observed linearity of these results is on the order of the accuracy to which the speed of the rotating wheel has been set for individual data readings. The absolute accuracy of the rotating wheel data is shown to be consistent with the level of repeatability of the cell calibrations. The preliminary turbulent pipe flow data show consistent turbulence intensity values, and mean axial velocity profiles generally agree with pitot probe data. However, there is at present an offset error in the radial velocity which is on the order of 5-10 % of the mean axial velocity.
Experimental and theoretical investigation of an inverse-pinch coaxial pulsed plasma thruster

The theoretical optimization of an inductively-driven inverse-pinch coaxial pulsed plasma thruster is examined by utilizing and enhancing the magnetohydrodynamic computer code MACH2 with a new circuit model. The simulations show that substantial gains in thrust efficiency can be achieved if circuit design assures that the propellant temperature always remains below the effective decomposition temperature. Concurrently, the experimental efforts with a inverse-pinch coaxial PPT have concentrated on obtaining an axisymmetric discharge in accordance to theoretical optimization. Two ignition schemes have been investigated consistently exhibited that magnetic field variations in the azimuthaf direction are substantial in very low background pressure operation. Alternatively, discharge images of experiments performed with elevated background pressure (>1 mT) show an azimuthally-uniform current discharge on a consistent basis which can serve as guidance toward higher initial density spark-plugs.
Delooping level of Nakayama algebras

We give another proof of the recent result of Ringel, which asserts equality between the finitistic dimension and delooping level of Nakayama algebras. The main tool is the syzygy filtration method introduced in our earlier work.
Influence Spread in the Heterogeneous Multiplex Linear Threshold Model

The linear threshold model (LTM) has been used to study spread on single-layer networks defined by one inter-agent sensing modality and agents homogeneous in protocol. We define and analyze the heterogeneous multiplex LTM to study spread on multi-layer networks with each layer representing a different sensing modality and agents heterogeneous in protocol. Protocols are designed to distinguish signals from different layers: an agent becomes active if a sufficient number of its neighbors in each of any a of the m layers is active. We focus on Protocol OR, when a=1, and Protocol AND, when a=m, which model agents that are most and least readily activated, respectively. We develop theory and algorithms to compute the size of the spread at steady state for any set of initially active agents and to analyze the role of distinguished sensing modalities, network structure, and heterogeneity. We show how heterogeneity manages the tension in spreading dynamics between sensitivity to inputs and robustness to disturbances.
ASIST: Annotation-free synthetic instance segmentation and tracking by adversarial simulations

Abstract Background The quantitative analysis of microscope videos often requires instance segmentation and tracking of cellular and subcellular objects. The traditional method consists of two stages: (1) performing instance object segmentation of each frame, and (2) associating objects frame-by-frame. Recently, pixel-embedding-based deep learning approaches these two steps simultaneously as a single stage holistic solution. Pixel-embedding-based learning forces similar feature representation of pixels from the same object, while maximizing the difference of feature representations from different objects. However, such deep learning methods require consistent annotations not only spatially (for segmentation), but also temporally (for tracking). In computer vision, annotated training data with consistent segmentation and tracking is resource intensive, the severity of which is multiplied in microscopy imaging due to (1) dense objects (e.g., overlapping or touching), and (2) high dynamics (e.g., irregular motion and mitosis). Adversarial simulations have provided successful solutions to alleviate the lack of such annotations in dynamics scenes in computer vision, such as using simulated environments (e.g., computer games) to train real-world self-driving systems. Methods In this paper, we propose an annotation-free synthetic instance segmentation and tracking (ASIST) method with adversarial simulation and single-stage pixel-embedding based learning. Contribution The contribution of this paper is three-fold: (1) the proposed method aggregates adversarial simulations and single-stage pixel-embedding based deep learning (2) the method is assessed with both the cellular (i.e., HeLa cells); and subcellular (i.e., microvilli) objects; and (3) to the best of our knowledge, this is the first study to explore annotation-free instance segmentation and tracking study for microscope videos. Results The ASIST method achieved an important step forward, when compared with fully supervised approaches: ASIST shows 7%–11% higher segmentation, detection and tracking performance on microvilli relative to fully supervised methods, and comparable performance on Hela cell videos.
Uncertainty-Aware Reliable Text Classification

Deep neural networks have significantly contributed to the success in predictive accuracy for classification tasks. However, they tend to make over-confident predictions in real-world settings, where domain shifting and out-of-distribution (OOD) examples exist. Most research on uncertainty estimation focuses on computer vision because it provides visual validation on uncertainty quality. However, few have been presented in the natural language process domain. Unlike Bayesian methods that indirectly infer uncertainty through weight uncertainties, current evidential uncertainty-based methods explicitly model the uncertainty of class probabilities through subjective opinions. They further consider inherent uncertainty in data with different root causes, vacuity (i.e., uncertainty due to a lack of evidence) and dissonance (i.e., uncertainty due to conflicting evidence). In our paper, we firstly apply evidential uncertainty in OOD detection for text classification tasks. We propose an inexpensive framework that adopts both auxiliary outliers and pseudo off-manifold samples to train the model with prior knowledge of a certain class, which has high vacuity for OOD samples. Extensive empirical experiments demonstrate that our model based on evidential uncertainty outperforms other counterparts for detecting OOD examples. Our approach can be easily deployed to traditional recurrent neural networks and fine-tuned pre-trained transformers.
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks

Deep reinforcement learning offers a model-free alternative to supervised deep learning and classical optimization for solving the transmit power control problem in wireless networks. The multi-agent deep reinforcement learning approach considers each transmitter as an individual learning agent that determines its transmit power level by observing the local wireless environment. Following a certain policy, these agents learn to collaboratively maximize a global objective, e.g., a sum-rate utility function. This multi-agent scheme is easily scalable and practically applicable to large-scale cellular networks. In this work, we present a distributively executed continuous power control algorithm with the help of deep actor-critic learning, and more specifically, by adapting deep deterministic policy gradient. Furthermore, we integrate the proposed power control algorithm to a time-slotted system where devices are mobile and channel conditions change rapidly. We demonstrate the functionality of the proposed algorithm using simulation results.
Covariance Steering with Optimal Risk Allocation

This paper extends the optimal covariance steering problem for linear stochastic systems subject to chance constraints to account for optimal risk allocation. Previous works have assumed a uniform risk allocation to cast the optimal control problem as a semi-definite program (SDP), which can be solved efficiently using standard SDP solvers. We adopt an Iterative Risk Allocation (IRA) formalism, which uses a two-stage approach to solve the optimal risk allocation problem for covariance steering. The upper-stage of IRA optimizes the risk, which is proved to be a convex problem, while the lower-stage optimizes the controller with the new constraints. This is done iteratively so as to find the optimal risk allocation that achieves the lowest total cost. The proposed framework results in solutions that tend to maximize the terminal covariance, while still satisfying the chance constraints, thus leading to less conservative solutions than previous methodologies. We also introduce two novel convex relaxation methods to approximate quadratic chance constraints as second-order cone constraints. We finally demonstrate the approach to a spacecraft rendezvous problem and compare the results.
Experimental and Analytical Load-Life Relation for AISI 9310 Steel Spur Gears

Life tests were conducted at three different loads with three groups of 8.9 cm pitch diameter spur gears made of vacuum arc remelted VAR AISI 9310 steel. Life was found to vary inversely with load to the 4.3 and 5.1 power at the L10 sub and L50 sub life levels, respectively. The Weibull slope varied linearly with maximum Hertz contact stress, having an average value of 2.5. The test data when compared to AGMA standards showed a steeper slope for the load-life diagram.
Sustained Online Amplification of COVID-19 Elites in the United States

In the absence of clear, consistent guidelines about the COVID-19 pandemic in the United States, many people use social media to learn about the virus, public health directives, vaccine distributio...
DGEN Aeropropulsion Research Turbofan (DART): Lossless Projection of Measured Engine Noise Spectra to a 1-Foot-Radius Arc

Baseline noise and aerodynamic data have been acquired for the DGEN Aeropropulsion Research Turbofan (DART) test rig. The DART is a fully-mobile engine test rig featuring a DGEN380 geared turbofan producing approximately 500 lbs. of thrust at sea level and a self-contained control room. Baseline noise data were acquired using 5 microphone arrays, varying distance, configuration, and angle to reflect the measurement locations at several other test facilities. Noise data were acquired at one array location on each test day to establish the repeatability of the measurements. The noise data from the different arrays is analyzed to show the limitations of projecting the results to a common radius when the noise sources are distributed and the measurement location is not in the geometric far-field.
Pseudo-Direct Solution to the Boundary-Layer Equations for Separated Flow

This paper describes a procedure for the automatic iteration of an inverse boundary-layer technique to a prescribed pressure distribution in a separated flow. The technique is demonstrated by computation of two transonic airfoil flows and two externally generated shock boundary-layer interaction flows. These results are compared to experimental data and to solutions of the full Navier-Stokes equations. These comparisons indicate that substantial economies can be obtained by applying methods like the present, in lieu of full Navier-Stokes methods, in zonal calculation schemes for design purposes. The optimization technique leading to convergence is described in detail and a table of typical computation time is presented.
A Numerical Analysis of the British Experimental Rotor Program Blade

Two Computational Fluid Dynamic codes which solve the compressible full-potential and the Reynolds-Averaged Thin-Layer Navier-Stokes equations were used to analyze the nonrotating aerodynamic characteristics of the British Experimental Rotor Program (BERP) helicopter blade at three flow regimes: low angle of attack, high angle of attack and transonic. Excellent agreement was found between the numerical results and experiment. In the low angle of attack regime, the BERP had less induced drag than a comparable aspect ratio rectangular planform wing. At high angle of attack, the blade attained high-lift by maintaining attached flow at the outermost spanwise locations. In the transonic regime, the BERP design reduces the shock strength at the outer spanwise locations which affects wave drag and shock-induced separation. Overall, the BERP blade exhibited many favorable aerodynamic characteristics in comparison to conventional helicopter rotor blades.
Agreement Between Trend-Based and Qualitative Analysis of the Retinal Nerve Fiber Layer Thickness for Glaucoma Progression on Spectral-Domain Optical Coherence Tomography

To evaluate the agreement between trend-based analysis and qualitative assessment of the retinal nerve fiber layer (RNFL) thickness for glaucomatous progression on spectral-domain optical coherence tomography (SDOCT). Retrospective review of 190 eyes from 103 patients with glaucoma or suspected glaucoma that underwent SDOCT imaging during four consecutive clinic visits. Trend-based progression was characterized by a significantly negative slope. Progression by qualitative analysis was determined by review of raw SDOCT B-scans. The slope was significantly greater in those with progression than without progression for both trend-based and qualitative analysis (p 0.05). Poor agreement was found between a trend-based and qualitative analysis of change in RNFL on SDOCT. Careful qualitative review of SDOCT imaging may identify specific areas of glaucoma progression not captured by trend-based methods, especially in the presence of artifacts. Such an approach may also prove useful for detecting glaucoma progression in a clinical setting when there are few data points available.
Operator Growth Bounds from Graph Theory

Let A and B be local operators in Hamiltonian quantum systems with N degrees of freedom and finite-dimensional Hilbert space. We prove that the commutator norm $$\Vert [A(t),B]\Vert $$ is upper bounded by a topological combinatorial problem: counting irreducible weighted paths between two points on the Hamiltonian’s factor graph. Our bounds sharpen existing Lieb–Robinson bounds by removing extraneous growth. In quantum systems drawn from zero-mean random ensembles with few-body interactions, we prove stronger bounds on the ensemble-averaged out-of-time-ordered correlator $${\mathbb {E}}\left[ \Vert { [A(t),B]} \Vert _{\mathrm {F}}^2\right] $$ . In such quantum systems on Erdos–Renyi factor graphs, we prove that the scrambling time $$t_{\mathrm {s}}$$ , at which $$\Vert { [A(t),B]} \Vert _{\mathrm {F}}={\Theta }(1)$$ , is almost surely $$t_{\mathrm {s}}={\Omega }(\sqrt{\log N})$$ ; we further prove $$t_{\mathrm {s}}={\Omega }(\log N) $$ to high order in perturbation theory in 1/N. We constrain infinite temperature quantum chaos in the q-local Sachdev-Ye-Kitaev model at any order in 1/N; at leading order, our upper bound on the Lyapunov exponent is within a factor of 2 of the known result at any $$q>2$$ . We also speculate on the implications of our theorems for conjectured holographic descriptions of quantum gravity.
The impact of vaccination to control COVID-19 burden in the United States: A simulation modeling approach.

Introduction Vaccination programs aim to control the COVID-19 pandemic. However, the relative impacts of vaccine coverage, effectiveness, and capacity in the context of nonpharmaceutical interventions such as mask use and physical distancing on the spread of SARS-CoV-2 are unclear. Our objective was to examine the impact of vaccination on the control of SARS-CoV-2 using our previously developed agent-based simulation model. Methods We applied our agent-based model to replicate COVID-19-related events in 1) Dane County, Wisconsin; 2) Milwaukee metropolitan area, Wisconsin; 3) New York City (NYC). We evaluated the impact of vaccination considering the proportion of the population vaccinated, probability that a vaccinated individual gains immunity, vaccination capacity, and adherence to nonpharmaceutical interventions. We estimated the timing of pandemic control, defined as the date after which only a small number of new cases occur. Results The timing of pandemic control depends highly on vaccination coverage, effectiveness, and adherence to nonpharmaceutical interventions. In Dane County and Milwaukee, if 50% of the population is vaccinated with a daily vaccination capacity of 0.25% of the population, vaccine effectiveness of 90%, and the adherence to nonpharmaceutical interventions is 60%, controlled spread could be achieved by June 2021 versus October 2021 in Dane County and November 2021 in Milwaukee without vaccine. Discussion In controlling the spread of SARS-CoV-2, the impact of vaccination varies widely depending not only on effectiveness and coverage, but also concurrent adherence to nonpharmaceutical interventions.
Ensemble Estimation of Generalized Mutual Information With Applications to Genomics

Mutual information is a measure of the dependence between random variables that has been used successfully in myriad applications in many fields. Generalized mutual information measures that go beyond classical Shannon mutual information have also received much interest in these applications. We derive the mean squared error convergence rates of kernel density-based plug-in estimators of general mutual information measures between two multidimensional random variables $\mathbf {X} $ and $\mathbf {Y} $ for two cases: 1) $\mathbf {X} $ and $\mathbf {Y} $ are continuous; 2) $\mathbf {X} $ and $\mathbf {Y} $ may have a mixture of discrete and continuous components. Using the derived rates, we propose an ensemble estimator of these information measures called GENIE by taking a weighted sum of the plug-in estimators with varied bandwidths. The resulting ensemble estimators achieve the $1/N$ parametric mean squared error convergence rate when the conditional densities of the continuous variables are sufficiently smooth. To the best of our knowledge, this is the first nonparametric mutual information estimator known to achieve the parametric convergence rate for the mixture case, which frequently arises in applications (e.g. variable selection in classification). The estimator is simple to implement and it uses the solution to an offline convex optimization problem and simple plug-in estimators. A central limit theorem is also derived for the ensemble estimators and minimax rates are derived for the continuous case. We demonstrate the ensemble estimator for the mixed case on simulated data and apply the proposed estimator to analyze gene relationships in single cell data.
The PD-1 checkpoint receptor maintains tolerance of self-reactive CD8 T cell in skin

Peripheral tolerance results from self-reactive T cell anergy/deletion shortly after antigen encounter. However, the frequent occurrences of immune-related Adverse Events (irAEs) following checkpoint inhibitor (CPI) treatment suggest a hypothesis that immunologically healthy individuals have self-reactive effector T cells that are kept in a non-pathogenic state by checkpoint receptors, instead of anergy/deletion. We expressed self-antigens in healthy skin and found that antigen-specific CD8 T cells infiltrated the tissue, but remained tolerant, despite having a transcriptional program that resembled effector T cells found after CPIs. These self-reactive PD-1+ CD8 T cells drove IFNg-dependent increases in PD-L1 on skin myeloid cells. Blockade of PD-1 or PD-1/CTLA-4 led to post-transcriptional upregulation of effector proteins by antigen-specific CD8 T cells and elimination of antigen-expressing epithelial cells, resulting in localized tissue pathology with features of human cutaneous irAEs. We hypothesize that myeloid cells in healthy skin prevent pathology from self-reactive effector CD8 T cells via PD-1/PD-L1.
Regulation of PKR by RNA: Formation of Active and Inactive Dimers

PKR is a member of the eIF2α family of protein kinases that inhibit translational initiation in response to stress stimuli and functions as a key mediator of the interferon-induced antiviral response. PKR contains a dsRNA binding domain that bind to duplex regions present in viral RNAs, resulting in kinase activation and autophosphorylation. An emerging theme in the regulation of protein kinases is the allosteric linkage of dimerization and activation. The PKR kinase domain forms a back-to-back parallel dimer which is implicated in activation. We have developed a sensitive homo-FRET assay for kinase domain dimerization to directly probe the relationship between RNA binding, activation, and dimerization. In the case of perfect duplex RNAs, dimerization is correlated with activation and dsRNAs containing 30 bp or more efficiently induce kinase domain dimerization and activation. However, more complex duplex RNAs containing a 10-15 bp 2'-O-methyl RNA barrier produce kinase dimers but do not activate. Similarly, inactivating mutations within the PKR dimer interface that disrupt key electrostatic and hydrogen binding interactions fail to abolish dimerization. Our data support a model where activating RNAs induce formation of a back-to-back parallel PKR kinase dimer whereas nonactivating RNAs either fail to induce dimerization or produce an alternative, inactive dimer configuration, providing an additional mechanism for distinguishing between host and pathogen RNA.
Trends in Medical and Surgical Admission Length of Stay by Race/Ethnicity and Socioeconomic Status: A Time Series Analysis.

Background Length of stay (LOS), a metric of hospital efficiency, differs by race/ethnicity and socioeconomic status (SES) and longer LOS is associated with adverse health outcomes. Historically, projects to improve LOS efficiency have yielded LOS reductions by 0.3 to 0.7 days per admission. Objective To assess differences in average adjusted length of stay (aALOS) over time by race/ethnicity, and SES stratified by discharge destination (home or non-home). Method Data were obtained from 2009-2014 Healthcare Cost and Utilization Project State Inpatient Datasets for New York, New Jersey, and Florida. Multivariate generalized linear models were used to examine trends in aALOS differences by race/ethnicity, and by high vs low SES patients (defined first vs fourth quartile of median income by zip code) controlling for patient, disease and hospital characteristics. Results For those discharged home, racial/ethnic and SES aALOS differences remained stable from 2009 to 2014. However, among those discharged to non-home destinations, Black vs White aALOS differences increased from 0.21 days in Q1 2009, (95% confidence interval (CI): 0.13 to 0.30) to 0.32 days in Q3 2013, (95% CI: 0.23 to 0.40), and for low vs high SES patients from 0.03 days in Q1 2009 (95% CI: -0.04 to 0.1) to 0.26 days, (95% CI: 0.19 to 0.34). Notably, for patients not discharged home, racial/ethnic and SES aALOS differences increased and persisted after Q3 2011, coinciding with the introduction of the Affordable Care Act (ACA). Conclusion Further research to understand the ACA's policy impact on hospital efficiencies, and relationship to racial/ethnic and SES differences in LOS is warranted.
A Reduced Order Model Approach to Inverse Scattering in Lossy Layered Media

We introduce a reduced order model (ROM) methodology for inverse electromagnetic wave scattering in layered lossy media, using data gathered by an antenna which generates a probing wave and measures the time resolved reflected wave. We recast the wave propagation problem as a passive infinite-dimensional dynamical system, whose transfer function is expressed in terms of the measurements at the antenna. The ROM is a low-dimensional dynamical system that approximates this transfer function. While there are many possible ROM realizations, we are interested in one that preserves passivity and in addition is: (1) data driven (i.e., is constructed only from the measurements) and (2) it consists of a matrix with special sparse algebraic structure, whose entries contain spatially localized information about the unknown dielectric permittivity and electrical conductivity of the layered medium. Localized means in the intervals of a special finite difference grid. The main result of the paper is to show with analysis and numerical simulations that these unknowns can be extracted efficiently from the ROM.
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D location and whose outputs are the following scene properties at that input location: volume density, surface normal, material parameters, distance to the first surface intersection in any direction, and visibility of the external environment in any direction. Together, these allow us to render novel views of the object under arbitrary lighting, including indirect illumination effects. The predicted visibility and surface intersection fields are critical to our model’s ability to simulate direct and indirect illumination during training, because the brute-force techniques used by prior work are intractable for lighting conditions outside of controlled setups with a single light. Our method outperforms alternative approaches for recovering relightable 3D scene representations, and performs well in complex lighting settings that have posed a significant challenge to prior work.
Thresholds versus fractional expectation-thresholds

Proving a conjecture of Talagrand, a fractional version of the 'expectation-threshold' conjecture of Kalai and the second author, we show for any increasing family $F$ on a finite set $X$ that $p_c (F) =O( q_f (F) \log \ell(F))$, where $p_c(F)$ and $q_f(F)$ are the threshold and 'fractional expectation-threshold' of $F$, and $\ell(F)$ is the largest size of a minimal member of $F$. This easily implies several heretofore difficult results and conjectures in probabilistic combinatorics, including thresholds for perfect hypergraph matchings (Johansson--Kahn--Vu), bounded-degree spanning trees (Montgomery), and bounded-degree spanning graphs (new). We also resolve (and vastly extend) the 'axial' version of the random multi-dimensional assignment problem (earlier considered by Martin--Mezard--Rivoire and Frieze--Sorkin). Our approach builds on a recent breakthrough of Alweiss, Lovett, Wu and Zhang on the Erdős--Rado 'Sunflower Conjecture'.
Jet trajectories and surface pressures induced on a body of revolution with various dual jet configurations

A jet in a cross flow is of interest in practical situations including jet-powered VTOL aircraft. Three aspects of the problem have received little prior study. First is the effect of the angle of the jet to the crossflow. Second is the performance of dual-jet configurations. The third item for further study is a jet injected from a body of revolution as opposed to a flat plate. The Test Plan for this work was designed to address these three aspects. The experiments were conducted in the 7 x 10 tunnel at NASA Ames at velocities 14.5 - 35.8 m/sec (47.6 - 117.4 ft/sec). Detailed pressure distributions are presented for single and dual jets over a range of velocity ratios from 3 to 8, spacings from 2 to 6 diameters and injection angles of 90, 75 and 60 degrees. Some flowfield measurements are also presented, and it is shown that a simple analysis is capable of predicting the trajectories of the jets.
Smoothed Nested Testing on Directed Acyclic Graphs

We consider the problem of multiple hypothesis testing when there is a logical nested structure to the hypotheses. When one hypothesis is nested inside another, the outer hypothesis must be false if the inner hypothesis is false. We model the nested structure as a directed acyclic graph, including chain and tree graphs as special cases. Each node in the graph is a hypothesis and rejecting a node requires also rejecting all of its ancestors. We propose a general framework for adjusting node-level test statistics using the known logical constraints. Within this framework, we study a smoothing procedure that combines each node with all of its descendants to form a more powerful statistic. We prove a broad class of smoothing strategies can be used with existing selection procedures to control the familywise error rate, false discovery exceedance rate, or false discovery rate, so long as the original test statistics are independent under the null. When the null statistics are not independent but are derived from positively-correlated normal observations, we prove control for all three error rates when the smoothing method is arithmetic averaging of the observations. Simulations and an application to a real biology dataset demonstrate that smoothing leads to substantial power gains.
Aspergillus fumigatus In-Host HOG Pathway Mutation for Cystic Fibrosis Lung Microenvironment Persistence.

The prevalence of Aspergillus fumigatus colonization in individuals with cystic fibrosis (CF) and subsequent fungal persistence in the lung is increasingly recognized. However, there is no consensus for clinical management of A. fumigatus in CF individuals, due largely to uncertainty surrounding A. fumigatus CF pathogenesis and virulence mechanisms. To address this gap in knowledge, a longitudinal series of A. fumigatus isolates from an individual with CF were collected over 4.5 years. Isolate genotypes were defined with whole-genome sequencing that revealed both transitory and persistent A. fumigatus in the lung. Persistent lineage isolates grew most readily in a low-oxygen culture environment, and conidia were more sensitive to oxidative stress-inducing conditions than those from nonpersistent isolates. Closely related persistent isolates harbored a unique allele of the high-osmolarity glycerol (HOG) pathway mitogen-activated protein kinase kinase, Pbs2 (pbs2C2). Data suggest this novel pbs2C2 allele arose in vivo and is necessary for the fungal response to osmotic stress in a low-oxygen environment through hyperactivation of the HOG (SakA) signaling pathway. Hyperactivation of the HOG pathway through pbs2C2 comes at the cost of decreased conidial stress resistance in the presence of atmospheric oxygen levels. These novel findings shed light on pathoadaptive mechanisms of A. fumigatus in CF, lay the foundation for identifying persistent A. fumigatus isolates that may require antifungal therapy, and highlight considerations for successful culture of persistent Aspergillus CF isolates. IMPORTANCE Aspergillus fumigatus infection causes a spectrum of clinical manifestations. For individuals with cystic fibrosis (CF), allergic bronchopulmonary aspergillosis (ABPA) is an established complication, but there is a growing appreciation for A. fumigatus airway persistence in CF disease progression. There currently is little consensus for clinical management of A. fumigatus long-term culture positivity in CF. A better understanding of A. fumigatus pathogenesis mechanisms in CF is expected to yield insights into when antifungal therapies are warranted. Here, a 4.5-year longitudinal collection of A. fumigatus isolates from a patient with CF identified a persistent lineage that harbors a unique allele of the Pbs2 mitogen-activated protein kinase kinase (MAPKK) necessary for unique CF-relevant stress phenotypes. Importantly for A. fumigatus CF patient diagnostics, this allele provides increased fitness under CF lung-like conditions at a cost of reduced in vitro growth under standard laboratory conditions. These data illustrate a molecular mechanism for A. fumigatus CF lung persistence with implications for diagnostics and antifungal therapy.
Transporting Robotic Swarms via Mean-Field Feedback Control

With the rapid development of AI and robotics, deploying a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for transporting a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed -- individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution li
A proof of the theta operator conjecture

Abstract In the context of the (generalized) Delta Conjecture and its compositional form, D'Adderio, Iraci, and Vanden Wyngaerd recently stated a conjecture relating two symmetric function operators, D k and Θ k . We prove this Theta Operator Conjecture, finding it as a consequence of the five-term relation of Mellit and Garsia. As a result, we find surprising ways of writing the D k operators. Even though we deal specifically with the relation between the D k and Θ k operators, our work introduces a method for finding relations between Θ k and other plethystic operators which are important in this area of study.
Private Multi-Group Aggregation

We study the differentially private multi-group aggregation (PMGA) problem. This setting involves a single server and $n$ users. Each user belongs to one of $k$ distinct groups and holds a discrete value representing his data. The goal is to design schemes that allow the server to find the aggregate (sum) of the values in each group (with high accuracy), under communication and local differential privacy constraints. The privacy constraint guarantees that the user's group remains private. This is motivated by applications where a user's group can reveal sensitive information, such as his religious and political beliefs, health condition, or race. We propose a novel scheme, dubbed Query and Aggregate (Q&A) for PMGA. The novelty of Q&A is that it is an interactive aggregation scheme. In Q&A, each user is assigned a random query matrix, to which he sends the server an answer based on his group and value. We characterize the Q&A scheme's performance in terms of accuracy (MSE), privacy, and communication. Private aggregation schemes for related settings in the literature are predominantly non-interactive and based on randomized response. We compare Q&A to the Randomized Group (RG) scheme, which adapts existing schemes to the PMGA setting. We observe that typically Q&A outperforms RG, in terms of utility vs. privacy, in the high privacy regime. Moreover, an attractive property of Q&A is that its communication cost per user does not depend on the number of groups.
Hahn polynomials and the Burnside process

We study a natural Markov chain on $$\{0,1,\ldots ,n\}$$ with eigenvectors the Hahn polynomials. This explicit diagonalization makes it possible to get sharp rates of convergence to stationarity. The process, the Burnside process, is a special case of the celebrated ‘Swendsen–Wang’ or ‘data augmentation’ algorithm. The description involves the beta-binomial distribution and Mallows model on permutations. It introduces a useful generalization of the Burnside process.
A New combinatorial approach to optimal embeddings of rectangles

An important problem in graph embeddings and parallel computing is to embed a rectangular grid into other graphs. We present a novel, general, combinatorial approach to (one-to-one) embedding rectangular grids into their ideal rectangular grids and optimal hypercubes. In contrast to earlier approaches of Aleliunas and Rosenberg, and Ellis, our approach is based on a special kind of doubly stochastic matrix. We prove that any rectangular grid can be embedded into its ideal rectangular grid with dilation equal to the ceiling of the compression ratio, which is bothoptimal up to a multiplicative constant and a substantial generalization of previous work. We also show that any rectangular grid can be embedded into its nearly ideal square grid with dilation at most 3. Finally, we show that any rectangular grid can be embedded into itsoptimal hypercube withoptimal dilation 2, a result previously obtained, after much research, through anad hoc approach. Our results also imply optimal simulations of two-dimensional mesh-connected parallel machines by hypercubes and mesh-connected machines, where each processor in the guest machine is simulated by exactly one processor in the host.
Measurement Space Partitioning for Estimation and Prediction

An important and challenging problem in the evaluation of baseball players is the quantification of batted-ball talent. This problem has traditionally been addressed using linear regression or machine learning methods. We use large sets of trajectory measurements acquired by in-game sensors to show that the predictive value of a batted ball depends on its physical properties. This knowledge is exploited to estimate batted-ball distributions defined over a multidimensional measurement space from observed distributions by using regression parameters that adapt to batted ball properties. This process is central to a new method for estimating batted-ball talent. The domain of the batted-ball distributions is defined by a partition of measurement space that is selected to optimize the accuracy of the estimates. We present examples illustrating facets of the new approach and use a set of experiments to show that the new method generates estimates that are significantly more accurate than those generated using current methods. The new methodology supports the use of fine-grained contextual adjustments and we show that this process further improves the accuracy of the technique.
Beyond Road Extraction: A Dataset for Map Update Using Aerial Images

The increasing availability of satellite and aerial imagery has sparked substantial interest in automatically updating street maps by processing aerial images. Until now, the community has largely focused on road extraction, where road networks are inferred from scratch from an aerial image. However, given that relatively high-quality maps exist in most parts of the world, in practice, inference approaches must be applied to update existing maps rather than infer new ones. With recent road extraction methods showing high accuracy, we argue that it is time to transition to the more practical map update task, where an existing map is updated by adding, removing, and shifting roads, without introducing errors in parts of the existing map that remain up-to-date. In this paper, we develop a new dataset called MUNO21 for the map update task, and show that it poses several new and interesting research challenges. We evaluate several state-of-the-art road extraction methods on MUNO21, and find that substantial further improvements in accuracy will be needed to realize automatic map update.
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots

Developing robust walking controllers for bipedal robots is a challenging endeavor. Traditional model-based locomotion controllers require simplifying assumptions and careful modelling; any small errors can result in unstable control. To address these challenges for bipedal locomotion, we present a model-free reinforcement learning framework for training robust locomotion policies in simulation, which can then be transferred to a real bipedal Cassie robot. To facilitate sim-to-real transfer, domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. The learned policies enable Cassie to perform a set of diverse and dynamic behaviors, while also being more robust than traditional controllers and prior learning-based methods that use residual control. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw. (Video1)
Rabia: Simplifying State-Machine Replication Through Randomization

We introduce Rabia, a simple and high performance framework for implementing state-machine replication (SMR) within a datacenter. The main innovation of Rabia is in using randomization to simplify the design. Rabia provides the following two features: (i) It does not need any fail-over protocol and supports trivial auxiliary protocols like log compaction, snapshotting, and reconfiguration, components that are often considered the most challenging when developing SMR systems; and (ii) It provides high performance, up to 1.5x higher throughput than the closest competitor (i.e., EPaxos) in a favorable setup (same availability zone with three replicas) and is comparable with a larger number of replicas or when deployed in multiple availability zones.