cssi/SciSciGPT-SciSciNet huggingface dataset:
Which workers bear the burden of social distancing

Using data from O∗NET, we construct two measures of an occupation’s potential exposure to social distancing measures: (i) the ability to conduct that job from home and (ii) the degree of physical proximity to others the job requires. After validating these measures with comparable measures from ATUS as well as realized work-from-home rates during the pandemic, we employ the measures to study the characteristics of workers in these types of jobs. Our results show that workers in low-work-from-home and high-physical-proximity jobs are more economically vulnerable across various measures constructed from the CPS and PSID: they are less educated, of lower income, have fewer liquid assets relative to income, and are more likely renters. Consistent with the idea that high physical proximity or low work-from-home occupations were more exposed to the Coronavirus shock, we show that the types of workers predicted to be employed in them experienced greater declines in employment during the pandemic. We conclude by comparing the aggregate employment losses in these occupations to their employment losses in the 2008 recession, and find evidence that these occupations were disproportionately exposed to the pandemic shock, and not just comprised of more cyclically sensitive workers.
Towards a Dimension-Free Understanding of Adaptive Linear Control

We study the problem of adaptive control of the linear quadratic regulator for systems in very high, or even infinite dimension. We demonstrate that while sublinear regret requires finite dimensional inputs, the ambient state dimension of the system need not be bounded in order to perform online control. We provide the first regret bounds for LQR which hold for infinite dimensional systems, replacing dependence on ambient dimension with more natural notions of problem complexity. Our guarantees arise from a novel perturbation bound for certainty equivalence which scales with the prediction error in estimating the system parameters, without requiring consistent parameter recovery in more stringent measures like the operator norm. When specialized to finite dimensional settings, our bounds recover near optimal dimension and time horizon dependence.
DeepDive: An Integrative Algorithm/Architecture Co-Design for Deep Separable Convolutional Neural Networks

Deep Separable Convolutional Neural Network (DSCNN) has become the emerging paradigm by offering modular networks with structural sparsity to achieve higher accuracy with relatively lower operations and parameters. However, there is a lack of customized architectures that can provide flexible solutions that fit the sparsity of the DSCNNs. This paper introduces DeepDive, a fully-functional vertical co-design framework, for power-efficient implementation of DSCNNs on edge FPGAs. DeepDive's architecture supports crucial heterogeneous Compute Units (CUs) to fully support DSCNNs with various convolutional operators interconnected with structural sparsity. It offers FPGA-aware training and online quantization combined with modular synthesizable C++ CUs, customized for DSCNNs. The execution results on Xilinx's ZCU102 FPGA board demonstrate 47.4 and 233.3 FPS/Watt for MobileNet-V2 and a compact version of EfficientNet, respectively, as two state-of-the-art depthwise separable CNNs. These comparisons showcase how DeepDive improves FPS/Watt by 2.2× and 1.51× over Jetson Nano high and low power modes, respectively. It also enhances FPS/Watt by about 2.27× and 37.25× over two other FPGA implementations.
OAO-3 end of mission power subsystem evaluation

End of mission tests were performed on the OAO-3 power subsystem in three component areas: solar array, nickel-cadmium batteries and the On-Board Processor (OBP) power boost operation. Solar array evaluation consisted of analyzing array performance characteristics and comparing them to earlier flight data. Measured solar array degradation of 14.1 to 17.7% after 8 1/3 years is in good agreement with theortical radiation damage losses. Battery discharge characteristics were compared to results of laboratory life cycle tests performed on similar cells. Comparison of cell voltage profils reveals close correlation and confirms the validity of real time life cycle simulation. The successful operation of the system in the OBP/power boost regulation mode demonstrates the excellent life, reliability and greater system utilization of power subsystems using maximum power trackers.
States that give the maximum signal-to-quantum noise ratio for a fixed energy

Under a radiation power constraint, the maximum signal-to-quantum noise ratio obtainable for any state of a radiation field is found. This maximum value is achieved by the two-photon coherent states introduced previously to describe two-photon lasers.
Optical modeling for dynamics and control analysis

This paper presents a coordinate-free ray-trace analysis of optical beam trains consisting of mirrors, lenses and reference surfaces. The analysis leads to optical models that can be directly integrated with standard structure and control models for integrated instrument design, analysis and simulation. This capability is required for a coming generation of spaceborn optical instruments which use controlled optical elements supported by flexible structures. New results include analytic formulas for optical sensitivities as functions of structural and control geometric parameters. An example problem calculates a linear (small-motion) optical model, derives optical gains for small- and large-angle controlled mirrors, and shows the propagation of a wavefront.
Quadratic-stretch elasticity:

A nonlinear small-strain elastic theory is constructed from a systematic expansion in Biot strains, truncated at quadratic order. The primary motivation is the desire for a clean separation between...
Single-cell classification using graph convolutional networks

Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures. In this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods. Results indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.
Cost-effectiveness of dengue vaccination in Puerto Rico.

An effective and widely used vaccine could reduce the burden of dengue virus (DENV) around the world. DENV is endemic in Puerto Rico, where the dengue vaccine CYD-TDV is currently under consideration as a control measure. CYD-TDV has demonstrated efficacy in clinical trials in vaccinees who had prior dengue virus infection. However, in vaccinees who had no prior dengue virus infection, the vaccine had a modestly elevated risk of hospitalization and severe disease. The WHO therefore recommended a strategy of pre-vaccination screening and vaccination of seropositive persons. To estimate the cost-effectiveness and benefits of this intervention (i.e., screening and vaccination of seropositive persons) in Puerto Rico, we simulated 10 years of the intervention in 9-year-olds using an agent-based model. Across the entire population, we found that 5.5% (4.6%-6.3%) of dengue hospitalizations could be averted. However, we also found that 0.057 (0.045-0.073) additional hospitalizations could occur for every 1,000 people in Puerto Rico due to DENV-naive children who were vaccinated following a false-positive test results for prior exposure. The ratio of the averted hospitalizations among all vaccinees to additional hospitalizations among DENV-naive vaccinees was estimated to be 19 (13-24). At a base case cost of vaccination of 382 USD, we found an incremental cost-effectiveness ratio of 122,000 USD per QALY gained. Our estimates can provide information for considerations to introduce the CYD-TDV vaccine in Puerto Rico.
How Robust are Randomized Smoothing based Defenses to Data Poisoning

Predictions of certifiably robust classifiers remain constant in a neighborhood of a point, making them resilient to test-time attacks with a guarantee. In this work, we present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality in achieving high certified adversarial robustness. Specifically, we propose a novel bilevel optimization based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Unlike other poisoning attacks that reduce the accuracy of the poisoned models on a small set of target points, our attack reduces the average certified radius (ACR) of an entire target class in the dataset. Moreover, our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods such as Gaussian data augmentation[8], MACER[36], and SmoothAdv[29] that achieve high certified adversarial robustness. To make the attack harder to detect, we use clean-label poisoning points with imperceptible distortions. The effectiveness of the proposed method is evaluated by poisoning MNIST and CIFAR10 datasets and training deep neural networks using previously mentioned training methods and certifying the robustness with randomized smoothing. The ACR of the target class, for models trained on generated poison data, can be reduced by more than 30%. Moreover, the poisoned data is transferable to models trained with different training methods and models with different architectures.
osr1 couples intermediate mesoderm cell fate with temporal dynamics of vessel progenitor cell differentiation

Transcriptional regulatory networks refine gene expression boundaries to define the dimensions of organ progenitor territories. Kidney progenitors originate within the intermediate mesoderm (IM), but the pathways that establish the boundary between the IM and neighboring vessel progenitors are poorly understood. Here, we delineate roles for the zinc-finger transcription factor Osr1 in kidney and vessel progenitor development. Zebrafish osr1 mutants display decreased IM formation and premature emergence of lateral vessel progenitors (LVPs). These phenotypes contrast with the increased IM and absent LVPs observed with loss of the bHLH transcription factor Hand2, and loss of hand2 partially suppresses osr1 mutant phenotypes. hand2 and osr1 are expressed together in the posterior mesoderm, but osr1 expression decreases dramatically prior to LVP emergence. Overexpressing osr1 during this timeframe inhibits LVP development while enhancing IM formation, and can rescue the osr1 mutant phenotype. Together, our data demonstrate that osr1 modulates the extent of IM formation and the temporal dynamics of LVP development, suggesting that a balance between levels of osr1 and hand2 expression is essential to demarcate the kidney and vessel progenitor territories.
Aggregate accruals and market returns: The role of aggregate M&A activity

Abstract Extant literature documents that aggregate accruals positively predict future market returns and attributes this relation to either changes in discount rates or systematic earnings management. We offer an alternative explanation: aggregate merger and acquisition (M&A) activity drives this relation. M&A activity affects the magnitude of accruals, which in turn drives the market return predictability of aggregate accruals. We find that the ability of both aggregate accruals and discretionary aggregate accruals (a measure of systematic earnings management) to predict market returns disappears after controlling for aggregate M&A activity. Furthermore, aggregate M&A activity predicts future market returns, consistent with a price response to improvements in macroeconomic outcomes due to aggregate M&A activity.
Detection of SARS-CoV-2-Specific IgA in the Human Milk of COVID-19 Vaccinated Lactating Health Care Workers.

Background: In 2019, a deadly virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 2019 (COVID-19), emerged. In December 2020, two mRNA-based COVID-19 vaccines were approved for use in the United States, which provide immunity to those receiving the vaccine. Maternally derived antibodies are a key element of infants' immunity. Certain vaccines given to pregnant and lactating mothers provide immunity to infants through transmission across the placenta, umbilical cord (IgG), and human milk (IgA). Human milk produced by mothers with a history of COVID-19 infection contains SARS-CoV-2 IgA and IgG. The purpose of this study is to determine whether SARS-CoV-2-specific immunoglobulins are found in human milk after the COVID-19 vaccination, and to characterize the types of immunoglobulins present. Methods: This is a prospective observational study conducted at Shands Hospital, University of Florida, from December 2020 to March 2021. Twenty-two lactating health care workers who received the SARS-CoV-2 mRNA vaccine (Pfizer/BioNTech or Moderna) made up the sample group. Plasma and human milk were collected at three time points (prevaccination, post-first vaccine dose, and post-second vaccine dose). SARS-CoV-2-specific IgA and IgG in human milk and in plasma were measured by enzyme-linked immunosorbent assay (ELISA). Maternal demographics were compiled. Results: We found significant secretion of SARS-CoV-2-specific IgA and IgG in human milk and plasma after SARS-CoV-2 vaccination. Conclusions: Our results show that the mRNA-based COVID-19 vaccines induce SARS-CoV-2-specific IgA and IgG secretion in human milk. Further studies are needed to determine the duration of this immune response, its capacity to neutralize the COVID-19 virus, the transfer of passive immunity to breastfeeding infants, and the potential therapeutic use of human milk IgA to combat SARS-CoV-2 infections and COVID-19.
Every Bit Counts: Second-Order Analysis of Cooperation in the Multiple-Access Channel

The work at hand presents a finite-blocklength analysis of the multiple access channel (MAC) sum-rate under the cooperation facilitator (CF) model. The CF model, in which independent encoders coordinate through an intermediary node, is known to show significant rate benefits, even when the rate of cooperation is limited. We continue this line of study for cooperation rates which are sub-linear in the blocklength n. Roughly speaking, our results show that if the facilitator transmits log $K$ bits, then there is a sum-rate benefit of order √log K/n compared to the best-known achievable rate. This result extends across a wide range of K: even a single bit of cooperation is shown to provide a sum-rate benefit of order 1/√n.
GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories

Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an underlying block code, followed by rearranging and concatenating the partitioned components in a “convolutional” manner. The number of partitioned components determines the “memory” of SC codes. While adopting higher memories results in improved SC code performance, obtaining optimal SC codes with high memory is known to be hard. In this paper, we investigate the relation between the performance of SC codes and the density distribution of partitioning matrices. We propose a probabilistic framework that obtains (locally) optimal density distributions via gradient descent. Starting from random partitioning matrices abiding by the obtained distribution, we perform low complexity optimization algorithms over the cycle properties to construct high memory, high performance quasi-cyclic SC codes. Simulation results show that codes obtained through our proposed method notably outperform state-of-the-art SC codes with the same constraint length and codes with uniform partitioning.
Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure.

Abstract Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.
Geometry of the Wiman–Edge monodromy

The Wiman–Edge pencil is a pencil of genus 6 curves for which the generic member has automorphism group the alternating group A5. There is a unique smooth member, the Wiman sextic, with automorphis...
Hessian estimates for Lagrangian mean curvature equation

In this paper, we derive a priori interior Hessian estimates for the Lagrangian mean curvature equation if the Lagrangian phase is supercritical and has bounded second derivatives.
Spin Nernst Effect of Antiferromagnetic Magnons in the Presence of Spin Diffusion

The magnon spin Nernst effect was recently proposed as an intrinsic effect in antiferromagnets, where spin diffusion and boundary spin transmission have been ignored. However, diffusion processes are essential for converting a bulk spin current into boundary spin accumulation, which determines the spin injection rate into detectors through imperfect transmission. We formulate a diffusive theory to describe the detection of the magnon spin Nernst effect with boundary conditions reflecting real device geometry. Because of the spin diffusion effect, the output signals in both electronic and optical detection grow rapidly with increasing system size in the transverse dimension, which eventually saturate. Counterintuitively, the measurable signals are even functions of magnetic field, making optical detection more favorable than electronic detection.
Man versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence

Audit firms are investing billions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Although firms assume AI will enhance audit quality, a growing body of research documents that individuals often exhibit “algorithm aversion” – the tendency to discount computer-based advice more heavily than human advice, although the advice is identical otherwise. Therefore, we conduct an experiment to examine how algorithm aversion manifests in auditor judgments. Consistent with theory, we find that auditors receiving contradictory evidence from their firm’s AI system (instead of a human specialist) propose smaller adjustments to management’s complex estimates, particularly when management develops their estimates using relatively objective (versus subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.
Theory and simulations of condensin mediated loop extrusion in DNA.

Condensation of hundreds of mega-base-pair-long human chromosomes in a small nuclear volume is a spectacular biological phenomenon. This process is driven by the formation of chromosome loops. The ATP consuming motor, condensin, interacts with chromatin segments to actively extrude loops. Motivated by real-time imaging of loop extrusion (LE), we created an analytically solvable model, predicting the LE velocity and step size distribution as a function of external load. The theory fits the available experimental data quantitatively, and suggests that condensin must undergo a large conformational change, induced by ATP binding, bringing distant parts of the motor to proximity. Simulations using a simple model confirm that the motor transitions between an open and a closed state in order to extrude loops by a scrunching mechanism, similar to that proposed in DNA bubble formation during bacterial transcription. Changes in the orientation of the motor domains are transmitted over ~50 nm, connecting the motor head and the hinge, thus providing an allosteric basis for LE. How chromosomes, which are polymers with nearly billion base pairs, are packaged in the restricted nuclear volume is not well understood. Here, the authors combine polymer physics, nonequilibrium fluctuation theorem, and simulations to quantitatively predict the force-dependent velocity and step-size distribution of condensin, which facilitates the folding of chromosomes by loop extrusion.
Decision Tree Heuristics Can Fail, Even in the Smoothed Setting

Greedy decision tree learning heuristics are mainstays of machine learning practice, but theoretical justification for their empirical success remains elusive. In fact, it has long been known that there are simple target functions for which they fail badly (Kearns and Mansour, STOC 1996). Recent work of Brutzkus, Daniely, and Malach (COLT 2020) considered the smoothed analysis model as a possible avenue towards resolving this disconnect. Within the smoothed setting and for targets f that are k-juntas, they showed that these heuristics successfully learn f with depth-k decision tree hypotheses. They conjectured that the same guarantee holds more generally for targets that are depth-k decision trees. We provide a counterexample to this conjecture: we construct targets that are depth-k decision trees and show that even in the smoothed setting, these heuristics build trees of depth 2^{Ω(k)} before achieving high accuracy. We also show that the guarantees of Brutzkus et al. cannot extend to the agnostic setting: there are targets that are very close to k-juntas, for which these heuristics build trees of depth 2^{Ω(k)} before achieving high accuracy.
Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms

The cells and their spatial patterns in the tumor microenvironment (TME) play a key role in tumor evolution, and yet the latter remains an understudied topic in computational pathology. This study, to the best of our knowledge, is among the first to hybridize local and global graph methods to profile orchestration and interaction of cellular components. To address the challenge in hematolymphoid cancers, where the cell classes in TME may be unclear, we first implemented cell-level unsupervised learning and identified two new cell subtypes. Local cell graphs or supercells were built for each image by considering the individual cell’s geospatial location and classes. Then, we applied supercell level clustering and identified two new cell communities. In the end, we built global graphs to abstract spatial interaction patterns and extract features for disease diagnosis. We evaluate the proposed algorithm on H&E slides of 60 hematolymphoid neoplasms and further compared it with three cell level graph-based algorithms, including the global cell graph, cluster cell graph, and FLocK. The proposed algorithm achieved a mean diagnosis accuracy of 0.703 with the repeated 5-fold cross-validation scheme. In conclusion, our algorithm shows superior performance over the existing methods and can be potentially applied to other cancer types.
Entropic singularities give rise to quantum transmission.

When can noiseless quantum information be sent across noisy quantum devices? And at what maximum rate? These questions lie at the heart of quantum technology, but remain unanswered because of non-additivity— a fundamental synergy which allows quantum devices (aka quantum channels) to send more information than expected. Previously, non-additivity was known to occur in very noisy channels with coherent information much smaller than that of a perfect channel; but, our work shows non-additivity in a simple low-noise channel. Our results extend even further. We prove a general theorem concerning positivity of a channel’s coherent information. A corollary of this theorem gives a simple dimensional test for a channel’s capacity. Applying this corollary solves an open problem by characterizing all qubit channels whose complement has non-zero capacity. Another application shows a wide class of zero quantum capacity qubit channels can assist an incomplete erasure channel in sending quantum information. These results arise from introducing and linking logarithmic singularities in the von-Neumann entropy with quantum transmission: changes in entropy caused by this singularity are a mechanism responsible for both positivity and non-additivity of the coherent information. Analysis of such singularities may be useful in other physics problems. Non-additivity of the quantum channel coherent information is known to occur in some very noisy channels, but its fundamental origin is unclear. Here, the author explains its link with log singularity of quantum entropy, and shows that it can also come up for low-noise channels.
A pressure-correction and bound-preserving discretization of the phase-field method for variable density two-phase flows

Abstract In this paper, we present an efficient numerical algorithm for solving the time-dependent Cahn–Hilliard–Navier–Stokes equations that model the flow of two phases with different densities. The pressure-correction step in the projection method consists of a Poisson problem with a modified right-hand side. Spatial discretization is based on discontinuous Galerkin methods with piecewise linear or piecewise quadratic polynomials. One important contribution of this work is the formulation of flux and slope limiting techniques that successfully eliminate the bulk shift, overshoot and undershoot in the order parameter. The bound-preserving property of the discrete order parameter is proved. Several numerical results demonstrate that the proposed numerical algorithm is effective and robust for modeling two-component immiscible flows in porous structures and digital rocks.
Koopman NMPC: Koopman-based Learning and Nonlinear Model Predictive Control of Control-affine Systems

Koopman-based learning methods can potentially be practical and powerful tools for dynamical robotic systems. However, common methods to construct Koopman representations seek to learn lifted linear models that cannot capture nonlinear actuation effects inherent in many robotic systems. This paper presents a learning and control methodology that is a first step towards overcoming this limitation. Using the Koopman canonical transform, control-affine dynamics can be expressed by a lifted bilinear model. The learned model is used for nonlinear model predictive control (NMPC) design where the bilinear structure can be exploited to improve computational efficiency. The benefits for control-affine dynamics compared to existing Koopman-based methods are highlighted through an example of a simulated planar quadrotor. Prediction error is greatly reduced and closed loop performance similar to NMPC with full model knowledge is achieved.
Generalized Shape Metrics on Neural Representations

Understanding the operation of biological and artificial networks remains a difficult and important challenge. To identify general principles, researchers are increasingly interested in surveying large collections of networks that are trained on, or biologically adapted to, similar tasks. A standardized set of analysis tools is now needed to identify how network-level covariates -- such as architecture, anatomical brain region, and model organism -- impact neural representations (hidden layer activations). Here, we provide a rigorous foundation for these analyses by defining a broad family of metric spaces that quantify representational dissimilarity. Using this framework we modify existing representational similarity measures based on canonical correlation analysis to satisfy the triangle inequality, formulate a novel metric that respects the inductive biases in convolutional layers, and identify approximate Euclidean embeddings that enable network representations to be incorporated into essentially any off-the-shelf machine learning method. We demonstrate these methods on large-scale datasets from biology (Allen Institute Brain Observatory) and deep learning (NAS-Bench-101). In doing so, we identify relationships between neural representations that are interpretable in terms of anatomical features and model performance.
Faa$T: A Transparent Auto-Scaling Cache for Serverless Applications

Function-as-a-Service (FaaS) has become an increasingly popular way for users to deploy their applications without the burden of managing the underlying infrastructure. However, existing FaaS platforms rely on remote storage to maintain state, limiting the set of applications that can be run efficiently. Recent caching work for FaaS platforms has tried to address this problem, but has fallen short: it disregards the widely different characteristics of FaaS applications, does not scale the cache based on data access patterns, or requires changes to applications. To address these limitations, we present Faa$T, a transparent auto-scaling distributed cache for serverless applications. Each application gets its own cache. After a function executes and the application becomes inactive, the cache is unloaded from memory with the application. Upon reloading for the next invocation, Faa$T pre-warms the cache with objects likely to be accessed. In addition to traditional compute-based scaling, Faa$T scales based on working set and object sizes to manage cache space and I/O bandwidth. We motivate our design with a comprehensive study of data access patterns on Azure Functions. We implement Faa$T for Azure Functions, and show that Faa$T can improve performance by up to 92% (57% on average) for challenging applications, and reduce cost for most users compared to state-of-the-art caching systems, i.e. the cost of having to stand up additional serverful resources.
Counterfactual Maximum Likelihood Estimation for Training Deep Networks

Although deep learning models have driven state-of-the-art performance on a wide array of tasks, they are prone to learning spurious correlations that should not be learned as predictive clues. To mitigate this problem, we propose a causality-based training framework to reduce the spurious correlations caused by observable confounders. We give theoretical analysis on the underlying general Structural Causal Model (SCM) and propose to perform Maximum Likelihood Estimation (MLE) on the interventional distribution instead of the observational distribution, namely Counterfactual Maximum Likelihood Estimation (CMLE). As the interventional distribution, in general, is hidden from the observational data, we then derive two different upper bounds of the expected negative log-likelihood and propose two general algorithms, Implicit CMLE and Explicit CMLE, for causal predictions of deep learning models using observational data. We conduct experiments on two real-world tasks: Natural Language Inference (NLI) and Image Captioning. The results show that CMLE methods outperform the regular MLE method in terms of out-of-domain generalization performance and reducing spurious correlations, while maintaining comparable performance on the regular evaluations.
Full-wave spectral-domain analysis of compensation of microstrip discontinuities using triangular subdomain functions

The authors present a full-wave spectral-domain analysis to investigate compensation of a variety of microstrip discontinuities including bends and T-junctions. To properly model the discontinuities with miters as well as 90 degrees corners, vector-valued triangular subdomain functions are used as both expansion and testing functions in the moment method procedure. Special consideration is given to the numerical treatment of the reaction integral between two triangular subdomains such that rather complicated geometrical configurations can be handled very efficiently. Comparison of some numerical results with available experimental data shows excellent agreement. The losses due to radiation and surface wave in some discontinuities are also discussed. >
The West Los Angeles Preterm Birth Prevention Project

Objective: The primary objective of this prospective study was to test whether preterm birth prevention education plus increased clinic visits and selected prophylactic interventions reduce preterm birth. Study Design: Eight West Los Angeles prenatal county clinics, comparable with respect to selected demographics, were randomized to be either experimental or control clinics. High-risk patients in all clinics were identified with a risk scoring system derived from a similar population. High-risk patients ( N = 1774) in experimental clinics were offered a program of education and more frequent visits and were randomized to receive various secondary intervention protocols in addition to the basic interventions of education and more frequent visits. Control clinic patients (N = 880) received standard county care. Results: Preterm birth rates were 19% lower among the experimental high-risk patients (7.4% vs 9.1 %), and differences were significant ( p Conclusion: The 19% reduction in preterm birth rate observed in the experimental clinips suggest an overall program benefit from a protocol that offered education, more frequent visits, and greater attention given to patients while the selected interventions were applied.
Laboratory Correlates of Adoptive Immunotherapy with Recombinant Interleukin-2 and Lymphokine-activated Killer Cells in Humans

Adoptive immunotherapy with interleukin 2 (IL-2) and lymphokine-activated killer (LAK) cells (IL-2/LAK) is a technically demanding cancer therapy dependent upon large scale isolation and culture of lymphocytes. An important question is whether this technology can be accomplished routinely outside of highly specialized centers. In addition, no systematic examination of laboratory correlates of IL-2/LAK therapy in humans has been reported to date. The objectives of this report are to address two issues relevant to IL-2/LAK therapy. (a) Can IL-2/LAK therapy be accomplished outside of previously identified centers of expertise? (b) What are the relevant laboratory/clinical parameter correlations? The six institutions in the National Cancer Institute extramural trial treated 83 evaluable patients with renal cancer, malignant melanoma, or colon cancer with IL-2/LAK by a uniform protocol. Patients received 5 days of IL-2 priming, then daily leukaphereses for 5 days starting 48 h after IL-2 to harvest cells. Mononuclear cells were isolated, then cultured in roller bottles in 1-liter aliquots for 3 to 4 days at a cell density of 1.5 x 10(6) per ml with recombinant IL-2, 1500 units per ml. Cells were harvested and administered to patients with additional IL-2. Administration of IL-2 regularly induced lymphopenia and rebound lymphocytosis. Leukapheresis yields and numbers of LAK cells generated in culture and reinfused into patients correlated directly with peak lymphocyte counts achieved by IL-2 administration. Mean mononuclear cell recovery per 5 days of leukapheresis (+/- SEM) was 14.3 +/- 0.8 x 10(10). Average volume of cells cultured per patient was 95 liters (range, 41 to 235). Mean yield of cells harvested from cultures was 53%. Mean total number of LAK cells infused per patient was 7.6 +/- 0.4 x 10(10) (range, 2 to 15.2 x 10(10]. LAK activity was measured in vitro by lysis of 51Cr-labeled natural killer-resistant Daudi and fresh tumor targets. LAK effector cells regularly lysed these targets in vitro. Neither tumor reduction nor clinical toxicity correlated with dose or with cytolytic activity of LAK cells, or with other laboratory parameters including base-line lymphocyte count and IL-2-induced lymphocytosis. We conclude: (a) large quantities of LAK effector cells with tumoricidal activity can be generated routinely at different centers; (b) neither in vitro LAK activity nor numbers of LAK cells infused were predictive of clinical efficacy or toxicity. There is a need to identify other laboratory or clinical parameters more predictive of IL-2/LAK therapeutic efficacy or toxicity.
Three‐dimensional cultures of mouse submandibular and parotid glands: a comparative study

Freshly isolated salivary cells can be plated on an extracellular matrix, such as growth factor-reduced Matrigel (GFR-MG), to induce the formation of three-dimensional (3D) structures. Cells grown on GFR-MG are able to form round structures with hollow lumina, capable of sustaining amylase expression. In contrast, cells grown on plastic do not exhibit these features. Our recent studies have used mouse parotid gland (PG) cells, grown on different extracellular matrices, as a model for acinar formation. However, PG cells were not able to respond to the secretory agonist carbachol beyond 5 days and did not sustain polarity over time, regardless of the substratum. An alternative option relies in the use of mouse submandibular glands (SMG), which are more anatomically accessible and yield a larger number of cells. We compared SMG and PG cell clusters (partially dissociated glands) for their ability to form hollow round structures, sustain amylase and maintain secretory function when grown on GFR-MG. The results were as follows: (a) SMG cell clusters formed more organized and larger structures than PG cell clusters; (b) both SMG and PG cell clusters maintained α-amylase expression over time; (c) SMG cell clusters maintained agonist-induced secretory responses over time; and (d) SMG cell clusters maintained secretory granules and cell-cell junctions. These results indicate that mouse SMG cell clusters are more amenable for the development of a bioengineered salivary gland than PG cell clusters, as they form more organized and functional structures. Copyright © 2014 John Wiley & Sons, Ltd.
A low molecular weight component of serum inhibits biofilm formation in Staphylococcus aureus.

Abstract Staphylococcus aureus has a variety of genes that can influence the process of biofilm formation. The ability to establish a biofilm is an important virulence factor for this pathogen, and yet, the regulation of this process in vivo is not well understood. S. aureus can form biofilms on intravenous catheters and this process plays a key role in the pathogenesis of catheter infections. In order to investigate whether or not serum is conducive to the process of biofilm formation, we grew S. aureus in serum and analyzed biofilm thickness and expression of biofilm-related genes. Whereas serum supported planktonic bacterial growth, it was a potent inhibitor of biofilm formation. The inhibitory serum component had a molecular weight less than 3000 kDa. The component was protease-resistant and heat stable. The serum component induced a significant increase in the transcription of the intercellular adhesin gene icaA and the fibronectin binding protein gene fnbA. Transcription of other biofilm-related genes was affected in a strain-dependent manner. These results reveal that serum inhibits biofilm formation despite the fact that biofilms form on intravenous catheters. This may suggest that in vivo, biofilm formation is “selected for” by the force of blood flow and/or immune pressure rather than “induced” by serum.
Incorporation and controlled release of a hydrophilic antibiotic using poly(lactide-co-glycolide)-based electrospun nanofibrous scaffolds

The successful incorporation and sustained release of a hydrophilic antibiotic drug (Mefoxin®, cefoxitin sodium) from electrospun poly(lactide-co-glycolide) (PLGA)-based nanofibrous scaffolds without the loss of structure and bioactivity was demonstrated. The morphology and density of the electrospun scaffold was found to be dependent on the drug concentration, which could be attributed to the effect of ionic salt on the electrospinning process. The drug release behavior from the electrospun scaffolds and its antimicrobial effects on Staphylococcus aureus cultures were also investigated. In all tested scaffolds, the maximum dosage of drug was released after 1 h of incubation in water at 37 °C. The usage of the amphiphilic block copolymer (PEG-b-PLA) reduced the cumulative amount of the released drug at earlier time points and prolonged the drug release rate at longer times (up to a 1-week period). The antibiotic drug released from these electrospun scaffolds was effective in their ability to inhibit Staphylococcus aureus growth (>90%). The combination of mechanical barriers based on non-woven nanofibrous biodegradable scaffolds and their capability for local delivery of antibiotics increases their desired utility in biomedical applications, particularly in the prevention of post-surgical adhesions and infections.
The impact of race on prostate cancer detection and choice of treatment in men undergoing a contemporary extended biopsy approach.

Abstract Introduction African American men have a higher rate of prostate cancer mortality compared with their Caucasian American counterparts. However, it remains unclear as to whether such differences are due to biologic or socioeconomic influences. This study sought to determine if there are differences in demographic and clinical characteristics between African American and Caucasian American men in a modern cohort undergoing extended biopsy approach, and evaluated the subsequent choice of therapy in patients diagnosed with prostate cancer. Methods A retrospective review was performed from a consecutive series of 500 men undergoing prostate biopsy at our institution between 2003 and 2005. All patients underwent a contemporary 10–12 biopsy scheme. Demographic, clinical, and pathologic variables as well as treatment choice (those with positive biopsy) were stratified and evaluated with regard to race—African American, Caucasian American, and other (Hispanic, Asian, American Indian). Results 65% were Caucasian American, 29% African American, and 7% other. The overall positive biopsy rate was 44%. African American men were significantly younger than Caucasian American but were not younger than “other” (61.6 vs. 64.3 vs. 61.5 years). No differences were observed with regard to prostate specific antigen density (PSAD), prostate volume, or rate of abnormal digital rectal exam (DRE). The positive biopsy rate was not different between Caucasian American and African American (46% vs. 46%), but significantly lower in other men (16%). These differences were maintained on odds ratio modeling, including age-adjusted and multivariate models. Of the 223 men with positive biopsies, information on treatment choice demonstrated that African American men had a significantly higher rate of choice of XRT (OR = 2.12) and rate of avoidance of surgery (OR = 0.35) than Caucasian American men. Conclusions In men undergoing prostate biopsy using an extended (10–12 core) biopsy scheme, no differences were observed with regard to positive biopsy rate or other clinical or biochemical parameters [except for age and prostate specific antigen (PSA) level] between African American and Caucasian American men. Of those with a positive biopsy, African American men were more likely to avoid surgery and choose XRT in our population.
Growth performance and chestnut blight incidence (Cryphonectria parasitica) of backcrossed chestnut seedlings in surface mine restoration

Anthropogenic disturbances such as mining for coal have caused significant disturbance to the Appalachian forests of North America. Recovery of these disturbances is highly dependent on restoration methods that encourage natural succession. Unfortunately, current reclamation protocols have resulted in soil compaction and aggressive herbaceous groundcovers that impede the recovery of native trees. To overcome this, methods such as deep ripping and plow and disking were applied to a reclaimed mine land in Ohio, USA Plantings of pure American chestnut (Castanea dentata) and two seedling types (BC1F3 and BC2F3) bred for resistance to chestnut blight fungus (Cryphonectria parasitica) were assessed among different soil preparation treatments over five field seasons. Seedling mortality due to natural infection by chestnut blight was recorded and related to the disease resistance potential of the BC1F3 and BC2F3 seedlings. The growth and survival of chestnuts in plots that employed either ripping, plow disking, or the combination of the two methods were significantly greater than the control plots. After five seasons, differences existed among the soil treatments; plots that applied deep ripping had the highest survival and growth. When chestnut types were compared, pure American chestnut was the tallest. However, BC2F3 chestnut seedlings had the highest survival and lower disease incidence. Results suggest that employing deep ripping with backcrossed chestnut seedlings provides a method for establishing hardwood seedlings in soils impacted by surface mining. Planting methods that promote vigorous growth can be applied more broadly to other regions where anthropogenic disturbances create soil conditions that hinder seedling establishment.
High-resolution HIXSE studies of the chemical environment of sulfur implanted in quartz glass

High-resolution sulfur Kα X-ray satellite spectra produced by 30 MeV Cl ion irradiation of quartz glass containing ion-implanted sulfur have been obtained. The normalized emission spectra exhibited variations in the intensity distribution of the satellite structure for different implant doses. Correlation of parameters describing the satellite intensity profile to the dose and the local chemical environment suggest that a method for nondestructively probing implant profiles, free from restrictions on the Z of the implant and matrix, may be possible.
Time, temperature and excess stress: Relaxation in strained heterostructures

Abstract We present examples of calculations of the time evolution of temperature and excess stress during growth and subsequent annealing of simple strained heterostructures. We show how to estimate strain relaxation in technologically interesting cases for which relaxation is small. These examples can be a starting point for calculating relaxation in complex strained heterostructures having more complicated fabrication and processing sequences.