cssi/SciSciGPT-SciSciNet huggingface dataset:
The effects of driver licensing laws on immigrant travel

Abstract Car use is critical to improving access to opportunities, especially for low-wage immigrants whose jobs are dispersed and when transit service is minimal. But many states have restricted the ability of undocumented immigrants to obtain drivers licenses, making it potentially difficult for them to improve their economic standing. The effects of these laws have been tested for their association with traffic safety but not on mode choice itself. Using the two most recent versions of the National Household Travel Survey, I fit a series of difference-in-difference models to estimate the effect of permissive immigrant driver licensing on travel outcomes. Permissive licensing increased the rate of giving rides by about 13% and increased the rate of getting a ride by about 6.5%, but changes to driving alone were insignificant. Results suggest permissive licensing has beneficial accessibility impacts for all immigrants in addition to the positive safety and economic externalities documented elsewhere.
On the interaction of species capable of explosive growth

In the classical Lotka-Volterra population models, the interacting species affect each other's growth rate. We propose an alternative model, in which the species affect each other through the limitation coefficients, rather then through the growth rates. This appears to be more realistic: the presence of foxes is not likely to diminish the fertility of rabbits, but will contribute to limiting rabbit's population. Both the cases of predation and of competition are considered, as well as competition in case of periodic coefficients. Our model becomes linear when one switches to the reciprocals of the variables. In another direction we use a similar idea to derive a multiplicity result for a class of periodic equations.
Rapid formation of super-Earths around low-mass stars

NASA's TESS mission is expected to discover hundreds of M dwarf planets. However, few studies focus on how planets form around low-mass stars. We aim to better characterize the formation process of M dwarf planets to fill this gap and aid in the interpretation of TESS results. We use ten sets of N-body planet formation simulations which vary in whether a gas disc is present, initial range of embryo semi-major axes, and initial solid surface density profile. Each simulation begins with 147 equal-mass embryos around a 0.2 solar mass star and runs for 100 Myr. We find that planets form rapidly, with most collisions occurring within the first 1 Myr. The presence of a gas disc reduces the final number of planets relative to a gas-free environment and causes planets to migrate inward. We find that roughly a quarter of planetary systems experience their final giant impact inside the gas disc, suggesting that some super-Earths may be able to reaccrete an extended gaseous envelope after their final giant impact, though these may be affected by additional process such as photoevaporation. In addition, we find that the final distribution of planets does not retain a memory of the slope of the initial surface density profile, regardless of whether or not a gas disc is present. Thus, our results suggest that present-day observations are unlikely to provide sufficient information to accurately reverse-engineer the initial distribution of solids.
Complexity and graded regulation of neuronal cell-type–specific alternative splicing revealed by single-cell RNA sequencing

The enormous cellular diversity in the mammalian brain, which is highly prototypical and organized in a hierarchical manner, is dictated by cell-type-specific gene-regulatory programs at the molecular level. Although prevalent in the brain, the contribution of alternative splicing (AS) to the molecular diversity across neuronal cell types is just starting to emerge. Here, we systematically investigated AS regulation across over 100 transcriptomically defined neuronal types of the adult mouse cortex using deep single-cell RNA-sequencing data. We found distinct splicing programs between glutamatergic and GABAergic neurons and between subclasses within each neuronal class. These programs consist of overlapping sets of alternative exons showing differential splicing at multiple hierarchical levels. Using an integrative approach, our analysis suggests that RNA-binding proteins (RBPs) Celf1/2, Mbnl2, and Khdrbs3 are preferentially expressed and more active in glutamatergic neurons, while Elavl2 and Qk are preferentially expressed and more active in GABAergic neurons. Importantly, these and additional RBPs also contribute to differential splicing between neuronal subclasses at multiple hierarchical levels, and some RBPs contribute to splicing dynamics that do not conform to the hierarchical structure defined by the transcriptional profiles. Thus, our results suggest graded regulation of AS across neuronal cell types, which may provide a molecular mechanism to specify neuronal identity and function that are orthogonal to established classifications based on transcriptional regulation.
Late 19th century navigational uncertainties and their influence on sea surface temperature estimates

Accurate estimates of historical changes in sea surface temperatures (SSTs) and their uncertainties are important for documenting and understanding historical changes in climate. A source of uncertainty that has not previously been quantified in historical SST estimates stems from position errors. A Bayesian inference framework is proposed for quantifying errors in reported positions and their implications on SST estimates. The analysis framework is applied to data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS3.0) in 1885, a time when astronomical and chronometer estimation of position was common but predated the use of radio signals. Focus is upon a subset of 943 ship tracks from ICOADS3.0 that report their position every two hours to a precision of 0.01∘ longitude and latitude. These data are interpreted as positions determined by dead reckoning that are periodically updated by celestial correction techniques. The posterior medians of uncertainties in celestial correction are 33.1 km (0.30∘ on the equator) in longitude and 24.4 km (0.22∘) in latitude, respectively. Celestial navigation uncertainties being smaller in latitude than longitude is qualitatively consistent with the relative difficulty of obtaining astronomical estimates. The posterior medians for two-hourly dead reckoning uncertainties are 19.2% for ship speed and 13.2∘ for ship heading, leading to random position uncertainties with median 0.18∘ (20 km on the equator) in longitude and 0.15∘ (17 km) in latitude. Reported ship tracks also contain systematic position uncertainties relating to precursor dead-reckoning positions not being updated after obtaining celestial position estimates, indicating that more accurate positions can be provided for SST observations. Finally, we translate position errors into SST uncertainties by sampling an ensemble of SSTs from the Multiscale Ultrahigh Resolution Sea Surface Temperature (MURSST) data set. Evolving technology for determining ship position, heterogeneous reporting and archiving of position information, and seasonal and spatial changes in navigational uncertainty and SST gradients together imply that accounting for positional error in SST estimates over the span of the instrumental record will require substantial additional effort.
Extending the Near-Infrared Emission Range of Indium Phosphide Quantum Dots for Multiplexed In Vivo Imaging

This report of the reddest emitting indium phosphide quantum dots (InP QDs) to date demonstrates tunable, near-infrared (NIR) photoluminescence (PL) as well as PL multiplexing in the first optical tissue window while avoiding toxic constituents. This synthesis overcomes the InP "growth bottleneck" and extends the emission peak of InP QDs deeper into the first optical tissue window using an inverted QD heterostructure, specifically ZnSe/InP/ZnS core/shell/shell nanoparticles. The QDs exhibit InP shell thickness-dependent tunable emission with peaks ranging from 515-845 nm. The high absorptivity of InP yields effective photoexcitation of the QDs with UV, visible, and NIR wavelengths. These nanoparticles extend the range of tunable direct-bandgap emission from InP-based nanostructures, effectively overcoming a synthetic barrier that has prevented InP-based QDs from reaching their full potential as NIR imaging agents. Multiplexed lymph node imaging in a mouse model demonstrates the potential of the NIR-emitting InP particles for in vivo imaging.
Selection and sorting in totally monotone arrays

A two-dimensional arrayA={a[i, j]} is calledtotally monotone if, for alli 1<i 2 andj 1<j 2,a[i 1,j 1]<a[i 1,j 2] impliesa[i 2,j 1]<a[i 2,j 2]. Totally monotone arrays were introduced in 1987 by Aggarwal, Klawe, Moran, Shor, and Wilber, who showed that several problems in computational geometry and VLSI river routing could be reduced to the problem of finding a maximum entry in each row of a totally monotone array. In this paper we consider several selection and sorting problems involving totally monotone arrays and give a number of applications of solutions for these problems. In particular, we obtain the following results for anm × n totally monotone arrayA:
Drosophila RASopathy models identify disease subtype differences and biomarkers of drug efficacy

RASopathies represent a family of mostly autosomal dominant diseases that are caused by missense variants in the rat sarcoma viral oncogene/mitogen activated protein kinase (RAS/MAPK) pathway including KRAS, NRAS, BRAF, RAF1, and SHP2. These variants are associated with overlapping but distinct phenotypes that affect the heart, craniofacial, skeletal, lymphatic, and nervous systems. Here, we report an analysis of 13 Drosophila transgenic lines, each expressing a different human RASopathy isoform. Similar to their human counterparts, each Drosophila line displayed common aspects but also important differences including distinct signaling pathways such as the Hippo and SAPK/JNK signaling networks. We identified multiple classes of clinically relevant drugs-including statins and histone deacetylase inhibitors-that improved viability across most RASopathy lines; in contrast, several canonical RAS pathway inhibitors proved less broadly effective. Overall, our study compares and contrasts a large number of RASopathy-associated variants including their therapeutic responses.
Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods

Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients. The recent success of deep learning techniques in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works into their algorithmic components and discuss each separately. For methods that provide evaluations on public benchmark datasets, we report comparisons between their results.
NP-ODE: Neural process aided ordinary differential equations for uncertainty quantification of finite element analysis

Finite Element Analysis (FEA) has been widely used to generate simulations of complex nonlinear systems. Despite its strength and accuracy, FEA usually has two limitations: (i) running high-fidelit...
Enzymatic β-Oxidation of the Cholesterol Side Chain in Mycobacterium tuberculosis Bifurcates Stereospecifically at Hydration of 3-Oxo-cholest-4,22-dien-24-oyl-CoA.

The unique ability of Mycobacterium tuberculosis (Mtb) to utilize host lipids such as cholesterol for survival, persistence, and virulence has made the metabolic pathway of cholesterol an area of great interest for therapeutics development. Herein, we identify and characterize two genes from the Cho-region (genomic locus responsible for cholesterol catabolism) of the Mtb genome, chsH3 (Rv3538) and chsB1 (Rv3502c). Their protein products catalyze two sequential stereospecific hydration and dehydrogenation steps in the β-oxidation of the cholesterol side chain. ChsH3 favors the 22S hydration of 3-oxo-cholest-4,22-dien-24-oyl-CoA in contrast to the previously reported EchA19 (Rv3516), which catalyzes formation of the (22R)-hydroxy-3-oxo-cholest-4-en-24-oyl-CoA from the same enoyl-CoA substrate. ChsB1 is stereospecific and catalyzes dehydrogenation of the ChsH3 product but not the EchA19 product. The X-ray crystallographic structure of the ChsB1 apo-protein was determined at a resolution of 2.03 A, and the holo-enzyme with bound NAD+ cofactor was determined at a resolution of 2.21 A. The homodimeric structure is representative of a classical NAD+-utilizing short-chain type alcohol dehydrogenase/reductase, including a Rossmann-fold motif, but exhibits a unique substrate binding site architecture that is of greater length and width than its homologous counterparts, likely to accommodate the bulky steroid substrate. Intriguingly, Mtb utilizes hydratases from the MaoC-like family in sterol side-chain catabolism in contrast to fatty acid β-oxidation in other species that utilize the evolutionarily distinct crotonase family of hydratases.
A Therapeutic Jurisprudence Analysis of the Use of Eminent Domain to Create a Leasehold

Therapeutic jurisprudence provides an excellent tool to analyze and guide the development of the law on the use of eminent domain to create leaseholds. The objective of these takings is for the condemnor to become a tenant under a “lease,” rather than the fee simple owner. I am perhaps the only scholar who has written extensively on the topic of takings to create a leasehold. In a previous work, I provided an exhaustive analysis of the conclusion that government can use eminent domain to create a leasehold. That work went on to conclude that there are circumstances in which government should use eminent domain to create a leasehold, but that difficult problems can arise in such takings. They necessitate refinements in arriving at just compensation. That work also concluded that there is at least one situation in which government should not be allowed to use eminent domain to create a leasehold. I labeled such takings Kelo-type takings, wherein the government uses its power of eminent domain with the objective of creating a leasehold that it will then transfer to a private party for private use. My argument that the use of such Kelo-type takings to create leaseholds should not be allowed was based primarily on public policy considerations. I concluded that the problems arising from takings that create private leaseholds are much worse than those encountered in situations such as Kelo, in which government acquires a fee simple from the condemnee and then makes a transfer to a private party, because the form disrupts the social contract between government and the people. Any such conclusion demands reexamination on theoretical grounds, which is done in this Article.1 In order to re-examine the question, it formally extends the jurisprudential philosophy of therapeutic justice to eminent domain in general and specifically to takings to create leaseholds. The principles underlying therapeutic jurisprudence, as well as the illuminating insights derived from its application, confirm the prior conclusion.
Probabilistic State Estimation in Water Networks

State estimation (SE) in water distribution networks (WDNs), the problem of estimating all unknown network heads and flows given select measurements, is challenging due to the nonconvexity of hydraulic models and significant uncertainty from water demands, network parameters, and measurements. To this end, a probabilistic modeling for SE in WDNs is proposed. After linearizing the nonlinear hydraulic WDN model, the proposed probabilistic SE (PSE) shows that the covariance matrix of unknown system states (unmeasured heads and flows) can be linearly expressed by the covariance matrix of three uncertainty sources (i.e., measurement noise, network parameters, and water demands). Instead of providing deterministic results for unknown states, the proposed PSE approach: 1) regards the system states and uncertainty sources as random variables and yields variances of individual unknown states; 2) considers thorough modeling of various types of valves and measurement scenarios in WDNs; and 3) is also useful for uncertainty quantification, extended period simulations, and confidence limit analysis. The effectiveness and scalability of the proposed approach are tested using several WDN case studies.
Gene body methylation is under selection in Arabidopsis thaliana.

In plants, mammals and insects, some genes are methylated in the CG dinucleotide context, a phenomenon called gene body methylation (gbM). It has been controversial whether this phenomenon has any functional role. Here, we took advantage of the availability of 876 leaf methylomes in Arabidopsis thaliana to characterize the population frequency of methylation at the gene level and to estimate the site-frequency spectrum of allelic states. Using a population genetics model specifically designed for epigenetic data, we found that genes with ancestral gbM are under significant selection to remain methylated. Conversely, ancestrally unmethylated genes were under selection to remain unmethylated. Repeating the analyses at the level of individual cytosines confirmed these results. Estimated selection coefficients were small, on the order of 4 Nes = 1.4, which is similar to the magnitude of selection acting on codon usage. We also estimated that A. thaliana is losing gbM threefold more rapidly than gaining it, which could be due to a recent reduction in the efficacy of selection after a switch to selfing. Finally, we investigated the potential function of gbM through its link with gene expression. Across genes with polymorphic methylation states, the expression of gene body methylated alleles was consistently and significantly higher than unmethylated alleles. Although it is difficult to disentangle genetic from epigenetic effects, our work suggests that gbM has a small but measurable effect on fitness, perhaps due to its association to a phenotype-like gene expression.
Multiple quadrotors carrying a flexible hose: dynamics, differential flatness and control

Abstract Using quadrotors UAVs for cooperative payload transportation using cables has been actively gaining interest in the recent years. Understanding the dynamics of these complex multi-agent systems would help towards designing safe and reliable systems. In this work, we study one such multi-agent system comprising of multiple quadrotors transporting a flexible hose. We model the hose as a series of smaller discrete links and derive a generalized coordinate-free dynamics for the same. We show that certain configurations of this under-actuated system are differentially-flat. We linearize the dynamics using variation-based linearization and present a linear time-varying LQR to track desired trajectories. Finally, we present numerical simulations to validate the dynamics, flatness and control.
Effects of Monetary Policy on Food Inequality in India

Food insecurity and hunger are significant components of standard poverty measures and continue to be pressing issues in emerging market economies. However, these issues have received less attentio...
Online Robust Control of Nonlinear Systems with Large Uncertainty

Robust control is a core approach for controlling systems with performance guarantees that are robust to modeling error, and is widely used in real-world systems. However, current robust control approaches can only handle small system uncertainty, and thus require significant effort in system identification prior to controller design. We present an online approach that robustly controls a nonlinear system under large model uncertainty. Our approach is based on decomposing the problem into two sub-problems, "robust control design" (which assumes small model uncertainty) and "chasing consistent models", which can be solved using existing tools from control theory and online learning, respectively. We provide a learning convergence analysis that yields a finite mistake bound on the number of times performance requirements are not met and can provide strong safety guarantees, by bounding the worst-case state deviation. To the best of our knowledge, this is the first approach for online robust control of nonlinear systems with such learning theoretic and safety guarantees. We also show how to instantiate this framework for general robotic systems, demonstrating the practicality of our approach.
On the role of system software in energy management of neuromorphic computing

Neuromorphic computing systems such as DYNAPs and Loihi have recently been introduced to the computing community to improve performance and energy efficiency of machine learning programs, especially those that are implemented using Spiking Neural Network (SNN). The role of a system software for neuromorphic systems is to cluster a large machine learning model (e.g., with many neurons and synapses) and map these clusters to the computing resources of the hardware. In this work, we formulate the energy consumption of a neuromorphic hardware, considering the power consumed by neurons and synapses, and the energy consumed in communicating spikes on the interconnect. Based on such formulation, we first evaluate the role of a system software in managing the energy consumption of neuromorphic systems. Next, we formulate a simple heuristic-based mapping approach to place the neurons and synapses onto the computing resources to reduce energy consumption. We evaluate our approach with 10 machine learning applications and demonstrate that the proposed mapping approach leads to a significant reduction of energy consumption of neuromorphic computing systems.
Time-inhomogeneous Gaussian stochastic volatility models: Large deviations and super roughness

Abstract We introduce time-inhomogeneous stochastic volatility models, in which the volatility is described by a nonnegative function of a Volterra type continuous Gaussian process that may have very rough sample paths. The main results obtained in the paper are sample path and small-noise large deviation principles for the log-price process in a time-inhomogeneous super rough Gaussian model under very mild restrictions. We use these results to study the asymptotic behavior of binary barrier options, exit time probability functions, and call options.
Enhancing into the Codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders

Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech out-put. However, these models are tightly coupled with speech content, and produce unintended outputs in noisy conditions. Based on VQ-VAE autoencoders with WaveRNN decoders, we develop compressor-enhancer encoders and accompanying decoders, and show that they operate well in noisy conditions. We also observe that a compressor-enhancer model performs better on clean speech inputs than a compressor model trained only on clean speech.
A termination-independent role of Rat1 in cotranscriptional splicing.

Rat1 is a 5'→3' exoribonuclease in budding yeast. It is a highly conserved protein with homologs being present in fission yeast, flies, worms, mice and humans. Rat1 and its human homolog Xrn2 have been implicated in multiple nuclear processes. Here we report a novel role of Rat1 in mRNA splicing. We observed an increase in the level of unspliced transcripts in mutants of Rat1. Accumulation of unspliced transcripts was not due to the surveillance role of Rat1 in degrading unspliced mRNA, or an indirect effect of Rat1 function in termination of transcription or on the level of splicing factors in the cell, or due to an increased elongation rate in Rat1 mutants. ChIP-Seq analysis revealed Rat1 crosslinking to the introns of a subset of yeast genes. Mass spectrometry and coimmunoprecipitation revealed an interaction of Rat1 with the Clf1, Isy1, Yju2, Prp43 and Sub2 splicing factors. Furthermore, recruitment of splicing factors on the intron was compromised in the Rat1 mutant. Based on these findings we propose that Rat1 has a novel role in splicing of mRNA in budding yeast. Rat1, however, is not a general splicing factor as it crosslinks to only long introns with an average length of 400 nucleotides.
Optimization of quantum-dot qubit fabrication via machine learning

Precise nanofabrication represents a critical challenge to developing semiconductor quantum-dot qubits for practical quantum computation. Here, we design and train a convolutional neural network to interpret scanning electron micrographs and quantify qualitative features affecting device functionality. The high-throughput strategy is exemplified by optimizing a model lithographic process within a five-dimensional design space and by demonstrating a robust approach to address lithographic proximity effects. The results emphasize the benefits of machine learning for developing stable processes, shortening development cycles, and enforcing quality control during qubit fabrication.
Extending color vision methods to bands beyond the visible

There is a rapid growth in sensor technology in regions of the electromagnetic spectrum beyond the visible. Also, the parallel growth of powerful, inexpensive computers and digital electronics has made many new imaging applications possible. Although there are many approaches to sensor fusion, this paper provides a discussion of relevant infrared phenomenology and attempts to apply known methods of human color vision to achieve image fusion. Two specific topics of importance are color contrast enhancement and color constancy.
A Tribute to Margaret Grace Stineman, MD.

ABSTRACT The field of physical medicine and rehabilitation mourns the death of Margaret Grace Stineman, MD. She was an incredibly productive researcher who helped to shape the delivery of rehabilitation care. She was a trusted colleague, mentor, and friend to many. Her outstanding accomplishments were acknowledged by her numerous awards and her election into honorary societies. Dr Stineman spent her career at the University of Pennsylvania and retired as Professor Emeritus in 2014. She is survived by her mother and innumerable colleagues and friends who were touched by her passion, intelligence, and dedication.
Effects of Coulomb coupling on friction in strongly magnetized plasmas

The friction force on a test particle traveling through a plasma that is both strongly coupled and strongly magnetized is studied using molecular dynamics simulations. In addition to the usual stopping power component aligned antiparallel to the velocity, a transverse component that is perpendicular to both the velocity and Lorentz force is observed. This component, which was previously only characterized in weakly coupled plasmas, is found to increase in both absolute and relative magnitude in the strongly coupled regime. Strong coupling is also observed to induce a third component of the friction force in the direction of the Lorentz force. These first-principles simulations reveal novel physics associated with collisions in strongly coupled, strongly magnetized plasmas that are not predicted by existing kinetic theories. The effect is expected to influence macroscopic transport in a number of laboratory experiments and astrophysical plasmas.
How many complaints against police officers can be abated by incapacitating a few “bad apples?”

The notion that the unjustified use of force by police officers is concentrated amongst a few "bad apples" is a popular descriptor which has gained traction in scholarly research and achieved considerable influence among policymakers. But is removing the bad apples likely to have an appreciable effect on police misconduct? Leveraging a simple policy simulation and data from the Chicago Police Department, we estimate that removing the top 10 percent of officers identified based on ex ante risk and replacing them with officers drawn from the middle of the risk distribution would have led to only a 4-6 percent reduction in use of force incidents in Chicago over a ten-year period. Our analysis suggests that surgically removing predictably problematic police officers is unlikely to have a large impact on citizen complaints. By assembling some of the first empirical evidence on the likely magnitude of incapacitation effects, we provide critical support for the idea that early warning systems must be designed, above all, to deter problematic behavior and promote accountability.
Hybrid Polymer-Garnet Materials for All-Solid-State Energy Storage Devices

Hybrid electrolyte materials comprising polymer-ionic salt matrixes embedded with garnet particles constitute a promising class of materials for the realization of all-solid-state batteries. In addition to providing solutions to the safety issues inherent to current liquid electrolytes, hybrid polymer electrolytes offer advantages over other solid-state electrolytes. This is because their functional properties such as ionic conductivity, electrochemical stability, and mechanical and thermal properties can be tailored to a particular application by independently optimizing the properties of the constituent materials. This independent optimization permits the rational design of solid-state electrolytes, thereby solving the current bottlenecks that prevent their practical implementation into battery devices. This Mini-Review starts with a survey of solid-state electrolytes, focusing on their materials and ion transport limitations. Next, we summarize the current understanding of transport mechanisms in composite polymer electrolytes (CPEs) with the purpose of identifying materials' solutions for further improving their properties. The overall goal of the Mini-Review is to foster heightened research interest in these hybrid structures to rapidly advance development of future all-solid-state battery devices.
CXCR3/CXCR3 Ligand Biological Axis Impairs RENCA Tumor Growth by a Mechanism of Immunoangiostasis

Metastatic renal cell carcinoma (RCC) responds poorly to chemo- or radiation therapy but appears to respond to systemic immunotherapy (i.e., IL-2 and/or IFN-alpha), albeit with only 5-10% durable response. The CXCR3/CXCR3 ligand biological axis plays an important role in mediating type 1 cytokine-dependent cell-mediated immunity, which could be beneficial for attenuating RCC if optimized. We found that systemic IL-2 induced the expression of CXCR3 on circulating mononuclear cells but impaired the CXCR3 ligand chemotactic gradient from plasma to tumor by increasing circulating CXCR3 ligand levels in a murine model of RCC. Moreover, the antitumor effect of systemic IL-2 was CXCR3-dependent, as IL-2 failed to inhibit tumor growth and angiogenesis in CXCR3-/- mice. We hypothesized that the immunotherapeutic effect of the CXCR3/CXCR3 ligand biological axis could be optimized by first priming with systemic IL-2 to induce CXCR3 expression on circulating mononuclear cells followed by enhancing the intratumor CXCR3 ligand levels to establish optimal CXCR3-dependent chemotactic gradient. We found that combined systemic IL-2 with an intratumor CXCR3 ligand (CXCL9) lead to significantly greater reduction in tumor growth and angiogenesis, increased tumor necrosis, and increased intratumor infiltration of CXCR3+ mononuclear cells, as compared with either IL-2 or CXCL9 alone. The enhanced antitumor effect of the combined strategy was associated with a more optimized CXCR3-dependent chemotactic gradient and increased tumor-specific immune response. These data suggest that the combined strategy of systemic IL-2 with intratumor CXCR3 ligand is more efficacious than either strategy alone for reducing tumor-associated angiogenesis and augmenting tumor-associated immunity, the concept of immunoangiostasis.
Convergence rates for Bayesian estimation and testing in monotone regression

Shape restrictions such as monotonicity on functions often arise naturally in statistical modeling. We consider a Bayesian approach to the estimation of a monotone regression function and testing for monotonicity. We construct a prior distribution using piecewise constant functions. For estimation, a prior imposing monotonicity of the heights of these steps is sensible, but the resulting posterior is harder to analyze theoretically. We consider a “projection-posterior” approach, where a conjugate normal prior is used, but the monotonicity constraint is imposed on posterior samples by a projection map onto the space of monotone functions. We show that the resulting posterior contracts at the optimal rate n−1∕3 under the L1-metric and at a nearly optimal rate under the empirical Lp-metrics for 0<p≤2. The projection-posterior approach is also computationally more convenient. We also construct a Bayesian test for the hypothesis of monotonicity using the posterior probability of a shrinking neighborhood of the set of monotone functions. We show that the resulting test has a universal consistency property and obtain the separation rate which ensures that the resulting power function approaches one.
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning.

In this paper we revisit the idea of pseudo-labeling in the context of semi-supervised learning where a learning algorithm has access to a small set of labeled samples and a large set of unlabeled samples. Pseudo-labeling works by applying pseudo-labels to samples in the unlabeled set by using a model trained on combination of the labeled samples and any previously pseudo-labeled samples, and iteratively repeating this process in a self-training cycle. Current methods seem to have abandoned this approach in favor of consistency regularization methods that train models under a combination of different styles of self-supervised losses on the unlabeled samples and standard supervised losses on the labeled samples. We empirically demonstrate that pseudo-labeling can in fact be competitive with the state-of-the-art, while being more resilient to out-of-distribution samples in the unlabeled set. We identify two key factors that allow pseudo-labeling to achieve such remarkable results (1) applying curriculum learning principles and (2) avoiding concept drift by restarting model parameters before each self-training cycle. We obtain 94.91% accuracy on CIFAR-10 using only 4,000 labeled samples, and 68.87% top-1 accuracy on Imagenet-ILSVRC using only 10% of the labeled samples.
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pretraining. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pretraining data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [54] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.1
Near-Body Grid Adaption for Overset Grids

A solution adaption capability for curvilinear near-body grids has been implemented in the OVERFLOW overset grid computational fluid dynamics code. The approach follows closely that used for the Cartesian off-body grids, but inserts refined grids in the computational space of original near-body grids. Refined curvilinear grids are generated using parametric cubic interpolation, with one-sided biasing based on curvature and stretching ratio of the original grid. Sensor functions, grid marking, and solution interpolation tasks are implemented in the same fashion as for off-body grids. A goal-oriented procedure, based on largest error first, is included for controlling growth rate and maximum size of the adapted grid system. The adaption process is almost entirely parallelized using MPI, resulting in a capability suitable for viscous, moving body simulations. Two- and three-dimensional examples are presented.
RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images

Robust and accurate nuclei centroid detection is important for the understanding of biological structures in fluorescence microscopy images. Existing automated nuclei localization methods face three main challenges: (1) Most of object detection methods work only on 2D images and are difficult to extend to 3D volumes; (2) Segmentation-based models can be used on 3D volumes but it is computational expensive for large microscopy volumes and they have difficulty distinguishing different instances of objects; (3) Hand annotated ground truth is limited for 3D microscopy volumes. To address these issues, we present a scalable approach for nuclei centroid detection of 3D microscopy volumes. We describe the RCNN-SliceNet to detect 2D nuclei centroids for each slice of the volume from different directions and 3D agglomerative hierarchical clustering (AHC) is used to estimate the 3D centroids of nuclei in a volume. The model was trained with the synthetic microscopy data generated using Spatially Constrained Cycle-Consistent Adversarial Networks (SpCycle-GAN) and tested on different types of real 3D microscopy data. Extensive experimental results demonstrate that our proposed method can accurately count and detect the nuclei centroids in a 3D microscopy volume.
A Co-Fractionation Mass Spectrometry-based Prediction of Protein Complex Assemblies in the Developing Rice Aleurone-subaleurone

Multiprotein complexes execute and coordinate diverse cellular processes such as organelle biogenesis, vesicle trafficking, cell signaling, and metabolism. Knowledge about their composition and localization provides useful clues about the mechanisms of cellular homeostasis and system-level control. This is of great biological importance and practical significance in heterotrophic rice (Oryza sativa) endosperm and aleurone-subaleurone tissues, which are a primary source of seed vitamins and stored energy. Dozens of protein complexes have been implicated in the synthesis, transport, and storage of seed proteins, lipids, vitamins, and minerals. Mutations in protein complexes that control RNA transport result in aberrant endosperm with shrunken and floury phenotypes, significantly reducing seed yield and quality. The purpose of this study was to broadly predict protein complex composition in the aleurone-subaleurone layers of developing rice seeds using co-fractionation mass spectrometry. Following orthogonal chromatographic separations of biological replicates, thousands of protein elution profiles were subjected to distance-based clustering to enable large-scale multimerization state measurements and protein complex predictions. The predicted complexes had predicted functions across diverse functional categories, including novel heteromeric RNA binding protein complexes that may influence seed quality. This effective and open-ended proteomics pipeline provides useful clues about system-level posttranslational control during the early stages of rice seed development.
The essence of parallel Algol

We consider a parallel Algol-like language, combining procedures with shared-variable parallelism. Procedures permit encapsulation of common parallel programming idioms. Local variables provide a way to restrict interference between parallel commands. The combination of local variables, procedures, and parallelism supports a form of concurrent object-oriented programming. We provide a denotational semantics for this language, simultaneously adapting possible worlds to the parallel setting and generalizing transition traces to the procedural setting. This semantics supports reasoning about safety and liveness properties of parallel programs, and validates a number of natural laws of program equivalence based on noninterference properties of local variables. The semantics also validates familiar laws of functional programming. We also provide a relationally parallel semantics. This semantics supports standard methods of reasoning about representational independence, adapted to shared-variable programs. The clean design of the programming language and its semantics shows that procedures and shared-variable parallelism can be combined smoothly.
Conformal Thin Film Packaging for SiC Sensor Circuits in Harsh Environments

In this investigation sputtered silicon carbide annealed at 300 C for one hour is used as a conformal thin film package. A RF magnetron sputterer was used to deposit 500 nm silicon carbide films on gold metal structures on alumina wafers. To determine the reliability and resistance to immersion in harsh environments, samples were submerged in gold etchant for 24 hours, in BOE for 24 hours, and in an O2 plasma etch for one hour. The adhesion strength of the thin film was measured by a pull test before and after the chemical immersion, which indicated that the film has an adhesion strength better than 10(exp 8) N/m2; this is similar to the adhesion of the gold layer to the alumina wafer. MIM capacitors are used to determine the dielectric constant, which is dependent on the SiC anneal temperature. Finally, to demonstrate that the SiC, conformal, thin film may be used to package RF circuits and sensors, an LC resonator circuit was fabricated and tested with and without the conformal SiC thin film packaging. The results indicate that the SiC coating adds no appreciable degradation to the circuits RF performance. Index Terms Sputter, silicon carbide, MIM capacitors, LC resonators, gold etchants, BOE, O2 plasma
Functions with integer-valued divided differences

Abstract Let s 0 , s 1 , s 2 , … be a sequence of rational numbers whose mth divided difference δ m s is integer-valued. We prove that s n is a polynomial function in n if | s n | ≪ θ n for some positive number θ satisfying θ e 1 + 1 2 + ⋯ + 1 m − 1 .
On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions

When applying imitation learning techniques to fit a policy from expert demonstrations, one can take advantage of prior stability/robustness assumptions on the expert's policy and incorporate such control-theoretic prior knowledge explicitly into the learning process. In this paper, we formulate the imitation learning of linear policies as a constrained optimization problem, and present efficient methods which can be used to enforce stability and robustness constraints during the learning processes. Specifically, we show that one can guarantee the closed-loop stability and robustness by posing linear matrix inequality (LMI) constraints on the fitted policy. Then both the projected gradient descent method and the alternating direction method of multipliers (ADMM) method can be applied to solve the resultant constrained policy fitting problem. Finally, we provide numerical results to demonstrate the effectiveness of our methods in producing linear polices with various stability and robustness guarantees.
The Realization Problem for Dilation Surfaces

Dilation surfaces, or twisted quadratic differentials, are variants of translation surfaces. In this paper, we study the question of what elements or subgroups of the mapping class group can be realized as affine automorphisms of dilation surfaces. We show that dilation surfaces can have exotic Dehn twists in their affine automorphism groups and will establish that only certain types of mapping class group elements can arise as affine automorphisms of dilation surfaces. We also generalize a construction of Thurston that constructs a translation surface from a pair of filling multicurves to dilation surfaces. This construction will give us dilation surfaces that realize a pair of Dehn multitwists in their affine automorphism groups.
Multiperiodic orbits from interacting soft spots in cyclically sheared amorphous solids

When an amorphous solid is deformed cyclically, it may reach a steady state in which the paths of constituent particles trace out closed loops that repeat in each driving cycle. A remarkable variant has been noticed in simulations where the period of particle motions is a multiple of the period of driving, but the reasons for this behavior have remained unclear. Motivated by mesoscopic features of displacement fields in experiments on jammed solids, we propose and analyze a simple model of interacting soft spots-locations where particles rearrange under stress and that resemble two-level systems with hysteresis. We show that multiperiodic behavior can arise among just three or more soft spots that interact with each other, but in all cases it requires frustrated interactions, illuminating this otherwise elusive type of interaction. We suggest directions for seeking this signature of frustration in experiments and for achieving it in designed systems.
Optimal nonparametric inference via deep neural network

Abstract Deep neural network is a state-of-art method in modern science and technology. Much statistical literature have been devoted to understanding its performance in nonparametric estimation, whereas the results are suboptimal due to a redundant logarithmic sacrifice. In this paper, we show that such log-factors are not necessary. We derive upper bounds for the L 2 minimax risk in nonparametric estimation. Sufficient conditions on network architectures are provided such that the upper bounds become optimal (without log-sacrifice). Our proof relies on an explicitly constructed network estimator based on tensor product B-splines. We also derive asymptotic distributions for the constructed network and a relating hypothesis testing procedure. The testing procedure is further proved as minimax optimal under suitable network architectures.