cssi/SciSciGPT-SciSciNet huggingface dataset:
Upper bounds for the spectral function on homogeneous spaces via volume growth

We use spectral embeddings to give upper bounds on the spectral function of the Laplace–Beltrami operator on homogeneous spaces in terms of the volume growth of balls. In the case of compact manifolds, our bounds extend the 1980 lower bound of Peter Li for the smallest positive eigenvalue to all eigenvalues. We also improve Li’s bound itself. Our bounds translate to explicit upper bounds on the heat kernel for both compact and noncompact homogeneous spaces.
Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses.

The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG ). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e., pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.
Self‐Powered Sensors Enabled by Wide‐Bandgap Perovskite Indoor Photovoltaic Cells

We present a new approach to ubiquitous sensing for indoor applications, using high-efficiency and low-cost indoor perovksite photovoltaic cells as external power sources for backscatter sensors. We demonstrate wide-bandgap perovskite photovoltaic cells for indoor light energy harvesting with the 1.63eV and 1.84 eV devices demonstrate efficiencies of 21% and 18.5% respectively under indoor compact fluorescent lighting, with a champion open-circuit voltage of 0.95 V in a 1.84 eV cell under a light intensity of 0.16 mW/cm2. Subsequently, we demonstrate a wireless temperature sensor self-powered by a perovskite indoor light-harvesting module. We connect three perovskite photovoltaic cells in series to create a module that produces 14.5 uW output power under 0.16 mW/cm2 of compact fluorescent illumination with an efficiency of 13.2%. We use this module as an external power source for a battery-assisted RFID temperature sensor and demonstrate a read range by of 5.1 meters while maintaining very high frequency measurements every 1.24 seconds. Our combined indoor perovskite photovoltaic modules and backscatter radio-frequency sensors are further discussed as a route to ubiquitous sensing in buildings given their potential to be manufactured in an integrated manner at very low-cost, their lack of a need for battery replacement and the high frequency data collection possible.
Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera

Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving. However, depth completion faces 3 main challenges: the irregularly spaced pattern in the sparse depth input, the difficulty in handling multiple sensor modalities (when color images are available), as well as the lack of dense, pixel-level ground truth depth labels for training. In this work, we address all these challenges. Specifically, we develop a deep regression model to learn a direct mapping from sparse depth (and color images) input to dense depth prediction. We also propose a self-supervised training framework that requires only sequences of color and sparse depth images, without the need for dense depth labels. Our experiments demonstrate that the self-supervised framework outperforms a number of existing solutions trained with semi-dense annotations. Furthermore, when trained with semi-dense annotations, our network attains state-of-the-art accuracy and is the winning approach on the KITTI depth completion benchmark1 at the time of submission.1http://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_completion
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals

We consider the problem of computing the best-fitting ReLU with respect to square-loss on a training set when the examples have been drawn according to a spherical Gaussian distribution (the labels can be arbitrary). Let $\opt < 1$ be the population loss of the best-fitting ReLU. We prove: \begin{itemize} \item Finding a ReLU with square-loss $\opt + \epsilon$ is as hard as the problem of learning sparse parities with noise, widely thought to be computationally intractable. This is the first hardness result for learning a ReLU with respect to Gaussian marginals, and our results imply --{\em unconditionally}-- that gradient descent cannot converge to the global minimum in polynomial time. \item There exists an efficient approximation algorithm for finding the best-fitting ReLU that achieves error $O(\opt^{2/3})$. The algorithm uses a novel reduction to noisy halfspace learning with respect to $0/1$ loss. \end{itemize} Prior work due to Soltanolkotabi \cite{soltanolkotabi2017learning} showed that gradient descent {\em can} find the best-fitting ReLU with respect to Gaussian marginals, if the training set is {\em exactly} labeled by a ReLU.
Efficient Constructions for Almost-Everywhere Secure Computation

We study the problem of almost-everywhere reliable message transmission; a key component in designing efficient and secure Multi-party Computation (MPC) protocols for sparsely connected networks. The goal is to design low-degree networks which allow a large fraction of honest nodes to communicate reliably even when a small constant fraction of nodes experience byzantine corruption and deviate arbitrarily from the assigned protocol.
The SKYNIVI Experience: Evoking Startle and Frustration in Dyads and Single Drivers

To study naturalistic in-cabin emotion we developed SKYNIVI, a modified open source driving simulator, with scenarios designed to elicit startle and frustration. We target generating these emotions because we believe that by detecting these it will be possible for autonomous vehicles to learn to drive better. We show how to use SKYNIVI to develop datasets that capture naturalistic emotions in drivers and passengers for algorithmic development. We recruited 51 participants as dyads and single drivers to participate in two different scenarios. We show that we were able to evoke hundreds of instances of our target emotions in this cohort and present an analysis of factors we found to impact emotional expression including: scenario design $\mathbf{(\mathrm{p} , demographic factors, personality and baseline affect $\mathbf{(\mathrm{p} . We find that having a second person in the vehicle impacts observed expressions of emotion $\mathbf{(\mathrm{p} even when no difference in baseline affect is reported.
Primal–dual weak Galerkin finite element methods for elliptic Cauchy problems

Abstract The authors propose and analyze a well-posed numerical scheme for a type of ill-posed elliptic Cauchy problem by using a constrained minimization approach combined with the weak Galerkin finite element method. The resulting Euler–Lagrange formulation yields a system of equations involving the original equation for the primal variable and its adjoint for the dual variable, and is thus an example of the primal–dual weak Galerkin finite element method. This new primal–dual weak Galerkin algorithm is consistent in the sense that the system is symmetric, well-posed, and is satisfied by the exact solution. A certain stability and error estimates were derived in discrete Sobolev norms, including one in a weak L 2 topology. Some numerical results are reported to illustrate and validate the theory developed in the paper.
Composite mechanics for engine structures

Recent research activities and accomplishments at Lewis Research Center on composite mechanics for engine structures are summarized. The activities focused mainly on developing procedures for the computational simulation of composite intrinsic and structural behavior. The computational simulation encompasses all aspects of composite mechanics, advanced three-dimensional finite-element methods, damage tolerance, composite structural and dynamic response, and structural tailoring and optimization. 16 references.
A New Way of Estimating Distances to Foregoing Vehicles from a Monocular Camera with Error Modeling

A method that can robustly estimate the distance to a foregoing vehicle with a monocular camera is in high demand for developing inexpensive Adaptive Cruise Control (ACC) systems. The accuracy of the estimated distances heavily depends on the road geometry and the true distance to the target vehicle. That motivates us to develop a distance estimation method that has two important properties: (a) it can deal with non-planar road surfaces from the first principle, and (b) it can estimate not only the distance but its variance of the error through mathematical analyses. Experiments on the KITTI dataset demonstrated that the proposed distance estimator is more robust and stable than the planar assumption model, and we verified by Monte Carlo simulations that our error model correctly predicts the variance of the error in the estimated distances.
Mobile Phone Data for Children on the Move: Challenges and Opportunities

Today, 95% of the global population has 2G mobile phone coverage (GSMA 2017) and the number of individuals who own a mobile phone is at an all time high. Mobile phones generate rich data on billions of people across different societal contexts and have in the last decade helped redefine how we do research and build tools to understand society. As such, mobile phone data have the potential to revolutionize how we tackle humanitarian problems, such as many suffered by refugees all over the world (United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development. A world that counts: Mobilising the data revolution for sustainable development, 2014 [64]). While promising, mobile phone data and the new computational approaches bring both opportunities and challenges (Blumenstock in Estimating economic characteristics with phone data, pp. 72–76, 2018 [9]). Mobile phone traces contain detailed information regarding people’s whereabouts, social life, and even financial standing. Therefore, developing and adopting strategies that open data up to the wider humanitarian and international development community for analysis and research while simultaneously protecting the privacy of individuals are of paramount importance (UNDG 2018). Here we outline the challenging situation of children on the move and actions UNICEF is pushing in helping displaced children and youth globally, and discuss opportunities where mobile phone data can be used. We identify three key challenges: data access, data and algorithmic bias, and operationalization of research, which need to be addressed if mobile phone data are to be successfully applied in humanitarian contexts.
ME R-CNN: Multi-Expert R-CNN for Object Detection

We introduce Multi-Expert Region-based Convolutional Neural Network (ME R-CNN) which is equipped with multiple experts (ME) where each expert is learned to process a certain type of regions of interest (RoIs). This architecture better captures the appearance variations of the RoIs caused by different shapes, poses, and viewing angles. In order to direct each RoI to the appropriate expert, we devise a novel “learnable” network, which we call, expert assignment network (EAN). EAN automatically learns the optimal RoI-expert relationship even without any supervision of expert assignment. As the major components of ME R-CNN, ME and EAN, are mutually affecting each other while tied to a shared network, neither an alternating nor a naive end-to-end optimization is likely to fail. To address this problem, we introduce a practical training strategy which is tailored to optimize ME, EAN, and the shared network in an end-to-end fashion. We show that both of the architectures provide considerable performance increase over the baselines on PASCAL VOC 07, 12, and MS COCO datasets.
Unsupervised Representation Adversarial Learning Network: from Reconstruction to Generation

A good representation for arbitrarily complicated data should have the capability of semantic generation, clustering and reconstruction. Previous research has already achieved impressive performance on either one. This paper aims at learning a disentangled representation effective for all of them in an unsupervised way. To achieve all the three tasks together, we learn the forward and inverse mapping between data and representation on the basis of a symmetric adversarial process. In theory, we minimize the upper bound of the two conditional entropy loss between the latent variables and the observations together to achieve the cycle consistency. The newly proposed RepGAN is tested on MNIST, fashionMNIST, CelebA, and SVHN datasets to perform unsupervised classification, generation and reconstruction tasks. The result demonstrates that RepGAN is able to learn a useful and competitive representation. To the author’s knowledge, our work is the first one to achieve both a high unsupervised classification accuracy and low reconstruction error on MNIST.
Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifies volumes by checking if at least a sufficient number of voxels is segmented as tumors, by which we can provide radiologists with tumor locations. In order to deal with tumors with different scales, we train and test our volumetric segmentation networks with multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is used to filter out outliers and reduce false alarms. We collect a new dataset containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303 cases are normal, which is the largest set for PDAC tumors to the best of our knowledge. To offer the best trade-off between sensitivity and specificity, our proposed framework reports a sensitivity of \(94.1\%\) at a specificity of \(98.5\%\), which demonstrates the potential to make a clinical impact.
A Three-phase Approach to Grain Surface Chemistry in Protoplanetary Disks: Gas, Ice Surfaces, and Ice Mantles of Dust Grains

We study the effects of grain surface reactions on the chemistry of protoplanetary disks where gas, ice surface layers and icy mantles of dust grains are considered as three distinct phases. Gas phase and grain surface chemistry is found to be mainly driven by photo-reactions and dust temperature gradients. The icy disk interior has three distinct chemical regions: (i) the inner midplane with low FUV fluxes and warm dust ($\gtrsim 15$K) that lead to the formation of complex organic molecules, (ii) the outer midplane with higher FUV from the ISM and cold dust where hydrogenation reactions dominate and, (iii) a molecular layer above the midplane but below the water condensation front where photodissociation of ices affects gas phase compositions. Some common radicals, e.g., CN and C$_2$H, exhibit a two-layered vertical structure and are abundant near the CO photodissociation front and near the water condensation front. The 3-phase approximation in general leads to lower vertical column densities than 2-phase models for many gas-phase molecules due to reduced desorption, e.g., H$_2$O, CO$_2$, HCN and HCOOH decrease by $\sim$ two orders of magnitude. Finally, we find that many observed gas phase species originate near the water condensation front; photo-processes determine their column densities which do not vary significantly with key disk properties such as mass and dust/gas ratio.
Analysis of Distributed Congestion Control in Cellular Vehicle-to-Everything Networks

Cellular Vehicle-to-everything (C-V2X) communication has been proposed in the 3rd Generation Partnership Project release 14 standard to address the latency and reliability requirements of cooperative safety applications. Such applications can involve highly congested vehicular scenarios where the network experiences high data loads. Thus, a sophisticated congestion control solution is vital in order to maintain the network performance required for safety-related applications. With the aid of our high-fidelity link-level network simulator, we investigate the feasibility of implementing the distributed congestion control algorithm specified in SAE J2945/1 standard on top of the C-V2X stack. We describe our implementation and evaluate the performance of transmission rate and range control mechanisms using relevant metrics. Additionally, we identify areas for potential design enhancements and further investigation.
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning

Off-policy estimation for long-horizon problems is important in many real-life applications such as healthcare and robotics, where high-fidelity simulators may not be available and on-policy evaluation is expensive or impossible. Recently, \citet{liu18breaking} proposed an approach that avoids the curse of horizon suffered by typical importance-sampling-based methods. While showing promising results, this approach is limited in practice as it requires data being collected by a known behavior policy. In this work, we propose a novel approach that eliminates such limitations. In particular, we formulate the problem as solving for the fixed point of a "backward flow" operator and show that the fixed point solution gives the desired importance ratios of stationary distributions between the target and behavior policies. We analyze its asymptotic consistency and finite-sample generalization. Experiments on benchmarks verify the effectiveness of our proposed approach.
The effects of porous material on microbubble skin friction reduction

Abstract : Previous results have shown that the injection of a high concentration of microbubbles into a turbulent boundary layer can produce sizeable skin friction reductions. The present paper investigates the role of the porous material in this phenomenon. A variety of different porous surfaces have been tested: a series of sintered metal plates with widely different pore sizes; a specially constructed porous material, composed of plastic strips; and two different porous section lengths. The effects of gravity were determined by changing the orientation of the porous surface with respect to the boundary layer. A notable conclusion is that the skin friction reduction is not critically related to the characteristics of the porous material.
A statistical framework for measuring the temporal stability of human mobility patterns

Despite the growing popularity of human mobility studies that collect GPS location data, the problem of determining the minimum required length of GPS monitoring has not been addressed in the curre...
A comparison of eDNA to camera trapping for assessment of terrestrial mammal diversity

Before environmental DNA (eDNA) can establish itself as a robust tool for biodiversity monitoring, comparison with existing approaches is necessary, yet is lacking for terrestrial mammals. Moreover, much is unknown regarding the nature, spread and persistence of DNA shed by animals into terrestrial environments, or the optimal experimental design for understanding these potential biases. To address some of these challenges, we compared the detection of terrestrial mammals using eDNA analysis of soil samples against confirmed species observations from a long-term (approx. 9-year) camera-trapping study. At the same time, we considered multiple experimental parameters, including two sampling designs, two DNA extraction kits and two metabarcodes of different sizes. All mammals regularly recorded with cameras were detected in eDNA. In addition, eDNA reported many unrecorded small mammals whose presence in the study area is otherwise documented. A long metabarcode (≈220 bp) offering a high taxonomic resolution, achieved a similar efficiency as a shorter one (≈70 bp) and a phosphate buffer-based extraction gave similar results as a total DNA extraction method, for a fraction of the price. Our results support that eDNA-based monitoring should become a valuable part of ecosystem surveys, yet mitochondrial reference databases need to be enriched first.
Game of Variable Contributions to the Common Good Under Uncertainty

When multiple agents have an opportunity to invest in improving the common good, the free rider problem arises. Many businesses and policymakers grapple with these free rider problems arising from ...
Feasibility study of a compact neutron resonance transmission analysis instrument

Neutron Resonance Transmission Analysis (NRTA) uses resonant absorption of neutrons to infer the absolute isotopic composition of a target object, enabling applications in a broad range of fields such as archeology, enrichment analysis of nuclear fuel, and arms control treaty verification. In the past, NRTA involved large user facilities and complex detector systems. However, recent advances in the intensity of compact neutron sources have made compact neutron imaging designs increasingly feasible. This work describes the Monte Carlo (MC) based design of a compact epithermal NRTA radiographic instrument, which uses a moderated, compact deuterium-tritium neutron source and an epithermal neutron detector. Such an instrument would have a wide range of applications and would be especially impactful for scenarios such as nuclear inspection and arms control verification exercises, where system complexity and mobility may be of critical importance. The MC simulations presented in this work demonstrate accurate time-of-flight reconstructions for transmitted neutron energies, capable of differentiating isotopic compositions of nuclear material with high levels of accuracy. A new generation of miniaturized and increasingly more intense neutron sources will allow this technique to achieve measurements with greater precision and speed, with significant impact on a variety of engineering and societal problems.Neutron Resonance Transmission Analysis (NRTA) uses resonant absorption of neutrons to infer the absolute isotopic composition of a target object, enabling applications in a broad range of fields such as archeology, enrichment analysis of nuclear fuel, and arms control treaty verification. In the past, NRTA involved large user facilities and complex detector systems. However, recent advances in the intensity of compact neutron sources have made compact neutron imaging designs increasingly feasible. This work describes the Monte Carlo (MC) based design of a compact epithermal NRTA radiographic instrument, which uses a moderated, compact deuterium-tritium neutron source and an epithermal neutron detector. Such an instrument would have a wide range of applications and would be especially impactful for scenarios such as nuclear inspection and arms control verification exercises, where system complexity and mobility may be of critical importance. The MC simulations presented in this work demonstrate accurate t...
A Pan-Cancer and Polygenic Bayesian Hierarchical Model for the Effect of Somatic Mutations on Survival

We built a novel Bayesian hierarchical survival model based on the somatic mutation profile of patients across 50 genes and 27 cancer types. The pan-cancer quality allows for the model to "borrow" information across cancer types, motivated by the assumption that similar mutation profiles may have similar (but not necessarily identical) effects on survival across different tissues of origin or tumor types. The effect of a mutation at each gene was allowed to vary by cancer type, whereas the mean effect of each gene was shared across cancers. Within this framework, we considered 4 parametric survival models (normal, log-normal, exponential, and Weibull), and we compared their performance via a cross-validation approach in which we fit each model on training data and estimate the log-posterior predictive likelihood on test data. The log-normal model gave the best fit, and we investigated the partial effect of each gene on survival via a forward selection procedure. Through this we determined that mutations at TP53 and FAT4 were together the most useful for predicting patient survival. We validated the model via simulation to ensure that our algorithm for posterior computation gave nominal coverage rates. The code used for this analysis can be found at https://github.com/sarahsamorodnitsky/Pan-Cancer-Survival-Modeling.git, and the results are summarized at http://ericfrazerlock.com/surv_figs/SurvivalDisplay.html.
Intestinal Bacteria Maintain Adult Enteric Nervous System and Nitrergic Neurons via Toll-like Receptor 2-induced Neurogenesis in Mice.

Background & Aims The enteric nervous system (ENS) exists in close proximity to luminal bacteria. Intestinal microbes regulate ENS development, but little is known about their effects on adult enteric neurons. We investigated whether intestinal bacteria or their products affect the adult ENS via toll-like receptors (TLRs) in mice. Methods We performed studies with conventional C57/BL6, germ-free C57/BL6, Nestin-creERT2:tdTomato, Nestin-GFP, and ChAT-cre:tdTomato. Mice were given drinking water with ampicillin or without (controls). Germ-free mice were given drinking water with TLR2 agonist or without (controls). Some mice were given a blocking antibody against TLR2 or a TLR4 inhibitor. We performed whole gut transit, bead latency, and geometric center studies. Feces were collected and analyzed by 16S ribosomal RNA gene sequencing. Longitudinal muscle myenteric plexus (LMMP) tissues were collected, analyzed by immunohistochemistry, and levels of nitric oxide were measured. Cells were isolated from colonic LMMP of Nestin-creERT2:tdTomato mice and incubated with agonists of TLR2 (receptor for gram-positive bacteria), TLR4 (receptor for gram-negative bacteria), or distilled water (control) and analyzed by flow cytometry. Results Stool from mice given ampicillin had altered composition of gut microbiota with reduced abundance of gram-positive bacteria and increased abundance of gram-negative bacteria, compared with mice given only water. Mice given ampicillin had reduced colon motility compared with mice given only water, and their colonic LMMP had reduced numbers of nitrergic neurons, reduced neuronal nitric oxide synthase production, and reduced colonic neurogenesis. Numbers of colonic myenteric neurons increased after mice were switched from ampicillin to plain water, with increased markers of neurogenesis. Nestin-positive enteric neural precursor cells expressed TLR2 and TLR4. In cells isolated from the colonic LMMP, incubation with the TLR2 agonist increased the percentage of neurons originating from enteric neural precursor cells to approximately 10%, compared with approximately 0.01% in cells incubated with the TLR4 agonist or distilled water. Mice given an antibody against TLR2 had prolonged whole gut transit times; their colonic LMMP had reduced total neurons and a smaller proportion of nitrergic neurons per ganglion, and reduced markers of neurogenesis compared with mice given saline. Colonic LMMP of mice given the TLR4 inhibitor did not have reduced markers of neurogenesis. Colonic LMMP of germ-free mice given TLR2 agonist had increased neuronal numbers compared with control germ-free mice. Conclusions In the adult mouse colon, TLR2 promotes colonic neurogenesis, regulated by intestinal bacteria. Our findings indicate that colonic microbiota help maintain the adult ENS via a specific signaling pathway. Pharmacologic and probiotic approaches directed towards specific TLR2 signaling processes might be developed for treatment of colonic motility disorders related to use of antibiotics or other factors.
Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization

Full-waveform inversion is an important and widely used method to reconstruct subsurface velocity images. Waveform inversion is a typical nonlinear and ill-posed inverse problem. Existing physics-driven computational methods for solving waveform inversion suffer from the cycle-skipping and local-minima issues, and do not mention that solving waveform inversion is computationally expensive. In recent years, data-driven methods become a promising way to solve the waveform-inversion problem. However, most deep-learning frameworks suffer from the generalization and overfitting issue. In this article, we developed a real-time data-driven technique and we call it VelocityGAN, to reconstruct accurately the subsurface velocities. Our VelocityGAN is built on a generative adversarial network (GAN) and trained end to end to learn a mapping function from the raw seismic waveform data to the velocity image. Different from other encoder–decoder-based data-driven seismic waveform-inversion approaches, our VelocityGAN learns regularization from data and further imposes the regularization to the generator so that inversion accuracy is improved. We further develop a transfer-learning strategy based on VelocityGAN to alleviate the generalization issue. A series of experiments is conducted on the synthetic seismic reflection data to evaluate the effectiveness, efficiency, and generalization of VelocityGAN. We not only compare it with the existing physics-driven approaches and data-driven frameworks but also conduct several transfer-learning experiments. The experimental results show that VelocityGAN achieves the state-of-the-art performance among the baselines and can improve the generalization results to some extent.
A slip law for glaciers on deformable beds

Slip of marine-terminating ice streams over beds of deformable till is responsible for most of the contribution of the West Antarctic Ice Sheet to sea level rise. Flow models of the ice sheet and till-bedded glaciers elsewhere require a law that relates slip resistance, slip velocity, and water pressure at the bed. We present results of experiments in which pressurized ice at its melting temperature is slid over a water-saturated till bed. Steady-state slip resistance increases with slip velocity owing to sliding of ice across the bed, but above a threshold velocity, till shears at its rate-independent Coulomb strength. These results motivate a generalized slip law for glacier-flow models that combines processes of hard-bedded sliding and bed deformation.
Thermal Modeling of Direct Digital Melt-Deposition Processes

Additive manufacturing involves creating three-dimensional (3D) objects by depositing materials layer-by-layer. The freeform nature of the method permits the production of components with complex geometry. Deposition processes provide one more capability, which is the addition of multiple materials in a discrete manner to create “heterogeneous” objects with locally controlled composition and microstructure. The result is direct digital manufacturing (DDM) by which dissimilar materials are added voxel-by-voxel (a voxel is volumetric pixel) following a predetermined tool-path. A typical example is functionally gradient material such as a gear with a tough core and a wear-resistant surface. The inherent complexity of DDM processes is such that process modeling based on direct physics-based theory is difficult, especially due to a lack of temperature-dependent thermophysical properties and particularly when dealing with melt-deposition processes. In order to overcome this difficulty, an inverse problem approach is proposed for the development of thermal models that can represent multi-material, direct digital melt deposition. This approach is based on the construction of a numerical-algorithmic framework for modeling anisotropic diffusivity such as that which would occur during energy deposition within a heterogeneous workpiece. This framework consists of path-weighted integral formulations of heat diffusion according to spatial variations in material composition and requires consideration of parameter sensitivity issues.
Multi-frame interferometric imaging with a femtosecond stroboscopic pulse train for observing irreversible phenomena

We describe a high-speed single-shot multi-frame interferometric imaging technique enabling multiple interferometric images with femtosecond exposure time over a 50 ns event window to be recorded, following a single laser-induced excitation event. The stroboscopic illumination of a framing camera is made possible through the use of a doubling cavity that produces a femtosecond pulse train that is synchronized to the gated exposure windows of the individual frames of the camera. The imaging system utilizes a Michelson interferometer to extract phase and ultimately displacement information. We demonstrate the method by monitoring laser-induced deformation and the propagation of high-amplitude acoustic waves in a silicon nitride membrane. The method is applicable to a wide range of fast irreversible phenomena such as crack branching, shock-induced material damage, cavitation, and dielectric breakdown.
Links between autobiographical memory richness and temporal discounting in older adults

When making choices between smaller, sooner rewards and larger, later ones, people tend to discount future outcomes. Individual differences in temporal discounting in older adults have been associated with episodic memory abilities and entorhinal cortical thickness. The cause of this association between better memory and more future-oriented choice remains unclear, however. One possibility is that people with perceptually richer recollections are more patient because they also imagine the future more vividly. Alternatively, perhaps people whose memories focus more on the meaning of events (i.e., are more “gist-based”) show reduced temporal discounting, since imagining the future depends on interactions between semantic and episodic memory. We examined which categories of episodic details – perception-based or gist-based – are associated with temporal discounting in older adults. Older adults whose autobiographical memories were richer in perception-based details showed reduced temporal discounting. Furthermore, in an exploratory neuroanatomical analysis, both discount rates and perception-based details correlated with entorhinal cortical thickness. Retrieving autobiographical memories before choice did not affect temporal discounting, however, suggesting that activating episodic memory circuitry at the time of choice is insufficient to alter discounting in older adults. These findings elucidate the role of episodic memory in decision making, which will inform interventions to nudge intertemporal choices.
The interaction between p53 and Mdm2 is independent of MEG3–p53 association

The ability of the long noncoding RNA MEG3 to suppress cell proliferation led to its recognition as a tumor suppressor. MEG3 has previously been shown to bind to p53 in vitro, which led us to hypothesize that MEG3 functions by disrupting the interaction of p53 and its E3 ubiquitin ligase Mdm2. To test this hypothesis in vivo, we built a cross-nearest neighbor/Monte Carlo analytical method based on two color direct stochastic optical reconstruction microscopy (dSTORM), a single-molecule localization microscopy (SMLM) technique. Our data support the interaction of MEG3 and p53. Surprisingly, this association had no effect on the binding of p53 and Mdm2, distinct from the most commonly proposed model for the mechanism of MEG3 action. Additionally, our mathematical approach to analyzing SMLM data has general applicability to assessing molecular interactions in a native cellular context.
Ectopic hepatocellular carcinoma in the adrenal gland with inferior vena cava thrombosis and right atrial extension

Filippos-Paschalis R et al1 described a very interesting and unique case of ectopic hepatocellular carcinoma (HCC) in the adrenal gland with inferior vena cava thrombosis and right atrial extension. The patient developed respiratory failure and required an urgent operation. The right adrenal gland was removed through the abdominal approach, but cardiopulmonary bypass (CBP) was needed in removing the right atrium extension. The ascending aorta, superior vena cava, and the right femoral vein were cannulated for arterial and venous access, respectively. They achieved systemic hypothermia (250 C), and antegrade cold cardioplegia was administered. The aorta was cross-clamped, and another vascular clamp was placed between the left common carotid artery and left subclavian artery. The adrenal gland, the right atrium tumor, and IVC tumor thrombus were removed successfully. During the placement of the venous cannulas, the authors were very careful to avoid dislodging the tumor thrombus. The surgery was meticulously planned, and the patient had an uneventful post-operative course.Ectopic hepatocellular carcinoma in the adrenal gland is a very rare tumor, but all adrenal tumors can extend into the IVC and even into the right atrium.2,3 Of note, renal cell carcinoma (RCC) can have the same behavior of vascular extension into the IVC and right atrium.4 Once these tumors extend into the IVC and go into the chest, hepatic veins can be obstructed, causing Budd-Chiari syndrome (BCS).5 Figure 1 showed that hepatic veins and IVC were dilated and obstructed; thus, the patient probably had BCS in this situation. Under such condition, the use of CPB is a must in order to remove the tumor from the hepatic vein and to avoid liver congestion. Also, the patient presented to the emergency department with signs and symptoms of pulmonary emboli (PE). Some of these patients can present with PE, which is a tumor thrombus that embolizes into the pulmonary arteries. In some cases the PEs also need to be removed if it is safe for the patient.6The use of CPB is indicated in cases like the one described by Filippos-Paschalis et al.1 The tumor was probably too bulky to be removed without the use of CBP; otherwise, the risk of … developing with the use of CBP may be unacceptably high. There are select cases of RCC and adrenal HCC with tumor thrombus extension which can be removed safely from the right atrium and IVC without the use of CPB.2,7,8 It is important to remember that these tumors do not cause thrombosis of the IVC, as the tumor thrombus (different from thrombosis) extends into the IVC. Tumor thrombus can cause blood thrombosis below its location,9 making it difficult to be able to place a cannula in the femoral veins.These complex extreme surgeries usually require a multidisciplinary team or a transplant surgeon who specializes in approaching these types of cases.
COVID-19 Exposes Need for Progressive Criminal Justice Reform.

Modeling conducted by the Centers for Disease Control and Prevention calculates that as many as 160 to 214 million people in the United States could become infected by the 2019 novel coronavirus (S...
Developing Countries and Environmental Protection: The Effects of Ex Ante Versus Ex Post Contracting

In the 1992 Rio Earth Summit, developing countries (DCs) were adamant that in order to protect the environment for the future, new institutions were needed that would channel resources from the wealthy developed countries to the poor DCs. With this backdrop, I analyze the problem faced by an asymmetrically informed supranational governmental authority (SNGA) that wishes to design an International Environmental Agreement (IEA). The SNGA cannot contract directly with polluting firms in the various DCs; instead, the SNGA must deal with such firms through their governments. I study this tripartite hierarchical interaction and focus on the properties of the optimal ex ante and ex post IEAs, which can be implemented by the SNGA in two different scenarios. The analysis suggests that IEAs are not inherently doomed due to a basic monitoring and enforcement problem stemming from national sovereignty. Further, desirable levels of pollution abatement can result in a number of contractual settings.
Chapter 2 – Descriptive Statistics

Publisher Summary This chapter discusses descriptive statistics and presents graphs and tables that describe a set of data, as are quantities that are used to summarize certain of the key properties of the data set. To draw conclusions from data, it is necessary to have an understanding of the data's origin. The chapter deals with data whose set of values is grouped into different intervals. It also discusses ways of summarizing data sets by use of statistics, which are numerical quantities whose values are determined by the data. There are three statistics that are used to indicate the center of the data set: the sample mean, the sample median, and the sample mode. The statistics are used to indicate the spread of the values in the data set and introduces the sample variance and its square root, called the sample standard deviation. The chapter also deals with sample percentiles and Chebyshev's inequality for sample data. A graphical technique, called the scatter diagram, for presenting paired value data is also introduced.
A better incision for pectus excavatum repair: avoiding the keloid triangle

The most common approach to the pepair of pectus excavatum and pectus carinatum deformities is via a central transverse submammary incision. The subsequent suprasternal scar is conspicuous and prone to hypertrophic and keloid scarring. To avoid the “keloid triangle” and to produce a less noticeable scar, we have utilized bilateral inframammary incisions for repairs of five female and two male patients with pectus defects. This approach provides excellent access for cartilage resection, sternotomy, and sternal tupport without increasing opearative time or compromising operative exposure. On follow-up for up to 25 months, all patients have had excellent cosmetic and functional results. Chest wall configuration and stability, wound healing, and scar formation have all been without complication. No keloid or hypertrophic scars have developed. To date, there has been no recurrence of pectus defects. We believe bilateral inframammary incisions are a superior approach for pectus repairs by enchancing cosmesis with less noticeable scars and fewer hypertropic and keloid scars, all without compromising operative exposure or increasing operative time.
LEARNING-BY-DOING, ORGANIZATIONAL FORGETTING, AND INDUSTRY DYNAMICS

Learning-by-doing and organizational forgetting have been shown to be important in a variety of industrial settings. This paper provides a general model of dynamic competition that accounts for these economic fundamentals and shows how they shape industry structure and dynamics. Previously obtained results regarding the dominance properties of flrms’ pricing behavior no longer hold in this more general setting. We show that forgetting does not simply negate learning. Rather, learning and forgetting are distinct economic forces. In particular, a model with learning and forgetting can give rise to aggressive pricing behavior, market dominance, and multiple equilibria, whereas a model with learning alone cannot.
A review on sars-cov-2 virology, pathophysiology, animal models, and anti-viral interventions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of CoV disease 2019 (COVID-19) is a highly pathogenic and transmissible CoV that is presently plaguing the global human population and economy. No proven effective antiviral therapy or vaccine currently exists, and supportive care remains to be the cornerstone treatment. Through previous lessons learned from SARS-CoV-1 and MERS-CoV studies, scientific groups worldwide have rapidly expanded the knowledge pertaining to SARS-CoV-2 virology that includes in vitro and in vivo models for testing of antiviral therapies and randomized clinical trials. In the present narrative, we review SARS-CoV-2 virology, clinical features, pathophysiology, and animal models with a specific focus on the antiviral and adjunctive therapies currently being tested or that require testing in animal models and randomized clinical trials.
Induction of cytochrome P4501A1 by photooxidized tryptophan in Hepa lclc7 cells

Mouse hepatoma Hepa-lclc7 (Hepa-1) cells were cultivated in the presence of UV-irradiated amino acids. The results demonstrated that all of the amino acids tested, UV-oxidized tryptophan caused the highest induction of 7-ethoxyresorufin O-deethylase (EROD) activity compared with the controls (P < 0.01). The induction of EROD activity by oxidized tryptophan was dose dependent, and maximal induction was obtained at 12 hr after administration. Studies with various Hepa-1 mutants, which are defective in either the aryl hydrocarbon (Ah) receptor or Ah receptor nuclear translocator protein, indicated that the induction of EROD activity by oxidized tryptophan occurs through the Ah receptor. Gel mobility shift assays using nuclear extracts of Hepa-1 cells revealed that oxidized products of tryptophan can induce both Ah receptor transformation and binding of the liganded Ah receptor complex to its specific DNA recognition site. CYP1A1 mRNA, quantified by reverse transcription-polymerase chain reaction, and CYP1A1 protein were induced markedly in the oxidized tryptophan group compared with the controls. Injection of isolated oxidized tryptophan products into adult male rats caused significant induction of EROD activity in the pulmonary and hepatic microsomes compared with the controls (P < 0.01). These results demonstrated that oxidized tryptophan induces Ah receptor activation and binding of the liganded Ah receptor complex to its specific DNA recognition site, thereby initiating transcription and translation of the CYP1A1 gene with concomitant increase of EROD activity in Hepa-1 cells. Induction of EROD activity in the liver and lungs after injection of isolated oxidized tryptophan products into rats suggests that a similar mechanism may be operative in vivo.
On diffusion processes with $B(\mathbb{R}^{2}, VMO)$ coefficients and "good" Green's functions of the corresponding operators

The solvability in Sobolev spaces with special mixed norms is proved for nondivergence form second order parabolic equations. The usefulness of mixed norms in our problem is similar to that in the theory of Navier–Stokes equation, where mixed norms are indispensable and quite powerful. We obtain better regularity properties of solutions which allow us to apply the results to proving weak uniqueness of solutions of the corresponding stochastic Ito equations. The leading coefficients of our operators are assumed to be measurable in the time variable and two coordinates of space variables, and be almost in VMO with respect to the other coordinates. This solvability result implies the weak uniqueness of solutions of the corresponding stochastic Ito equations in the class of “good” solutions (which is nonempty). This also implies uniqueness of a Green’s function in the class of “good” ones (which is always nonempty).