cssi/SciSciGPT-SciSciNet huggingface dataset:
A note on new Bernstein-type inequalities for the log-likelihood function of Bernoulli variables

Abstract We prove a new Bernstein-type inequality for the log-likelihood function of Bernoulli variables. In contrast to classical Bernstein’s inequality and Hoeffding’s inequality when applied to this log-likelihood, the new bound is independent of the parameters of the Bernoulli variables and therefore does not blow up as the parameters approach 0 or 1. The new inequality strengthens certain theoretical results on likelihood-based methods for community detection in networks and can be applied to other likelihood-based methods for binary data.
Optimal local well-posedness theory for the kinetic wave equation

Abstract We prove local existence and uniqueness results for the (space-homogeneous) 4-wave kinetic equation in wave turbulence theory. We consider collision operators defined by radial, but general dispersion relations satisfying suitable bounds, and we prove two local well-posedness theorems in nearly critical weighted spaces.
Dynamic Time Warping in Strongly Subquadratic Time: Algorithms for the Low-Distance Regime and Approximate Evaluation.

Dynamic time warping distance (DTW) is a widely used distance measure between time series. The best known algorithms for computing DTW run in near quadratic time, and conditional lower bounds prohibit the existence of significantly faster algorithms. The lower bounds do not prevent a faster algorithm for the special case in which the DTW is small, however. For an arbitrary metric space $\Sigma$ with distances normalized so that the smallest non-zero distance is one, we present an algorithm which computes $\operatorname{dtw}(x, y)$ for two strings $x$ and $y$ over $\Sigma$ in time $O(n \cdot \operatorname{dtw}(x, y))$. We also present an approximation algorithm which computes $\operatorname{dtw}(x, y)$ within a factor of $O(n^\epsilon)$ in time $\tilde{O}(n^{2 - \epsilon})$ for $0 < \epsilon < 1$. The algorithm allows for the strings $x$ and $y$ to be taken over an arbitrary well-separated tree metric with logarithmic depth and at most exponential aspect ratio. Extending our techniques further, we also obtain the first approximation algorithm for edit distance to work with characters taken from an arbitrary metric space, providing an $n^\epsilon$-approximation in time $\tilde{O}(n^{2 - \epsilon})$, with high probability. Additionally, we present a simple reduction from computing edit distance to computing DTW. Applying our reduction to a conditional lower bound of Bringmann and Kunnemann pertaining to edit distance over $\{0, 1\}$, we obtain a conditional lower bound for computing DTW over a three letter alphabet (with distances of zero and one). This improves on a previous result of Abboud, Backurs, and Williams. With a similar approach, we prove a reduction from computing edit distance to computing longest LCS length. This means that one can recover conditional lower bounds for LCS directly from those for edit distance, which was not previously thought to be the case.
Do LGBT-Supportive Corporate Policies Enhance Firm Performance?

Prior research provides evidence that lesbian, gay, bisexual, and transgender (LGBT)-supportive corporate policies are related to important human resource functions, such as enhanced recruitment and retention. In addition, prior research indicates that investors view the adoption of such policies positively. We examine the firm-performance mechanisms underlying favorable stock-market reactions based on an integration of perspectives from corporate social responsibility and the business case for diversity. Specifically, we estimate a hierarchical linear model (HLM) to account for the nested nature of our data (firms nested within states) and find that (1) the presence of LGBT-supportive policies is associated with higher firm value, productivity, and profitability; (2) the firm-value and profitability benefits associated with LGBT-supportive policies are larger for companies engaged in research and development (RD and (3) the firm-value and profitability benefits of LGBT-supportive policies persist in the presence of state antidiscrimination laws. In supplemental analyses, we find that firms implementing (discontinuing) LGBT-supportive policies experience increases (decreases) in firm value, productivity, and profitability. We are among the first to link LGBT-supportive policies specifically to financial performance outcomes as well as to develop and test a multilevel model of these relationships. Our results have important implications for theory and research on LGBT issues in organizations, human resource managers, and policymakers.
Inertia and Overwithholding: Explaining the Prevalence of Income Tax Refunds

Over three-quarters of US taxpayers receive income tax refunds, which are effectively zero-interest loans to the government. Previous explanations include precautionary and/or forced savings motives. I present evidence on a third explanation: inertia. I find that following a change in tax liability, prepayments are only adjusted by 29 percent of the tax change after one year and 61 percent after three years. Adjustment increases with income and experience, and for EITC recipients, I rule out adjustment greater than 2 percent. Thus, policies affecting default-withholding rules are no longer neutral decisions, but rather, may affect consumption smoothing, particularly for low-income taxpayers. (JEL D14, H24, K34)
Mutualism increases diversity, stability, and function of multiplex networks that integrate pollinators into food webs

Ecosystems are composed of complex networks of many species interacting in different ways. While ecologists have long studied food webs of feeding interactions, recent studies increasingly focus on mutualistic networks including plants that exchange food for reproductive services provided by animals such as pollinators. Here, we synthesize both types of consumer-resource interactions to better understand the controversial effects of mutualism on ecosystems at the species, guild, and whole-community levels. We find that consumer-resource mechanisms underlying plant-pollinator mutualisms can increase persistence, productivity, abundance, and temporal stability of both mutualists and non-mutualists in food webs. These effects strongly increase with floral reward productivity and are qualitatively robust to variation in the prevalence of mutualism and pollinators feeding upon resources in addition to rewards. This work advances the ability of mechanistic network theory to synthesize different types of interactions and illustrates how mutualism can enhance the diversity, stability, and function of complex ecosystems.
2 – Modeling and Theory

Publisher Summary This chapter presents the theory behind the single-, dual-, and triple-fractal analysis and presents the expressions for both the binding and the dissociation phases. A biosensor system, wherein either the antigen, antibody, analyte, or substrate is attached to the surface, along with its different complexities, which include heterogeneities on the surface and in solution, diffusion-coupled reaction, time-varying adsorption or binding rate coefficients, can be characterized as a fractal system. The interaction between the analyte and the receptor on the solid biosensor surface is detected either by a change in the refractive index or by changes in the fluorometric intensity or ultraviolet light intensity. The SPR biosensor protocol analyzes the binding, dissociation where applicable, and kinetic curves by using classical saturation models involving analyte–receptor binding with 1:1, 1:2, ratios, generally under diffusion-free conditions and assuming that the receptors are homogeneously distributed over the sensor surface. In analyte–receptor binding the analyte–receptor binds with the active site on the surface and the product is released. In this sense the catalytic surface exhibits an unchanging fractal surface to the reactant in the absence of fouling and other complications. In the case of analyte–receptor association, the biosensor surface exhibits a changing fractal surface to the analyte in solution. One resorts to a triple-fractal analysis if the dual-fractal analysis does not provide a reasonable fit. The equation for the fractal analysis equation is generic in nature, and one may easily extend the single- and the dual-fractal analysis equations to describe the binding and/or the dissociation kinetics for a triple-fractal analysis.
Efficiency of Excitation by Electron Impact and Anomalous Scattering in Mercury Vapor

The efficiency of excitation by electron impact of the 6.67 volt resonance level in the mercury atom has been studied as a function of the energy of the incident electrons. The electrons that have lost energy are separated out by a small retarding field and measured. The number of collisions is calculated from the experimentally determined value of the mean free path. The range covered is 0.4 volt from 6.67 to 7.07 giving a value for every 0.1 volt of the interval. The efficiency reaches a maximum of about 0.06 at 6.77 volts and then falls off to 0.04 at 7.0 volts.The number of electrons scattered elastically at large angles by mercury vapor as a function of their energy was measured. The energy range covered was from 2 to 10 volts in steps of 0.2 of a volt. Certain very definite singularities were found. These correspond to an increase in large angle scattering and occur most prominently at 4.9, 5.7, and 6.3 volts. A less prominent group of singularities was also observed at 9.6, 10.3 and 11.1 volts.
The role of the C2A domain of synaptotagmin 1 in asynchronous neurotransmitter release

Following nerve stimulation, there are two distinct phases of Ca2+-dependent neurotransmitter release: a fast, synchronous release phase, and a prolonged, asynchronous release phase. Each of these phases is tightly regulated and mediated by distinct mechanisms. Synaptotagmin 1 is the major Ca2+ sensor that triggers fast, synchronous neurotransmitter release upon Ca2+ binding by its C2A and C2B domains. It has also been implicated in the inhibition of asynchronous neurotransmitter release, as blocking Ca2+ binding by the C2A domain of synaptotagmin 1 results in increased asynchronous release. However, the mutation used to block Ca2+ binding in the previous experiments (aspartate to asparagine mutations, sytD-N) had the unintended side effect of mimicking Ca2+ binding, raising the possibility that the increase in asynchronous release was directly caused by ostensibly constitutive Ca2+ binding. Thus, rather than modulating an asynchronous sensor, sytD-N may be mimicking one. To directly test the C2A inhibition hypothesis, we utilized an alternate C2A mutation that we designed to block Ca2+ binding without mimicking it (an aspartate to glutamate mutation, sytD-E). Analysis of both the original sytD-N mutation and our alternate sytD-E mutation at the Drosophila neuromuscular junction showed differential effects on asynchronous release, as well as on synchronous release and the frequency of spontaneous release. Importantly, we found that asynchronous release is not increased in the sytD-E mutant. Thus, our work provides new mechanistic insight into synaptotagmin 1 function during Ca2+-evoked synaptic transmission and demonstrates that Ca2+ binding by the C2A domain of synaptotagmin 1 does not inhibit asynchronous neurotransmitter release in vivo.
Mediators of Ischemic Renal Injury

Acute renal failure is a serious consequence of renal ischemia. The diagnosis carries an associated high mortality rate. When blood flow to the kidneys is inadequate to supply metabolic demands, a number of pathophysiological changes occur that ultimately result in cell death and tissue dysfunction, characterized by a marked reduction in glomerular filtration rate and associated accumulation of systemic toxins and disorders of fluid and electrolyte metabolism. In this chapter we review the factors that have been implicated as mediators of the renal tissue damage associated with ischemia and reperfusion.
Brain Age Estimation Using LSTM on Children's Brain MRI

Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-term memory (LSTM), which consists of four parts: 2D ResNet18 for feature extraction on 2D images, a pooling layer for feature reduction over the sequences, an LSTM layer, and a final regression layer. We apply the proposed method on a public multisite NIH-PD dataset and evaluate generalization on a second multisite dataset, which shows that the proposed 2D-ResNet18+LSTM method provides better results than traditional 3D based neural network for brain age estimation.
Thermal Effects on Fluid Mixing in the Eye

Age-related macular degeneration (AMD) is the leading cause of central vision loss in the developed world. Wet AMD can be managed through serial intravitreal injections of anti-vascular endothelial growth factor (anti-VEGF) agents. However, sometimes the treatment is ineffective. Given that the half-life of the drug is limited, inefficient mixing of the injected drug in the vitreous chamber of the eye may contribute to the ineffectiveness. Here, we introduce thermal heating as a means of enhancing the mixing-process in the vitreous chamber and investigate parameters that potentially influence its effectiveness. Our in vitro studies reveal the importance of the heating location on the eye. A significant increase in the mixing and delivery of drugs to the targeted area (the macula) could be achieved by placing heating pads to induce a current, against gravity, in the vitreous. The presented results can potentially help in the development of a better strategy for intravitreal injection, subsequently improving the quality of patient care.
NSEEN: Neural Semantic Embedding for Entity Normalization.

Much of human knowledge is encoded in text, available in scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into machine-processable structures, such as knowledge graphs. An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets. However, entity normalization is a challenging problem; there often are many textual forms for a canonical entity that may not be captured in the reference set, and entities mentioned in text may include many syntactic variations, or errors. The problem is particularly acute in scientific domains, such as biology. To address this problem, we have developed a general, scalable solution based on a deep Siamese neural network model to embed the semantic information about the entities, as well as their syntactic variations. We use these embeddings for fast mapping of new entities to large reference sets, and empirically show the effectiveness of our framework in challenging bio-entity normalization datasets.
EfficientDet: Scalable and Efficient Object Detection

Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDetD7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4x – 9x smaller and using 13x – 42x fewer FLOPs than previous detector.
On the Planar Two-Center Problem and Circular Hulls.

Given a set S of n points in the Euclidean plane, the two-center problem is to find two congruent disks of smallest radius whose union covers all points of S. Previously, Eppstein [SODA'97] gave a randomized algorithm of O(nlog²n) expected time and Chan [CGTA'99] presented a deterministic algorithm of O(nlog² nlog²log n) time. In this paper, we propose an O(nlog² n) time deterministic algorithm, which improves Chan’s deterministic algorithm and matches the randomized bound of Eppstein. If S is in convex position, we solve the problem in O(nlog nlog log n) deterministic time. Our results rely on new techniques for dynamically maintaining circular hulls under point insertions and deletions, which are of independent interest.
Modern solid-state laser materials

The discovery of the tunable alexandrite (Cr: BeAl2O4) solid-state laser by Walling et al.1 in 1978 and the rediscovery of tunable cobalt and nickel doped MgF2 layers by Moulton et al.2 in 1978 signaled a revival of interest in the research and development of new solid-state lasers not seen since the mid-1960s. Through the efforts of several groups of researchers around the world, several new (i.e., modern) solid-state lasers have appeared in the past year or so. These efforts have focused on two distinct types of laser: (1) phonon-terminated wavelength-tunable lasers using a variety of chromium (Cr3+) doped crystalline host materials (e.g., oxide garnets,3 emerald,4 fluoride perovskites5), and (2) 1060-nm neodymium (Nd3+) lasers with increased efficiency (relative to the industry standard, Nd:YAG) achieved through effective Cr sensitization of Nd luminescence in co-doped oxide garnets.6,7 To date, the gadolinium scandium gallium garnet (QSGG), singly doped with Cr or co-doped with Nd/Cr, has attracted the most attention.
Do only tax incentives matter? Labor supply and demand responses to an unusually large and salient tax break

Abstract I explore labor supply responses to an unusually large and salient notch generated by the “Mini-Job” program in Germany. Using administrative data, I document three findings. First, despite the unusually large magnitude of incentives, earnings elasticities are modest, even after accounting for frictions. Second, the observed response cannot be fully attributed to labor supply alone; rather, the observed outcomes are highly dependent on the availability of jobs and, so are strongly influenced by labor demand incentives. Third, I show that these firm incentives are likely driven by the fact that mini-job workers receive lower fringe-benefit payments.
Efficient polynomial chaos expansion for uncertainty quantification in power systems

Abstract Growing uncertainty from renewable energy integration and distributed energy resources motivate the need for advanced tools to quantify the effect of uncertainty and assess the risks it poses to secure system operation. Polynomial chaos expansion (PCE) has been recently proposed as a tool for uncertainty quantification in power systems. The method produces highly accurate results, but has proved to be computationally challenging to scale to large systems. We propose a modified algorithm based on PCE with significantly improved computational efficiency that retains the desired high level of accuracy of the standard PCE. Our method uses computational enhancements by exploiting the sparsity structure and algebraic properties of the power flow equations. We show the scalability of the method on the 1354 pegase test system, assess the quality of the uncertainty quantification in terms of accuracy and robustness, and demonstrate an example application to solving the chance constrained optimal power flow problem.
Hermite-Discontinuous Galerkin Overset Grid Methods for the Scalar Wave Equation

We present high order accurate numerical methods for the wave equation that combines efficient Hermite methods with geometrically flexible discontinuous Galerkin methods by using overset grids. Near boundaries we use thin boundary fitted curvilinear grids and in the volume we use Cartesian grids so that the computational complexity of the solvers approaches a structured Cartesian Hermite method. Unlike many other overset methods we do not need to add artificial dissipation but we find that the built-in dissipation of the Hermite and discontinuous Galerkin methods is sufficient to maintain the stability. By numerical experiments we demonstrate the stability, accuracy, efficiency, and the applicability of the methods to forward and inverse problems.
Consumption Inequality Among Children: Evidence from Child Fostering in Malawi

The share of household resources devoted to a child may depend on their gender, birth order, or relationship to the household head. However, it is challenging to determine whether parents favour certain children over others as consumption data are collected at the household level and goods are shared among family members. I develop a new methodology using the collective household framework to identify consumption inequality between different types of children. I apply this method to child fostering in Malawi. I find little evidence of inequality between foster and non-foster children.
Genomic Sequencing for Newborn Screening: Results of the NC NEXUS Project

Summary Newborn screening (NBS) was established as a public health program in the 1960s and is crucial for facilitating detection of certain medical conditions in which early intervention can prevent serious, life-threatening health problems. Genomic sequencing can potentially expand the screening for rare hereditary disorders, but many questions surround its possible use for this purpose. We examined the use of exome sequencing (ES) for NBS in the North Carolina Newborn Exome Sequencing for Universal Screening (NC NEXUS) project, comparing the yield from ES used in a screening versus a diagnostic context. We enrolled healthy newborns and children with metabolic diseases or hearing loss (106 participants total). ES confirmed the participant’s underlying diagnosis in 15 out of 17 (88%) children with metabolic disorders and in 5 out of 28 (∼18%) children with hearing loss. We discovered actionable findings in four participants that would not have been detected by standard NBS. A subset of parents was eligible to receive additional information for their child about childhood-onset conditions with low or no clinical actionability, clinically actionable adult-onset conditions, and carrier status for autosomal-recessive conditions. We found pathogenic variants associated with hereditary breast and/or ovarian cancer in two children, a likely pathogenic variant in the gene associated with Lowe syndrome in one child, and an average of 1.8 reportable variants per child for carrier results. These results highlight the benefits and limitations of using genomic sequencing for NBS and the challenges of using such technology in future precision medicine approaches.
Bacterial Genome Wide Association Studies (bGWAS) and Transcriptomics Identifies Cryptic Antimicrobial Resistance Mechanisms in Acinetobacter baumannii.

Antimicrobial resistance (AMR) in the nosocomial pathogen, Acinetobacter baumannii, is becoming a serious public health threat. While some mechanisms of AMR have been reported, understanding novel mechanisms of resistance is critical for identifying emerging resistance. One of the first steps in identifying novel AMR mechanisms is performing genotype/phenotype association studies. However, performing genotype/phenotype association studies is complicated by the plastic nature of the A. baumannii pan-genome. In this study, we compared the antibiograms of 12 antimicrobials associated with multiple drug families for 84 A. baumannii isolates, many isolated in Arizona, USA. in silico screening of these genomes for known AMR mechanisms failed to identify clear correlations for most drugs. We then performed a bacterial genome wide association study (bGWAS) looking for associations between all possible 21-mers; this approach generally failed to identify mechanisms that explained the resistance phenotype. In order to decrease the genomic noise associated with population stratification, we compared four phylogenetically-related pairs of isolates with differing susceptibility profiles. RNA-Sequencing (RNA-Seq) was performed on paired isolates and differentially expressed genes were identified. In these isolate pairs, we identified five different potential mechanisms, highlighting the difficulty of broad AMR surveillance in this species. To verify and validate differential expression, amplicon sequencing was performed. These results suggest that a diagnostic platform based on gene expression rather than genomics alone may be beneficial in certain surveillance efforts. The implementation of such advanced diagnostics coupled with increased AMR surveillance will potentially improve A. baumannii infection treatment and patient outcomes.
Covert Computation in Self-Assembled Circuits

Traditionally, computation within self-assembly models is hard to conceal because the self-assembly process generates a crystalline assembly whose computational history is inherently part of the structure itself. With no way to remove information from the computation, this computational model offers a unique problem: how can computational input and computation be hidden while still computing and reporting the final output? Designing such systems is inherently motivated by privacy concerns in biomedical computing and applications in cryptography. In this paper we propose the problem of performing “covert computation” within tile self-assembly that seeks to design self-assembly systems that “conceal” both the input and computational history of performed computations. We achieve these results within the growth-only restricted abstract Tile Assembly Model (aTAM) with positive and negative interactions. We show that general-case covert computation is possible by implementing a set of basic covert logic gates capable of simulating any circuit (functionally complete). To further motivate the study of covert computation, we apply our new framework to resolve an outstanding complexity question; we use our covert circuitry to show that the unique assembly verification problem within the growth-only aTAM with negative interactions is coNP-complete.
The schooling and labor market effects of eliminating university tuition in Ecuador

Abstract This paper estimates the effects of a 2008 policy that eliminated tuition fees at public universities in Ecuador. We use a difference-in-differences strategy that exploits variation across cohorts differentially exposed to the policy, as well as geographic variation in access to public universities. We find that the tuition fee elimination significantly increased college participation and shifted people into higher-skilled jobs. We detect no statistically significant effects on income, though standard errors are large. Overall, the bulk of the benefits of this fee elimination were enjoyed by those of higher socioeconomic status.
The Non-Democratic Roots of Mass Education: Evidence from 200 Years

Because primary education is often conceptualized as a pro-poor redistributive policy, a common argument is that democratization increases its provision. But primary education can also serve the goals of autocrats, including redistribution, promoting loyalty, nation-building, and/or industrialization. To examine the relationship between democratization and education provision empirically, I leverage new datasets covering 109 countries and 200 years. Difference-in-differences and interrupted time series estimates find that, on average, democratization had no or little impact on primary school enrollment rates. When unpacking this average null result, I find that, consistent with median voter theories, democratization can lead to an expansion of primary schooling, but the key condition under which it does—when a majority lacked access to primary schooling before democratization—rarely holds. Around the world, state-controlled primary schooling emerged a century before democratization, and in three-fourths of countries that democratized, a majority already had access to primary education before democratization.
A political economy model of the Ganges pollution cleanup problem

We study pollution cleanup in the Ganges in Varanasi, India. Voters elect politicians and elected politicians decide how much pollution to clean up. Between the two time periods, there is an election. Politicians are sincere or insincere. The marginal cost of public funds ζ measures how efficiently elected politicians transform tax receipts into pollution cleanup. Voters have identical per period utility functions. We ascertain the equilibrium outcome and per period voter welfare. Second, we show that an increase in ζ reduces the equilibrium pollution cleanup and voter welfare. Third, an insincere politician can delay the revelation of his insincerity. We show that a critical value of ζ,ζ^*, exists such that the insincere incumbent separates and loses the election if and only if ζ>ζ^* and that he pools and is re-elected otherwise. Finally, we note that an increase in ζ can raise voter welfare when politicians are more likely to be insincere.
Scaled Autonomy: Enabling Human Operators to Control Robot Fleets

Autonomous robots often encounter challenging situations where their control policies fail and an expert human operator must briefly intervene, e.g., through teleoperation. In settings where multiple robots act in separate environments, a single human operator can manage a fleet of robots by identifying and teleoperating one robot at any given time. The key challenge is that users have limited attention: as the number of robots increases, users lose the ability to decide which robot requires teleoperation the most. Our goal is to automate this decision, thereby enabling users to supervise more robots than their attention would normally allow for. Our insight is that we can model the user’s choice of which robot to control as an approximately optimal decision that maximizes the user’s utility function. We learn a model of the user’s preferences from observations of the user’s choices in easy settings with a few robots, and use it in challenging settings with more robots to automatically identify which robot the user would most likely choose to control, if they were able to evaluate the states of all robots at all times. We run simulation experiments and a user study with twelve participants that show our method can be used to assist users in performing a simulated navigation task. We also run a hardware demonstration that illustrates how our method can be applied to a real-world mobile robot navigation task.
Targeted transfer learning to improve performance in small medical physics datasets.

Purpose To perform an in-depth evaluation of current state of the art techniques in training neural networks to identify appropriate approaches in small datasets. Method In total, 112,120 frontal-view X-ray images from the NIH ChestXray14 dataset were used in our analysis. Two tasks were studied: unbalanced multi-label classification of 14 diseases, and binary classification of pneumonia vs non-pneumonia. All datasets were randomly split into training, validation, and testing (70%, 10%, and 20%). Two popular convolution neural networks (CNNs), DensNet121 and ResNet50, were trained using PyTorch. We performed several experiments to test: (a) whether transfer learning using pretrained networks on ImageNet are of value to medical imaging/physics tasks (e.g., predicting toxicity from radiographic images after training on images from the internet), (b) whether using pretrained networks trained on problems that are similar to the target task helps transfer learning (e.g., using X-ray pretrained networks for X-ray target tasks), (c) whether freeze deep layers or change all weights provides an optimal transfer learning strategy, (d) the best strategy for the learning rate policy, and (e) what quantity of data is needed in order to appropriately deploy these various strategies (N = 50 to N = 77 880). Results In the multi-label problem, DensNet121 needed at least 1600 patients to be comparable to, and 10 000 to outperform, radiomics-based logistic regression. In classifying pneumonia vs non-pneumonia, both CNN and radiomics-based methods performed poorly when N 15% increase on AUC) comes from a good selection of the transfer learning dataset, dropout, cycling learning rate, and freezing and unfreezing of deep layers as training progresses. In contrast, if sufficient data are available (>35 000), little or no tweaking is needed to obtain impressive performance. While transfer learning using X-ray images from other anatomical sites improves performance, we also observed a similar boost by using pretrained networks from ImageNet. Having source images from the same anatomical site, however, outperforms every other methodology, by up to 15%. In this case, DL models can be trained with as little as N = 50. Conclusions While training DL models in small datasets (N 35 000). Using transfer learning with images from the same anatomical site can yield remarkable performance in new tasks with as few as N = 50. Surprisingly, we did not find any advantage for using images from other anatomical sites over networks that have been trained using ImageNet. This indicates that features learned may not be as general as currently believed, and performance decays rapidly even by just changing the anatomical site of the images.
Frequency-dependent functional renormalization group for interacting fermionic systems

We derive an expansion of the functional renormalization (fRG) equations that treats the frequency and momentum dependencies of the vertices in a systematic manner. The scheme extends the channel-decomposed fRG equations to the frequency domain and reformulates them as a series of linear integral equations in the particle-particle, particle-hole and particle-hole exchange channels. We show that the linearity of the equations offers numerous computational advantages and leads to converged, stable solutions for a variety of Hamiltonians. As the expansion is in the coupling between channels, the truncations that are necessary to making the scheme computationally viable still lead to equations that treat contributions from all channels equally. As a first benchmark we apply the two-loop fRG equations to the single impurity Anderson model. We consider the sources of error within the fRG, the computational cost associated with each, and how the choice of regulator affects the flow of the fRG. We then use the optimal truncation scheme to study the Extended Hubbard Hamiltonian in one and two dimensions. We find that in many cases of interest the fRG flow converges to a stable vertex and self-energy from which we can extract the various correlation functions and susceptibilities of interest.
Robust Submodular Minimization with Applications to Cooperative Modeling

Robust Optimization is becoming increasingly important in machine learning applications. This paper studies the problem of robust submodular minimization subject to combinatorial constraints. Constrained Submodular Minimization arises in several applications such as co-operative cuts in image segmentation, co-operative matchings in image correspondence, etc. Many of these models are defined over clusterings of data points (for example pixels in images), and it is important for these models to be robust to perturbations and uncertainty in the data. While several existing papers have studied robust submodular maximization, ours is the first work to study the minimization version under a broad range of combinatorial constraints including cardinality, knapsack, matroid as well as graph-based constraints such as cuts, paths, matchings, and trees. In each case, we provide scalable approximation algorithms and also study hardness bounds. Finally, we empirically demonstrate the utility of our algorithms on synthetic and real-world datasets.
Learning When-to-Treat Policies

Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may cho...
Teaching Explicit Social-Emotional Skills With Contextual Supports for Students With Intensive Intervention Needs:

Social-cognitive and emotional factors as well as behavior problems contribute to the social difficulties experienced by many students with or at high risk for emotional and behavioral disorders (E...
The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. III. Substructures in Protostellar Disks

The prevalence of substructures in $\sim1-10$ Myr old protoplanetary disks, which are often linked to planet formation, has raised the question of how early such features form, and as a corollary, how early planet formation begins. Here we present observations of seven protostellar disks (aged $\sim0.1-1$ Myr) from the VLA/ALMA Nascent Disk and Multiplicity Survey of Orion Protostars (VANDAM: Orion) that show clear substructures, thereby demonstrating that these features can form early in the lifetimes of disks. We use simple analytic models as well as detailed radiative transfer modeling to characterize their structure. In particular we show that at least four of the sources have relatively massive envelopes, indicating that they are particularly young, likely the youngest disks with substructures known to-date. Several of these disks also have emission from an inner disk that is offset from the center of the ring structure. Given the size of the cleared out regions of the disk, it is unclear, however, whether these features are related to planet formation, or rather if they are signposts of close-separation binary formation at early times.
V-Dream: Immersive Exploration of Generative Design Solution Space

Generative Design workflows have introduced alternative paradigms in the domain of computational design, allowing designers to generate large pools of valid solutions by defining a set of goals and constraints. However, analyzing and narrowing down the generated solution space, which usually consists of various high-dimensional properties, has been a major challenge in current generative workflows. By taking advantage of the interactive unbounded spatial exploration, and the visual immersion offered in virtual reality platforms, we propose V-Dream, a virtual reality generative analysis framework for exploring large-scale solution spaces. V-Dream proposes a hybrid search workflow in which a spatial stochastic search approach is combined with a recommender system allowing users to pick desired candidates and eliminate the undesired ones iteratively. In each cycle, V-Dream reorganizes the remaining options in clusters based on the defined features. Moreover, our framework allows users to inspect design solutions and evaluate their performance metrics in various hierarchical levels, assisting them in narrowing down the solution space through iterative cycles of search/select/re-clustering of the solutions in an immersive fashion. Finally, we present a prototype of our proposed framework, illustrating how users can navigate and narrow down desired solutions from a pool of over 16000 monitor stands generated by Autodesk’s Dreamcatcher software.
Dipole–phonon quantum logic with alkaline-earth monoxide and monosulfide cations

Dipole-phonon quantum logic (DPQL) leverages the interaction between polar molecular ions and the motional modes of a trapped-ion Coulomb crystal to provide a potentially scalable route to quantum information science. Here, we study a class of candidate molecular ions for DPQL, the cationic alkaline-earth monoxides and monosulfides, which possess suitable structure for DPQL and can be produced in existing atomic ion experiments with little additional complexity. We present calculations of DPQL operations for one of these molecules, CaO+, and discuss progress towards experimental realization. We also further develop the theory of DPQL to include state preparation and measurement and entanglement of multiple molecular ions.
Assurance of Distributed Algorithms and Systems: Runtime Checking of Safety and Liveness

This paper presents a general framework and methods for complete programming and checking of distributed algorithms at a high-level, as in pseudocode languages, but precisely specified and directly executable, as in formal specification languages and practical programming languages, respectively. The checking framework, as well as the writing of distributed algorithms and specification of their safety and liveness properties, use DistAlgo, a high-level language for distributed algorithms. We give a complete executable specification of the checking framework, with a complete example algorithm and example safety and liveness properties.
On the Sharp Time Decay Rates for the 2D Generalized Quasi-geostrophic Equation and the Boussinesq System

We compute the sharp time decay rates of the solutions of the IVP for quasi-geostrophic equation and the Boussinesq model, subject to fractional dissipation. Moreover, we explicitly identify the asymptotic profiles, the kernel of the $$\alpha $$ stable processes, which are analogues of the Oseen vortices.
From stochastic quantization to bulk quantization: Schwinger-Dyson equations and S-matrix

In stochastic quantization, ordinary 4-dimensional Euclidean quantum field theory is expressed as a functional integral over fields in 5 dimensions with a fictitious 5th time. However a broader framework, which we call``bulk quantization, is required for extension to fermions, and for the increased power afforded by the higher symmetry of the 5-dimensional action that is topological when expressed in terms of auxiliary fields. Within the broader framework, we give a direct proof by means of Schwinger-Dyson equations that a time-slice of the 5-dimensional theory is equivalent to the usual 4-dimensional theory. The proof does not rely on the conjecture that the relevant stochastic process relaxes to an equilibrium distribution. Rather, it depends on the higher symmetry of the 5-dimensional action which includes a BRST-type topological invariance, and invariance under translation and inversion in the 5-th time. We express the physical S-matrix directly in terms of the truncated 5-dimensional correlation functions, for which ``going off the mass-shell'' means going from the 3 physical degrees of freedom to 5 independent variables. We derive the Landau-Cutokosky rules of the 5-dimensional theory which include the physical unitarity relation.
Neon lights up a controversy: the solar ne/o abundance

The standard solar model was so reliable that it could predict the existence of the massive neutrino. Helioseismology measurements were so precise that they could determine the depth of the convection zone. This agreement between theory and observation was the envy of all astrophysics—until recently, when sophisticated three-dimensional hydrodynamic calculations of the solar atmosphere reduced the metal content by a factor of almost 2. Antia & Basu suggested that a higher value of the solar neon abundance, ANe/AO = 0.52, would resolve this controversy. Drake & Testa presented evidence in favor of this idea from a sample of 21 Chandra stars with enhanced values of the neon abundance, ANe/AO = 0.41. In this Letter, we have analyzed solar active region spectra from the archive of the Flat Crystal Spectrometer on the Solar Maximum Mission, a NASA mission from the 1980s, as well as full-Sun spectra from the pioneering days of X-ray astronomy in the 1960s. These data are consistent with the standard neon-to-oxygen abundance value, ANe/AO = 0.15 (Grevesse & Sauval). We conclude, therefore, that the enhanced-neon hypothesis will not resolve the current controversy.
Baryon Spectra and AdS/CFT Correspondence

We provide a detailed map between wrapped D3-branes in Anti-de Sitter (AdS) backgrounds and dibaryon operators in the corresponding conformal field theory (CFT). The effective five dimensional action governing the dynamics of AdS space contains a U(1)R gauge field that mediates interactions between objects possessing R-charge. We show that the U(1)R charge of these wrapped D3-branes as measured by the gauge field matches the R-charge of the dibaryons expected from field theory considerations. We are able, through a careful probe brane calculation in an AdS5 × T1,1 background, to understand the exact relation between the mass of the wrapped D3-brane and the dimension of the corresponding dibaryon. We also make some steps toward matching the counting of dibaryon operators in the CFT with the ground states of a supersymmetric quantum mechanical system whose target space is the moduli space of D-branes. Finally, we discuss BPS excitations of the D3-brane and compare them with higher dimension operators in the CFT.
QCD corrections to massive color-octet vector boson pair production

This paper describes the calculation of the next-to-leading order (NLO) QCD corrections to massive color-octet vector boson pair production at hadron colliders. As a concrete framework, a two-site coloron model with an internal parity is chosen, which can be regarded as an effective low-energy approximation of Kaluza-Klein gluon physics in universal extra dimensions. The renormalization procedure involves several subtleties, which are discussed in detail. The impact of the NLO corrections is relatively modest, amounting to a reduction of 11-14% in the total cross-section, but they significantly reduce the scale dependence of the LO result.
Open-System Quantum Annealing in Mean-Field Models with Exponential Degeneracy

Intrinsic noise is unavoidable in quantum devices and represents a hindrance to implementing quantum computation. Despite the presence of noise, a quantum-annealing algorithm that involves quantum tunneling may provide computational advantages over simulated annealing.
Quantum Criticality via Magnetic Branes

Holographic methods are used to investigate the low temperature limit, including quantum critical behavior, of strongly coupled 4-dimensional gauge theories in the presence of an external magnetic field, and finite charge density. In addition to the metric, the dual gravity theory contains a Maxwell field with Chern-Simons coupling. In the absence of charge, the magnetic field induces an RG flow to an infrared \(\mathit{AdS}_{3} \times {\bf R}^{2}\) geometry, which is dual to a 2-dimensional CFT representing strongly interacting fermions in the lowest Landau level. Two asymptotic Virasoro algebras and one chiral Kac-Moody algebra arise as emergent symmetries in the IR. Including a nonzero charge density reveals a quantum critical point when the magnetic field reaches a critical value whose scale is set by the charge density. The critical theory is probed by the study of long-distance correlation functions of the boundary stress tensor and current. All quantities of major physical interest in this system, such as critical exponents and scaling functions, can be computed analytically. We also study an asymptotically AdS 6 system whose magnetic field induced quantum critical point is governed by an IR Lifshitz geometry, holographically dual to a D=2+1 field theory. The behavior of these holographic theories shares important similarities with that of real world quantum critical systems obtained by tuning a magnetic field, and may be relevant to materials such as Strontium Ruthenates.
What is the Mass of a Gap-opening Planet?

NASA through Hubble Fellowship - Space Telescope Science Institute [HST-HF-51320.01-A, NAS 5-26555]; Natural Sciences and Engineering Research Council of Canada; Center for Integrative Planetary Science at the University of California, Berkeley; Sagan Fellowship Program - NASA; Jet Propulsion Laboratory (JPL)