cssi/SciSciGPT-SciSciNet huggingface dataset:
Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro

COVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro . Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.
Naturalized Epistemology and the Law of Evidence Revisited

We revisit Naturalized Epistemology and the Law of Evidence, published twenty years ago. The evolution of the relative plausibility theory of juridical proof is offered as evidence of the advantage of a naturalized approach to the study of the field and law evidence. Various alternative explanations of aspects of juridical proof from other disciplines are examined and their shortcomings described. These competing explanations are similar in their reductive, a priori approaches that are at odds with an empirically oriented naturalized approach. The shortcomings of the various a priori approaches are driven by their common methodological commitments to employing weird hypotheticals to engage in intuition mining about the American legal systems that in turn ignore important aspects of the actual legal systems and consistently make impossible epistemological demands. As a result, both their descriptions of and prescriptions for American legal systems are implausible, unlike the relative plausibility theory.
Super-pancyclic hypergraphs and bipartite graphs

Abstract We find Dirac-type sufficient conditions for a hypergraph H with few edges to be hamiltonian. We also show that these conditions guarantee that H is super-pancyclic, i.e., for each A ⊆ V ( H ) with | A | ≥ 3 , H contains a Berge cycle with vertex set A. We mostly use the language of bipartite graphs, because every bipartite graph is the incidence graph of a multihypergraph. In particular, we extend some results of Jackson on the existence of long cycles in bipartite graphs where the vertices in one part have high minimum degree. Moreover, we prove a conjecture of Jackson from 1981 on long cycles in 2-connected bipartite graphs.
Spillovers from Mass Advertising: An Identification Strategy

By definition, mass advertising decisions cannot target local levels, which provides opportunities to identify the effect of advertising.
ArduCode: Predictive Framework for Automation Engineering

Automation engineering is the task of integrating, via software, various sensors, actuators, and controls to automate a real-world process. Today, automation engineering is supported by a suite of software tools, including integrated development environments (IDEs), hardware configurators, compilers, and runtimes. These tools focus on the automation code itself but leave the automation engineer unassisted in their decision-making. This can lead to longer software development cycles due to the imperfections in the decision-making, which arise when integrating software and hardware. To address this problem, this article addresses multiple challenges often faced in automation engineering and proposes machine learning-based solutions to assist engineers tackle these challenges. We show that machine learning can be leveraged to assist the automation engineer in classifying automation code, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators. We validate our architecture on two real data sets consisting of 2927 Arduino projects and 683 programmable logic controller (PLC) projects. Our results show that paragraph embedding techniques can be utilized to classify automation using code snippets with precision close to human annotation, giving an $F_{1}$ -score of 72%. Furthermore, we show that such embedding techniques can help us find similar code snippets with high accuracy. Finally, we use autoencoder models for hardware recommendation and achieve a $p\text{@}3$ of 0.79 and $p\text{@}5$ of 0.95. We also present the implementation of ArduCode in a proof-of-concept user interface integrated into an existing automation engineering system platform. Note to Practitioners —This article is motivated by the use of artificial intelligence methods to improve the efficiency and quality of the automation engineering software development process. Our goal is to develop and integrate intelligent assistants in existing automation engineering development tools to minimally disrupt existing workflows. Practitioners should be able to adapt our framework to other tools and data. Our contributions address important practical problems: 1) we address the lack of realistic data sets in automation engineering with two publicly available data sources; 2) we make the reference implementation of our algorithms publicly available on GitHub for other practitioners to have a starting point for future research; and 3) we demonstrate the integration of our framework as an add-on to an existing automation engineering toolchain.
Algorithmic averaging for studying periodic orbits of planar differential systems

One of the main open problems in the qualitative theory of real planar differential systems is the study of limit cycles. In this article, we present an algorithmic approach for detecting how many limit cycles can bifurcate from the periodic orbits of a given polynomial differential center when it is perturbed inside a class of polynomial differential systems via the averaging method. We propose four symbolic algorithms to implement the averaging method. The first algorithm is based on the change of polar coordinates that allows one to transform a considered differential system to the normal form of averaging. The second algorithm is used to derive the solutions of certain differential systems associated to the unperturbed term of the normal of averaging. The third algorithm exploits the partial Bell polynomials and allows one to compute the integral formula of the averaged functions at any order. The last algorithm is based on the aforementioned algorithms and determines the exact expressions of the averaged functions for the considered differential systems. The implementation of our algorithms is discussed and evaluated using several examples. The experimental results have extended the existing relevant results for certain classes of differential systems.
A Chern–Weil formula for the Chern character of a perfect curved module

Let $k$ be a field of characteristic 0 and $\mathcal{A}$ a curved $k$-algebra. We obtain a Chern-Weil-type formula for the Chern character of a perfect $\mathcal{A}$-module taking values in $HN_0^{II}(\mathcal{A})$, the negative cyclic homology of the second kind associated to $\mathcal{A}$, when $\mathcal{A}$ satisfies a certain smoothness condition.
A GMM Based Algorithm To Generate Point-Cloud And Its Application To Neuroimaging

Recent years have witnessed the emergence of 3D medical imaging techniques with the development of 3D sensors and technology. Due to the presence of noise in image acquisition, registration is a must, yet incur error which propagates to the subsequent analysis. An alternative way to analyze medical imaging is by understanding the 3D shapes represented in terms of point-cloud. Though in the medical imaging community, 3D point-cloud is not a “go-to” choice, it is a “natural” way to capture 3D shapes. Another hurdle presented in applying deep learning techniques to medical imaging is the lack of samples. A way to overcome this limitation is by generating samples using GAN like schemes. However, due to different modality in medical images, standard generative models can not be applied directly. In this work, we use the advantage of the 3D point-cloud representation of medical images and propose a Gaussian mixture model based generation and interpolation scheme. For interpolation, given two 3D structures represented as point-clouds, we can generate point-clouds in between, and the experimental validation shows the goodness of the interpolated samples. We also generate new point-clouds for subjects with and without dementia and show that the generated samples are indeed closely matched to the respective training samples from the same class.
Rigidity results for von Neumann algebras arising from mixing extensions of profinite actions of groups on probability spaces

Motivated by Popa’s seminal work Popa (Invent Math 165:409-45, 2006), in this paper, we provide a fairly large class of examples of group actions $$\Gamma \curvearrowright X$$ satisfying the extended Neshveyev–Stormer rigidity phenomenon Neshveyev and Stormer (J Funct Anal 195(2):239-261, 2002): whenever $$\Lambda \curvearrowright Y$$ is a free ergodic pmp action and there is a $$*$$ -isomorphism $$\Theta :L^\infty (X)\rtimes \Gamma {\rightarrow }L^\infty (Y)\rtimes \Lambda $$ such that $$\Theta (L(\Gamma ))=L(\Lambda )$$ then the actions $$\Gamma \curvearrowright X$$ and $$\Lambda \curvearrowright Y$$ are conjugate (in a way compatible with $$\Theta $$ ). We also obtain a complete description of the intermediate subalgebras of all (possibly non-free) compact extensions of group actions in the same spirit as the recent results of Suzuki (Complete descriptions of intermediate operator algebras by intermediate extensions of dynamical systems, To appear in Comm Math Phy. ArXiv Preprint: arXiv:1805.02077 , 2020). This yields new consequences to the study of rigidity for crossed product von Neumann algebras and to the classification of subfactors of finite Jones index.
An autoregulatory switch in sex-specific phf7 transcription causes loss of sexual identity and tumors in the Drosophila female germline

Maintenance of germ cell sexual identity is essential for reproduction. Entry into the spermatogenesis or oogenesis pathway requires that the appropriate gene network is activated and the antagonist network is silenced. For example, in Drosophila female germ cells, forced expression of the testis-specific PHD finger protein 7 (PHF7) disrupts oogenesis leading to either an agametic or germ cell tumor phenotype. Here we show that PHF7 expressing ovarian germ cells inappropriately express hundreds of genes, many of which are male germline genes. We find that the majority of genes under PHF7 control in female germ cells are not under PHF7 control in male germ cells, suggesting that PHF7 is acting in a tissue-specific manner. Remarkably, transcriptional reprogramming includes a positive autoregulatory feedback mechanism in which ectopic PHF7 overcomes its own transcriptional repression through promoter switching. Furthermore, we find that tumorigenic capacity is dependent on the dosage of phf7. This study reveals that ectopic PHF7 in female germ cells leads to a loss of sexual identity and promotion of a regulatory circuit beneficial for tumor initiation and progression.
Local Structure of Glassy Lithium Phosphorus Oxynitride Thin Films: A Combined Experimental and Ab Initio Approach

Lithium phosphorus oxynitride (LiPON) is an amorphous solid-state lithium ion conductor displaying exemplary cyclability against lithium metal anodes. There is no definitive explanation for this stability due to the limited understanding of the structure of LiPON. Herein, we provide a structural model of RF-sputtered LiPON. Information about the short-range structure results from 1D and 2D solid-state NMR experiments. These results are compared with first principles chemical shielding calculations of Li-P-O/N crystals and ab initio molecular dynamics-generated amorphous LiPON models to unequivocally identify the glassy structure as primarily isolated phosphate monomers with N incorporated in both apical and as bridging sites in phosphate dimers. Structural results suggest LiPON's stability is a result of its glassy character. Free-standing LiPON films are produced that exhibit a high degree of flexibility, highlighting the unique mechanical properties of glassy materials.
Neural Dynamics on Complex Networks

Learning continuous-time dynamics on complex networks is crucial for understanding, predicting, and controlling complex systems in science and engineering. However, this task is very challenging due to the combinatorial complexities in the structures of high dimensional systems, their elusive continuous-time nonlinear dynamics, and their structural-dynamic dependencies. To address these challenges, we propose to combine Ordinary Differential Equation Systems (ODEs) and Graph Neural Networks (GNNs) to learn continuous-time dynamics on complex networks in a data-driven manner. We model differential equation systems by GNNs. Instead of mapping through a discrete number of neural layers in the forward process, we integrate GNN layers over continuous time numerically, leading to capturing continuous-time dynamics on graphs. Our model can be interpreted as a Continuous-time GNN model or a Graph Neural ODEs model. Our model can be utilized for continuous-time network dynamics prediction, structured sequence prediction (a regularly-sampled case), and node semi-supervised classification tasks (a one-snapshot case) in a unified framework. We validate our model by extensive experiments in the above three scenarios. The promising experimental results demonstrate our model's capability of jointly capturing the structure and dynamics of complex systems in a unified framework.
Stratification of Systemic Lupus Erythematosus Patients Using Gene Expression Data to Reveal Expression of Distinct Immune Pathways.

Abstract Systemic lupus erythematosus (SLE) is the tenth leading cause of death in females 15-24 years old in the US. The diversity of symptoms and immune pathways expressed in SLE patients causes difficulties in treating SLE as well as in new clinical trials. This study used unsupervised learning on gene expression data from adult SLE patients to separate patients into clusters. The dimensionality of the gene expression data was reduced by three separate methods (PCA, UMAP, and a simple linear autoencoder) and the results from each of these methods were used to separate patients into six clusters with k-means clustering. The clusters revealed three separate immune pathways in the SLE patients that caused SLE. These pathways were: (1) high interferon levels, (2) high autoantibody levels, and (3) dysregulation of the mitochondrial apoptosis pathway. The first two pathways have been extensively studied in SLE. However, mitochondrial apoptosis has not been investigated before to the best of our knowledge as a standalone cause of SLE, independent of autoantibody production, indicating that mitochondrial proteins could lead to a new set of therapeutic targets for SLE in future research.
Dynamic, multimodal hydrogel actuators using porphyrin-based visible light photoredox catalysis in a thermoresponsive polymer network

Hydrogels that can respond to multiple external stimuli represent the next generation of advanced functional biomaterials. Here, a series of multimodal hydrogels were synthesized that can contract and expand reversibly over several cycles while changing their mechanical properties in response to blue and red light, as well as heat (∼50 °C). The light-responsive behavior was achieved through a photoredox-based mechanism consisting of photoinduced electron transfer from a zinc porphyrin photocatalyst in its excited state to oligoviologen-based macrocrosslinkers, both of which were integrated into the hydrogel polymer network during gel formation. Orthogonal thermoresponsive properties were also realized by introducing N-isopropyl acrylamide (NIPAM) monomer simultaneously with hydroxyethyl acrylate (HEA) in the pre-gel mixture to produce a statistical 60 : 40 HEA : NIPAM polymer network. The resultant hydrogel actuators – crosslinked with either a styrenated viologen dimer (2V4+-St) or hexamer (6V12+-St) – were exposed to red or blue light, or heat, for up to 5 h, and their rate of contraction, as well as the corresponding changes in their physical properties (i.e., stiffness, tensile strength, Young's modulus, etc.), were measured. The combined application of blue light and heat to the 6V12+-St-based hydrogels was also demonstrated, resulting in hydrogels with more than two-fold faster contraction kinetics and dramatically enhanced mechanical robustness when fully contracted. We envision that the reported materials and the corresponding methods of remotely manipulating the dynamic hydrogels may serve as a useful blueprint for future adaptive materials used in biomedical applications.
Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping

A segmentation-based architecture is proposed to decompose objects into multiple primitive shapes from monocular depth input for robotic manipulation. The backbone deep network is trained on synthetic data with 6 classes of primitive shapes generated by a simulation engine. Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape primitive region. The grasps are priority ordered via proposed ranking algorithm, with the first feasible one chosen for execution. On task-free grasping of individual objects, the method achieves a 94% success rate. On task-oriented grasping, it achieves a 76% success rate. Overall, the method supports the hypothesis that shape primitives can support task-free and task-relevant grasp prediction.
UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation

The fusion of multiple sensor modalities, especially through deep learning architectures, has been an active area of study. However, an under-explored aspect of such work is whether the methods can be robust to degradation across their input modalities, especially when they must generalize to degradation not seen during training. In this work, we propose an uncertainty-aware fusion scheme to effectively fuse inputs that might suffer from a range of known and unknown degradation. Specifically, we analyze a number of uncertainty measures, each of which captures a different aspect of uncertainty, and we propose a novel way to fuse degraded inputs by scaling modality-specific output softmax probabilities. We additionally propose a novel data-dependent spatial temperature scaling method to complement these existing uncertainty measures. Finally, we integrate the uncertainty-scaled output from each modality using a probabilistic noisy-or fusion method. In a photo-realistic simulation environment (AirSim), we show that our method achieves significantly better results on a semantic segmentation task, as compared to state-of-art fusion architectures, on a range of degradation (e.g. fog, snow, frost, and various other types of noise), some of which are unknown during training.
Unified Cross-Modality Feature Disentangler for Unsupervised Multi-domain MRI Abdomen Organs Segmentation

Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style scaling code, which modulates the generator to produce multi-modality images according to the target domain code from the image content features. Finally, we introduce a joint distribution matching discriminator that combines the translated images with task-relevant segmentation probability maps to further constrain and regularize image-to-image (I2I) translations. We performed extensive comparisons to multiple state-of-the-art I2I translation and segmentation methods. Our approach resulted in the lowest average multi-domain image reconstruction error of 1.34 ± 0.04. Our approach produced an average Dice similarity coefficient (DSC) of 0.85 for T1w and 0.90 for T2w MRI for multi-organ segmentation, which was highly comparable to a fully supervised MRI multi-organ segmentation network (DSC of 0.86 for T1w and 0.90 for T2w MRI) .
Distributed Networked Real-Time Learning

Many machine learning algorithms have been developed under the assumption that datasets are already available in batch form. Yet, in many application domains, data are only available sequentially overtime via compute nodes in different geographic locations. In this article, we consider the problem of learning a model when streaming data cannot be transferred to a single location in a timely fashion. In such cases, a distributed architecture for learning which relies on a network of interconnected “local” nodes is required. We propose a distributed scheme in which every local node implements stochastic gradient updates based upon a local data stream. To ensure robust estimation, a network regularization penalty is used to maintain a measure of cohesion in the ensemble of models. We show that the ensemble average approximates a stationary point and characterizes the degree to which individual models differ from the ensemble average. We compare the results with federated learning to conclude that the proposed approach is more robust to heterogeneity in data streams (data rates and estimation quality). We illustrate the results with an application to image classification with a deep learning model based upon convolutional neural networks.
Assessment of Controlled Substance Distribution to U.S. Veterinary Teaching Institutions From 2006 to 2019.

Objective: To evaluate the changing pattern of distribution of Schedule II and III opioids, barbiturates, and stimulants to veterinary educational institutions in the United States. Design: Longitudinal study. Sample: Veterinary teaching institutions that use Schedule II and III drugs. Procedures: Distribution of controlled substances to veterinary teaching institutions was obtained from the Drug Enforcement Administration’s Automated Reports and Consolidated Orders System (ARCOS) for opioids (e.g. methadone, fentanyl, codeine), barbiturates (pentobarbital, butalbital), and stimulants (amphetamine, methylphenidate, lisdexamfetamine) from 2006 - 2019. Opioids were converted to their morphine milligram equivalents (MME) for evaluation over time. Results: Controlled substance distribution to veterinary schools exhibited dynamic, and agent specific, changes. The total MME for eleven opioids peaked in 2013 and decreased by 17.3% in 2019. Methadone accounted for two-fifths (42.3%) and fentanyl over one-third (35.4%) of the total MME in 2019. Pentobarbital distribution was greatest by weight of all substances studied and peaked in 2011 at 69.4 kg. Stimulants underwent a pronounced decline and were very modest by 2014. Conclusions and Clinical Relevance: Opioids by total MME in veterinary teaching practice have undergone more modest changes than opioids used with humans. Hydrocodone, codeine and recently fentanyl use have declined while methadone increased. Stimulant distribution decreased to become negligible. Together, this pattern of findings warrant continued monitoring.
Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion

RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification. EMBR-seq results in 90% of the sequenced RNA molecules from an E. coli culture deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq uses a single or a few oligonucleotides per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits for bacterial rRNA depletion, we show that EMBR-seq can be used to successfully quantify the transcriptome from more than 500-fold lower starting total RNA. EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.
Drosophila hedgehog can act as a morphogen in the absence of regulated Ci processing.

Morphogens play a crucial role in determining how cells are organized in developing organisms. These chemical signals act over a wide area, and the amount of signal each cell receives typically initiates a sequence of events that spatially pattern the multiple cells of an organ or tissue. One of the most well-studied groups of morphogens are the hedgehog proteins, which are involved in the development of many animals, ranging from flies to humans. In fruit flies, hedgehog proteins kickstart a cascade of molecular changes that switch on a set of 'target' genes. They do this by ultimately altering the activity of a protein called cubitus interruptus, which comes in two lengths: a long version called Ci-155 and a short version called Ci-75. When hedgehog is absent, Ci-155 is kept in an inactive state in the cytoplasm, where it is slowly converted into its shorter form, Ci-75: this repressor protein is then able to access the nucleus, where it switches ‘off’ the target genes. However, when a hedgehog signal is present, the processing of Ci into its shorter form is inhibited. Instead, Ci-155 becomes activated by a separate mechanism that allows the long form protein to enter the nucleus and switch ‘on’ the target genes. But it was unclear whether hedgehog requires both of these mechanisms in order to act as a morphogen and regulate the activity of developmental genes. To answer this question, Little et al. mutated the gene for Ci in the embryo of fruit flies, so that the Ci-155 protein could no longer be processed into Ci-75. Examining the developing wings of these flies revealed that the genes targeted by hedgehog are still activated in the correct pattern. In some parts of the wing, Ci-75 is required to switch off specific sets of genes. But when Little et al. blocked these genes, by adding a gene that constantly produces the Ci repressor in the presence or absence of hedgehog, the adult flies still developed normally structured wings. This suggests that hedgehog does not need to regulate the processing of Ci-155 into Ci-75 in order to perform its developmental role. Previous work showed that when one of the major mechanisms used by hedgehog to activate Ci-155 is blocked, fruit flies are still able to develop normal wings. Taken together with the findings of Little et al., this suggests that the two mechanisms induced by hedgehog can compensate for each other, and independently regulate the development of the fruit fly wing. These mechanisms, which are also found in humans, have been linked to birth defects and several common types of cancer, and understanding how they work could help the development of new treatments.
Study of self-interaction errors in density functional predictions of dipole polarizabilities and ionization energies of water clusters using Perdew-Zunger and locally scaled self-interaction corrected methods.

We studied the effect of self-interaction error (SIE) on the static dipole polarizabilities of water clusters modeled with three increasingly sophisticated, non-empirical density functional approximations (DFAs), viz., the local spin density approximation (LDA), the Perdew–Burke–Ernzerhof (PBE) generalized-gradient approximation (GGA), and the strongly constrained and appropriately normed (SCAN) meta-GGA, using the Perdew–Zunger self-interaction-correction (PZ-SIC) energy functional in the Fermi–Lowdin orbital SIC framework. Our results show that while all three DFAs overestimate the cluster polarizabilities, the description systematically improves from LDA to PBE to SCAN. The self-correlation free SCAN predicts polarizabilities quite accurately with a mean absolute error (MAE) of 0.53 bohr3 with respect to coupled cluster singles and doubles (CCSD) values. Removing SIE using PZ-SIC correctly reduces the DFA polarizabilities, but overcorrects, resulting in underestimated polarizabilities in SIC-LDA, SIC-PBE, and SIC-SCAN. Finally, we applied a recently proposed locally scaled SIC (LSIC) method using a quasi self-consistent scheme and using the kinetic energy density ratio as an iso-orbital indicator. The results show that the LSIC polarizabilities are in excellent agreement with mean absolute errors of 0.08 bohr3 for LSIC-LDA and 0.06 bohr3 for LSIC-PBE with most recent CCSD polarizabilities. Likewise, the ionization energy estimates as absolute of highest occupied energy eigenvalue predicted by LSIC are also in excellent agreement with CCSD(T) ionization energies with MAEs of 0.4 eV for LSIC-LDA and 0.06 eV for LSIC-PBE. The LSIC-LDA predictions of ionization energies are comparable to the reported GW ionization energies, while the LSIC-PBE ionization energies are more accurate than the reported GW results.
Micromachining imposed subsurface plastic deformation in single-crystal aluminum

Abstract Mechanical removal of metal induces deformation and changes to microstructural characteristics of the newly created surfaces. The mode and extent of deformation can be difficult to predict since it depends on the local crystallographic orientation, which varies significantly for polycrystalline metals. In this work, we analyzed the deformation mode and extent beneath machined surfaces of different crystallographic orientations. This was accomplished by orthogonal micromachining of single-crystal aluminum along six different crystallographic orientations orthogonal to the sample [111] zone-axis, followed by electron backscatter diffraction (EBSD) analysis to evaluate the resulting subsurface microstructure and crystal lattice rotation. The results indicate that differences in the initial material crystallographic orientation produce significant variations in the depth of deformation (compared to the uncut chip thickness), the degree of grain refinement and the extent of lattice rotations. We grouped the orientation as “hard” or “soft” based on the measured cutting force. The soft orientations exhibit deformation modes consisting of shear bands and lattice rotations; whereas hard orientations exhibit deformation modes consistent with strain hardening: localized dynamic recrystallization, highly entangled dislocations and minimal crystal lattice rotations. The depth of subsurface deformation for some orientations was extensive, reaching depths far greater than the uncut chip thicknesses. Overall, we conclude that the cutting force required to machine a given orientation does provide some insight on the local deformation mode, and orientations can be easier or harder to machine based on local susceptibility to shear and lattice rotation.
The black branes of M-theory

Abstract We present a class of black p-brane solutions of M-theory which were hitherto known only in the extremal supersymmetric limit, and calculate their macroscopic entropy and temperature.
Changes in Vegetation Cover of Yukon River Drainages in Interior Alaska: Estimated from MODIS Greenness Trends, 2000 to 2018

Trends and transitions in the growing season MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in ecosystems of the Yukon River basin in interior Alaska. Statistical analysis of changes in the NDVI time series was conducted using the “Breaks for Additive Seasonal and Trend” method (BFAST). This structural change analysis indicated that NDVI breakpoints and negative 18-yr trends in vegetation greenness over the years since 2000 could be explained in large part by the impacts of severe wildfires, commonly affecting shrubland and forested ecosystems at relatively low elevations (< 300 m). At least one NDVI breakpoint was detected at 29% of the MODIS pixels within the Yukon River basin study area. The warmest and wettest years in the study time period were found to be associated with a sizeable fraction (30%) of NDVI breakpoints. Among pixels with no NDVI breakpoints detected, both forest and shrubland trends were strongly skewed toward positive trend values. Results from gradual NDVI trend analysis supported the hypothesis that air temperature warming has enhanced the rates of (unburned) vegetation growth in shrubland and woodlands across interior Alaska over the past two decades.
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction

This paper presents an adaptive image sampling algorithm based on Deep Learning (DL). It consists of an adaptive sampling mask generation network which is jointly trained with an image inpainting network. The sampling rate is controlled by the mask generation network, and a binarization strategy is investigated to make the sampling mask binary. In addition to the image sampling and reconstruction process, we show how it can be extended and used to speed up raster scanning such as the X-Ray fluorescence (XRF) image scanning process. Recently XRF laboratory-based systems have evolved into lightweight and portable instruments thanks to technological advancements in both X-Ray generation and detection. However, the scanning time of an XRF image is usually long due to the long exposure requirements (e.g., 100 μs - 1 ms per point). We propose an XRF image in painting approach to address the long scanning times, thus speeding up the scanning process, while being able to reconstruct a high quality XRF image. The proposed adaptive image sampling algorithm is applied to the RGB image of the scanning target to generate the sampling mask. The XRF scanner is then driven according to the sampling mask to scan a subset of the total image pixels. Finally, we inpaint the scanned XRF image by fusing the RGB image to reconstruct the full scan XRF image. The experiments show that the proposed adaptive sampling algorithm is able to effectively sample the image and achieve a better reconstruction accuracy than that of existing methods.
Interpreting Torsional Oscillator Measurements: Effect of Shear Modulus and Supersolidity

The torsional oscillator is the chief instrument for investigating supersolidity in solid 4He. These oscillators can be sensitive to the elastic properties of the solid helium, which show anomalies over the same range of temperature in which the supersolid phenomenon appears. In this report we present a detailed study of the influence of the elastic properties of the solid on the periods of torsional oscillators for the various designs that have been commonly employed in supersolid measurements. We show how to design an oscillator which measures supersolidity, and how to design one which predominantly measures elasticity. We describe the use of multiple frequency TOs for the separation of the elastic and supersolid phenomena.
Strategies for advantageous differential transport of ions in magnetic fusion devices

In a variety of magnetized plasma geometries, it has long been known that highly charged impurities tend to accumulate in regions of higher density. This “collisional pinch” is modified in the presence of additional forces, such as those might be found in systems with gravity, fast rotation, or non-negligible space charge. In the case of a rotating, cylindrical plasma, there is a regime in which the radially outermost ion species is intermediate in both mass and charge. This could have implications for fusion devices and plasma mass filters.
Entropic and Near-Field Improvements of Thermoradiative Cells.

A p-n junction maintained at above ambient temperature can work as a heat engine, converting some of the supplied heat into electricity and rejecting entropy by interband emission. Such thermoradiative cells have potential to harvest low-grade heat into electricity. By analyzing the entropy content of different spectral components of thermal radiation, we identify an approach to increase the efficiency of thermoradiative cells via spectrally selecting long-wavelength photons for radiative exchange. Furthermore, we predict that the near-field photon extraction by coupling photons generated from interband electronic transition to phonon polariton modes on the surface of a heat sink can increase the conversion efficiency as well as the power generation density, providing more opportunities to efficiently utilize terrestrial emission for clean energy. An ideal InSb thermoradiative cell can achieve a maximum efficiency and power density up to 20.4% and 327 Wm−2, respectively, between a hot source at 500 K and a cold sink at 300 K. However, sub-bandgap and non-radiative losses will significantly degrade the cell performance.
Hecke algebra correspondences for the metaplectic group

Over a p-adic field of odd residual characteristic, Gan and Savin proved a correspondence between the Bernstein components of the even and odd Weil representations of the metaplectic group and the components of the trivial representation of the equal rank odd orthogonal groups. In this paper, we extend their result to the case of even residual characteristic.
Exact Synchronization for Finite-State Sources

We analyze how an observer synchronizes to the internal state of a finite-state information source, using the ϵ-machine causal representation. Here, we treat the case of exact synchronization, when it is possible for the observer to synchronize completely after a finite number of observations. The more difficult case of strictly asymptotic synchronization is treated in a sequel. In both cases, we find that an observer, on average, will synchronize to the source state exponentially fast and that, as a result, the average accuracy in an observer’s predictions of the source output approaches its optimal level exponentially fast as well. Additionally, we show here how to analytically calculate the synchronization rate for exact ϵ-machines and provide an efficient polynomial-time algorithm to test ϵ-machines for exactness.
Principal Component Projection with Low-Degree Polynomials

In this paper, we consider approximations of principal component projection (PCP) without explicitly computing principal components. This problem has been studied in several recent works. The main feature of existing approaches is viewing the PCP matrix as a matrix function. This underlying function is the composition of a step function with a rational function. To find an approximate PCP, the step function is approximated by a polynomial while the rational function is evaluated by a fast ridge regression solver. In this work, we further improve this process by replacing the rational function with carefully constructed polynomials of low degree. We characterize the properties of polynomials that are suitable for approximating PCP, and then establish an optimization problem to select the optimal one from those polynomials. We show theoretically and confirm numerically that the resulting approximate PCP approach with optimal polynomials is indeed effective for approximations of principal component projection.
Bounded cohomology of finitely generated Kleinian groups

Any action of a group $${\Gamma}$$ on $${\mathbb{H}^3}$$ by isometries yields a class in degree three bounded cohomology by pulling back the volume cocycle to $${\Gamma}$$ . We prove that the bounded cohomology of finitely generated Kleinian groups without parabolic elements distinguishes the asymptotic geometry of geometrically infinite ends of hyperbolic 3-manifolds. That is, if two homotopy equivalent hyperbolic manifolds with infinite volume and without parabolic cusps have different geometrically infinite end invariants, then they define a 2 dimensional subspace of bounded cohomology. Our techniques apply to classes of hyperbolic 3-manifolds that have sufficiently different end invariants, and we give explicit bases for vector subspaces whose dimension is uncountable. We also show that these bases are uniformly separated in pseudo-norm, extending results of Soma. The technical machinery of the Ending Lamination Theorem allows us to analyze the geometrically infinite ends of hyperbolic 3-manifolds with unbounded geometry.
Marked tubes and the graph multiplihedron

Given a graph G , we construct a convex polytope whose face poset is based on marked subgraphs of G . Dubbed the graph multiplihedron, we provide a realization using integer coordinates. Not only does this yield a natural generalization of the multiplihedron, but features of this polytope appear in works related to quilted disks, bordered Riemann surfaces and operadic structures. Certain examples of graph multiplihedra are related to Minkowski sums of simplices and cubes and others to the permutohedron. 52B11; 18D50, 55P48
Strong Negative Type in Spheres

It is known that spheres have negative type, but only subsets with at most one pair of antipodal points have strict negative type. These are conditions on the (angular) distances within any finite subset of points. We show that subsets with at most one pair of antipodal points have strong negative type, a condition on every probability distribution of points. This implies that the function of expected distances to points determines uniquely the probability measure on such a set. It also implies that the distance covariance test for stochastic independence, introduced by Szekely, Rizzo and Bakirov, is consistent against all alternatives in such sets. Similarly, it allows tests of goodness of fit, equality of distributions, and hierarchical clustering with angular distances. We prove this by showing an analogue of the Cramer--Wold theorem.
The Milky Way's stellar halo - lumpy or triaxial?

We present minimum chi-squared fits of power law and Hernquist density profiles to F-turnoff stars in eight 2.5° wide stripes of SDSS data: five in the North Galactic Cap and three in the South Galactic cap. Portions of the stellar Galactic halo that are known to contain large streams of tidal debris or other lumpy structure, or that may include significant contamination from the thick disk, are avoided. The data strongly favor a model that is not symmetric about the Galaxy's axis of rotation. If included as a free parameter, the best fit to the center of the spheroid is surprisingly ≈ 3kpc from the Galactic center in the direction of the Sun's motion. The model fits favor a low value of the density of halo stars at the solar position. The alternative to a non-axisymmetric stellar distribution is that our fits are contaminated by previously unidentified lumpy substructure.
PROBING THE MASSES AND RADII OF DONOR STARS IN ECLIPSING X-RAY BINARIES WITH THE SWIFT BURST ALERT TELESCOPE

Physical parameters of both the mass donor and compact object can be constrained in X-ray binaries with well-defined eclipses, as our survey of wind-fed supergiant X-ray binaries IGR J16393-4643, IGR J16418-4532, IGR J16479-4514, IGR J18027-2016, and XTE J1855-026 reveals. Using the orbital period and Kepler’s third law, we express the eclipse half-angle in terms of radius, inclination angle, and the sum of the masses. Pulse-timing and radial velocity curves can give masses of both the donor and compact object as in the case of the “double-lined” binaries IGR J18027-2016 and XTE J1855-026. The eclipse half angles are , , 32 ± 2,34 ± 2, and degrees for IGR J16393-4643, IGR J16418-4532, IGR J16479-4514, IGR J18027-2016, and XTE 1855-026, respectively. In wind-fed systems, the primary not exceeding the Roche-lobe size provides an upper limit on system parameters. In IGR J16393-4643, spectral types of B0 V or B0-5 III are found to be consistent with the eclipse duration and Roche-lobe, but the previously proposed donor stars in IGR J16418-4532 and IGR J16479-4514 were found to be inconsistent with the Roche-lobe size. Stars with spectral types O7.5 I and earlier are possible. For IGR J18027-2016, the mass and radius of the donor star lie between 18.6–19.4 and 17.4–19.5 . We constrain the neutron star mass between 1.37 and 1.43 .We find the mass and radius of the donor star in XTE J1855-026 to lie between 19.6–20.2 and 21.5–23.0 . The neutron star mass was constrained to 1.77–1.82 . Eclipse profiles are asymmetric in IGR J18027-2016 and XTE J1855-026, which we attribute to accretion wakes.
At What Frequency Should the Kelly Bettor Bet

We study the problem of optimizing the betting frequency in a dynamic game setting using Kelly's celebrated expected logarithmic growth criterion as the performance metric. The game is defined by a sequence of bets with independent and identically distributed returns $X$ ( $k$ ). The bettor selects the fraction of wealth $K$ wagered at $k=0$ and waits $n$ steps before updating the bet size. Between updates, the proceeds from the previous bets remain at risk in the spirit of “buy and hold.” Within this context, the main questions we consider are as follows: How does the optimal performance, we call it $g_{n}^{*}$ , change with $n?$ Does the high-frequency case, $n=1$ , always lead to the best performance? What are the effects of accrued interest and transaction costs? First, we provide rather complete answers to these questions for the important special case when $X(k)\in\{-1,1\}$ is a Bernoulli random variable with probability $p$ that $X(k)=1$ . This leads us to analysis of more general probability distributions for $X$ (k) and leads to two conjectures. The first conjecture is simple to state: Absent transaction costs, $g_{n}^{*}$ is non-increasing in n. The second conjecture involves the technical condition which we call the sufficient attractiveness inequality. We first prove that satisfaction of this inequality is sufficient to guarantee that the low-frequency bettor using any $n > 1$ can match the performance of the high-frequency bettor using $n=1$ . Subsequently, we conjecture, and provide supporting evidence, that this condition is also necessary.