cssi/SciSciGPT-SciSciNet huggingface dataset:
The structure and free resolutions of the symbolic powers of star configurations of hypersurfaces

Star configurations of points are configurations with known (and conjectured) extremal behaviors among all configurations of points in $\mathbb P_k^n$; additional interest come from their rich structure, which allows them to be studied using tools from algebraic geometry, combinatorics, commutative algebra and representation theory. In the present paper we investigate the more general problem of determining the structure of symbolic powers of a wide generalization of star configurations of points (introduced by Geramita, Harbourne, Migliore and Nagel) called star configurations of hypersurfaces in $\mathbb P_k^n$. Here (1) we provide explicit minimal generating sets of the symbolic powers $I^{(m)}$ of these ideals $I$, (2) we introduce a notion of $\delta$-c.i. quotients, which generalize ideals with linear quotients, and show that $I^{(m)}$ have $\delta$-c.i. quotients, (3) we show that the shape of the Betti tables of these symbolic powers is determined by certain "Koszul" strands and we prove that a little bit more than the bottom half of the Betti table has a regular, almost hypnotic, pattern, and (4) we provide a closed formula for all the graded Betti numbers in these strands. As a special case of (2) we deduce that symbolic powers of ideals of star configurations of points have linear quotients. We also improve and extend results by Galetto, Geramita, Shin and Van Tuyl, and provide explicit new general formulas for the minimal number of generators and the symbolic defects of star configurations. Finally, inspired by Young tableaux, we introduce a technical tool which may be of independent interest: it is a "canonical" way of writing any monomial in any given set of polynomials. Our methods are characteristic--free.
Nuclease-mediated depletion biases in ribosome footprint profiling libraries

Ribosome footprint profiling is a high-throughput sequencing-based technique that provides detailed and global views of translation in living cells. An essential part of this technology is removal of unwanted, normally very abundant, ribosomal RNA sequences that dominate libraries and increase sequencing costs. The most effective commercial solution (Ribo-Zero) has been discontinued as a standalone product and a number of new, experimentally distinct commercial applications have emerged on the market. Here we evaluated several commercially available alternatives designed for RNA-seq of human samples and find them generally unsuitable for ribosome footprint profiling. We instead recommend the use of custom-designed biotinylated oligos, which were widely used in early ribosome profiling studies. Importantly, we warn that depletion solutions based on targeted nuclease cleavage significantly perturb the high-resolution information that can be derived from the data, and thus do not recommend their use for any applications that require precise determination of the ends of RNA fragments.
A transference inequality for rational approximation to points in geometric progression

We establish a transference inequality conjectured by Badziahin and Bugeaud relating exponents of rational approximation of points in geometric progression.
Prediction of repurposed drugs for treating lung injury in COVID-19

Background: Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient treatment for COVID-19 induced lung injury and acute respiratory failure. Methods: Inhibition of angiotensin-converting enzyme 2 (ACE2) caused by the spike protein of SARS-CoV-2 is the most plausible mechanism of lung injury in COVID-19. We performed drug repositioning analysis to identify drug candidates that reverse gene expression pattern in L1000 lung cell line HCC515 treated with ACE2 inhibitor. We confirmed these drug candidates by similar bioinformatics analysis using lung tissues from patients deceased from COVID-19. We further investigated deregulated genes and pathways related to lung injury, as well as the gene-pathway-drug candidate relationships. Results: We propose two candidate drugs, COL-3 (a chemically modified tetracycline) and CGP-60474 (a cyclin-dependent kinase inhibitor), for treating lung injuries in COVID-19. Further bioinformatics analysis shows that 12 significantly enriched pathways (P-value <0.05) overlap between HCC515 cells treated with ACE2 inhibitor and human COVID-19 patient lung tissues. These include signaling pathways known to be associated with lung injury such as TNF signaling, MAPK signaling and chemokine signaling pathways. All 12 pathways are targeted in COL-3 treated HCC515 cells, in which genes such as RHOA, RAC2, FAS, CDC42 have reduced expression. CGP-60474 shares 11 of 12 pathways with COL-3 and common target genes such as RHOA. It also uniquely targets other genes related to lung injury, such as CALR and MMP14. Conclusions: This study shows that ACE2 inhibition is likely part of the mechanisms leading to lung injury in COVID-19, and that compounds such as COL-3 and CGP-60474 have potential as repurposed drugs for its treatment.
Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms

We introduce a framework for analyzing and designing EIS inversion algorithms. Our framework stems from the observation of four features common to well-defined EIS inversion algorithms, namely (1) the representation of unknown distributions, (2) the minimization of a metric of error to estimate parameters arising from the chosen representation, subject to constraints on (3) the complexity control parameters, and (4) a means for choosing optimal control parameter values. These features must be present to overcome the ill-posed nature of EIS inversion problems. We review three established EIS inversion algorithms to illustrate the pervasiveness of these features, and show the utility of the framework by resolving ambiguities concerning three more algorithms. Our framework is then used to design the generalized EIS inversion (gEISi) algorithm, which uses Gaussian basis function representation, modality control parameter, and cross-validation for choosing the optimal control parameter value. The gEISi algorithm is applicable to the generalized EIS inversion problem, which allows for a wider range of underlying models. We also considered the construction of credible intervals for distributions arising from the algorithm. The algorithm is able to accurately reproduce distributions which have been difficult to obtain using existing algorithms. It is provided gratis on the repository this https URL.
Conditional Importance Sampling for Off-Policy Learning

The principal contribution of this paper is a conceptual framework for off-policy reinforcement learning, based on conditional expectations of importance sampling ratios. This framework yields new perspectives and understanding of existing off-policy algorithms, and reveals a broad space of unexplored algorithms. We theoretically analyse this space, and concretely investigate several algorithms that arise from this framework.
Impact of Thiol-Disulfide Balance on the Binding of Covid-19 Spike Protein with Angiotensin-Converting Enzyme 2 Receptor

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to an ongoing pandemic of coronavirus disease (COVID-19), which started in 2019. This is a member of Coronaviridae family in the genus Betacoronavirus, which also includes SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). The angiotensin-converting enzyme 2 (ACE2) is the functional receptor for SARS-CoV and SARS-CoV-2 to enter the host cells. In particular, the interaction of viral spike proteins with ACE2 is a critical step in the viral replication cycle. The receptor-binding domain of the viral spike proteins and ACE2 have several cysteine residues. In this study, the role of thiol-disulfide balance on the interactions between SARS-CoV/CoV-2 spike proteins and ACE2 was investigated using molecular dynamics simulations. The study revealed that the binding affinity was significantly impaired when all of the disulfide bonds of both ACE2 and SARS-CoV/CoV-2 spike proteins were reduced to thiol groups. The impact on the binding affinity was less severe when the disulfide bridges of only one of the binding partners were reduced to thiols. This computational finding possibly provides a molecular basis for the differential COVID-19 cellular recognition due to the oxidative stress.
Bounds for the rank of the finite part of operator $K$-theory

We derive a lower and an upper bound for the rank of the finite part of operator $K$-theory groups of maximal and reduced $C^*$-algebras of finitely generated groups. The lower bound is based on the amount of polynomially growing conjugacy classes of finite order elements in the group. The upper bound is based on the amount of torsion elements in the group. We use the lower bound to give lower bounds for the structure group $S(M)$ and the group of positive scalar curvature metrics $P(M)$ for an oriented manifold $M$. We define a class of groups called "polynomially full groups" for which the upper bound and the lower bound we derive are the same. We show that the class of polynomially full groups contains all virtually nilpotent groups. As example, we give explicit formulas for the ranks of the finite parts of operator $K$-theory groups for the finitely generated abelian groups, the symmetric groups and the dihedral groups.
A Super-Oxidized Radical Cationic Icosahedral Boron Cluster.

While the icosahedral closo-[B12H12]2– cluster does not display reversible electrochemical behavior, perfunctionalization of this species via substitution of all 12 B–H vertices with alkoxy or benz...
Bandwidth Optimized Parallel Algorithms for Sparse Matrix-Matrix Multiplication using Propagation Blocking

Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. It is well known that SpGEMM is a memory-bound operation, and its peak performance is expected to be bound by the memory bandwidth. Yet, existing algorithms fail to saturate the memory bandwidth, resulting in suboptimal performance under the Roofline model. In this paper, we characterize existing SpGEMM algorithms based on their memory access patterns and develop practical lower and upper bounds for SpGEMM performance. We then develop an SpGEMM algorithm based on the outer product. The newly developed algorithm called PB-SpGEMM saturates memory bandwidth by using the propagation blocking technique and by performing in-cache sorting and merging. For many practical matrices, PB-SpGEMM runs 20%-50% faster than the state-of-the-art heap and hash SpGEMM algorithms on modern multicore processors. Most importantly, PB-SpGEMM attains performance predicted by the Roofline model, and its performance remains stable with respect to matrix size and sparsity.
On the odd order composition factors of finite linear groups

In this paper, we study the product of orders of composition factors of odd order in a composition series of a finite linear group. First we generalize a result by Manz and Wolf about the order of solvable linear groups of odd order. Then we use this result to find bounds for the product of orders of composition factors of odd order in a composition series of a finite linear group.
Probabilistic analysis of masked loads with aggregated photovoltaic production

Abstract In this paper we present a probabilistic analysis framework to estimate behind-the-meter photovoltaic generation in real time. We develop a forward model consisting of a spatiotemporal stochastic process that represents the photovoltaic generation and a stochastic differential equation with jumps that represents the demand. We employ this model to disaggregate the behind-the-meter photovoltaic generation using net load and irradiance measurements.
Interacting regional policies in containing a disease.

Regional quarantine policies, in which a portion of a population surrounding infections is locked down, are an important tool to contain disease. However, jurisdictional governments—such as cities, counties, states, and countries—act with minimal coordination across borders. We show that a regional quarantine policy’s effectiveness depends on whether 1) the network of interactions satisfies a growth balance condition, 2) infections have a short delay in detection, and 3) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are outward looking and proactive: triggering quarantines in reaction to neighbors’ infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments—those that wait for nontrivial internal infection rates before quarantining—impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.
Microfluidics-enabled 96-well perfusion system for high-throughput tissue engineering and long-term all-optical electrophysiology

This work demonstrates a novel high-throughput (HT) microfluidics-enabled uninterrupted perfusion system (HT-μUPS) and validates its use with chronic all-optical electrophysiology in human excitable cells. HT-μUPS consists of a soft multichannel microfluidic plate cover which could button on a commercial HT 96-well plate. Herein, we demonstrate the manufacturing process of the system and its usages in acute and chronic all-optical electrophysiological studies of human induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CM) and engineered excitable (spiking HEK) cells. HT-μUPS perfusion maintained functional voltage and calcium responses in iPSC-CM and spiking HEK cells under spontaneous conditions and under optogenetic pacing. Long-term culture with HT-μUPS improved cell viability and optogenetically-tracked calcium responses in spiking HEK cells. The simplicity of this design and its compatibility with HT all-optical electrophysiology can empower cell-based assays for personalized medicine using patient-derived cells.
Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing

Automated writing evaluation systems can improve students’ writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback. Existing research on revision of argumentative writing in such systems, however, has focused on the types of revisions students make (e.g., surface vs. content) rather than the extent to which revisions actually respond to the feedback provided and improve the essay. We introduce an annotation scheme to capture the nature of sentence-level revisions of evidence use and reasoning (the ‘RER’ scheme) and apply it to 5th- and 6th-grade students’ argumentative essays. We show that reliable manual annotation can be achieved and that revision annotations correlate with a holistic assessment of essay improvement in line with the feedback provided. Furthermore, we explore the feasibility of automatically classifying revisions according to our scheme.
PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

SUMMARY Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene-disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data. AVAILABILITY AND IMPLEMENTATION A demo PheWAS-ME application is publicly available at https://prod.tbilab.org/phewas_me/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas_me_info/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity_explorer). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Discovering hierarchies among intermetallic crystal structures

The crystal structure of a compound plays an important role in determining its properties. Here we analyze over 4000 intermetallic compounds, and we identify a hierarchical relationship between their crystal structures. By considering each intermetallic compound as a particular chemical ordering over the sites of a higher symmetry crystal structure, we determine that most intermetallic compounds can be derived from a small number of parent crystal structures. While many compounds are chemical orderings over the sites of simple crystal structures such as body-centered-cubic (bcc) and face-centered-cubic (fcc) structures, the majority map onto more complex parent crystal structures. Surprisingly, many intermetallic compounds map onto parent crystal structures that differ from those of their elemental constituents. We find that the occurrence of several of the more complex parent crystal structures, such as the Laves phases, can be understood in terms of simple descriptors such as the ratio of atomic radii and electronegativity differences.
Stochastic Iterative Hard Thresholding for Low-Tucker-Rank Tensor Recovery

Low-rank tensor recovery problems have been widely studied in many signal processing and machine learning applications. Tensor rank is typically defined under certain tensor decomposition. In particular, Tucker decomposition is known as one of the most popular tensor decompositions. In recent years, researchers have developed many state-of-the-art algorithms to address the problem of low-Tucker-rank tensor recovery. Motivated by the favorable properties of the stochastic algorithms, such as stochastic gradient descent and stochastic iterative hard thresholding, we aim to extend the stochastic iterative hard thresholding algorithm from vectors to tensors in order to address the problem of recovering a low-Tucker-rank tensor from its linear measurements. We have also developed linear convergence analysis for the proposed method and conducted a series of experiments with both synthetic and real data to illustrate the performance of the proposed method.
Intelligent Export Diversification: An Export Recommendation System with Machine Learning

This paper presents a set of collaborative filtering algorithms that produce product recommendations to diversify and optimize a country's export structure in support of sustainable long-term growth. The recommendation system is able to accurately predict the historical trends in export content and structure for high-growth countries, such as China, India, Poland, and Chile, over 20-year spans. As a contemporary case study, the system is applied to Paraguay, to create recommendations for the country's export diversification strategy.
Direct detection of light ''Ge-phobic'' exothermic dark matter

We present comparisons of direct dark matter (DM) detection data for light WIMPs with exothermic scattering with nuclei (exoDM), both assuming the Standard Halo Model (SHM) and in a halo model — independent manner. Exothermic interactions favor light targets, thus reducing the importance of upper limits derived from xenon targets, the most restrictive of which is at present the LUX limit. In our SHM analysis the CDMS-II-Si and CoGeNT regions become allowed by these bounds, however the recent SuperCDMS limit rejects both regions for exoDM with isospin-conserving couplings. An isospin-violating coupling of the exoDM, in particular one with a neutron to proton coupling ratio of −0.8 (which we call “Ge-phobic”), maximally reduces the DM coupling to germanium and allows the CDMS-II-Si region to become compatible with all bounds. This is also clearly shown in our halo-independent analysis.
The Weak Localization for the Alloy-Type Anderson Model on a Cubic Lattice

We consider alloy type random Schrodinger operators on a cubic lattice whose randomness is generated by the sign-indefinite single-site potential. We derive Anderson localization for this class of models in the Lifshitz tails regime, i.e. when the coupling parameter λ is small, for the energies E≤−Cλ 2.
Interstellar Phase Transitions Stimulated by Time-dependent Heating

We use three-dimensional hydrodynamic numerical simulations to study phase transformations occurring in a clumpy interstellar gas exposed to time-dependent volumetric heating. To mimic conditions in the Galactic interstellar medium, we take a numerical model of a turbulent multiphase medium from Kritsuk & Norman computed in a periodic box with mean density n0 = 0.25 cm-3 and mean pressure P0/k ~ 103.4 K cm-3. A second model with n0 = 1 cm-3 is also considered. Variations of the heating rate on a timescale of 1-10 Myr applied thereafter cause pressure variations in the gas and shifting of the thermal equilibrium curve in the phase plane. This stimulates mass transfer between the gas phases via thermal instability, converting 5%-10% of the thermal energy into kinetic energy of gas motions. The experiments demonstrate that recurrent substantial heating episodes can maintain turbulence at this level. Possible applications to the interstellar gas heated by variable far-ultraviolet background radiation produced by short-living massive stars are discussed.
Weathering adaptation: Grid infrastructure planning in a changing climate

Abstract Decisions related to electric power systems planning and operations rely on assumptions and insights informed by historic weather data and records of past performance. Evolving climate trends are, however, changing the energy use patterns and operating conditions of grid assets, thus altering the nature and severity of risks the system faces. Because grid assets remain in operation for decades, planning for evolving risks will require incorporating climate projections into grid infrastructure planning processes. The current work traces a pathway for climate-aware decision-making in the electricity sector. We evaluate the suitability of using existing climate models and data for electricity planning and discuss their limitations. We review the interactions between grid infrastructure and climate by synthesizing what is known about how changing environmental operating conditions would impact infrastructure utilization, constraints, and performance. We contextualize our findings by presenting a case study of California, examining if and where climate data can be integrated into infrastructure planning processes. The core contribution of the work is a series of nine recommendations detailing advancements in climate projections, grid modeling architecture, and disaster preparedness that would be needed to ensure that infrastructure planning decisions are robust to uncertainty and risks associated with evolving climate conditions.
OPTIMAL LINEAR IMAGE COMBINATION

A simple, yet general, formalism for the optimized linear combination of astrophysical images is constructed and demonstrated. The formalism allows the user to combine multiple undersampled images to provide oversampled output at high precision. The proposed method is general and may be used for any configuration of input pixels and point spread function; it also provides the noise covariance in the output image along with a powerful metric for describing undesired distortion to the image convolution kernel. The method explicitly provides knowledge and control of the inevitable compromise between noise and fidelity in the output image. We present a first prototype implementation of the method, outlining the steps taken to generate an efficient algorithm. This implementation is then put to practical use in reconstructing fully sampled output images using simulated, undersampled input exposures that are designed to mimic the proposed Wide-field InfraRed Survey Telescope (WFIRST). We examine results using randomly rotated and dithered input images, while also assessing better-known "ideal" dither patterns: comparing results, we illustrate the use of the method as a survey design tool. Finally, we use the method to test the robustness of linear image combination when subjected to practical realities such as missing input pixels and focal plane plate scale variations.
Functionals and the Quantum Master Equation

The quantum master equation is usually formulated in terms of functionals of the components of mappings (fields in physpeak) from a space–time manifold M into a finite-dimensional vector space. The master equation is the sum of two terms one of which is the antibracket (odd poisson bracket) of functionals and the other is the Laplacian of a functional. Both of these terms seem to depend on the fact that the mappings on which the functionals act are vector-valued. It turns out that neither the Laplacian nor the antibracket is well-defined for sections of an arbitrary vector bundle. We show that if the functionals are permitted to have their values in an appropriate graded tensor algebra whose factors are the dual of the space of smooth functions on M, then both the antibracket and the Laplace operator can be invariantly defined. This permits one to develop the Batalin–Vilkovisky approach to BRST cohomology for functionals of sections of an arbitrary vector bundle.
Randall-Sundrum models and the regularized AdS/CFT correspondence

It has been proposed that Randall-Sundrum models can be holographically described by a regularized (broken) conformal field theory. We analyze the foundations of this duality using a regularized version of the AdS/CFT correspondence. We compare two- and three-point correlation functions and find the same behaviour in both descriptions. In particular, we show that the regularization of the deformed CFT generates kinetic terms for the sources, which hence can be naturally treated as dynamical fields. We also discuss the counterterms required for two- and three-point correlators in the renormalized AdS/CFT correspondence.
Fuzzy overlapping community quality metrics

Modularity is widely used to effectively measure the strength of the disjoint community structure found by community detection algorithms. Several overlapping extensions of modularity were proposed to measure the quality of overlapping community structure. However, all these extensions differ just in the way they define the belonging coefficient and belonging function. Yet, there is lack of systematic comparison of different extensions. To fill this gap, we overview overlapping extensions of modularity and generalize them with a uniform definition enabling application of different belonging coefficients and belonging functions to select the best. In addition, we extend localized modularity, modularity density, and eight local community quality metrics to enable their usages for overlapping communities. The experimental results on a large number of real networks and synthetic networks using overlapping extensions of modularity, overlapping modularity density, and local metrics show that the best results are obtained when the product of the belonging coefficients of two nodes is used as the belonging function. Moreover, the results may be used to guide researchers on which metrics to adopt when measuring the quality of overlapping community structure.
Jet quenching in hot strongly coupled gauge theories simplified

Theoretical studies of jet stopping in strongly-coupled QCD-like plasmas have used gauge-gravity duality to find that the maximum stopping distance scales like E1/3 for large jet energies E. In recent work studying jets that are created by finite-size sources in the gauge theory, we found an additional scale: the typical (as opposed to maximum) jet stopping distance scales like (EL)1/4, where L is the size of the space-time region where the jet is created. In this paper, we show that the results of our previous, somewhat involved computation in the gravity dual, and the (EL)1/4 scale in particular, can be very easily reproduced and understood in terms of the distance that high-energy particles travel in AdS5-Schwarzschild space before falling into the black brane. We also investigate how stopping distances depend on the conformal dimension of the source operator used to create the jet.
The universal n-pointed surface bundle only has n sections

The classifying space BDiff(Sg,n) of the orientation-preserving diffeomorphism group of a surface Sg of genus g > 1 fixing n points pointwise has a universal bundle Sg →UDiff(Sg,n) →ug,nBDiff(Sg,n). The n fixed points provide n sections {si|1 ≤ i ≤ n} of ug,n. In this paper we prove a conjecture of R. Hain that any section of π is homotopic to some si. Let PConfn(Sg) be the space of ordered n-tuple of distinct points on Sg. As part of the proof of Hain’s conjecture, we prove a result of independent interest: any surjective homomorphism π1(PConfn(Sg)) → π1(Sg) is equal to one of the forgetful homomorphisms {pi : π1(PConfn(Sg)) → π1(Sg)|1 ≤ i ≤ n}, possibly post-composed with an automorphism of π1(Sg). We also classify sections of the universal hyperelliptic surface bundle.
Scaling of plate tectonic convection with pseudoplastic rheology

[1] The scaling of plate tectonic convection is investigated by simulating thermal convection with pseudoplastic rheology and strongly temperature-dependent viscosity. The effect of mantle melting is also explored with additional depth-dependent viscosity. Heat flow scaling can be constructed with only two parameters, the internal Rayleigh number and the lithospheric viscosity contrast, the latter of which is determined entirely by rheological properties. The critical viscosity contrast for the transition between plate tectonic and stagnant lid convection is found to be proportional to the square root of the internal Rayleigh number. The relation between mantle temperature and surface heat flux on Earth is discussed on the basis of these scaling laws, and the inverse relationship between them, as previously suggested from the consideration of global energy balance, is confirmed by this fully dynamic approach. In the presence of surface water to reduce the effective friction coefficient, the operation of plate tectonics is suggested to be plausible throughout the Earth history.
Generalized involution models for wreath products

We prove that if a finite group H has a generalized involution model, as defined by Bump and Ginzburg, then the wreath product H ≀ S n also has a generalized involution model. This extends the work of Baddeley concerning involution models for wreath products. As an application, we construct a Gel’fand model for wreath products of the form A ≀ S n with A abelian, and give an alternate proof of a recent result due to Adin, Postnikov and Roichman describing a particularly elegant Gel’fand model for the wreath product ℤ r ≀ S n . We conclude by discussing some notable properties of this representation and its decomposition into irreducible constituents, proving a conjecture of Adin, Postnikov and Roichman.
Nucleon Structure from Lattice QCD Calculations

Parton distribution and correlation functions describe the relation between a hadron and the quarks and gluons (or collectively, the partons) within it, and carry rich information on hadron's partonic structure that cannot be calculated by QCD perturbation theory. In this talk, I will review what lattice QCD can and cannot do for calculating the parton distribution and correlation functions, and the new ideas and efforts around the world to explore nucleon structure from lattice QCD calculations by combining the strength of both lattice QCD and perturbative QCD in such a way that is complementary to our on-going effort to extract these fundamental functions from experimental data.
The size of p-branes

Abstract We obtain form factors for scattering of gravitons and anti-symmetric tensor particles off Dirichlet p -branes in Type II superstring theory. As expected, the form factor of the −1-brane (the D-instanton) exhibits point-like behavior: in fact it is saturated by the dilaton tadpole graph. In contrast, p -branes with p > −1 acquire size of order the string scale due to quantum effects and exhibit Regge behavior. We find their leading form factors in closed form and show that they contain an infinite sequence of poles associated with p -brane excitations. Finally, we argue that the p -brane form factors for scattering of R-R bosons will have the same stringy features found with NS-NS states.
Solving Markov decision processes for network-level post-hazard recovery via simulation optimization and rollout

Computation of optimal recovery decisions for community resilience assurance post-hazard is a combinatorial decision-making problem under uncertainty. It involves solving a large-scale optimization problem, which is significantly aggravated by the introduction of uncertainty. In this paper, we draw upon established tools from multiple research communities to provide an effective solution to this challenging problem. We provide a stochastic model of damage to the water network (WN) within a testbed community following a severe earthquake and compute near-optimal recovery actions for restoration of the water network. We formulate this stochastic decision-making problem as a Markov Decision Process (MDP), and solve it using a popular class of heuristic algorithms known as rollout. A simulation-based representation of MDPs is utilized in conjunction with rollout and the Optimal Computing Budget Allocation (OCBA) algorithm to address the resulting stochastic simulation optimization problem. Our method employs non-myopic planning with efficient use of simulation budget. We show, through simulation results, that rollout fused with OCBA performs competitively with respect to rollout with total equal allocation (TEA) at a meagre simulation budget of 5-10% of rollout with TEA, which is a crucial step towards addressing large-scale community recovery problems following natural disasters.
A Definitive Survey for Lyman Limit Systems at z~3.5 with the Sloan Digital Sky Survey

We perform a semi-automated survey for τ912 ≥ 2 Lyman limit systems (LLSs) in quasar spectra from the Sloan Digital Sky Survey, Data Release 7. From a starting sample of 2473 quasars with z em = 3.6-5.0, we analyze 429 spectra meeting strict selection criteria for a total redshift path Δz = 93.8 and identify 190 intervening systems at z LLS ≥ 3.3. The incidence of τ912 ≥ 2 LLSs per unit redshift, lτ≥2(z), is well described by a single power law at these redshifts: , with z * ≡ 3.7, C LLS = 1.9 ± 0.2, and γLLS = 5.1 ± 1.5 (68% c.l.). These values are systematically lower than previous estimates (especially at z < 4) but are consistent with recent measurements of the mean free path to ionizing radiation. Extrapolations of this power law to z = 0 are inconsistent with previous estimations of l(z) at z < 1 and may indicate a break at z 2, similar to that observed for the Lyα forest. Our results also indicate that the systems giving rise to LLS absorption decrease by 50% in comoving number density and/or physical size from z = 4 to 3.3, perhaps due to an enhanced extragalactic ultraviolet background. The observations place an integral constraint on the H I frequency distribution f(N H I , X) and indicate that the power-law slope is likely shallower than β = –1 at . Including other constraints on f(N H I , X) from the literature, we infer that β is steeper than β = –1.7 at , implying at least two inflections in f(N H I , X). We also perform a survey for proximate LLSs (PLLSs) and find that lPLLS(z) is systematically lower (25%) than intervening systems. Finally, we estimate that systematic effects impose an uncertainty of 10%-20% in the l(z) measurements; these effects may limit the precision of all future surveys.
A HIGH-PT TRIGGER FOR THE HERA-B EXPERIMENT

We have constructed a high-pT trigger for the HERA-B experiment at DESY. The HERA-B experiment produces B mesons by inserting wire targets into the halo of the proton beam circulating in HERA. The high-pt trigger records events that contain tracks that have high transverse momentum with respect to the beam. Such a trigger is efficient for recording B → π+π-, B → K- π+, Bs → K+ K-, , and other topical hadronic B decays. These decays provide sensitivity to the internal angles alpha and gamma of the CKM unitarity triangle, and they also can be used to measure or constrain the mixing parameter xs.
MOTIVIC INVARIANTS OF p-ADIC FIELDS

We provide a complete analysis of the motivic Adams spec- tral sequences converging to the bigraded coefficients of the 2-complete algebraic Johnson-Wilson spectraBPGLhni overp-adic fields. These spec- tra interpolate between integral motivic cohomology (n = 0), a connec- tive version of algebraic K-theory (n = 1), and the algebraic Brown- Peterson spectrum (n = 1). We deduce that, over p-adic fields, the 2- complete BPGLhni splits over 2-complete BPGLh0i, implying that the slice spectral sequence forBPGL collapses. This is the first in a series of two papers investigating motivic invari- ants ofp-adic fields, and it lays the groundwork for an understanding of the motivic Adams-Novikov spectral sequence over such base fields.
On a Poisson summation formula for noncommutative tori

It is proved that a maximal abelian subalgebra of the noncommutative torus commutes with the Laplace operator on a complex torus. As a corollary, one gets an analog of the Poisson summation formula for noncommutative tori.
Volumes of highly twisted knots and links

We show that for a large class of knots and links with complements in S admitting a hyperbolic structure, we can determine bounds on the volume of the link complement from combinatorial information given by a link diagram. Specifically, there is a universal constant C such that if a knot or link admits a prime, twist reduced diagram with at least 2 twist regions and at least C crossings per twist region, then the link complement is hyperbolic with volume bounded below by 3.3515 times the number of twist regions in the diagram. C is at most 113.
Athermal shearing of frictionless cross-shaped particles of varying aspect ratio

We use numerical simulations to study the shear-driven steady-state flow of athermal, frictionless, overdamped, two dimensional cross-shaped particles of varying aspect ratios, and make comparison with the behavior of rod-shaped and staple-shaped particles. We find that the extent of non-convexity of the particle shape plays an important role in determining both the value of the jamming packing fraction as well as the rotational motion and orientational ordering of the particles.
Identifying defect-tolerant semiconductors with high minority-carrier lifetimes: beyond hybrid lead halide perovskites

The emergence of methyl-ammonium lead halide (MAPbX3) perovskites motivates the identification of unique properties giving rise to exceptional bulk transport properties, and identifying future materials with similar properties. Here, we propose that this “defect tolerance” emerges from fundamental electronic-structure properties, including the orbital character of the conduction and valence band extrema, the chargecarrier effective masses, and the static dielectric constant. We use MaterialsProject.org searches and detailed electronic-structure calculations to demonstrate these properties in other materials than MAPbX3. This framework of materials discovery may be applied more broadly, to accelerate discovery of new semiconductors based on emerging understanding of recent successes.
Concurrent Optimization of Vehicle Dynamics and Powertrain Operation Using Connectivity and Automation

Connected and automated vehicles (CAVs) provide the most intriguing opportunity to reduce energy consumption and travel delays. In this paper, we propose a two-level control architecture for CAVs to optimize (1) the vehicle's speed profile, aimed at minimizing stop-and-go driving, and (2) the powertrain efficiency of the vehicle for the optimal speed profile derived in (1). The proposed hierarchical control framework can be implemented onboard the vehicle in real time with minimal computational effort. We evaluate the effectiveness of the efficiency of the proposed architecture through simulation in Mcity using a 100% penetration rate of CAVs. The results show that the proposed approach yields significant benefits in terms of energy efficiency.
Relating zeta functions of discrete and quantum graphs

We write the spectral zeta function of the Laplace operator on an equilateral metric graph in terms of the spectral zeta function of the normalized Laplace operator on the corresponding discrete graph. To do this, we apply a relation between the spectrum of the Laplacian on a discrete graph and that of the Laplacian on an equilateral metric graph. As a by-product, we determine how the multiplicity of eigenvalues of the quantum graph, that are also in the spectrum of the graph with Dirichlet conditions at the vertices, depends on the graph geometry. Finally we apply the result to calculate the vacuum energy and spectral determinant of a complete bipartite graph and compare our results with those for a star graph, a graph in which all vertices are connected to a central vertex by a single edge.
Stochastic particle production in a de Sitter background

We explore non-adiabatic particle production in a de Sitter universe for a scalar spectator field, by allowing the effective mass $m^2(t)$ of this field and the cosmic time interval between non-adiabatic events to vary stochastically. Two main scenarios are considered depending on the (non-stochastic) mass $M$ of the spectator field: the conformal case with $M^2=2H^2$, and the case of a massless field. We make use of the transfer matrix formalism to parametrize the evolution of the system in terms of the "occupation number", and two phases associated with the transfer matrix; these are used to construct the evolution of the spectator field. Assuming short-time interactions approximated by Dirac-delta functions, we numerically track the change of these parameters and the field in all regimes: sub- and super-horizon with weak and strong scattering. In all cases a log-normally distributed field amplitude is observed, and the logarithm of the field amplitude approximately satisfies the properties of a Wiener process outside the horizon. We derive a Fokker-Planck equation for the evolution of the transfer matrix parameters, which allows us to calculate analytically non-trivial distributions and moments in the weak-scattering limit.
Characterizing Earth Analogs in Reflected Light: Atmospheric Retrieval Studies for Future Space Telescopes

Space-based high contrast imaging mission concepts for studying rocky exoplanets in reflected light are currently under community study. We develop an inverse modeling framework to estimate the science return of such missions given different instrument design considerations. By combining an exoplanet albedo model, an instrument noise model, and an ensemble Markov chain Monte Carlo sampler, we explore retrievals of atmospheric and planetary properties for Earth twins as a function of signal-to-noise ratio (SNR) and resolution ($R$). Our forward model includes Rayleigh scattering, single-layer water clouds with patchy coverage, and pressure-dependent absorption due to water vapor, oxygen, and ozone. We simulate data at $R = 70$ and $R = 140$ from 0.4-1.0 $\mu$m with SNR $ = 5, 10, 15, 20$ at 550 nm (i.e., for HabEx/LUVOIR-type instruments). At these same SNR, we simulate data for WFIRST paired with a starshade, which includes two photometric points between 0.48-0.6 $\mu$m and $R = 50$ spectroscopy from 0.6-0.97 $\mu$m. Given our noise model for WFIRST-type detectors, we find that weak detections of water vapor, ozone, and oxygen can be achieved with observations with at least $R = 70$ / SNR$\ = 15$, or $R = 140$ / SNR$\ = 10$ for improved detections. Meaningful constraints are only achieved with $R = 140$ / SNR$\ = 20$ data. The WFIRST data offer limited diagnostic information, needing at least SNR = 20 to weakly detect gases. Most scenarios place limits on planetary radius, but cannot constrain surface gravity and, thus, planetary mass.
Parallel Accelerated Vector Similarity Calculations for Genomics Applications

Abstract The surge in availability of genomic data holds promise for enabling determination of genetic causes of observed individual traits, with applications to problems such as discovery of the genetic roots of phenotypes, be they molecular phenotypes such as gene expression or metabolite concentrations, or complex phenotypes such as diseases. However, the growing sizes of these datasets and the quadratic, cubic or higher scaling characteristics of the relevant algorithms pose a serious computational challenge necessitating use of leadership scale computing. In this paper we describe a new approach to performing vector similarity metrics calculations, suitable for parallel systems equipped with graphics processing units (GPUs) or Intel Xeon Phi processors. Our primary focus is the Proportional Similarity metric applied to Genome Wide Association Studies (GWAS) and Phenome Wide Association Studies (PheWAS). We describe the implementation of the algorithms on accelerated processors, methods used for eliminating redundant calculations due to symmetries, and techniques for efficient mapping of the calculations to many-node parallel systems. Results are presented demonstrating high per-node performance and parallel scalability with rates of more than five quadrillion (5 × 1015) elementwise comparisons achieved per second on the ORNL Titan system. In a companion paper we describe corresponding techniques applied to calculations of the Custom Correlation Coefficient for comparative genomics applications.
Polyharmonic and Related Kernels on Manifolds: Interpolation and Approximation

This article is devoted to developing a theory for effective kernel interpolation and approximation in a general setting. For a wide class of compact, connected Cź Riemannian manifolds, including the important cases of spheres and SO(3), and using techniques involving differential geometry and Lie groups, we establish that the kernels obtained as fundamental solutions of certain partial differential operators generate Lagrange functions that are uniformly bounded and decay away from their center at an algebraic rate, and in certain cases, an exponential rate. An immediate corollary is that the corresponding Lebesgue constants for interpolation as well as for L2 minimization are uniformly bounded with a constant whose only dependence on the set of data sites is reflected in the mesh ratio, which measures the uniformity of the data. The kernels considered here include the restricted surface splines on spheres, as well as surface splines for SO(3), both of which have elementary closed-form representations that are computationally implementable. In addition to obtaining bounded Lebesgue constants in this setting, we also establish a "zeros lemma" for domains on compact Riemannian manifolds--one that holds in as much generality as the corresponding Euclidean zeros lemma (on Lipschitz domains satisfying interior cone conditions) with constants that clearly demonstrate the influence of the geometry of the boundary (via cone parameters) as well as that of the Riemannian metric.