cssi/SciSciGPT-SciSciNet huggingface dataset:
TROM: A Testing-Based Method for Finding Transcriptomic Similarity of Biological Samples

Comparative transcriptomics has gained increasing popularity in genomic research thanks to the development of high-throughput technologies including microarray and next-generation RNA sequencing that have generated numerous transcriptomic data. An important question is to understand the conservation and divergence of biological processes in different species. We propose a testing-based method TROM (Transcriptome Overlap Measure) for comparing transcriptomes within or between different species, and provide a different perspective, in contrast to traditional correlation analyses, about capturing transcriptomic similarity. Specifically, the TROM method focuses on identifying associated genes that capture molecular characteristics of biological samples, and subsequently comparing the biological samples by testing the overlap of their associated genes. We use simulation and real data studies to demonstrate that TROM is more powerful in identifying similar transcriptomes and more robust to stochastic gene expression noise than Pearson and Spearman correlations. We apply TROM to compare the developmental stages of six Drosophila species, C. elegans, S. purpuratus, D. rerio and mouse liver, and find interesting correspondence patterns that imply conserved gene expression programs in the development of these species. The TROM method is available as an R package on CRAN (https://cran.r-project.org/package=TROM) with manuals and source codes available at http://www.stat.ucla.edu/~jingyi.li/software-and-data/trom.html.
A determinacy approach to Borel combinatorics

We introduce a new method, involving infinite games and Borel determinacy, which we use to answer several well-known questions in Borel combinatorics.
Curves of constant curvature and torsion in the 3-sphere

We describe the curves of constant (geodesic) curvature and torsion in the three-dimensional round sphere. These curves are the trajectory of a point whose motion is the superposition of two circular motions in orthogonal planes. The global behavior may be periodic or the curve may be dense in a Clifford torus embedded in the 3-sphere. This behavior is very different from that of helices in three-dimensional Euclidean space, which also have constant curvature and torsion.
The Longevity of Water Ice on Ganymedes and Europas around Migrated Giant Planets

The gas giant planets in the Solar System have a retinue of icy moons, and we expect giant exoplanets to have similar satellite systems. If a Jupiter-like planet were to migrate toward its parent star the icy moons orbiting it would evaporate, creating atmospheres and possible habitable surface oceans. Here, we examine how long the surface ice and possible oceans would last before being hydrodynamically lost to space. The hydrodynamic loss rate from the moons is determined, in large part, by the stellar flux available for absorption, which increases as the giant planet and icy moons migrate closer to the star. At some planet–star distance the stellar flux incident on the icy moons becomes so great that they enter a runaway greenhouse state. This runaway greenhouse state rapidly transfers all available surface water to the atmosphere as vapor, where it is easily lost from the small moons. However, for icy moons of Ganymede's size around a Sun-like star we found that surface water (either ice or liquid) can persist indefinitely outside the runaway greenhouse orbital distance. In contrast, the surface water on smaller moons of Europa's size will only persist on timescales greater than 1 Gyr at distances ranging 1.49–0.74 au around a Sun-like star for Bond albedos of 0.2 and 0.8, where the lower albedo becomes relevant if ice melts. Consequently, small moons can lose their icy shells, which would create a torus of H atoms around their host planet that might be detectable in future observations.
Vibration-induced conductivity fluctuation measurement for soil bulk density analysis

Soil bulk density affects water storage, water and nutrient movement, and plant root activity in the soil profile. Its measurement is difficult in field conditions. Vibration-induced conductivity fluctuation was investigated to quantify soil bulk density with possible field applications in the future. The AC electrical conductivity of soil was measured using a pair of blade-like electrodes while exposing the soil to periodic vibration. The blades were positioned longitudinally and transversally to the direction of the induced vibration to enable the calculation of a normalized index. The normalized index was expected to provide data independent from the vibration strength and to reduce the effect of soil salinity and water content. The experiment was conducted on natural and salinized fine sand at two moisture conditions and four bulk densities. The blade-shaped electrodes improved electrode-soil contact compared to cylindrical electrodes, and thereby, reduced measurement noise. Simulations on a simplified resistor lattice indicate that the transversal effect increases as soil bulk density decreases. Measurement of dry sand showed a negative correlation between the normalized conductivity fluctuation and soil bulk density for both longitudinal and transversal settings. The decrease in the transversal signal was smaller than expected. The wet natural and salinized soils performed very similarly as hypothesized, but their normalized VICOF response was not significant to bulk density changes. This lack of sensitivity might be attributed to the heavy electrodes and/or the specific vibration method used. The effects of electrode material, vibration method and soil properties on the experiment need further study.
Irrational Monodromies of Vacuum Energy

We present a theory with axion flux monodromies coupled to gravity, that reduces to the local vacuum energy sequester below the axion mass scales. If the axion potentials include a term generated by nonperturbative couplings to gauge sectors, with a decay constant incommensurate with monodromy periods, the low energy potential germinates a landscape of irrational axion vacua, with arbitrarily small cosmological constants. The sensitivity of the values of cosmological constants to unknown UV physics can be greatly reduced. The variation of the cosmological constant in each vacuum, from one order in perturbation theory to the next, can be much smaller than the naive cut­off. The nonperturbative transitions in the early universe between the vacua populate this landscape, similar to the case of irrational axion. In such a landscape of vacua a small cosmological constant can naturally emerge.
Epitaxial co-deposition growth of CaGe2 films by molecular beam epitaxy for large area germanane

Two-dimensional crystals are an important class of materials for novel physics, chemistry, and engineering. Germanane (GeH), the germanium-based analogue of graphane (CH), is of particular interest due to its direct band gap and spin-orbit coupling. Here, we report the successful co-deposition growth of CaGe2 films on Ge(111) substrates by molecular beam epitaxy and their subsequent conversion to germanane by immersion in hydrochloric acid. We find that the growth of CaGe2 occurs within an adsorption-limited growth regime, which ensures stoichiometry of the film. We utilize in situ reflection high energy electron diffraction (RHEED) to explore the growth temperature window and find the best RHEED patterns at 750 °C. Finally, the CaGe2 films are immersed in hydrochloric acid to convert the films to germanane. Auger electron spectroscopy of the resulting film indicates the removal of Ca, and RHEED patterns indicate a single-crystal film with an in-plane orientation dictated by the underlying Ge(111) substrate. X-ray diffraction and Raman spectroscopy indicate that the resulting films are indeed germanane. Ex situ atomic force microscopy shows that the grain size of the germanane is on the order of a few micrometers, being primarily limited by terraces induced by the miscut of the Ge substrate. Thus, optimization of the substrate could lead to the long-term goal of large area germanane films.
PATCHWORK: A Multipatch Infrastructure for Multiphysics/Multiscale/Multiframe Fluid Simulations

We present a "multipatch" infrastructure for the numerical simulation of fluid problems in which subregions require different grid scales, different grid geometries, different physical equations, or different reference frames. Its key element is a sophisticated client–router–server framework for efficiently linking processors supporting different regions ("patches") that must exchange boundary data. This infrastructure may be used with a wide variety of fluid dynamics codes; the only requirement is that their primary dependent variables be the same in all patches, e.g., fluid mass density, internal energy density, and velocity. Its structure can accommodate either Newtonian or relativistic dynamics. The overhead imposed by this system is both problem and computer cluster architecture dependent. Compared to a conventional simulation using the same number of cells and processors employed on a problem not requiring multipatch methods, the cell update per processor rate decreases by an amount that can range from negligible to a factor of a few; however, even in these problems, the infrastructure can permit substantial decreases in the total number of cell updates required.
Branes ending on branes in a tachyon model

In a tachyon model proposed by Minahan and Zwiebach and derived in the boundary string field theory, we construct various new solutions which correspond to nontrivial brane configurations in string theory. Our solutions include Dp-D(p−2) bound states, (F, Dp) bound states, string junctions, D(p−2)-branes ending on a Dp-brane, D(p−2)-branes suspended between parallel Dp-branes and their non-commutative generalizations. We find the Bogomol'nyi bounds and the BPS equations for some of our solutions, and check the physical consistency of our solutions with the D-brane picture by looking at the distributions of their energies and RR-charges in space. We also give conjectures for a few other brane configurations.
Optimal Permutation Recovery in Permuted Monotone Matrix Model.

Motivated by recent research on quantifying bacterial growth dynamics based on genome assemblies, we consider a permuted monotone matrix model Y=ΘΠ+Z, where the rows represent different samples, th...
Relaxation of the composite Higgs little hierarchy

We describe a composite Higgs scenario in which a cosmological relaxation mechanism naturally gives rise to a hierarchy between the weak scale and the scale of spontaneous global symmetry breaking. This is achieved through the scanning of sources of explicit global symmetry breaking by a relaxion field during an exponentially long period of inflation in the early universe. We explore this mechanism in detail in a specific composite Higgs scenario with QCD-like dynamics, based on an ultraviolet SU(N )TC ‘technicolor’ confining gauge theory with three Dirac technifermion flavors. We find that we can successfully generate a hierarchy of scales ξ≡〈h〉2/F 2 ≳ 1.2 × 10− 4 (i.e., compositeness scales F π ∼ 20 TeV) without tuning. This evades all current electroweak precision bounds on our (custodial violating) model. While directly observing the heavy composite states in this model will be challenging, a future electroweak precision measurement program can probe most of the natural parameter space for the model. We also highlight signatures of more general composite Higgs models in the cosmological relaxation framework, including some implications for flavor and dark matter.
ARE THE EFFECTS OF STRUCTURE FORMATION SEEN IN THE CENTRAL METALLICITY OF GALAXY CLUSTERS

A sample of 46 nearby clusters observed with Chandra is analyzed to produce radial density, temperature, entropy, and metallicity profiles, as well as other morphological measurements. The entropy profiles are computed to larger radii than in previous Chandra cluster sample analyses. We find that the iron mass fraction measured in the inner shows a larger dispersion across the sample of low-mass clusters?than it does for the sample of high-mass clusters. We interpret this finding as the result of the mixing of more halos in large clusters than in small clusters, leading to an averaging of the metallicity in the large clusters, and thus less dispersion of metallicity. This interpretation lends support to the idea that the low-entropy, metal-rich gas of merging halos reaches the clusters? centers, which explains observations of core-collapse supernova product?metallicity peaks, and which is seen in hydrodynamical simulations. The gas in these merging halos would have to reach cluster centers without mixing in the outer regions. On the other hand, the metallicity dispersion does not change with mass in the outer regions of the clusters, suggesting that most of the outer metals originate from a source with a more uniform metallicity level, such as during pre-enrichment. We also measure a correlation between the metal content in low-mass clusters and the morphological disturbance of their intracluster medium, as measured by centroid shift. This suggests an alternative interpretation, whereby transitional metallicity boosts in the center of low-mass clusters account for the larger dispersion of their metallicities.
A polyhedral study of the static probabilistic lot-sizing problem

We study the polyhedral structure of the static probabilistic lot-sizing problem and propose valid inequalities that integrate information from the chance constraint and the binary setup variables. We prove that the proposed inequalities subsume existing inequalities for this problem, and they are facet-defining under certain conditions. In addition, we show that they give the convex hull description of a related stochastic lot-sizing problem. We propose a new formulation that exploits the simple recourse structure, which significantly reduces the number of variables and constraints of the deterministic equivalent program. This reformulation can be applied to general chance-constrained programs with simple recourse. The computational results show that the proposed inequalities and the new formulation are effective for the static probabilistic lot-sizing problems.
Global Hopf Bifurcation for Differential-Algebraic Equations with State-Dependent Delay

We develop a global Hopf bifurcation theory for differential equations with a state-dependent delay governed by an algebraic equation, using the $$S^1$$ -equivariant degree. We apply the global Hopf bifurcation theory to a model of genetic regulatory dynamics with threshold type state-dependent delay vanishing at the stationary state, for a description of the global continuation of the periodic oscillations.
Advances in non-relativistic matter via complex Langevin approaches

The recent progress in understanding the mathematics of complex stochastic quantization, as well as its application to quantum chromodynamics in situations that have a complex phase problem (e.g. finite quark density, real time), has opened up an intriguing possibility for non-relativistic many-body physics which has so far remained largely unexplored. In this brief contribution, I review a few specific examples of advances in the characterization of the thermodynamics of non-relativistic matter in a variety of one-dimensional cases affected by the sign problem: repulsive interactions, finite polarization, finite mass imbalance, and projection to finite systems to obtain virial coefficients.
Little Evidence of Modified Genetic Effect of rs16969968 on Heavy Smoking Based on Age of Onset of Smoking

INTRODUCTION Tobacco smoking is the leading cause of preventable death globally. Smoking quantity, measured in cigarettes per day, is influenced both by the age of onset of regular smoking (AOS) and by genetic factors, including a strong effect of the nonsynonymous single-nucleotide polymorphism rs16969968. A previous study by Hartz et al. reported an interaction between these two factors, whereby rs16969968 risk allele carriers who started smoking earlier showed increased risk for heavy smoking compared with those who started later. This finding has yet to be replicated in a large, independent sample. METHODS We performed a preregistered, direct replication attempt of the rs16969968 × AOS interaction on smoking quantity in 128 383 unrelated individuals from the UK Biobank, meta-analyzed across ancestry groups. We fit statistical association models mirroring the original publication as well as formal interaction tests on multiple phenotypic and analytical scales. RESULTS We replicated the main effects of rs16969968 and AOS on cigarettes per day but failed to replicate the interaction using previous methods. Nominal significance of the rs16969968 × AOS interaction term depended strongly on the scale of analysis and the particular phenotype, as did associations stratified by early/late AOS. No interaction tests passed genome-wide correction (α = 5e-8), and all estimated interaction effect sizes were much smaller in magnitude than previous estimates. CONCLUSIONS We failed to replicate the strong rs16969968 × AOS interaction effect previously reported. If such gene-moderator interactions influence complex traits, they likely depend on scale of measurement, and current biobanks lack the power to detect significant genome-wide associations given the minute effect sizes expected. IMPLICATIONS We failed to replicate the strong rs16969968 × AOS interaction effect on smoking quantity previously reported. If such gene-moderator interactions influence complex traits, current biobanks lack the power to detect significant genome-wide associations given the minute effect sizes expected. Furthermore, many potential interaction effects are likely to depend on the scale of measurement employed.
Universal Properties of Cuprate Superconductors: Tc Phase Diagram, Room-Temperature Thermopower, Neutron Spin Resonance, and STM Incommensurability Explained in Terms of Chiral Plaquette Pairing

We report that four properties of cuprates and their evolution with doping are consequences of simply counting four-site plaquettes arising from doping, (1) the universal T_c phase diagram (superconductivity between ~0.05 and ~0.27 doping per CuO_2 plane and optimal T_c at ~0.16), (2) the universal doping dependence of the room-temperature thermopower, (3) the superconducting neutron spin resonance peak (the “41 meV peak”), and (4) the dispersionless scanning tunneling conductance incommensurability. Properties (1), (3), and (4) are explained with no adjustable parameters, and (2) is explained with exactly one. The successful quantitative interpretation of four very distinct aspects of cuprate phenomenology by a simple counting rule provides strong evidence for four-site plaquette percolation in these materials. This suggests that inhomogeneity, percolation, and plaquettes play an essential role in cuprates. This geometric analysis may provide a useful guide to search for new compositions and structures with improved superconducting properties.
Nonlinear Mixed Modes in Red Giants

Turbulent motions in the convective envelope of red giants excite a rich spectrum of solar-like oscillation modes. Observations by CoRoT and Kepler have shown that the mode amplitudes increase dramatically as the stars ascend the red giant branch, i.e., as the frequency of maximum power, $\nu_\mathrm{max}$, decreases. Most studies nonetheless assume that the modes are well described by the linearized fluid equations. We investigate to what extent the linear approximation is justified as a function of stellar mass $M$ and $\nu_\mathrm{max}$, focusing on dipole mixed modes with frequency near $\nu_\mathrm{max}$. A useful measure of a mode's nonlinearity is the product of its radial wavenumber and its radial displacement, $k_r \xi_r$ (i.e., its shear). We show that $k_r \xi_r \propto \nu_\mathrm{max}^{-9/2}$, implying that the nonlinearity of mixed modes increases significantly as a star evolves. The modes are weakly nonlinear ($k_r \xi_r > 10^{-3}$) for $\nu_\mathrm{max} \lesssim 150 \, \mu\mathrm{Hz}$ and strongly nonlinear ($k_r \xi_r > 1$) for $\nu_\mathrm{max} \lesssim 30 \, \mu\mathrm{Hz}$, with only a mild dependence on $M$ over the range we consider ($1.0 - 2.0 M_\odot$). A weakly nonlinear mixed mode can excite secondary waves in the stellar core through the parametric instability, resulting in enhanced, but partial, damping of the mode. By contrast, a strongly nonlinear mode breaks as it propagates through the core and is fully damped there. Evaluating the impact of nonlinear effects on observables such as mode amplitudes and linewidths requires large mode network simulations. We plan to carry out such calculations in the future and investigate whether nonlinear damping can explain why some red giants exhibit dipole modes with unusually small amplitudes, known as depressed modes.
Conditions for existence of dual certificates in rank-one semidefinite problems

Several signal recovery tasks can be relaxed into semidenite programs with rank-one minimizers. A common technique for proving these programs succeed is to construct a dual certicate. Unfortunately, dual certicates may not exist under some formulations of semidenite programs. In order to put problems into a form where dual certicate arguments are possible, it is important to develop conditions under which the certicates exist. In this paper, we provide an example where dual certicates do not exist. We then present a completeness condition under which they are guaranteed to exist. For programs that do not satisfy the completeness condition, we present a completion process which produces an equivalent program that does satisfy the condition. The important message of this paper is that dual certicates may not
Monoids of injective maps closed under conjugation by permutations

Let Ω be a countably infinite set, Inj(Ω) the monoid of all injective endomaps of Ω, and Sym(Ω) the group of all permutations of Ω. We classify all submonoids of Inj(Ω) that are closed under conjugation by elements of Sym(Ω).
Testing X-Ray Measurements of Galaxy Cluster Outskirts with Cosmological Simulations

The study of galaxy cluster outskirts has emerged as one of the new frontiers in extragalactic astrophysics and cosmology with the advent of new observations in X-ray and microwave. However, the thermodynamic properties and chemical enrichment of this diffuse and azimuthally asymmetric component of the intracluster medium (ICM) are still not well understood. This work, for the first time, systematically explores potential observational biases in these regions. To assess X-ray measurements of galaxy cluster properties at large radii (>R 500c ), we use mock Chandra analyses of cosmological galaxy cluster simulations. The pipeline is identical to that used for Chandra observations, but the biases discussed in this paper are relevant for all X-ray observations outside of R 500c . We find the following from our analysis: (1) filament regions can contribute as much as 50% at R 200c to the emission measure; (2) X-ray temperatures and metal abundances from model fitted mock X-ray spectra in a multi-temperature ICM respectively vary to the level of 10% and 50%; (3) resulting density profiles vary to within 10% out to R 200c , and gas mass, total mass, and baryon fractions all vary to within a few percent; (4) the bias from a metal abundance extrapolated a factor of five higher than the true metal abundance results in total mass measurements biased high by 20% and total gas measurements biased low by 10%; and (5) differences in projection and dynamical state of a cluster can lead to gas density slope measurements that differ by a factor of 15% and 30%, respectively. The presented results can partially account for some of the recent gas profile measurements in cluster outskirts by, e.g., Suzaku. Our findings are pertinent to future X-ray cosmological constraints from cluster outskirts, which are least affected by non-gravitational gas physics, as well as to measurements probing gas properties in filamentary structures.
Bounded outdegree and extremal length on discrete Riemann surfaces

Let $T$ be a triangulation of a Riemann surface. We show that the 1-skeleton of $T$ may be oriented so that there is a global bound on the outdegree of the vertices. Our application is to construct extremal metrics on triangulations formed from $T$ by attaching new edges and vertices and subdividing its faces. Such refinements provide a mechanism of convergence of the discrete triangulation to the classical surface. We will prove a bound on the distortion of the discrete extremal lengths of path families on $T$ under the refinement process. Our bound will depend only on the refinement and not on $T$. In particular, the result does not require bounded degree.
Dictionary learning of sound speed profiles

To provide constraints on the inversion of ocean sound speed profiles (SSPs), SSPs are often modeled using empirical orthogonal functions (EOFs). However, this regularization, which uses the leading order EOFs with a minimum-energy constraint on the coefficients, often yields low resolution SSP estimates. In this paper, it is shown that dictionary learning, a form of unsupervised machine learning, can improve SSP resolution by generating a dictionary of shape functions for sparse processing (e.g., compressive sensing) that optimally compress SSPs; both minimizing the reconstruction error and the number of coefficients. These learned dictionaries (LDs) are not constrained to be orthogonal and thus, fit the given signals such that each signal example is approximated using few LD entries. Here, LDs describing SSP observations from the HF-97 experiment and the South China Sea are generated using the K-SVD algorithm. These LDs better explain SSP variability and require fewer coefficients than EOFs, describing much of the variability with one coefficient. Thus, LDs improve the resolution of SSP estimates with negligible computational burden.
Furstenberg sets and Furstenberg schemes over finite fields

We give a lower bound for the size of a subset of $\mathbb F_q^n$ containing a rich k-plane in every direction, a k-plane Furstenberg set. The chief novelty of our method is that we use arguments on non-reduced subschemes and flat families to derive combinatorial facts about incidences between points and k-planes in space.
RUPRECHT 147: THE OLDEST NEARBY OPEN CLUSTER AS A NEW BENCHMARK FOR STELLAR ASTROPHYSICS

Ruprecht 147 is a hitherto unappreciated open cluster that holds great promise as a standard in fundamental stellar astrophysics. We have conducted a radial velocity survey of astrometric candidates with Lick, Palomar, and MMT observatories and have identified over 100 members, including 5 blue stragglers, 11 red giants, and 5 double-lined spectroscopic binaries (SB2s). We estimate the cluster metallicity from spectroscopic analysis, using Spectroscopy Made Easy (SME), and find it to be [M/H] = +0.07 ± 0.03. We have obtained deep CFHT/MegaCam g'r'i'z' photometry and fit Padova isochrones to the (g' – i') and Two Micron All Sky Survey (J – K_S ) color-magnitude diagrams, using the τ^2 maximum-likelihood procedure of Naylor, and an alternative method using two-dimensional cross-correlations developed in this work. We find best fits for Padova isochrones at age t = 2.5 ± 0.25 Gyr, m – M = 7.35 ± 0.1, and A_V = 0.25 ± 0.05, with additional uncertainty from the unresolved binary population and possibility of differential extinction across this large cluster. The inferred age is heavily dependent on our choice of stellar evolution model: fitting Dartmouth and PARSEC models yield age parameters of 3 Gyr and 3.25 Gyr, respectively. At ~300 pc and ~3 Gyr, Ruprecht 147 is by far the oldest nearby star cluster.
Bounds on layer potentials with rough inputs for higher order elliptic equations

In this paper we establish square-function estimates on the double and single layer potentials with rough inputs for divergence form elliptic operators, of arbitrary even order 2m, with variable t-independent coefficients in the upper half-space.
The TLA+ Toolbox

We discuss the workflows supported by the TLA+ Toolbox to write and verify specifications. We focus on features that are useful in industry because its users are primarily engineers. Two features are novel in the scope of formal IDEs: CloudTLC connects the Toolbox with cloud computing to scale up model checking. A Profiler helps to debug inefficient expressions and to pinpoint the source of state space explosion. For those who wish to contribute to the Toolbox or learn from its flaws, we present its technical architecture.
Minimal surfaces in the Heisenberg group

We investigate the minimal surface problem in the three dimensional Heisenberg group, H, equipped with its standard Carnot–Caratheodory metric. Using a particular surface measure, we characterize minimal surfaces in terms of a sub-elliptic partial differential equation and prove an existence result for the Plateau problem in this setting. Further, we provide a link between our minimal surfaces and Riemannian constant mean curvature surfaces in H equipped with different Riemannian metrics approximating the Carnot–Caratheodory metric. We generate a large library of examples of minimal surfaces and use these to show that the solution to the Dirichlet problem need not be unique. Moreover, we show that the minimal surfaces we construct are in fact X-minimal surfaces in the sense of Garofalo and Nhieu.
HOMFLY-PT homology for general link diagrams and braidlike isotopy

Khovanov and Rozansky’s categorification of the homfly-pt polynomial is invariant under braidlike isotopies for any general link diagram and Markov moves for braid closures. To define homfly-pt homology, they required a link to be presented as a braid closure, because they did not prove invariance under the other oriented Reidemeister moves. In this text we prove that the Reidemeister IIb move fails in homfly-pt homology by using virtual crossing filtrations of the author and Rozansky. The decategorification of homfly-pt homology for general link diagrams gives a deformed version of the homfly-pt polynomial, Pb(D), which can be used to detect nonbraidlike isotopies. Finally, we will use Pb(D) to prove that homfly-pt homology is not an invariant of virtual links, even when virtual links are presented as virtual braid closures.
Tidal stresses and energy gaps in microstate geometries

We compute energy gaps and study infalling massive geodesic probes in the new families of scaling, microstate geometries that have been constructed recently and for which the holographic duals are known. We find that in the deepest geometries, which have the lowest energy gaps, the geodesic deviation shows that the stress reaches the Planck scale long before the probe reaches the cap of the geometry. Such probes must therefore undergo a stringy transition as they fall into microstate geometry. We discuss the scales associated with this transition and comment on the implications for scrambling in microstate geometries.
A partial order on bipartitions from the generalized Springer correspondence

In \cite{Lusztig}, Lusztig gives an explicit formula for the bijection between the set of bipartitions and the set $\mathcal{N}$ of unipotent classes in a spin group which carry irreducible local systems equivariant for the spin group but not equivariant for the special orthogonal group. The set $\mathcal{N}$ has a natural partial order and therefore induces a partial order on bipartitions. We use the explicit formula given in \cite{Lusztig} to prove that this partial order on bipartitions is the same as the dominance order appeared in Dipper-James-Murphy's work.
Dempster-Shafer Theory and Statistical Inference with Weak Beliefs

The Dempster-Shafer (DS) theory is a powerful tool for probabilistic reasoning based on a formal calculus for combining evi- dence. DS theory has been widely used in computer science and engi- neering applications, but has yet to reach the statistical mainstream, perhaps because the DS belief functions do not satisfy long-run fre- quency properties. Recently, two of the authors proposed an extension of DS, called the weak belief (WB) approach, that can incorporate de- sirable frequency properties into the DS framework by systematically enlarging the focal elements. The present paper reviews and extends this WB approach. We present a general description of WB in the context of inferential models, its interplay with the DS calculus, and the maximal belief solution. New applications of the WB method in two high-dimensional hypothesis testing problems are given. Simula- tions show that the WB procedures, suitably calibrated, perform well compared to popular classical methods. Most importantly, the WB ap- proach combines the probabilistic reasoning of DS with the desirable frequency properties of classical statistics.
Going beyond the Debye Length: Overcoming Charge Screening Limitations in Next-Generation Bioelectronic Sensors

Electronic biosensors are a natural fit for field-deployable diagnostic devices because they can be miniaturized, mass produced, and integrated with circuitry. Unfortunately, progress in the development of such platforms has been hindered by the fact that mobile ions present in biological samples screen charges from the target molecule, greatly reducing sensor sensitivity. Under physiological conditions, the thickness of the resulting electric double layer is less than 1 nm, and it has generally been assumed that electronic detection beyond this distance is virtually impossible. However, a few recently described sensor design strategies seem to defy this conventional wisdom, exploiting the physics of electrical double layers in ways that traditional models do not capture. In the first strategy, charge screening is decreased by constraining the space in which double layers can form. The second strategy uses external stimuli to prevent double layers from reaching equilibrium, thereby effectively reducing charge screening. In this Perspective, we describe these relatively new concepts and offer theoretical insights into mechanisms that may enable electronic biosensing beyond the Debye length. If these concepts can be further developed and translated into practical electronic biosensors, we foresee exciting opportunities for the next generation of diagnostic technologies.
Learning Gradient Fields for Shape Generation

In this work, we propose a novel technique to generate shapes from point cloud data. A point cloud can be viewed as samples from a distribution of 3D points whose density is concentrated near the surface of the shape. Point cloud generation thus amounts to moving randomly sampled points to high-density areas. We generate point clouds by performing stochastic gradient ascent on an unnormalized probability density, thereby moving sampled points toward the high-likelihood regions. Our model directly predicts the gradient of the log density field and can be trained with a simple objective adapted from score-based generative models. We show that our method can reach state-of-the-art performance for point cloud auto-encoding and generation, while also allowing for extraction of a high-quality implicit surface. Code is available at https://github.com/RuojinCai/ShapeGF.
The ubiquitylome of developing cortical neurons.

Abstract TRIM9 and TRIM67 are neuronally-enriched E3 ubiquitin. Both genes are required for neuronal morphological responses to the axon guidance cue netrin-1. For example, our previously published work demonstrated that the actin polymerase VASP and the netrin receptor DCC exhibit TRIM9 dependent ubiquitylation that is lost upon netrin stimulation. Deletion of either gene in the mouse results in subtle neuroanatomical anomalies yet overt deficits in spatial learning and memory. Despite their role in neuronal form and function, the identity of few TRIM9 or TRIM67 substrates are known. Here we performed ubiquitin remnant profiling approach in cultured wildtype and knockout murine embryonic cortical neurons to identify ubiquitylated peptides and proteins, with the ultimate goal of identifying substrates of TRIM9 and TRIM67 that exhibited reduced ubiquitylation in the absence of the ligase. This work reveals the ubiquitylome of developing cortical neurons.
Privacy, speech, and values: what we have no business knowing

In the United States the ascendancy of speech protection is due to an expansive and unjustified view of the value or primacy of free expression and access to information. This is perhaps understandable, given that privacy has been understood as a mere interest, whereas speech rights have been seen as more fundamental. I have argued elsewhere that the "mere interest" view of privacy is false. Privacy, properly defined, is a necessary condition for human well-being or flourishing. The opening section of this article will provide an overview of this theory. Next, after a few remarks on speech absolutism, privacy absolutism, and balancing theories, I will sketch several of the dominant argument strands that have been offered in support of presumptively weighty speech rights. While these arguments, taken together, establish that free speech is important, they do not support the view that speech should nearly always trump privacy. In final section I will present and defend a way to balance free speech and privacy claims.
Recombinant ACE2 Expression Is Required for SARS-CoV-2 To Infect Primary Human Endothelial Cells and Induce Inflammatory and Procoagulative Responses.

ABSTRACT SARS-CoV-2 causes COVID-19, an acute respiratory distress syndrome (ARDS) characterized by pulmonary edema, viral pneumonia, multiorgan dysfunction, coagulopathy, and inflammation. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) receptors to infect and damage ciliated epithelial cells in the upper respiratory tract. In alveoli, gas exchange occurs across an epithelial-endothelial barrier that ties respiration to endothelial cell (EC) regulation of edema, coagulation, and inflammation. How SARS-CoV-2 dysregulates vascular functions to cause ARDS in COVID-19 patients remains an enigma focused on dysregulated EC responses. Whether SARS-CoV-2 directly or indirectly affects functions of the endothelium remains to be resolved and is critical to understanding SARS-CoV-2 pathogenesis and therapeutic targets. We demonstrate that primary human ECs lack ACE2 receptors at protein and RNA levels and that SARS-CoV-2 is incapable of directly infecting ECs derived from pulmonary, cardiac, brain, umbilical vein, or kidney tissues. In contrast, pulmonary ECs transduced with recombinant ACE2 receptors are infected by SARS-CoV-2 and result in high viral titers (∼1 × 107/ml), multinucleate syncytia, and EC lysis. SARS-CoV-2 infection of ACE2-expressing ECs elicits procoagulative and inflammatory responses observed in COVID-19 patients. The inability of SARS-CoV-2 to directly infect and lyse ECs without ACE2 expression explains the lack of vascular hemorrhage in COVID-19 patients and indicates that the endothelium is not a primary target of SARS-CoV-2 infection. These findings are consistent with SARS-CoV-2 indirectly activating EC programs that regulate thrombosis and endotheliitis in COVID-19 patients and focus strategies on therapeutically targeting epithelial and inflammatory responses that activate the endothelium or initiate limited ACE2-independent EC infection. IMPORTANCE SARS-CoV-2 infects pulmonary epithelial cells through ACE2 receptors and causes ARDS. COVID-19 causes progressive respiratory failure resulting from diffuse alveolar damage and systemic coagulopathy, thrombosis, and capillary inflammation that tie alveolar responses to EC dysfunction. This has prompted theories that SARS-CoV-2 directly infects ECs through ACE2 receptors, yet SARS-CoV-2 antigen has not been colocalized with ECs and prior studies indicate that ACE2 colocalizes with alveolar epithelial cells and vascular smooth muscle cells, not ECs. Here, we demonstrate that primary human ECs derived from lung, kidney, heart, brain, and umbilical veins require expression of recombinant ACE2 receptors in order to be infected by SARS-CoV-2. However, SARS-CoV-2 lytically infects ACE2-ECs and elicits procoagulative and inflammatory responses observed in COVID-19 patients. These findings suggest a novel mechanism of COVID-19 pathogenesis resulting from indirect EC activation, or infection of a small subset of ECs by an ACE2-independent mechanism, that transforms rationales and targets for therapeutic intervention.
Benchmarking network fabrics for data distributed training of deep neural networks

Artificial Intelligence/Machine Learning applications require the training of complex models on large amounts of labelled data. The large computational requirements for training deep models have necessitated the development of new methods for faster training. One such approach is the data parallel approach, where the training data is distributed across multiple compute nodes. This approach is simple to implement and supported by most of the commonly used machine learning frameworks. The data parallel approach leverages MPI for communicating gradients across all nodes. In this paper, we examine the effects of using different physical hardware interconnects and network-related software primitives for enabling data distributed deep learning. We compare the effect of using GPUDirect and NCCL on Ethernet and OmniPath fabrics. Our results show that using Ethernet-based networking in shared HPC systems does not have a significant effect on the training times for commonly used deep neural network architectures or traditional HPC applications such as Computational Fluid Dynamics.
Flavonols modulate lateral root emergence by scavenging reactive oxygen species in Arabidopsis thaliana

Flavonoids are a class of specialized metabolites with subclasses including flavonols and anthocyanins, which have unique properties as antioxidants. Flavonoids modulate plant development, but whether and how they impact lateral root development is unclear. We examined potential roles for flavonols in this process using Arabidopsis thaliana mutants with defects in genes encoding key enzymes in flavonoid biosynthesis. We observed the tt4 and fls1 mutants, which produce no flavonols, have increased lateral root emergence. The tt4 root phenotype was reversed by genetic and chemical complementation. To more specifically define the flavonoids involved, we tested an array of flavonoid biosynthetic mutants, eliminating roles for anthocyanins and the flavonols quercetin and isorhamnetin in modulating lateral root development. Instead, two tt7 mutant alleles, with defects in a branchpoint enzyme blocking quercetin biosynthesis, formed reduced numbers of lateral roots and tt7-2 had elevated levels of kaempferol. Using a flavonol-specific dye, we observed that in the tt7-2 mutant, kaempferol accumulated within lateral root primordia at higher levels than wild-type. These data are consistent with kaempferol, or downstream derivatives, acting as a negative regulator of lateral root emergence. We examined ROS accumulation using ROS-responsive probes and found reduced fluorescence of a superoxide-selective probe within the primordia of tt7-2 compared with wild-type, but not in the tt4 mutant, consistent with opposite effects of these mutants on lateral root emergence. These results support a model in which increased level of kaempferol in the lateral root primordia of tt7-2 reduces superoxide concentration and ROS-stimulated lateral root emergence.
Active Ag/Co Composite Chiral Nanohole Arrays

The magneto-chiroptical properties of Ag/Co composite chiral nanohole arrays (CCNAs) are investigated. The intrinsic circular dichroism of CCNA samples was induced by in-plane symmetry breaking by ...
Submodular Input Selection for Synchronization in Kuramoto Networks

Synchronization is an essential property of engineered and natural networked dynamical systems. The Kuramoto model of nonlinear synchronization has been widely studied in applications including entrainment of clock cells in brain networks and power system stability. Synchronization of Kuramoto networks has been found to be challenging in the presence of signed couplings between oscillators and when the network includes oscillators with heterogeneous natural frequencies. In this paper, we study the problem of minimum-set control input selection for synchronizing signed Kuramoto networks. We first derive sufficient conditions for synchronization in homogeneous as well as heterogeneous Kuramoto networks using a passivity-based framework. We then develop a submodular algorithm for selecting a minimum set of control inputs for a given Kuramoto network. We evaluate our approach through a numerical study on multiple classes of graphs, including undirected, directed, and cycle graphs.
Optimal Tracking Current Control of Switched Reluctance Motor Drives Using Reinforcement Q-Learning Scheduling

In this article, a novel Q-learning scheduling method for the current controller of a switched reluctance motor (SRM) drive is investigated. The Q-learning algorithm is a class of reinforcement learning approaches that can find the best forward-in-time solution of a linear control problem. An augmented system is constructed based on the reference current signal and the SRM model to allow for solving the algebraic Riccati equation of the current-tracking problem. This article introduces a new scheduled-Q-learning algorithm that utilizes a table of Q-cores that lies on the nonlinear surface of an SRM model without involving any information about the model parameters to track the reference current trajectory by scheduling the infinite horizon linear quadratic trackers (LQT) handled by Q-learning algorithms. Additionally, a linear interpolation algorithm is proposed to improve the transition of the LQT between trained Q-cores to ensure a smooth response as state variables evolve on the nonlinear surface of the model. Lastly, simulation and experimental results are provided to validate the effectiveness of the proposed control scheme.
Posterior impropriety of some sparse Bayesian learning models

Abstract Sparse Bayesian learning models are typically used for prediction in datasets with significantly greater number of covariates than observations. Such models often take a reproducing kernel Hilbert space (RKHS) approach to carry out the task of prediction and can be implemented using either proper or improper priors. In this article we show that a few sparse Bayesian learning models in the literature, when implemented using improper priors, lead to improper posteriors.
Productivity of cellular-Lindelöf spaces

Abstract The main purpose of this note is to prove that the product of a cellular-Lindelof space with a space of countable spread need not be cellular-Lindelof.
Why Do People Reject New Technologies and Stymie Organizational Changes of Which They Are in Favor? Exploring Misalignments between Social Interactions and Materiality

This article explores the relationship between users' interpretations of a new technology and failure of organizational change. I suggest that people form interpretations of a new technology not only based on their conversations with others, but also through their use of technology's material features directly. Through qualitative and quantitative analysis of ethnographic data on the implementation and use of a computer simulation technology at a major automotive firm, I show that engineers' communication with managers, coworkers, and customers led them to develop an interpretation about what the technology was supposed to do while their interactions with the material features of complementary technologies led them to develop an interpretation that the new simulation technology was not an efficient tool for that specific purpose. I show how the interpretations developed from people's material interactions moderate the effects of the interpretations developed through social interactions on willingness to use the technology in the future. I then demonstrate that, in this particular setting, engineers inadvertently stymied an organizational change of which they were very much in favor by reducing their use of the new technology. I conclude by discussing how misalignments between the information generated in users' interactions with others and with technologies' material features can lead to the failure of planned organizational change.