cssi/SciSciGPT-SciSciNet huggingface dataset:
Bertini theorems over finite fields

Let X be a smooth quasiprojective subscheme of Pn of dimension m iÝ 0 over Fq. Then there exist homogeneous polynomials f over Fq for which the intersection of X and the hypersurface f = 0 is smooth. In fact, the set of such f has a positive density, equal to ?AEX(m+1).1, where ?AEX(s) = ZX(q.s) is the zeta function of X. An analogue for regular quasiprojective schemes over Z is proved, assuming the abc conjecture and another conjecture.
Accurate Energetic Constraints for Passive Grasp Stability Analysis

Passive reaction effects in grasp stability analysis occur when the contact forces and joint torques applied by a grasp change in response to external disturbances applied to the grasped object. For example, nonbackdrivable actuators (e.g., highly geared servos) will passively resist external disturbances without an actively applied command; for numerous robot hands using such motors, these effects can be highly beneficial as they increase grasp resistance without requiring active control. In this article, we introduce a grasp stability analysis method that can model these effects, and, for a given grasp, distinguish between disturbances that will be passively resisted and those that will not. We find that, in order to achieve this, the grasp model must include accurate energetic constraints. One way to achieve this is to consider the maximum dissipation principle (MDP), a part of the Coulomb friction model that is rarely used in grasp stability analysis. However, the MDP constraints are nonconvex, and difficult to solve efficiently. We thus introduce a convex relaxation method, along with an algorithm that successively refines this relaxation locally in order to obtain solutions to arbitrary accuracy efficiently. Our resulting algorithm can determine if a grasp is passively stable, solve for equilibrium contact forces and compute optimal actuator commands for stability. Its implementation is publicly available as part of the open-source GraspIt! simulator.
Local multiresolution order in community detection

Community detection algorithms attempt to find the best clusters of nodes in an arbitrary complex network. Multi-scale (?multiresolution?) community detection extends the problem to identify the best network scale(s) for these clusters. The latter task is generally accomplished by analyzing community stability simultaneously for all clusters in the network. In the current work, we extend this general approach to define local multiresolution methods, which enable the extraction of well-defined local communities even if the global community structure is vaguely defined in an average sense. Toward this end, we propose measures analogous to variation of information and normalized mutual information that are used to quantitatively identify the best resolution(s) at the community level based on correlations between clusters in independently-solved systems. We demonstrate our method on two constructed networks as well as a real network and draw inferences about local community strength. Our approach is independent of the applied community detection algorithm save for the inherent requirement that the method be able to identify communities across different network scales, with appropriate changes to account for how different resolutions are evaluated or defined in a particular community detection method. It should, in principle, easily adapt to alternative community comparison measures.
The Velocity Distribution of the Nearest Interstellar Gas

The bulk flow velocity for the cluster of interstellar cloudlets within ~30 pc of the Sun is determined from optical and ultraviolet absorption line data, after omitting from the sample stars with circumstellar disks or variable emission lines and the active variable HR 1099. A total of 96 velocity components toward the remaining 60 stars yield a streaming velocity through the local standard of rest of -17.0 ± 4.6 km s-1, with an upstream direction of l = 23, b = -52 (using Hipparcos values for the solar apex motion). The velocity dispersion of the interstellar matter (ISM) within 30 pc is consistent with that of nearby diffuse clouds, but present statistics are inadequate to distinguish between a Gaussian or exponential distribution about the bulk flow velocity. The upstream direction of the bulk flow vector suggests an origin associated with the Loop I supernova remnant. Groupings of component velocities by region are seen, indicating regional departures from the bulk flow velocity or possibly separate clouds. The absorption components from the cloudlet feeding ISM into the solar system form one of the regional features. The nominal gradient between the velocities of upstream and downstream gas may be an artifact of the Sun's location near the edge of the local cloud complex. The Sun may emerge from the surrounding gas patch within several thousand years.
Average-value tverberg partitions via finite fourier analysis

The long-standing topological Tverberg conjecture claimed, for any continuous map from the boundary of an N(q, d):= (q-1)(d+1)-simplex to d-dimensional Euclidian space, the existence of q pairwise disjoint subfaces whose images have non-empty q-fold intersection. The affine cases, true for all q, constitute Tverberg’s famous 1966 generalization of the classical Radon’s Theorem. Although established for all prime powers in 1987 by Ozaydin, counterexamples to the conjecture, relying on 2014 work of Mabillard and Wagner, were first shown to exist for all non-prime-powers in 2015 by Frick. Starting with a reformulation of the topological Tverberg conjecture in terms of harmonic analysis on finite groups, we show that despite the failure of the conjecture, continuous maps below the tight dimension N(q, d) are nonetheless guaranteed q pairwise disjoint subfaces–including when q is not a prime power–which satisfy a variety of “average value” coincidences, the latter obtained as the vanishing of prescribed Fourier transforms.
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making

Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios. In these scenarios, full automation is often undesirable, not only due to the significance of the outcome, but also because human experts can draw on their domain knowledge complementary to the model's to ensure task success. We refer to these scenarios as AI-assisted decision making, where the individual strengths of the human and the AI come together to optimize the joint decision outcome. A key to their success is to appropriately calibrate human trust in the AI on a case-by-case basis; knowing when to trust or distrust the AI allows the human expert to appropriately apply their knowledge, improving decision outcomes in cases where the model is likely to perform poorly. This research conducts a case study of AI-assisted decision making in which humans and AI have comparable performance alone, and explores whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. Specifically, we study the effect of showing confidence score and local explanation for a particular prediction. Through two human experiments, we show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making, which may also depend on whether the human can bring in enough unique knowledge to complement the AI's errors. We also highlight the problems in using local explanation for AI-assisted decision making scenarios and invite the research community to explore new approaches to explainability for calibrating human trust in AI.
Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)

The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a transformed coordinate space. The space is transformed according to an estimation of the background statistics, which directly effects the performance of the statistic as a target detector. In this paper, the ACE detection statistic is used to detect buried explosive hazards with data from a Wideband Electromagnetic Induction (WEMI) sensor. Target signatures are based on a dictionary defined using a Discrete Spectrum of Relaxation Frequencies (DSRF) model. Results are summarized as a receiver operator curve (ROC) and compared to other leading methods.
Chain Ends and the Ultimate Strength of Polyethylene Fibers

We use large scale molecular dynamics (MD) simulations to determine the tensile yield mechanism of orthorhombic polyethylene (PE) crystals with finite chains spanning 102–104 carbons in length. We find the yield stress σy saturates for long chains at 6.3 GPa, agreeing well with experiments. We show chains do not break, but always yield by slip, after nucleation of 1D dislocations at chain ends. Dislocations are accurately described by a Frenkel-Kontorova model parametrized by the mechanical properties of an ideal crystal. We compute a dislocation core size ξ ≈ 25 A and determine the high and low strain rate limits of σy. Our results suggest characterizing the 1D dislocations of polymer crystals as an efficient method for numerically predicting the ultimate tensile strength of aligned fibers.
Visualizing edge states with an atomic Bose gas in the quantum Hall regime

Bringing ultracold atomic gases into the quantum Hall regime is challenging. We engineered an effective magnetic field in a two-dimensional lattice with an elongated-strip geometry, consisting of the sites of an optical lattice in the long direction and of three internal atomic spin states in the short direction. We imaged the localized states of atomic Bose-Einstein condensates in this strip; via excitation dynamics, we further observed both the skipping orbits of excited atoms traveling down the system’s edges, analogous to edge magnetoplasmons in two-dimensional electron systems, and a dynamical Hall effect for bulk excitations. Our technique involves minimal heating, which will be important for spectroscopic measurements of the Hofstadter butterfly and realizations of Laughlin’s charge pump.
Optical modular arithmetic

Nanoscale integrated photonic devices and circuits offer a path to ultra-low power computation at the few-photon level. Here we propose an optical circuit that performs a ubiquitous operation: the controlled, random-access readout of a collection of stored memory phases or, equivalently, the computation of the inner product of a vector of phases with a binary selector" vector, where the arithmetic is done modulo 2pi and the result is encoded in the phase of a coherent field. This circuit, a collection of cascaded interferometers driven by a coherent input field, demonstrates the use of coherence as a computational resource, and of the use of recently-developed mathematical tools for modeling optical circuits with many coupled parts. The construction extends in a straightforward way to the computation of matrix-vector and matrix-matrix products, and, with the inclusion of an optical feedback loop, to the computation of a weighted" readout of stored memory phases. We note some applications of these circuits for error correction and for computing tasks requiring fast vector inner products, e.g. statistical classification and some machine learning algorithms.
A Curious Case of Circular Polarization in the 25 GHz Methanol Maser Line toward OMC-1

We report the detection of a circular polarization signature in the Stokes $V$ profile of a 25 GHz Class I CH$_3$OH maser toward the high mass star forming region OMC-1. Such a feature usually constitutes a detection of the Zeeman effect. If due to a magnetic field in OMC-1, this would represent the first detection and discovery of the Zeeman effect in the 25 GHz Class I CH$_3$OH maser. The feature in Stokes $V$ is detected in two observations with different angular resolutions taken eight years apart with the Very Large Array (VLA); for our 2009 D-configuration observations, the fitted value for $z B_\text{los}$ is $152 \pm 12$ Hz, where $z$ is the Zeeman splitting factor and $B_\text{los}$ is the line-of-sight magnetic field. For our 2017 C-configuration observations, the fitted value for $zB_\text{los} = 149 \pm 19~$Hz, likely for the same maser spot. These correspond to $B_\text{los}$ in the range 171-214 mG, depending on which hyperfine transition is responsible for the maser line. While these $B_\text{los}$ values are high, they are not implausible. If the magnetic field increases in proportion to the molecular hydrogen density in shocked regions, then our detected fields predict values for the pre-shock magnetic field that are in agreement with observations. With $B_\text{los}$ =171-214 mG, the magnetic energy in the post-shocked regions where these 25 GHz Class I CH$_3$OH masers occur would dominate over the kinetic energy density and be at least of the order of the pressure in the shock, implying that the magnetic field would exert significant influence over the dynamics of these regions.
Consequences of a Change in the Galactic Environment of the Sun

The interaction of the heliosphere with interstellar clouds has attracted interest since the late 1920s, with a view to explaining apparent both quasi-periodic climate catastrophes as well as periodic mass extinctions. Until recently, however, models describing the solar wind-local interstellar medium (LISM) interaction self-consistently had not been developed. Here we describe the results of a two-dimensional simulation of the interaction between the heliosphere and an interstellar cloud with the same properties as currently, except that the H0 density is increased from the present value of n(H0)~0.2 cm-3 to 10 cm-3. The mutual interaction of interstellar neutral hydrogen and plasma is included. The heliospheric cavity is reduced considerably in size (approximately 10-14 AU to the termination shock in the upstream direction) and is highly dynamical. The interplanetary environment at the orbit of the Earth changes markedly, with the density of interstellar H0 increasing to ~2 cm-3. The termination shock itself experiences periods where it disappears, reforms, and disappears again. Considerable mixing of the shocked solar wind and LISM occurs because of Rayleigh-Taylor-like instabilities at the nose, driven by ion-neutral friction. Implications of two anomalously high concentrations of 10Be found in Antarctic ice cores, corresponding to 33,000 and 60,000 yr ago, and the absence of prior similar events are discussed in terms of density enhancements in the surrounding interstellar cloud. The calculation presented here supports past speculation that the Galactic environment of the Sun moderates the interplanetary environment at the orbit of the Earth and possibly also the terrestrial climate.
The lowest volume 3–orbifolds with high torsion

For each natural number n >= 4, we determine the unique lowest volume hyperbolic 3-orbifold whose torsion orders are bounded below by n. This lowest volume orbifold has base space the 3-sphere and singular locus the figure-8 knot, marked n. We apply this result to give sharp lower bounds on the volume of a hyperbolic manifold in terms of the order of elements in its symmetry group.
Restrictions and Extensions of Semibounded Operators

We study restriction and extension theory for semibounded Hermitian operators in the Hardy space $$\fancyscript{H}_{2}$$ of analytic functions on the disk $$\mathbb D $$ . Starting with the operator $${z\frac{d}{dz}}$$ , we show that, for every choice of a closed subset $$F\subset \mathbb T =\partial \mathbb D $$ of measure zero, there is a densely defined Hermitian restriction of $$z\frac{d}{dz}$$ corresponding to boundary functions vanishing on $$F$$ . For every such restriction operator, we classify all its selfadjoint extension, and for each we present a complete spectral picture. We prove that different sets $$F$$ with the same cardinality can lead to quite different boundary-value problems, inequivalent selfadjoint extension operators, and quite different spectral configurations. As a tool in our analysis, we prove that the von Neumann deficiency spaces, for a fixed set $$F$$ , have a natural presentation as reproducing kernel Hilbert spaces, with a Hurwitz zeta-function, restricted to $$F\times F$$ , as reproducing kernel.
SPACETIME EMERGENCE AND GENERAL COVARIANCE TRANSMUTATION

Spacetime emergence refers to the notion that classical spacetime "emerges" as an approximate macroscopic entity from a non-spatio-temporal structure present in a more complete theory of interacting fundamental constituents. In this paper, we propose a novel mechanism involving the "soldering" of internal and external spaces for the emergence of spacetime and the twin transmutation of general covariance. In the context of string theory, this mechanism points to a critical four-dimensional spacetime background.
On the Existence of Uniformly Most Powerful Bayesian Tests With Application to Non-Central Chi-Squared Tests

Uniformly most powerful Bayesian tests (UMPBT’s) are an objective class of Bayesian hypothesis tests that can be considered the Bayesian counterpart of classical uniformly most powerful tests. Because the rejection regions of UMPBT’s can be matched to the rejection regions of classical uniformly most powerful tests (UMPTs), UMPBT’s provide a mechanism for calibrating Bayesian evidence thresholds, Bayes factors, classical significance levels and p-values. The purpose of this article is to expand the application of UMPBT’s outside the class of exponential family models. Specifically, we introduce sufficient conditions for the existence of UMPBT’s and propose a unified approach for their derivation. An important application of our methodology is the extension of UMPBT’s to testing whether the non-centrality parameter of a chi-squared distribution is zero. The resulting tests have broad applicability, providing default alternative hypotheses to compute Bayes factors in, for example, Pearson’s chi-squared test for goodness-of-fit, tests of independence in contingency tables, and likelihood ratio, score and Wald tests.
Variational Integrators for Interconnected Lagrange–Dirac Systems

Interconnected systems are an important class of mathematical models, as they allow for the construction of complex, hierarchical, multiphysics, and multiscale models by the interconnection of simpler subsystems. Lagrange–Dirac mechanical systems provide a broad category of mathematical models that are closed under interconnection, and in this paper, we develop a framework for the interconnection of discrete Lagrange–Dirac mechanical systems, with a view toward constructing geometric structure-preserving discretizations of interconnected systems. This work builds on previous work on the interconnection of continuous Lagrange–Dirac systems (Jacobs and Yoshimura in J Geom Mech 6(1):67–98, 2014) and discrete Dirac variational integrators (Leok and Ohsawa in Found Comput Math 11(5), 529–562, 2011). We test our results by simulating some of the continuous examples given in Jacobs and Yoshimura (2014).
Causality and passivity in elastodynamics

What are the constraints placed on the constitutive tensors of elastodynamics by the requirements that the linear elastodynamic system under consideration be both causal (effects succeed causes) an...
Computing Equilibria of Prediction Markets via Persuasion.

We study the computation of equilibria in prediction markets in perhaps the most fundamental special case with two players and three trading opportunities. To do so, we show equivalence of prediction market equilibria with those of a simpler signaling game with commitment introduced by Kong and Schoenebeck [18]. We then extend their results by giving computationally efficient algorithms for additional parameter regimes. Our approach leverages a new connection between prediction markets and Bayesian persuasion, which also reveals interesting conceptual insights.
Accelerating and Parallelizing Lagrangian Simulations of Mixing-Limited Reactive Transport

Recent advances in random-walk particle-tracking have enabled direct simulation of mixing and reactions on particles by allowing the particles to interact with each other using a multi-point mass transfer scheme. The mass transfer scheme allows separation of mixing and spreading processes, among other advantages, but it is computationally expensive because its speed depends on the number of interacting particle pairs. This note explores methods for relieving the computational bottleneck caused by the mass transfer step, and we use these algorithms to develop a new parallel, interacting particle model. The new model is a combination of a sparse search algorithm and a novel domain-decomposition scheme, both of which offer significant speedup relative to the reference case--even when they are executed serially. We combine the strengths of these methods to create a parallel particle scheme that is highly accurate and efficient with run times that scale as $1 / P$ for a fixed number of particles, where $P$ is the number of computational cores being used. The new parallel model is a significant advance because it enables efficient simulation of large particle ensembles that are needed for environmental simulations, and also because it can naturally pair with parallel geochemical solvers to create a practical Lagrangian tool for simulating mixing and reactions in complex chemical systems.
The Promise of Property: Legal Optimism and Collective Efficacy in Chicago’s Urban Agriculture District

This article explains how and why community organizers in Chicago’s Englewood neighborhood have found promise in the opportunities that property law provides for addressing community problems. Drawing on interviews and ethnographic fieldwork, it examines the creation of neighborhood institutions for Chicago’s first urban agriculture district. These institutions have been informed by memories of slavery and sharecropping, and of the role played by food production and economic cooperation in struggles for African American self-determination. To keep ownership, use, and benefits of urban farmland local, organizers in Englewood founded a community land trust as a way to cultivate a sense of community ownership and control, and as a way to chip away at the alienation that blocks residents from addressing local problems. Prior studies have linked collective efficacy and residents’ individual sense of ownership; the experience in Englewood points to how collective efficacy could also be fostered by institutions that demonstrate collective, African-American ownership of community resources. The article discusses why organizers and residents in race-class subjugated communities may find promise in the sense of sovereignty and legal agency afforded by property.
PyMT5: multi-mode translation of natural language and Python code with transformers

Simultaneously modeling source code and natural language has many exciting applications in automated software development and understanding. Pursuant to achieving such technology, we introduce PyMT5, the Python method text-to-text transfer transformer, which is trained to translate between all pairs of Python method feature combinations: a single model that can both predict whole methods from natural language documentation strings (docstrings) and summarize code into docstrings of any common style. We present an analysis and modeling effort of a large-scale parallel corpus of 26 million Python methods and 7.7 million method-docstring pairs, demonstrating that for docstring and method generation, PyMT5~ outperforms similarly-sized auto-regressive language models (GPT2) which were English pre-trained or randomly initialized. On the CodeSearchNet test set, our best model predicts 92.1% syntactically correct method bodies, achieved a BLEU score of 8.59 for method generation and 16.3 for docstring generation (summarization), and achieved a ROUGE-L F-score of 24.8 for method generation and 36.7 for docstring generation.
The Red Supergiant Content of M31

We investigate the red supergiant (RSG) population of M31, obtaining the radial velocities of 255 stars. These data substantiate membership of our photometrically selected sample, demonstrating that Galactic foreground stars and extragalactic RSGs can be distinguished on the basis of B − V, V − R two-color diagrams. In addition, we use these spectra to measure effective temperatures and assign spectral types, deriving physical properties for 192 RSGs. Comparison with the solar metallicity Geneva evolutionary tracks indicates astonishingly good agreement. The most luminous RSGs in M31 are likely evolved from 25–30 M_⊙ stars, while the vast majority evolved from stars with initial masses of 20 M_⊙ or less. There is an interesting bifurcation in the distribution of RSGs with effective temperatures that increases with higher luminosities, with one sequence consisting of early K-type supergiants, and with the other consisting of M-type supergiants that become later (cooler) with increasing luminosities. This separation is only partially reflected in the evolutionary tracks, although that might be due to the mis-match in metallicities between the solar Geneva models and the higher-than-solar metallicity of M31. As the luminosities increase the median spectral type also increases; i.e., the higher mass RSGs spend more time at cooler temperatures than do those of lower luminosities, a result which is new to this study. Finally we discuss what would be needed observationally to successfully build a luminosity function that could be used to constrain the mass-loss rates of RSGs as our Geneva colleagues have suggested.
Tidal Decay and Stable Roche-Lobe Overflow of Short-Period Gaseous Exoplanets

Many gaseous exoplanets in short-period orbits are on the verge or are in the process of Roche-lobe overflow (RLO). Moreover, orbital stability analysis shows tides can drive many hot Jupiters to spiral inevitably toward their host stars. Thus, the coupled processes of orbital evolution and RLO likely shape the observed distribution of close-in exoplanets and may even be responsible for producing some of the short-period rocky planets. However, the exact outcome for an overflowing planet depends on its internal response to mass loss, and the accompanying orbital evolution can act to enhance or inhibit RLO. In this study, we apply the fully-featured and robust Modules for Experiments in Stellar Astrophysics suite to model RLO of short-period gaseous planets. We show that, although the detailed evolution may depend on several properties of the planetary system, it is largely determined by the core mass of the overflowing gas giant. In particular, we find that the orbital expansion that accompanies RLO often stops and reverses at a specific maximum period that depends on the core mass. We suggest that RLO may often strand the remnant of a gas giant near this orbital period, which provides an observational prediction that can corroborate the hypothesis that short-period gas giants undergo RLO. We conduct a preliminary comparison of this prediction to the observed population of small, short-period planets and find some planets in orbits that may be consistent with this picture. To the extent that we can establish some short-period planets are indeed the remnants of gas giants, that population can elucidate the properties of gas giant cores, the properties of which remain largely unconstrained.
MORE ON LOGARITHMIC SUMS OF CONVEX BODIES

We prove that the log-Brunn-Minkowski inequality (log-BMI) for the Lebesgue measure in dimension n would imply the log-BMI and, therefore, the B-conjecture for any log-concave density in dimension n. As a consequence, we prove the log-BMI and the B-conjecture for any log-concave density, in the plane. Moreover, we prove that the log-BMI reduces to the following: For each dimension n, there is a density fn, which satisfies an integrability assumption, so that the log-BMI holds for parallelepipeds with parallel facets, for the density fn. As byproduct of our methods, we study possible log-concavity of the function t 7→ | (K +p ·e t L) ◦ |, where p ≥ 1 and K, L are symmetric convex bodies, which we are able to prove in some instances and as a further application, we confirm the variance conjecture in a special class of convex bodies. Finally, we establish a non-trivial dual form of the log-BMI.
Laser cooling with adiabatic transfer on a Raman transition

Sawtooth Wave Adiabatic Passage (SWAP) laser cooling was recently demonstrated using a narrow-linewidth single-photon optical transition in atomic strontium and may prove useful for cooling other atoms and molecules. However, many atoms and molecules lack the appropriate narrow optical transition. Here we use such an atom, alt;supagt;87alt;/supagt;Rb, to demonstrate that two-photon Raman transitions with arbitrarily-tunable linewidths can be used to achieve 1D SWAP cooling without significantly populating the intermediate excited state. Unlike SWAP cooling on a narrow transition, Raman SWAP cooling allows for a final 1D temperature well below the Doppler cooling limit (here, 25 times lower); and the effective excited state decay rate can be modified in time, presenting another degree of freedom during the cooling process. We also develop a generic model for Raman Landau-Zener transitions in the presence of small residual free-space scattering for future applications of SWAP cooling in other atoms or molecules.
Refined metallicity indices for M dwarfs using the SLoWPoKES catalog of wide, low-mass binaries

We report the results from spectroscopic observations of 113 ultra-wide, low-mass binary systems, largely composed of M0-M3 dwarfs, from the SLoWPoKES catalog of common proper motion pairs identified in the Sloan Digital Sky Survey. Radial velocities of each binary member were used to confirm that they are comoving and, consequently, to further validate the high fidelity of the SLoWPoKES catalog. Ten stars appear to be spectroscopic binaries based on broad or split spectral features, supporting previous findings that wide binaries are likely to be hierarchical systems. We measured the Hα equivalent width of the stars in our sample and found that components of 81% of the observed pairs have similar Hα levels. The difference in Hα equivalent width among components with similar masses was smaller than the range of Hα variability for individual objects. We confirm that the Lepine et al. ζ-index traces iso-metallicity loci for most of our sample of M dwarfs. However, we find a small systematic bias in ζ, especially in the early-type M dwarfs. We use our sample to recalibrate the definition of ζ. While representing a small change in the definition, the new ζ is a significantly better predictor of iso-metallicity for the higher-mass M dwarfs.
Chandra observations of SNR RCW 103

We analyze three Chandra observations, with a combined exposure time of 99 ks, of the Galactic supernova remnant (SNR) RCW 103, a young SNR, with no previous clear detections of metal-rich ejecta. Based on our imaging and spectral analyses of these deep Chandra data, we find evidence for metal-rich ejecta emission scattered throughout the remnant. X-ray emission from the shocked ejecta is generally weak, and the shocked circumstellar medium (CSM) is a largely dominant component across the entire remnant. The CSM component shows abundances of ~0.5 solar, while Ne, Mg, Si, S, and Fe abundances of the ejecta are up to a few times solar. Comparison of these ejecta abundances with yields from supernova nucleosynthesis models suggests, together with the existence of a central neutron star, a progenitor mass of ~18–20 , though the Fe/Si ratios are larger than predicted. The shocked CSM emission suggests a progenitor with high mass-loss rate and subsolar metallicity.
A Combinatorial Approach to Small Ball Inequalities for Sums and Differences

Small ball inequalities have been extensively studied in the setting of Gaussian processes and associated Banach or Hilbert spaces. In this paper, we focus on studying small ball probabilities for sums or differences of independent, identically distributed random elements taking values in very general sets. Depending on the setting – abelian or non-abelian groups, or vector spaces, or Banach spaces – we provide a collection of inequalities relating different small ball probabilities that are sharp in many cases of interest. We prove these distribution-free probabilistic inequalities by showing that underlying them are inequalities of extremal combinatorial nature, related among other things to classical packing problems such as the kissing number problem. Applications are given to moment inequalities.
Aspects of the hypermultiplet moduli space in string duality

A type IIA string (or F-theory) compactified on a Calabi-Yau threefold is believed to be dual to a heterotic string on a K3 surface times a 2-torus (or on a K3 surface). We consider how the resulting moduli space of hypermultiplets is identified between these two pictures in the case of the E8 × E8 heterotic string. As examples we discuss SU(2)-bundles and G2-bundles on the K3 surface and the case of point-like instantons. We are lead to a rather beautiful identification between the integral cohomology of the Calabi-Yau threefold and some integral structures on the heterotic side somewhat reminiscent of mirror symmetry. We discuss the consequences for probing nonperturbative effects in the both the type IIA string and the heterotic string.
Elliptic nets and elliptic curves

An elliptic divisibility sequence is an integer recurrence sequence associated to an elliptic curve over the rationals together with a rational point on that curve. In this paper we present a higher-dimensional analogue over arbitrary base fields. Suppose E is an elliptic curve over a field K, and P1,…,Pn are points on E defined over K. To this information we associate an n-dimensional array of values in K satisfying a nonlinear recurrence relation. Arrays satisfying this relation are called elliptic nets. We demonstrate an explicit bijection between the set of elliptic nets and the set of elliptic curves with specified points. We also obtain Laurentness/integrality results for elliptic nets.
Automatic thresholding of SIFT descriptors

We introduce a method to perform automatic thresholding of SIFT descriptors that improves matching performance by at least 15.9% on the Oxford image matching benchmark. The method uses a contrario methodology to determine a unique bin magnitude threshold. This is done by building a generative uniform background model for descriptors and determining when bin magnitudes have reached a sufficient level. The presented method, called meaningful clamping, contrasts from the current SIFT implementation by efficiently computing a clamping threshold that is unique for every descriptor.
Hadronic Interactions from Lattice QCD

We present an overview of recent efforts to calculate the interactions among hadrons using lattice QCD. After outlining the techniques that are used to extract scattering parameters, we detail the latest calculations of meson–meson scattering, baryon–baryon scattering and multi-meson systems obtained with domain-wall valence quarks on the staggered MILC lattices by the NPLQCD collaboration. Estimates of the computational resources required to achieve precision results in the baryon sector are presented.
The ELM Survey. VI. Eleven New Double Degenerates

We present the discovery of 11 new double degenerate systems containing extremely low-mass white dwarfs (ELM WDs). Our radial velocity observations confirm that all of the targets have orbital periods $\leq$ 1 day. We perform spectroscopic fits and provide a complete set of physical and binary parameters. We review and compare recent evolutionary calculations and estimate that the systematic uncertainty in our mass determinations due to differences in the evolutionary models is small ($\approx$ 0.01 M$_{\odot}$). Five of the new systems will merge due to gravitational wave radiation within a Hubble time, bringing the total number of merger systems found in the ELM Survey to 38. We examine the ensemble properties of the current sample of ELM WD binaries, including the period distribution as a function of effective temperature, and the implications for the future evolution of these systems. We also revisit the empirical boundaries of instability strip of ELM WDs and identify new pulsating ELM WD candidates. Finally, we consider the kinematic properties of our sample of ELM WDs and estimate that a significant fraction of the WDs from the ELM Survey are members of the Galactic halo.
Modern Multiple Imputation with Functional Data

This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state-of-the-art methods, which face major challenges in the fitting of more complex non-linear models. Currently, many of these models cannot be consistently estimated unless the number of observed points per curve grows sufficiently quickly with the sample size, whereas, we show numerically that a modified approach with more modern multiple imputation methods can produce better estimates in general. We also propose a new imputation approach that combines the ideas of {\it MissForest} with {\it Local Linear Forest} and compare their performance with {\it PACE} and several other multivariate multiple imputation methods. This work is motivated by a longitudinal study on smoking cessation, in which the Electronic Health Records (EHR) from Penn State PaTH to Health allow for the collection of a great deal of data, with highly variable sampling. To illustrate our approach, we explore the relation between relapse and diastolic blood pressure. We also consider a variety of simulation schemes with varying levels of sparsity to validate our methods.
Towards cardiac intervention assistance: hardware-aware neural architecture exploration for real-time 3D cardiac cine MRI segmentation

Real-time cardiac magnetic resonance imaging (MRI) plays an increasingly important role in guiding various cardiac interventions. In order to provide better visual assistance, the cine MRI frames need to be segmented on-the-fly to avoid noticeable visual lag. In addition, considering reliability and patient data privacy, the computation is preferably done on local hardware. State-of-the-art MRI segmentation methods mostly focus on accuracy only, and can hardly be adopted for real-time application or on local hardware. In this work, we present the first hardware-aware multi-scale neural architecture search (NAS) framework for real-time 3D cardiac cine MRI segmentation. The proposed framework incorporates a latency regularization term into the loss function to handle realtime constraints, with the consideration of underlying hardware. In addition, the formulation is fully differentiable with respect to the architecture parameters, so that stochastic gradient descent (SGD) can be used for optimization to reduce the computation cost while maintaining optimization quality. Experimental results on ACDC MICCAI 2017 dataset demonstrate that our hardware-aware multi-scale NAS framework can reduce the latency by up to 3.5× and satisfy the real-time constraints, while still achieving competitive segmentation accuracy, compared with the state-of-the-art NAS segmentation framework.
Ionic structure and decay length in highly concentrated confined electrolytes

We use molecular dynamics simulations of the primitive model of electrolytes to study the ionic structure in aqueous monovalent electrolyte solutions confined by charged planar interfaces over a wide range of electrolyte concentrations, interfacial separations, surface charge densities, and ion sizes. The investigations are inspired by recent experiments that have directly measured the increase in the decay length for highly concentrated electrolytes with an increase in concentration. The behavior of ions in the nanoconfinement created by the interfaces is probed by evaluating the ionic density profiles, net charge densities, integrated charges, and decay lengths associated with the screening of the charged interface. The results show the presence of two distinct regimes of screening behavior as the concentration is changed from 0.1M to 2.5M for a wide range of electrolyte systems generated by tuning the interfacial separation, surface charge density, and ionic size. For low concentrations, the integrated charge exhibits a monotonic decay to 0 with a decay length that decreases sharply with increasing concentration. For high concentrations (≳1M), the integrated charge has a non-monotonic behavior signaling charge inversion and formation of structured layers of ions near the interfaces. The decay length under these conditions rises with increasing concentration. To complement the simulation results, a variational approach is developed that produces charge densities with characteristics consistent with those observed in simulations. The results demonstrate the relation between the rise in the strength of steric correlations and the changes in the screening behavior.
Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data.

In single-cell RNA sequencing (scRNA-seq), doublets form when two cells are encapsulated into one reaction volume. The existence of doublets, which appear to be-but are not-real cells, is a key confounder in scRNA-seq data analysis. Computational methods have been developed to detect doublets in scRNA-seq data; however, the scRNA-seq field lacks a comprehensive benchmarking of these methods, making it difficult for researchers to choose an appropriate method for specific analyses. We conducted a systematic benchmark study of nine cutting-edge computational doublet-detection methods. Our study included 16 real datasets, which contained experimentally annotated doublets, and 112 realistic synthetic datasets. We compared doublet-detection methods regarding detection accuracy under various experimental settings, impacts on downstream analyses, and computational efficiencies. Our results show that existing methods exhibited diverse performance and distinct advantages in different aspects. Overall, the DoubletFinder method has the best detection accuracy, and the cxds method has the highest computational efficiency. A record of this paper's transparent peer review process is included in the Supplemental Information.
A multi-dimensional, time-lapse, high content screening platform applied to schistosomiasis drug discovery.

Approximately 10% of the world’s population is at risk of schistosomiasis, a disease of poverty caused by the Schistosoma parasite. To facilitate drug discovery for this complex flatworm, we developed an automated high-content screen to quantify the multidimensional responses of Schistosoma mansoni post-infective larvae (somules) to chemical insult. We describe an integrated platform to process worms at scale, collect time-lapsed, bright-field images, segment highly variable and touching worms, and then store, visualize, and query dynamic phenotypes. To demonstrate the methodology, we treated somules with seven drugs that generated diverse responses and evaluated 45 static and kinetic response descriptors relative to concentration and time. For compound screening, we used the Mahalanobis distance to compare multidimensional phenotypic effects induced by 1323 approved drugs. Overall, we characterize both known anti-schistosomals and identify new bioactives. Apart from facilitating drug discovery, the multidimensional quantification provided by this platform will allow mapping of chemistry to phenotype. Steven Chen et al. develop an automated, time-lapsed, high-content screen to quantify the responses of Schistosoma mansoni larvae to chemical insult. They apply their method to evaluate 45 static and kinetic response endpoints for seven drugs and screen 1323 approved drugs. Their work identifies anti-schistosomal compounds and underscores the value of quantifying motion in phenotypic drug discovery.
Noise from a Vibrating Propeller

This paper is concerned with an analytical study of the noise from a vibrating propeller. The influence of airfoil thickness and of steady loading are also included to provide a basis for comparison. The analysis was based on the concept of distributing sources and doublets on the surface of the blade, which were multiplied by their appropriate strength factors. The noise in the plane of the propeller was dominated by the thickness noise. Moving the observer's position to the propeller axis, the thickness noise and loading noise were zero, and a pure sinusoidal noise was found, caused by the vibrations of the propeller.
Emergence of Quantum Mechanics from Iterated Maps

Iterated maps are deterministic models of dynamical systems in discrete time. A key feature of these models is the concept of invariant density associated with the asymptotic onset of stationarity. Drawing from the minimal fractality of spacetime near the Fermi scale, we show here that invariant density enables a step-by-step derivation of Quantum Mechanics from iterated maps.
Strings in Ramond-Ramond backgrounds from the Neveu-Schwarz-Ramond formalism

We treat RR flux backgrounds of type II string theory in the framework of closed superstring field theory based on the NSR formalism, focusing on two examples: (1) the pp-wave background supported by 5-form flux, and (2) AdS3 × S3 × M4 supported by mixed 3-form fluxes. In both cases, we analyze the classical string field solution perturbatively, and compute the correction to the dispersion relation of string states to quadratic order in the RR flux. In the first example, our result is in a delicate way consistent with that obtained from lightcone quantization of the Green-Schwarz string. In the second example, we will obtain numerically the mass corrections to pulsating type IIB strings in AdS3 × S3 × M4. Our results, valid at finite AdS radius, agree with previously known answers in the semiclassical limit and in the BMN limit respectively.
Deploying deep learning in OpenFOAM with TensorFlow

We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks. This module is constructed with the TensorFlow C API and is integrated into OpenFOAM as an application that may be linked at run time. Notably, our formulation precludes any restrictions related to the type of neural network architecture (i.e., convolutional, fully-connected, etc.). This allows for potential studies of complicated neural architectures for practical CFD problems. In addition, the proposed module outlines a path towards an open-source, unified and transparent framework for computational fluid dynamics and machine learning.
Types for tame p-adic groups

Let k be a non-archimedean local field with residual characteristic p. Let G be a connected reductive group over k that splits over a tamely ramified field extension of k. Suppose p does not divide the order of the Weyl group of G. Then we show that every smooth irreducible complex representation of G(k) contains an $\mathfrak{s}$-type of the form constructed by Kim and Yu and that every irreducible supercuspidal representation arises from Yu's construction. This improves an earlier result of Kim, which held only in characteristic zero and with a very large and ineffective bound on p. By contrast, our bound on p is explicit and tight, and our result holds in positive characteristic as well. Moreover, our approach is more explicit in extracting an input for Yu's construction from a given representation.
Kolmogorov width decay and poor approximators in machine learning: shallow neural networks, random feature models and neural tangent kernels

We establish a scale separation of Kolmogorov width type between subspaces of a given Banach space under the condition that a sequence of linear maps converges much faster on one of the subspaces. The general technique is then applied to show that reproducing kernel Hilbert spaces are poor $$L^{2}$$ -approximators for the class of two-layer neural networks in high dimension, and that multi-layer networks with small path norm are poor approximators for certain Lipschitz functions, also in the $$L^{2}$$ -topology.