cssi/SciSciGPT-SciSciNet huggingface dataset:
THE SPECTRUM OF THORIUM FROM 250 nm TO 5500 nm: RITZ WAVELENGTHS AND OPTIMIZED ENERGY LEVELS

We have made precise observations of a thorium-argon hollow cathode lamp emission spectrum in the region between 350 nm and 1175 nm using a high-resolution Fourier transform spectrometer. Our measurements are combined with results from seven previously published thorium line lists to re-optimize the energy levels of neutral, singly, and doubly ionized thorium (Th I, Th II, and Th III). Using the optimized level values, we calculate accurate Ritz wavelengths for 19, 874 thorium lines between 250 nm and 5500 nm (40, 000 cm–1 to 1800 cm–1). We have also found 102 new thorium energy levels. A systematic analysis of previous measurements in light of our new results allows us to identify and propose corrections for systematic errors in Palmer & Engleman and typographical errors and incorrect classifications in Kerber et al. We also found a large scatter with respect to the thorium line list of Lovis & Pepe. We anticipate that our Ritz wavelengths will lead to improved measurement accuracy for current and future spectrographs that make use of thorium-argon or thorium-neon lamps as calibration standards.
Amplification due to two-stream instability of self-electric and magnetic fields of an ion beam propagating in background plasma

Propagation of charged particle beams in background plasma as a method of space charge neutralization has been shown to achieve a high degree of charge and current neutralization and therefore enables nearly ballistic propagation and focusing of charged particle beams. Correspondingly, the use of plasmas for propagation of charged particle beams has important applications for transport and focusing of intense particle beams in inertial fusion and high energy density laboratory plasma physics. However, the streaming of beam ions through a background plasma can lead to the development of two-stream instability between the beam ions and the plasma electrons. The beam electric and magnetic fields enhanced by the two-stream instability can lead to defocusing of the ion beam. Using particle-in-cell simulations, we study the scaling of the instability-driven self-electromagnetic fields and consequent defocusing forces with the background plasma density and beam ion mass. We identify plasma parameters where the defocusing forces can be reduced.
Automatic detection and counting of retina cell nuclei using deep learning

The ability to automatically detect, classify, calculate the size, number, and grade of retinal cells and other biological objects is critically important in eye disease like age-related macular degeneration (AMD). In this paper, we developed an automated tool based on deep learning technique and Mask R-CNN model to analyze large datasets of transmission electron microscopy (TEM) images and quantify retinal cells with high speed and precision. We considered three categories for outer nuclear layer (ONL) cells: live, intermediate, and pyknotic. We trained the model using a dataset of 24 samples. We then optimized the hyper-parameters using another set of 6 samples. The results of this research, after applying to the test datasets, demonstrated that our method is highly accurate for automatically detecting, categorizing, and counting cell nuclei in the ONL of the retina. Performance of our model was tested using general metrics: general mean average precision (mAP) for detection; and precision, recall, F1-score, and accuracy for categorizing and counting.
A naturally large four-point function in single field inflation

Non-Gaussianities of the primordial density perturbations have emerged as a very powerful possible signal to test the dynamics that drove the period of inflation. While in general the most sensitive observable is the three-point function in this paper we show that there are technically natural inflationary models where the leading source of non-Gaussianity is the four-point function. Using the recently developed Effective Field Theory of Inflation, we are able to show that it is possible to impose an approximate parity symmetry and an approximate continuos shift symmetry on the inflaton fluctuations that allow, when the dispersion relation if of the form ω ~ csk, for a unique quartic operator, while approximately forbidding all the cubic ones. The resulting shape for the four-point function is unique. In the models where the dispersion relation is of the form ω ~ k2/M a similar construction can be carried out and additional shapes are possible.
Topics in D-geometry

We discuss the general theory of D-branes on Calabi-Yau, recent results from the theory of boundary states and new results on the spectrum of branes on the quintic Calabi-Yau.
MOSFIRE ABSORPTION LINE SPECTROSCOPY OF z > 2 QUIESCENT GALAXIES: PROBING A PERIOD OF RAPID SIZE GROWTH

Using the MOSFIRE near-infrared multi-slit spectrograph on the Keck 1 Telescope, we have secured high signal-to-noise ratio absorption line spectra for six massive galaxies with redshift 2 < z < 2.5. Five of these galaxies lie on the red sequence and show signatures of passive stellar populations in their rest-frame optical spectra. By fitting broadened spectral templates we have determined stellar velocity dispersions and, with broad-band HST and Spitzer photometry and imaging, stellar masses and effective radii. Using this enlarged sample of galaxies we confirm earlier suggestions that quiescent galaxies at z > 2 have small sizes and large velocity dispersions compared to local galaxies of similar stellar mass. The dynamical masses are in very good agreement with stellar masses (logM_∗/M_(dyn) = −0.02 ± 0.03), although the average stellar-to-dynamical mass ratio is larger than that found at lower redshift (−0.23 ± 0.05). By assuming evolution at fixed velocity dispersion, not only do we confirm a surprisingly rapid rate of size growth but we also consider the necessary evolutionary track on the mass-size plane and find a slope ɑ = d logR_e/d logM_∗ ≳ 2 inconsistent with most numerical simulations of minor mergers. Both results suggest an additional mechanism may be required to explain the size growth of early galaxies.
Privacy and Proportionality: Examining Mass Electronic Surveillance under Article 8 and the Fourth Amendment

An individual citizen’s right to privacy has found extensive protection in the modern democratic state. However, with the increase of technological innovation and new kinds of threats, democratic states must grapple with balancing a problem that can never be wholly solved—governments seeking to create the optimal degree of security inevitably conflicts with citizens’ optimal degree of privacy. This article examines one vehicle through which governments have prioritized national security at the expense of individual privacy: mass electronic surveillance. Employing the case study method, this article compares three cases where mass electronic surveillance measures were challenged before the European Court of Human Rights under Article 8 against four cases where such measures were challenged in the U.S. judicial system under the Fourth Amendment. This article seeks to determine how the treatment of privacy infringe- ments created by mass electronic surveillance differs when examined in these two differ- ent privacy regimes. I argue the Strasbourg Court’s use of what is known in the literature as the “proportionality analysis” provoked by Article 8(2) allows for more substantial protections of privacy rights in Europe than under the Fourth Amendment in the U.S..
Fusion systems of blocks of finite groups over arbitrary fields

To any block idempotent $b$ of a group algebra $kG$ of a finite group $G$ over a field $k$ of characteristic $p>0$, Puig associated a fusion system and proved that it is saturated if the $k$-algebra $kC_G(P)e$ is split, where $(P,e)$ is a maximal $kGb$-Brauer pair. We investigate in the non-split case how far the fusion system is from being saturated by describing it in an explicit way as being generated by the fusion system of a related block idempotent over a larger field together with a single automorphism of the defect group.
A Control-Oriented Notion of Finite State Approximation

We consider the problem of approximating discrete-time plants with finite-valued sensors and actuators by deterministic finite memory systems for the purpose of certified-by-design controller synthesis. Building on ideas from robust control, we propose a control-oriented notion of finite state approximation for these systems, demonstrate its relevance to the control synthesis problem, and discuss its key features.
Enhanced x-ray angular dispersion and x-ray spectrographs with resolving power beyond 10^{-8}

Spectrograph is an optical device that is used to disperse photons of different energies E into distinct directions and space locations, and to take a snapshot of the whole spectrum of photon energies with a spatially sensitive photon detector. Substantial advantage of a spectrograph over an ordinary spectral analyzer, is its ability to deal with many photon energies simultaneously, thus reducing exposure time per spectrum considerably. To realize a spectrograph, dispersing elements with large angular dispersion rate are required. In visible light optics this is easily achieved with diffraction gratings. In hard x-ray regime this is a problem. Here we show, on the example of CDW x-ray optics, that multi-crystal arrangements may feature cumulative angular dispersion rates more than an order of magnitude larger than those attainable in single Bragg reflections. This makes, first, hard x-ray spectrographs feasible, and, secondly, a resolving power beyond E/ΔE approximate > 108
Network Inference From Consensus Dynamics With Unknown Parameters

We explore the problem of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discrete-time consensus dynamics, subject to parameter uncertainty, taking place on the network. Specifically, we consider three problems in which we assume different levels of knowledge about the diffusion rates, observation times, and the input signal power of the dynamics. To solve these underdetermined problems, we propose a set of algorithms that leverage the spectral properties of the observed data and tools from convex optimization. Furthermore, we provide theoretical performance guarantees associated with these algorithms. We complement our theoretical work with numerical experiments, that demonstrate how our proposed methods outperform current state-of-the-art algorithms and showcase their effectiveness in recovering both synthetic and real-world networks.
Dipolophoresis in concentrated suspensions of ideally polarizable spheres

The dynamics of ideally polarizable spherical particles in concentrated suspensions under the effects of nonlinear electrokinetic phenomena is analysed using large-scale numerical simulations. Particles are assumed to carry no net charge and considered to undergo the combination of dielectrophoresis and induced-charge electrophoresis termed dipolophoresis. Chaotic motion and resulting hydrodynamic diffusion are known to be driven by the induced-charge electrophoresis, which dominates the dielectrophoresis. Up to a volume fraction . Moreover, this transition is visible in the pair distribution functions, which also reveal the change in the suspension microstructure in concentrated regimes. It appears that strong and massive repulsive contacts along the direction perpendicular to an electric field promote the non-trivial suspension behaviours observed in concentrated regimes.
A Framework to Control Functional Connectivity in the Human Brain

In this paper, we propose a framework to control brain-wide functional connectivity by selectively acting on the brain’s structure and parameters. Functional connectivity, which measures the degree of correlation between neural activities in different brain regions, can be used to distinguish between healthy and certain diseased brain dynamics and, possibly, as a control parameter to restore healthy functions. In this work, we use a collection of interconnected Kuramoto oscillators to model oscillatory neural activity, and show that functional connectivity is essentially regulated by the degree of synchronization between different clusters of oscillators. Then, we propose a minimally invasive method to correct the oscillators’ interconnections and frequencies to enforce arbitrary and stable synchronization patterns among the oscillators and, consequently, a desired pattern of functional connectivity. Additionally, we show that our synchronization-based framework is robust to parameter mismatches and numerical inaccuracies, and validate it using a realistic neurovascular model to simulate neural activity and functional connectivity in the human brain.
Deep Evidential Regression.

Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order to learn both aleatoric and epistemic uncertainty. We accomplish this by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution. We additionally impose priors during training such that the model is regularized when its predicted evidence is not aligned with the correct output. Our method does not rely on sampling during inference or on out-of-distribution (OOD) examples for training, thus enabling efficient and scalable uncertainty learning. We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.
Volume distortion in groups

Given a space Y in X , a cycle in Y may be filled with a chain in two ways: either by restricting the chain to Y or by allowing it to be anywhere in X . When the pair .G;H/ acts on .X;Y/, we define the k ‐volume distortion function of H in G to measure the large-scale difference between the volumes of such fillings. We show that these functions are quasi-isometry invariants, and thus independent of the choice of spaces, and provide several bounds in terms of other group properties, such as Dehn functions. We also compute the volume distortion in a number of examples, including characterizing the k ‐volume distortion of Z k in Z k AM Z, where M is a diagonalizable matrix. We use this to prove a conjecture of Gersten. 20F65; 20F67, 57M07
Knots, Music and DNA

Musical gestures connect the symbolic layer of the score to the physical layer of sound. I focus here on the mathematical theory of musical gestures, and I propose its generalization to include braids and knots. In this way, it is possible to extend the formalism to cover more case studies, especially regarding conducting gestures. Moreover, recent developments involving comparisons and similarities between gestures of orchestral musicians can be contextualized in the frame of braided monoidal categories. Because knots and braids can be applied to both music and biology (they apply to knotted proteins, for example), I end the article with a new musical rendition of DNA.
Spin-1/2 Maxwell Fields

Requiring covariance of Maxwell's equations without a priori imposing charge invariance allows for both spin-1 and spin-1/2 transformations of the complete Maxwell field and current. The spin-1/2 case yields new transformation rules, with new invariants, for all traditional Maxwell field and source quantities. The accompanying spin-1/2 representations of the Lorentz group employ the Minkowski metric, and consequently the primary spin-1/2 Maxwell invariants are also spin-1 invariants; for example, Φ 2−A 2, E 2−B 2+2i E⋅B−(∂0 Φ+∇⋅A)2. The associated Maxwell Lagrangian density is also the same for both spin-1 and spin-1/2 fields. However, in the spin-1/2 case, standard field and source quantities are complex and both charge and gauge invariance are lost. Requiring the potentials to satisfy the Klein–Gordon equation equates the Maxwell and field-potential equations with two Dirac equations of the Klein–Gordon mass, and thus one complex Klein–Gordon Maxwell field describes either two real vector fields or two Dirac fields, all of the same mass.
Dolgopyat's method and the fractal uncertainty principle

We show a fractal uncertainty principle with exponent 1 2 − δ + e , e > 0 , for Ahlfors–David regular subsets of ℝ of dimension δ ∈ ( 0 , 1 ) . This is an improvement over the volume bound 1 2 − δ , and e is estimated explicitly in terms of the regularity constant of the set. The proof uses a version of techniques originating in the works of Dolgopyat, Naud, and Stoyanov on spectral radii of transfer operators. Here the group invariance of the set is replaced by its fractal structure. As an application, we quantify the result of Naud on spectral gaps for convex cocompact hyperbolic surfaces and obtain a new spectral gap for open quantum baker maps.
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates

CPU simulators are useful tools for modeling CPU execution behavior. However, they suffer from inaccuracies due to the cost and complexity of setting their fine-grained parameters, such as the latencies of individual instructions. This complexity arises from the expertise required to design benchmarks and measurement frameworks that can precisely measure the values of parameters at such fine granularity. In some cases, these parameters do not necessarily have a physical realization and are therefore fundamentally approximate, or even unmeasurable.In this paper we present DiffTune, a system for learning the parameters of x86 basic block CPU simulators from coarse-grained end-to-end measurements. Given a simulator, DiffTune learns its parameters by first replacing the original simulator with a differentiable surrogate, another function that approximates the original function; by making the surrogate differentiable, DiffTune is then able to apply gradient-based optimization techniques even when the original function is non-differentiable, such as is the case with CPU simulators. With this differentiable surrogate, DiffTune then applies gradient-based optimization to produce values of the simulator’s parameters that minimize the simulator’s error on a dataset of ground truth end-to-end performance measurements. Finally, the learned parameters are plugged back into the original simulator.DiffTune is able to automatically learn the entire set of microarchitecture-specific parameters within the Intel x86 simulation model of llvm-mca, a basic block CPU simulator based on LLVM’s instruction scheduling model. DiffTune’s learned parameters lead llvm-mca to an average error that not only matches but lowers that of its original, expert-provided parameter values.
Wide-field Survey of Dwarf Satellite Systems around 10 Hosts in the Local Volume

We present the results of an extensive search for dwarf satellite galaxies around 10 primary host galaxies in the Local Volume (D$<$12 Mpc) using archival CFHT/MegaCam imaging data. The hosts span a wide range in properties, with stellar masses ranging from that of the LMC to ${\sim}3$ times that of the Milky Way (MW). The surveyed hosts are: NGC 1023, NGC 1156, NGC 2903, NGC 4258, NGC 4565, NGC 4631, NGC 5023, M51, M64, and M104. We detect satellite candidates using a consistent semi-automated detection algorithm that is optimized for the detection of low surface brightness objects. Depending on the host, our completeness limit is $M_g{\sim}-8$ to $-10$ (assuming the distance of the host). We detect objects with surface brightness down to $\mu_{0,g}{\sim}26$ mag arcsec$^{-2}$ at $\gtrsim90\%$ completeness. The survey areas of the six best-surveyed hosts cover most of the inner projected $R<150$ kpc area, which roughly doubles the number of MW-mass hosts surveyed at this level of area and luminosity completeness. The number of detected candidates range from 1 around M64 to 33 around NGC 4258. In total, 153 candidates are found, of which 93 are new. While we defer an analysis of the satellite luminosity functions of the hosts until distance information is available for the candidates, we do show that the candidates are primarily red, spheroid systems with properties roughly consistent with known satellites in the Local Group.
Signatures of Velocity-dependent Dark Matter Self-interactions in Milky Way-mass Halos

We explore the impact of elastic, anisotropic, velocity-dependent dark matter (DM) self-interactions on the host halo and subhalos of Milky Way (MW)--mass systems. We consider a generic self-interacting dark matter (SIDM) model parameterized by the masses of a light mediator and the DM particle. The ratio of these masses, $w$, sets the velocity scale above which momentum transfer due to DM self-interactions becomes inefficient. We perform high-resolution zoom-in simulations of an MW-mass halo for values of $w$ that span scenarios in which self-interactions either between the host and its subhalos or only within subhalos efficiently transfer momentum, and we study the effects of self-interactions on the host halo and on the abundance, radial distribution, orbital dynamics, and density profiles of subhalos in each case. The abundance and properties of surviving subhalos are consistent with being determined primarily by subhalo--host halo interactions. In particular, subhalos on radial orbits in models with larger values of the cross section at the host halo velocity scale are more susceptible to tidal disruption owing to mass loss from ram pressure stripping caused by self-interactions with the host. This mechanism suppresses the abundance of surviving subhalos relative to collisionless DM simulations, with stronger suppression for larger values of $w$. Thus, probes of subhalo abundance around MW-mass hosts can be used to place upper limits on the self-interaction cross section at velocity scales of $\sim 200\ \rm{km\ s}^{-1}$, and combining these measurements with the orbital properties and internal dynamics of subhalos may break degeneracies among velocity-dependent SIDM models.
Functional determinants and Casimir energy in higher dimensional spherically symmetric background potentials

In this paper we analyze the spectral zeta function associated with a Laplace operator acting on scalar functions on an N-dimensional Euclidean space in the presence of a spherically symmetric background potential. The obtained analytic continuation of the spectral zeta function is then used to derive very simple results for the functional determinant of the operator and the Casimir energy of the scalar field.
Zodiacal Exoplanets in Time (ZEIT). IX. A Flat Transmission Spectrum and a Highly Eccentric Orbit for the Young Neptune K2-25b as Revealed by Spitzer

Transiting planets in nearby young clusters offer the opportunity to study the atmospheres and dynamics of planets during their formative years. To this end, we focused on K2-25b -- a close-in ($P$=3.48 days), Neptune-sized exoplanet orbiting a M4.5 dwarf in the 650Myr Hyades cluster. We combined photometric observations of K2-25 covering a total of 44 transits and spanning >2 yr, drawn from a mix of space-based telescopes (Spitzer Space Telescope and K2) and ground-based facilities (Las Cumbres Observatory Global Telescope network and MEarth). The transit photometry spanned 0.6--4.5$\mu$m, which enabled our study of K2-25b's transmission spectrum. We combined and fit each dataset at a common wavelength within a Markov Chain Monte Carlo framework, yielding consistent planet parameters. The resulting transit depths ruled out a solar-composition atmosphere for K2-25b for the range of expected planetary masses and equilibrium temperature at a $>4\sigma$ confidence level, and are consistent with a flat transmission spectrum. Mass constraints and transit observations at a finer grid of wavelengths (e.g., from the Hubble Space Telescope) are needed to make more definitive statements about the presence of clouds or an atmosphere of high mean molecular weight. Our precise measurements of K2-25b's transit duration also enabled new constraints on the eccentricity of K2-25's orbit. We find K2-25b's orbit to be eccentric ($e>0.20$) for all reasonable stellar densities and independent of the observation wavelength or instrument. The high eccentricity is suggestive of a complex dynamical history and motivates future searches for additional planets or stellar companions.
A Hamilton-Jacobi Formalism for Thermodynamics

Abstract We show that classical thermodynamics has a formulation in terms of Hamilton–Jacobi theory, analogous to mechanics. Even though the thermodynamic variables come in conjugate pairs such as pressure/volume or temperature/entropy, the phase space is odd-dimensional. For a system with n thermodynamic degrees of freedom it is 2 n + 1 -dimensional. The equations of state of a substance pick out an n-dimensional submanifold. A family of substances whose equations of state depend on n parameters define a hypersurface of co-dimension one. This can be described by the vanishing of a function which plays the role of a Hamiltonian. The ordinary differential equations (characteristic equations) defined by this function describe a dynamical system on the hypersurface. Its orbits can be used to reconstruct the equations of state. The ‘time’ variable associated to this dynamics is related to, but is not identical to, entropy. After developing this formalism on well-grounded systems such as the van der Waals gases and the Curie–Weiss magnets, we derive a Hamilton–Jacobi equation for black hole thermodynamics in General Relativity. The cosmological constant appears as a constant of integration in this picture.
Magnetic dipole moment in relativistic quantum mechanics

We investigate relativistic quantum mechanics (RQM) for particles with arbitrary magnetic moment. We compare two well known RQM models: a) Dirac equation supplemented with an incremental Pauli term (DP); b) Klein-Gordon equations with full Pauli EM dipole moment term (KGP). We compare exact solutions to the external field cases in the limit of weak and strong (critical) fields for: i) homogeneous magnetic field, and ii) the Coulomb $ 1/r$-potential. For i) we consider the Landau energies and the Landau states as a function of the gyromagnetic factor (g-factor). For ii) we investigate contribution to the Lamb shift and the fine structure splitting. For both we address the limit of strong binding and show that these two formulations grossly disagree. We discuss possible experiments capable of distinguishing between KGP and DP models in laboratory. We describe the impact of our considerations in the astrophysical context (magnetars). We introduce novel RQM models of magnetic moments which can be further explored.
Splitting cubic circle graphs

We show that every 3-regular circle graph has at least two pairs of twin vertices; consequently no such graph is prime with respect to the split decomposition. We also deduce that up to isomorphism, K_4 and K_{3,3} are the only 3-connected, 3-regular circle graphs.
Pressure-anisotropy-induced nonlinearities in the kinetic magnetorotational instability.

In collisionless and weakly collisional plasmas, such as hot accretion flows onto compact objects, the magnetorotational instability (MRI) can differ significantly from the standard (collisional) MRI. In particular, pressure anisotropy with respect to the local magnetic-field direction can both change the linear MRI dispersion relation and cause nonlinear modifications to the mode structure and growth rate, even when the field and flow perturbations are very small. This work studies these pressure-anisotropy-induced nonlinearities in the weakly nonlinear, high-ion-beta regime, before the MRI saturates into strong turbulence. Our goal is to better understand how the saturation of the MRI in a low-collisionality plasma might differ from that in the collisional regime. We focus on two key effects: (i) the direct impact of self-induced pressure-anisotropy nonlinearities on the evolution of an MRI mode, and (ii) the influence of pressure anisotropy on the ‘parasitic instabilities’ that are suspected to cause the mode to break up into turbulence. Our main conclusions are: (i) The mirror instability regulates the pressure anisotropy in such a way that the linear MRI in a collisionless plasma is an approximate nonlinear solution once the mode amplitude becomes larger than the background field (just as in magnetohyrodynamics). This implies that differences between the collisionless and collisional MRI become unimportant at large amplitudes. (ii) The break up of large-amplitude MRI modes into turbulence via parasitic instabilities is similar in collisionless and collisional plasmas. Together, these conclusions suggest that the route to magnetorotational turbulence in a collisionless plasma may well be similar to that in a collisional plasma, as suggested by recent kinetic simulations. As a supplement to these findings, we offer guidance for the design of future kinetic simulations of magnetorotational turbulence.
Convergence of Phase-Field Free Energy and Boundary Force for Molecular Solvation

We study a phase-field variational model for the solvation of charged molecules with an implicit solvent. The solvation free-energy functional of all phase fields consists of the surface energy, solute excluded volume and solute-solvent van der Waals dispersion energy, and electrostatic free energy. The surface energy is defined by the van der Waals–Cahn–Hilliard functional with squared gradient and a double-well potential. The electrostatic part of free energy is defined through the electrostatic potential governed by the Poisson–Boltzmann equation in which the dielectric coefficient is defined through the underlying phase field. We prove the continuity of the electrostatics—its potential, free energy, and dielectric boundary force—with respect to the perturbation of the dielectric boundary. We also prove the $${\Gamma}$$ -convergence of the phase-field free-energy functionals to their sharp-interface limit, and the equivalence of the convergence of total free energies to that of all individual parts of free energy. We finally prove the convergence of phase-field forces to their sharp-interface limit. Such forces are defined as the negative first variations of the free-energy functional; and arise from stress tensors. In particular, we obtain the force convergence for the van der Waals–Cahn–Hilliard functionals with minimal assumptions.
The M theory lift of two O6? planes and four D6 branes

We solve for the effective actions on the Coulomb branches of a class of N = 2 supersymmetric theories by finding the complex structure of an M5-brane in an appropriate background hyperkahler geometry corresponding to the lift of two O6? orientifolds and four D6-branes to M theory. The resulting Seiberg-Witten curves are of finite genus, unlike other solutions proposed in the literature. The simplest theories in this class are the scale invariant Sp(k) theory with one antisymmetric and four fundamental hypermultiplets and the SU(k) theory with two antisymmetric and four fundamental hypermultiplets. Infinite classes of related theories are obtained by adding extra SU(k) factors with bifundamental matter and by turning on masses to flow down to various asymptotically free theories. The N = 4 supersymmetric SU(k) theory can be embedded in these asymptotically free theories, allowing a derivation of a subgroup of its S duality group as an exact equivalence of quantum field theories.
A High-Fidelity Open Embodied Avatar with Lip Syncing and Expression Capabilities

Embodied avatars as virtual agents have many applications and provide benefits over disembodied agents, allowing nonverbal social and interactional cues to be leveraged, in a similar manner to how humans interact with each other. We present an open embodied avatar built upon the Unreal Engine that can be controlled via a simple python programming interface. The avatar has lip syncing (phoneme control), head gesture and facial expression (using either facial action units or cardinal emotion categories) capabilities. We release code and models to illustrate how the avatar can be controlled like a puppet or used to create a simple conversational agent using public application programming interfaces (APIs). GITHUB link: https://github.com/danmcduff/AvatarSim
Leveraging rationales to improve human task performance

Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans. In response to the proliferation of such models, the field of Explainable AI (XAI) has sought to develop techniques that enhance the transparency and interpretability of machine learning methods. In this work, we consider a question not previously explored within the XAI and ML communities: Given a computational system whose performance exceeds that of its human user, can explainable AI capabilities be leveraged to improve the performance of the human? We study this question in the context of the game of Chess, for which computational game engines that surpass the performance of the average player are widely available. We introduce the Rationale-Generating Algorithm, an automated technique for generating rationales for utility-based computational methods, which we evaluate with a multi-day user study against two baselines. The results show that our approach produces rationales that lead to statistically significant improvement in human task performance, demonstrating that rationales automatically generated from an AI's internal task model can be used not only to explain what the system is doing, but also to instruct the user and ultimately improve their task performance.
Galaxy Ellipticity Measurements in the Near-infrared for Weak Lensing

We investigate the value of the near-infrared imaging from upcoming surveys for constraining the ellipticities of galaxies. We select galaxies between 0.5 ≤ z 1.0, the NIR-selected source density is higher by a factor of 1.4 and therefore the standard error in NIR-derived ellipticities is about 30% smaller, implying a more precise ellipticity measurement. The good performance of the NIR is mainly because galaxies have an intrinsically smoother light distribution in the NIR bands than in the optical, the latter tracing the clumpy star-forming regions. In addition, the NIR bands have a higher surface brightness per pixel than the optical images, while being less affected by dust attenuation. Despite the worse spatial sampling and resolution of Euclid NIR compared to optical, the NIR approach yields equivalent or more precise galaxy ellipticity measurements. If systematics that affect shape such as dithering strategy and point-spread function undersampling can be mitigated, inclusion of the NIR can improve galaxy ellipticity measurements over all redshifts. This is particularly important for upcoming weak lensing surveys, such as with Euclid and WFIRST.
Towers of regular self-covers and linear endomorphisms of tori

Let $M$ be a closed manifold that admits a self-cover $p:M \to M$ of degree >1. We say p is strongly regular if all its iterates are regular covers. In this case, we establish an algebraic structure theorem for the fundamental group of $M$: We prove that $\pi_1(M)$ surjects onto a nontrivial free abelian group $A$, and the self-cover is induced by a linear endomorphism of $A$. Under further hypotheses we show that a finite cover of $M$ admits the structure of a principal torus bundle. We show that this applies when $M$ is Kahler and $p$ is a strongly regular, holomorphic self-cover, and prove that a finite cover splits as a product with a torus factor.
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning

The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of widely available remote sensing data for the prediction of infrastructure quality in Africa. We train a convolutional neural network to predict ground truth labels from the Afrobarometer Round 6 survey using Landsat 8 and Sentinel 1 satellite imagery. Our best models predict infrastructure quality with AUROC scores of 0.881 on Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using Landsat 8. These performances are significantly better than models that leverage OpenStreetMap or nighttime light intensity on the same tasks. We also demonstrate that our trained model can accurately make predictions in an unseen country after fine-tuning on a small sample of images. Furthermore, the model can be deployed in regions with limited samples to predict infrastructure outcomes with higher performance than nearest neighbor spatial interpolation.
Socio-spatial Self-organizing Maps: Using Social Media to Assess Relevant Geographies for Exposure to Social Processes

Social media offers a unique window into attitudes like racism and homophobia, exposure to which are important, hard to measure and understudied social determinants of health. However, individual geo-located observations from social media are noisy and geographically inconsistent. Existing areas by which exposures are measured, like Zip codes, average over irrelevant administratively-defined boundaries. Hence, in order to enable studies of online social environmental measures like attitudes on social media and their possible relationship to health outcomes, first there is a need for a method to define the collective, underlying degree of social media attitudes by region. To address this, we create the Socio-spatial-Self organizing map, "SS-SOM" pipeline to best identify regions by their latent social attitude from Twitter posts. SS-SOMs use neural embedding for text-classification, and augment traditional SOMs to generate a controlled number of non-overlapping, topologically-constrained and topically-similar clusters. We find that not only are SS-SOMs robust to missing data, the exposure of a cohort of men who are susceptible to multiple racism and homophobia-linked health outcomes, changes by up to 42% using SS-SOM measures as compared to using Zip code-based measures.
Beyond the pseudo-time-dependent approach: Chemical models of dense core precursors

Context. Chemical models of dense cloud cores often utilize the so-called pseudo-time-dependent approximation, in which the physical conditions are held fixed and uniform as the chemistry occurs. In this approximation, the initial abundances chosen, which are totally atomic in nature except for molecular hydrogen, are artificial. A more detailed approach to the chemistry of dense cold cores should include the physical evolution during their early stages of formation. Aims. Our major goal is to investigate the initial synthesis of molecular ices and gas-phase molecules as cold molecular gas begins to form behind a shock in the diffuse interstellar medium. The abundances calculated as the conditions evolve can then be utilized as reasonable initial conditions for a theory of the chemistry of dense cores. Methods. Hydrodynamic shock-wave simulations of the early stages of cold core formation are used to determine the time-dependent physical conditions for a gas-grain chemical network. We follow the cold post-shock molecular evolution of ices and gas-phase molecules as the visual extinction increases with time to AV ≈ 3. (Note that instead of an equal sign, the approximately equal sign should remain.) At higher extinction, self-gravity becomes important. Results. As the newly condensed gas enters its cool post-shock phase, a large amount of CO is produced in the gas. As the CO forms, water ice is produced on grains, while accretion of CO produces CO ice. The production of CO2 ice from CO occurs via several surface mechanisms, while the production of CH4 ice is slowed by gas-phase conversion of C into CO.
Environment-assisted Quantum-enhanced Sensing with Electronic Spins in Diamond

The performance of solid-state quantum sensors based on electronic spin defects is often limited by the presence of environmental spin impurities that cause decoherence. A promising approach to improve these quantum sensors is to convert environment spins into useful resources for sensing, in particular, entangled states. However, the sensitivity enhancement that can be achieved from entangled states is limited by experimental constraints, such as control errors, decoherence, and time overheads. Here we experimentally demonstrate the efficient use of an unknown electronic spin defect in the proximity of a nitrogen-vacancy center in diamond to achieve both an entangled quantum sensor and a quantum memory for readout. We show that, whereas entanglement alone does not provide an enhancement in sensitivity, combining both entanglement and repetitive readout achieves an enhancement in performance over the use of a single-spin sensor, and more broadly we discuss regimes where sensitivity could be enhanced. Our results critically highlight the challenges in improving quantum sensors using entangled states of electronic spins, while providing an important benchmark in the quest for entanglement-assisted metrology.
Consensus in the Presence of Multiple Opinion Leaders: Effect of Bounded Confidence

The problem of analyzing the performance of networked agents exchanging evidence in a dynamic network has recently grown in importance. This problem has relevance in signal and data fusion network applications and in studying opinion and consensus dynamics in social networks. Due to its capability of handling a wider variety of uncertainties and ambiguities associated with evidence, we use the framework of Dempster-Shafer (DS) theory to capture the opinion of an agent. We then examine the consensus among agents in dynamic networks in which an agent can utilize either a cautious or receptive updating strategy. In particular, we examine the case of bounded confidence updating where an agent exchanges its opinion only with neighboring nodes possessing “similar” evidence. In a fusion network, this captures the case in which nodes only update their state based on evidence consistent with the node's own evidence. In opinion dynamics, this captures the notions of Social Judgment Theory (SJT) in which agents update their opinions only with other agents possessing opinions closer to their own. Focusing on the two special DS theoretic cases where an agent state is modeled as a Dirichlet body of evidence and a probability mass function (p.m.f.), we utilize results from matrix theory, graph theory, and networks to prove the existence of consensus agent states in several time-varying network cases of interest. For example, we show the existence of a consensus in which a subset of network nodes achieves a consensus that is adopted by follower network nodes. Of particular interest is the case of multiple opinion leaders, where we show that the agents do not reach a consensus in general, but rather converge to “opinion clusters”. Simulation results are provided to illustrate the main results.
A pseudo knockoff filter for correlated features

In 2015, Barber and Candes introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and prove that this method achieves exact FDR control. Inspired by the work of Barber and Candes (2015), we propose and analyze a pseudo-knockoff filter that inherits some advantages of the original knockoff filter and has more flexibility in constructing its knockoff matrix. Moreover, we perform a number of numerical experiments that seem to suggest that the pseudo knockoff filter with the half Lasso statistic has FDR control and offers more power than the original knockoff filter with the Lasso Path or the half Lasso Statistic for the numerical examples that we consider in this paper. Although we cannot establish rigorous FDR control for the pseudo knockoff filter, we provide some partial analysis of the pseudo knockoff filter with the half Lasso statistic and establish a uniform FDP bound and an expectation inequality.
A Small Delay and Correlation Time Limit of Stochastic Differential Delay Equations with State-Dependent Colored Noise

We consider a general stochastic differential delay equation (SDDE) with state-dependent colored noises and derive its limit as the time delays and the correlation times of the noises go to zero. The work is motivated by an experiment involving an electrical circuit with noisy, delayed feedback. An Ornstein–Uhlenbeck process is used to model the colored noise. The main methods used in the proof are a theorem about convergence of solutions of stochastic differential equations by Kurtz and Protter and a maximal inequality for sums of a stationary sequence of random variables by Peligrad and Utev.
Simulating quantum magnets with symmetric top molecules

A correspondence is established between the electric dipole matrix elements of a polyatomic symmetric top molecule in a state with nonzero projection of the total angular momentum on the symmetry axis of the molecule and the magnetic dipole matrix elements of a magnetic dipole associated with an elemental spin F. It is shown that this correspondence makes it possible to perform quantum simulation of the single-particle spectrum and the dipole-dipole interactions of magnetic dipoles in a static external magnetic field B with symmetric top molecules subject to a static external electric field E DC . It is further shown that no such correspondence exists for 1Σ molecules in static fields, such as the alkali metal dimers. The effective spin angular momentum of the simulated magnetic dipole corresponds to the rotational angular momentum of the symmetric top molecule, and so quantum simulation of arbitrarily large integer spins is possible. Further, taking the molecule CH3F as an example, it is shown that the characteristic dipole-dipole interaction energies of the simulated magnetic dipole are a factor of 620, 600, and 310 larger than for the highly magnetic atoms Chromium, Erbium, and Dysprosium, respectively. Several applications of the correspondence for many-body physics are presented, including long-range and anisotropic spin models with arbitrary integer spin S using symmetric top molecules in optical lattices, quantum simulation of molecular magnets, and spontaneous demagnetization of Bose-Einstein condensates due to dipole-dipole interactions. These results are expected to be relevant as cold symmetric top molecules reach quantum degeneracy through Stark deceleration and opto-electrical cooling.
On the Planar Elastica , Stress, and Material Stress

We revisit the classical problem of the planar Euler elastica with applied forces and moments, and present a classification of the shapes in terms of tangentially conserved quantities associated with spatial and material symmetries. We compare commonly used director, variational, and dynamical systems representations, and present several illustrative physical examples. We remark that an approach that employs only the shape equation for the tangential angle obscures physical information about the tension in the body.