cssi/SciSciGPT-SciSciNet huggingface dataset:
On the classification of 5d SCFTs

We determine all 5d SCFTs upto rank three by studying RG flows of 5d KK theories. Our analysis reveals the existence of new rank one and rank two 5d SCFTs not captured by previous classifications. In addition to that, we provide for the first time a systematic and conjecturally complete classification of rank three 5d SCFTs. Our methods are based on a recently studied geometric description of 5d KK theories, thus demonstrating the utility of these geometric descriptions. It is straightforward, though computationally intensive, to extend this work and systematically classify 5d SCFTs of higher ranks (greater than or equal to four) by using the geometric description of 5d KK theories.
Direct Determination of Supermassive Black Hole Properties with Gravitational-Wave Radiation from Surrounding Stellar-Mass Black Hole Binaries

A significant number of stellar-mass black-hole (BH) binaries may merge in galactic nuclei or in the surrounding gas disks. With purposed space-borne gravitational-wave observatories, we may use such a binary as a signal carrier to probe modulations induced by a central supermassive BH (SMBH), which further allows us to place constraints on the SMBH's properties. We show in particular the de Sitter precession of the inner stellar-mass binary's orbital angular momentum (AM) around the AM of the outer orbit will be detectable if the precession period is comparable to the duration of observation, typically a few years. Once detected, the precession can be combined with the Doppler shift arising from the outer orbital motion to determine the mass of the SMBH and the outer orbital separation individually and each with percent-level accuracy. If we further assume a joint detection by space-borne and ground-based detectors, the detectability threshold could be extended to a precession period of $\ensuremath{\sim}100\text{ }\text{ }\mathrm{yr}$.
ADHD in Adolescents: Commentary on the Special Issue of Ripple Effects in Self-Perceptions and Social Relationships:

Adolescence is a developmental period that affords both risk as well as enormous opportunity. Ripple effects can extend far and wide and speak to the nuance and complexity in understanding and trea...
State-space optimal feedback control of optogenetically driven neural activity

Objective.The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse.Approach.Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedback, where a parameter-adaptive Kalman filter provided robustness to model-mismatch.Main results.This model-based control scheme proved effective for feedback control of single-neuron firing rate in the thalamus of awake animals. Notably, the graded optical actuation utilized here did not synchronize simultaneously recorded neurons, but heterogeneity across the neuronal population resulted in a varied response to stimulation. Simulated multi-output feedback control provided better control of a heterogeneous population and demonstrated how the approach generalizes beyond single-neuron applications.Significance.To our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches laid out and tested here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in the face of injury or disease.
Objective sensory testing methods reveal a higher prevalence of olfactory loss in COVID-19 positive patients compared to subjective methods: A systematic review and meta-analysis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has currently infected over 6.5 million people worldwide. In response to the pandemic, numerous studies have tried to identify the causes and symptoms of the disease. Emerging evidence supports recently acquired anosmia (complete loss of smell) and hyposmia (partial loss of smell) as symptoms of COVID-19, but studies of olfactory dysfunction show a wide range of prevalence from 5% to 98%. We undertook a search of Pubmed/Medline and Google Scholar with the keywords "COVID-19," "smell," and/or "olfaction." We included any study that quantified smell loss (anosmia and hyposmia) as a symptom of COVID-19. Studies were grouped and compared based on the type of method used to measure smell loss-subjective measures, such as self-reported smell loss, versus objective measures using rated stimuli-to determine if prevalence differed by method type. For each study, 95% confidence intervals (CIs) were calculated from point estimates of olfactory disturbances. We identified 34 articles quantifying anosmia as a symptom of COVID-19 (6 objective and 28 subjective), collected from cases identified from January 16 to April 30, 2020. The pooled prevalence estimate of smell loss was 77% when assessed through objective measurements (95% CI of 61.4-89.2%) and 44% with subjective measurements (95% CI of 32.2-57.0%). Objective measures are a more sensitive method to identify smell loss as a result of infection with SARS-CoV-2; the use of subjective measures, while expedient during the early stages of the pandemic, underestimates the true prevalence of smell loss.
Diffusion Synthetic Acceleration for Heterogeneous Domains, Compatible with Voids

A standard approach to solving the S N transport equations is to use source iteration with diffusion synthetic acceleration (DSA). Although this approach is widely used and effective on many prob...
Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs

The COVID-19 pandemic caused by the novel SARS-Coronavirus-2 (n-SARS-CoV-2) has impacted people's lives in unprecedented ways. During the time of the pandemic, social vloggers have used social media to actively share their opinions or experiences in quarantine. This paper collected videos from YouTube to track emotional responses in conversational vlogs and their potential associations with events related to the pandemic. In particular, vlogs uploaded from locations in New York City were analyzed given that this was one of the first epicenters of the pandemic in the United States. We observed some common patterns in vloggers' acoustic and linguistic features across the time span of the quarantine, which is indicative of changes in emotional reactivity. Additionally, we investigated fluctuations of acoustic and linguistic patterns in relation to COVID-19 events in the New York area (e.g. the number of daily new cases, number of deaths, and extension of stay-at-home order and state of emergency). Our results indicate that acoustic features, such as zero-crossing-rate, jitter, and shimmer, can be valuable for analyzing emotional reactivity in social media videos. Our findings further indicate that some of the peaks of the acoustic and linguistic indices align with COVID-19 events, such as the peak in the number of deaths and emergency declaration.
Neural Network-Based Reconstruction in Compressed Sensing MRI Without Fully-Sampled Training Data

Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-sampled MR images, offering the potential to reduce scan times. Classical techniques minimize a regularized least-squares cost function using an expensive iterative optimization procedure. Recently, deep learning models have been developed that model the iterative nature of classical techniques by unrolling iterations in a neural network. While exhibiting superior performance, these methods require large quantities of ground-truth images and have shown to be non-robust to unseen data. In this paper, we explore a novel strategy to train an unrolled reconstruction network in an unsupervised fashion by adopting a loss function widely-used in classical optimization schemes. We demonstrate that this strategy achieves lower loss and is computationally cheap compared to classical optimization solvers while also exhibiting superior robustness compared to supervised models. Code is available at https://github.com/alanqrwang/HQSNet.
Chemotaxis in uncertain environments: Hedging bets with multiple receptor types

This work shows that cells should express multiple types of receptors when they are sufficiently uncertain about their environment, and also shows how cells can efficiently integrate information from these different receptor types.
On the Self-propulsion of a Rigid Body in a Viscous Liquid by Time-Periodic Boundary Data

Consider a rigid body, $${\mathscr {B}}$$ , constrained to move by translational motion in an unbounded viscous liquid. The driving mechanism is a given distribution of time-periodic velocity field, $${\varvec{v}}_*$$ , at the interface body-liquid, of magnitude $$\delta $$ (in appropriate function class). The main objective is to find conditions on $${\varvec{v}}_*$$ ensuring that $${\mathscr {B}}$$ performs a non-zero net motion, namely, $${\mathscr {B}}$$ can cover any given distance in a finite time. The approach to the problem depends on whether the averaged value of $${\varvec{v}}_*$$ over a period of time is (case (b)) or is not (case (a)) identically zero. In case (a) we solve the problem in a relatively straightforward way, by showing that, for small $$\delta $$ , it reduces to the study of a suitable and well-investigated time-independent Stokes (linear) problem. In case (b), however, the question is much more complicated, because we show that it cannot be brought to the study of a linear problem. Therefore, in case (b), self-propulsion is a genuinely nonlinear issue that we solve directly on the nonlinear system by a contradiction argument. In this way, we are able to give, also in case (b), sufficient conditions for self-propulsion (for small $$\delta $$ ). Finally, we demonstrate, by means of counterexamples, that such conditions are, in general, also necessary.
Human visual motion perception shows hallmarks of Bayesian structural inference.

Motion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about how humans identify the structure underlying a scene’s motion in the first place. We studied the computations governing human motion structure identification in two psychophysics experiments and found that perception of motion relations showed hallmarks of Bayesian structural inference. At the heart of our research lies a tractable task design that enabled us to reveal the signatures of probabilistic reasoning about latent structure. We found that a choice model based on the task’s Bayesian ideal observer accurately matched many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence—especially, when motion scenes were ambiguous and when object motion was hierarchically nested within other moving reference frames. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception.
Sampling requirements and approaches to detect ecosystem shifts

Abstract Environmental monitoring is a key component of understanding and managing ecosystems. Given that most monitoring efforts are still expensive and time-consuming, it is essential that monitoring programs are designed to be efficient and effective. In many situations, the expensive part of monitoring is not sample collection, but instead sample processing, which leads to only a subset of the samples being processed. For example, sediment or ice cores can be quickly obtained in the field, but they require weeks or months of processing in a laboratory setting. Standard sub-sampling approaches often involve equally-spaced sampling on depth. We use simulations to show how many samples, and which types of sampling approaches, are the most effective in detecting ecosystem change. We test these ideas with a case study of Cladocera community assemblage indicators reconstructed from a sediment core. We demonstrate that standard approaches to sample processing are less efficient than an iterative approach. For our case study, using an optimal sampling approach would have resulted in savings of 195 person–hours—thousands of dollars in labor costs. We also show that, compared with these standard approaches, fewer samples are typically needed to achieve high statistical power. We explain how our approach can be applied to monitoring programs that rely on video records, eDNA, remote sensing, and other common tools that allow re-sampling.
Quantizing Derived Mapping Stacks

In this paper we discuss several topological and geometric invariants obtained by quantizing σ-models. More precisely, we do not quantize the entire mapping stack of fields, but rather only the sub...
Live Imaging of Intracranial Lymphatics in the Zebrafish.

Rationale: The recent discovery of meningeal lymphatics in mammals is reshaping our understanding of fluid homeostasis and cellular waste management in the brain, but visualization and experimental...
Investigation of Band-Offsets at Monolayer–Multilayer MoS2 Junctions by Scanning Photocurrent Microscopy

The thickness-dependent band structure of MoS2 implies that discontinuities in energy bands exist at the interface of monolayer (1L) and multilayer (ML) thin films. The characteristics of such heterojunctions are analyzed here using current versus voltage measurements, scanning photocurrent microscopy, and finite element simulations of charge carrier transport. Rectifying I–V curves are consistently observed between contacts on opposite sides of 1L/ML junctions, and a strong bias-dependent photocurrent is observed at the junction. Finite element device simulations with varying carrier concentrations and electron affinities show that a type II band alignment at single layer/multilayer junctions reproduces both the rectifying electrical characteristics and the photocurrent response under bias. However, the zero-bias junction photocurrent and its energy dependence are not explained by conventional photovoltaic and photothermoelectric mechanisms, indicating the contributions of hot carriers.
D-BRANES ON ASYMMETRIC ORBIFOLDS

Abstract We construct D-brane states on an asymmetric orbifold of type IIA on a four-torus, which is modded out by T -duality. We find explicit boundary states charged under the twisted sector gauge fields. Unlike other cases, the boundary states involve an explicit dependence on the twist fields. The D-brane spectrum is consistent with the model being equivalent to type IIA on a four-torus.
A non-commutative formula for the isotropic magneto-electric response

A non-commutative formula for the isotropic magneto-electric response of disordered insulators under magnetic fields is derived using the methods of non-commutative geometry. Our result follows from an explicit evaluation of the Ito derivative with respect to the magnetic field of the non-commutative formula for the electric polarization reported in Schulz-Baldes and Teufel (2012 arXiv:1201.4812v1). The quantization, topological invariance and connection to a second Chern number of the magneto-electric response are discussed in the context of three-dimensional, disordered, time-reversal or inversion symmetric topological insulators.
Analyzing and generating multimode optical fields using self-configuring networks

Working with finite numbers of modes to describe, generate, and detect optical fields can be both mathematically economical and physically useful. Such a modal basis can map directly to various applications in communications, sensing, and processing. But, we need a way to generate and analyze such fields, including measurement and control of both the relative amplitudes and phases of the modal components. Ideally such an analysis scheme would operate directly on the field, without needing a separate, mutually coherent reference beam. Here, we show first how to measure all those relative amplitudes and phases automatically and simultaneously. The method repurposes a self-configuring network of 2×2 blocks, such as integrated Mach–Zehnder interferometers, that can automatically align itself to the optical field by a sequence of simple one-parameter power minimizations when network elements, such as phase shifters, are adjusted. The optical field is then directly deduced from the resulting settings of those elements. We show how the entire network can be calibrated for such measurements, automatically and with just two light beams. Then, using the same calibration and running the mesh backwards, we can also controllably generate an arbitrary multimode field. Explicit algorithms and formulas are given for operating this system.
Towards a Combinatorial Characterization of Bounded-Memory Learning

Combinatorial dimensions play an important role in the theory of machine learning. For example, VC dimension characterizes PAC learning, SQ dimension characterizes weak learning with statistical queries, and Littlestone dimension characterizes online learning. In this paper we aim to develop combinatorial dimensions that characterize bounded memory learning. We propose a candidate solution for the case of realizable strong learning under a known distribution, based on the SQ dimension of neighboring distributions. We prove both upper and lower bounds for our candidate solution, that match in some regime of parameters. In this parameter regime there is an equivalence between bounded memory and SQ learning. We conjecture that our characterization holds in a much wider regime of parameters.
Existence of least-perimeter partitions

We prove the existence of a perimeter-minimizing partition of n-dimensional space, R n , into regions of unit volume.
Maximal power output of a stochastic thermodynamic engine

Abstract Classical thermodynamics aimed to quantify the efficiency of thermodynamic engines, by bounding the maximal amount of mechanical energy produced, compared to the amount of heat required. While this was accomplished early on, by Carnot and Clausius, the more practical problem to quantify limits of power that can be delivered, remained elusive due to the fact that quasistatic processes require infinitely slow cycling, resulting in a vanishing power output. Recent insights, drawn from stochastic models, appear to bridge the gap between theory and practice in that they lead to physically meaningful expressions for the dissipation cost in operating a thermodynamic engine over a finite time window. Indeed, the problem to optimize power can be expressed as a stochastic control problem. Building on this framework of stochastic thermodynamics we derive bounds on the maximal power that can be drawn by cycling an overdamped ensemble of particles via a time-varying potential while alternating contact with heat baths of different temperature ( T c cold, and T h hot). Specifically, assuming a suitable bound M on the spatial gradient of the controlling potential, we show that the maximal achievable power is bounded by M 8 ( T h T c − 1 ) . Moreover, we show that this bound can be reached to within a factor of ( T h T c − 1 ) ∕ ( T h T c + 1 ) by operating the cyclic thermodynamic process with a quadratic potential.
A DEEP SEARCH FOR FAINT GALAXIES ASSOCIATED WITH VERY LOW-REDSHIFT C iv ABSORBERS. II. PROGRAM DESIGN, ABSORPTION-LINE MEASUREMENTS, AND ABSORBER STATISTICS

To investigate the evolution of metal-enriched gas over recent cosmic epochs as well as to characterize the diffuse, ionized, metal-enriched circumgalactic medium (CGM), we have conducted a blind survey for C IV absorption systems in 89 QSO sightlines observed with the Hubble Space Telescope (HST) Cosmic Origins Spectrograph (COS). We have identified 42 absorbers at z < 0.16, comprising the largest uniform blind sample size to date in this redshift range. Our measurements indicate an increasing C IV absorber number density per comoving path length (dN/dX = 7.5 +/- 1.1) and modestly increasing mass density relative to the critical density of the Universe (Omega(C IV) = 10.0 +/- 1.5 x 10^-8 ) from z ~ 1.5 to the present epoch, consistent with predictions from cosmological hydrodynamical simulations. Furthermore, the data support a functional form for the column density distribution function that deviates from a single power-law, also consistent with independent theoretical predictions. As the data also probe heavy element ions in addition to C IV at the same redshifts, we identify, measure, and search for correlations between column densities of these species where components appear aligned in velocity. Among these ion-ion correlations, we find evidence for tight correlations between C II and Si II, C II and Si III, and C IV and Si IV, suggesting that these pairs of species arise in similar ionization conditions. However, the evidence for correlations decreases as the difference in ionization potential increases. Finally, when controlling for observational bias, we find only marginal evidence for a correlation (86.8% likelihood) between the Doppler line width b(C IV) and column density N(C IV).
AutoAIViz: opening the blackbox of automated artificial intelligence with conditional parallel coordinates

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present only limited to no information about the process of how they select and generate model results. Thus, users often do not understand the process, neither do they trust the outputs. In this short paper, we provide a first user evaluation by 10 data scientists of an experimental system, AutoAIViz, that aims to visualize AutoAI's model generation process. We find that the proposed system helps users to complete the data science tasks, and increases their understanding, toward the goal of increasing trust in the AutoAI system.
Towards optimal extraction of cosmological information from nonlinear data

One of the main unsolved problems of cosmology is how to maximize the extraction of information from nonlinear data. If the data are nonlinear the usual approach is to employ a sequence of statistics (N-point statistics, counting statistics of clusters, density peaks or voids etc.), along with the corresponding covariance matrices. However, this approach is computationally prohibitive and has not been shown to be exhaustive in terms of information content. Here we instead develop a hierarchical Bayesian approach, expanding the likelihood around the maximum posterior of linear modes, which we solve for using optimization methods. By integrating out the modes using perturbative expansion of the likelihood we construct an initial power spectrum estimator, which for a fixed forward model contains all the cosmological information if the initial modes are gaussian distributed. We develop a method to construct the window and covariance matrix such that the estimator is explicitly unbiased and nearly optimal. We then generalize the method to include the forward model parameters, including cosmological and nuisance parameters, and primordial non-gaussianity. We apply the method in the simplified context of nonlinear structure formation, using either simplified 2-LPT dynamics or N-body simulations as the nonlinear mapping between linear and nonlinear density, and 2-LPT dynamics in the optimization steps used to reconstruct the initial density modes. We demonstrate that the method gives an unbiased estimator of the initial power spectrum, providing among other a near optimal reconstruction of linear baryonic acoustic oscillations.
Post-big bang processing of the primordial elements

We explore the Gnedin-Ostriker suggestion that a post-big bang photodissociation process may modify the primordial abundances of the light elements. We consider several specific models and discuss the general features that are necessary (but not necessarily sufficient) to make the model work. We find that with any significant processing, the final D and {sup 3}He abundances, which are independent of their initial standard big bang nucleosynthesis (SBBN) values, rise quickly to a level several orders of magnitude above the observationally inferred primordial values. Solutions for specific models show that the only initial abundances that can be photoprocessed into agreement with observations are those that undergo virtually no processing and are already in agreement with observation. Thus it is unlikely that this model can work for any nontrivial case unless an artificial density and/or photon distribution is invoked. {copyright} {ital 1996 The American Astronomical Society.}
Krall-Jacobi commutative algebras of partial differential operators

Abstract We construct a large family of commutative algebras of partial differential operators invariant under rotations. These algebras are isomorphic extensions of the algebras of ordinary differential operators introduced by Grunbaum and Yakimov corresponding to Darboux transformations at one end of the spectrum of the recurrence operator for the Jacobi polynomials. The construction is based on a new proof of their results which leads to a more detailed description of the one-dimensional theory. In particular, our approach establishes a conjecture by Haine concerning the explicit characterization of the Krall–Jacobi algebras of ordinary differential operators.
Multiparticle dynamics on the triangular lattice in interacting media

We study the motion of N particles moving on a two-dimensional triangular lattice, whose sites are occupied by either left or right rotators. These rotators deterministically scatter the particles to the left (right), changing orientation from left to right (right–left) after scattering a particle. This interplay between the scatterers and the particle's motion causes a single particle to propagate in one direction away from its initial position irrespective of the system's configuration of scatterers, i.e. the state of the medium through which the particle moves. For multiple particles we show that the particles' dynamics can be vastly different. Specifically, we show that a particle can become entangled with another particle potentially causing the particle's trajectory to become periodic and that this can happen when the particles have the same or differing speeds. We describe two classes of periodic orbits based on the particles' initial velocities. We also describe how a particle with an unbounded past trajectory implies that some, possibly other, particle(s) has an unbounded future trajectory in this and other related multiparticle models.
ROME (Radio Observations of Magnetized Exoplanets). II. HD 189733 Does Not Accrete Significant Material from its Exoplanet like a T Tauri Star from A Disk

It has been asserted that the primary star in the HD 189733 system steadily accretes evaporated exospheric gases from its ``hot Jupiter'' companion, rather like a T Tauri star accreting from a disk. We conduct statistical and periodogram analyses of the photometric time series of the primary, as acquired by the automated photoelectric telescope (APT), Microvariability and Oscillations of Stars (MOST), and Wise Observatory, to investigate this claim with the goal of revealing the presence of accretion shocks or photospheric accretion hotspots as are found in T Tauri systems such as AA Tau. None of the anticipated features were found. We re-analyze existing radio, optical, ultraviolet, and X-ray data within the framework of accreting T Tauri systems to determine physical quantities such as plasma density and temperature, accretion rate, and flare lengths. We find that with an accretion rate of $\dot{M}\sim10^{9}$ to 10$^{11}$ g s$^{-1}$, the star is more similar to a system that intermittently absorbs gas from sungrazing comets in outburst than classical T Tauri systems, which have accretion rates at least two orders of magnitude larger. If such accretion exists, it would result in undetectably low activity at all wavelengths. Alternatively, all of the emission properties observed thus far are in agreement with stellar activity from a magnetically active star.
Models for Discrete Quantum Gravity

We first discuss a framework for discrete quantum processes (DQP). It is shown that the set of q -probability operators is convex and its set of extreme elements is found. The property of consistency for a DQP is studied and the quadratic algebra of suitable sets is introduced. A classical sequential growth process is “quantized” to obtain a model for discrete quantum gravity called a quantum sequential growth process (QSGP). Two methods for constructing concrete examples of QSGP are provided.
Improved spectral imaging microscopy for cultural heritage through oblique illumination

This work presents the development of a flexible microscopic chemical imaging platform for cultural heritage that utilizes wavelength-tunable oblique illumination from a point source to obtain per-pixel reflectance spectra in the VIS–NIR range. The microscope light source can be adjusted on two axes allowing for a hemisphere of possible illumination directions. The synthesis of multiple illumination angles allows for the calculation of surface normal vectors, similar to phase gradients, and axial optical sectioning. The extraction of spectral reflectance images with high spatial resolutions from these data is demonstrated through the analysis of a replica cross-section, created from known painting reference materials, as well as a sample extracted from a painting by Pablo Picasso entitled La Misereuse accroupie (1902). These case studies show the rich microscale molecular information that may be obtained using this microscope and how the instrument overcomes challenges for spectral analysis commonly encountered on works of art with complex matrices composed of both inorganic minerals and organic lakes.
Electron correlation via frozen Gaussian dynamics

We investigate the accuracy and efficiency of the semiclassical frozen Gaussian method in describing electron dynamics in real time. Model systems of two soft-Coulomb-interacting electrons are used to study correlated dynamics under non-perturbative electric fields, as well as the excitation spectrum. The results show that a recently proposed method that combines exact-exchange with semiclassical correlation to propagate the one-body density-matrix holds promise for electron dynamics in many situations that either wavefunction or density-functional methods have difficulty describing. The results also however point out challenges in such a method that need to be addressed before it can become widely applicable.
Rational Period Functions on the Hecke Groups

We describe rational period functions on the Hecke groups and characterize the ones whose poles satisfy a certain symmetry. This generalizes part of the characterization of rational period functions on the modular group, which is one of the Hecke groups.
Noncommutative knot theory.

The classical abelian invariants of a knot are the Alexander module, which is the first homology group of the the unique infinite cyclic covering space of S 3 - K, considered as a module over the (commutative) Laurent polynomial ring, and the Blanchfield linking pairing defined on this module. From the perspective of the knot group, G, these invariants reflect the structure of G ( 1 ) /G ( 2 ) as a module over G/G ( 1 ) (here G ( n ) is the n t h term of the derived series of G). Hence any phenomenon associated to G ( 2 ) is invisible to abelian invariants. This paper begins the systematic study of invariants associated to solvable covering spaces of knot exteriors, in particular the study of what we call the n t h higher-order Alexander module, G ( + 1 ) /G ( n + 2 ) , considered as a Z[G/G ( n + 1 ) ]-module. We show that these modules share almost all of the properties of the classical Alexander module. They are torsion modules with higher-order Alexander polynomials whose degrees give lower bounds for the knot genus. The modules have presentation matrices derived either from a group presentation or from a Seifert surface. They admit higher-order linking forms exhibiting self-duality. There are applications to estimating knot genus and to detecting fibered, prime and alternating knots. There are also surprising applications to detecting symplectic structures on 4-manifolds. These modules are similar to but different from those considered by the author, Kent Orr and Peter Teichner and are special cases of the modules considered subsequently by Shelly Harvey for arbitrary 3-manifolds.
CIRCULATION AND DISSIPATION ON HOT JUPITERS

Many global circulation models predict supersonic zonal winds and large vertical shears in the atmospheres of short-period Jovian exoplanets. Using linear analysis and nonlinear local simulations, we investigate hydrodynamic dissipation mechanisms to balance the thermal acceleration of these winds. The adiabatic Richardson criterion remains a good guide to linear stability, although thermal diffusion allows some modes to violate it at very long wavelengths and very low growth rates. Nonlinearly, wind speeds saturate at Mach numbers {approx}2 and Richardson numbers {approx}<1/4 for a broad range of plausible diffusivities and forcing strengths. Turbulence and vertical mixing, though accompanied by weak shocks, dominate the dissipation, which appears to be the outcome of a recurrent Kelvin-Helmholtz instability. An explicit shear viscosity, as well as thermal diffusivity, is added to ZEUS to capture dissipation outside of shocks. The wind speed is neither monotonic nor single valued for a range of shear viscosities larger than about 10{sup -3} of the sound speed times the pressure scale height. Coarsening the numerical resolution can also increase the speed. Hence global simulations that are incapable of representing vertical turbulence and shocks, either because of reduced physics or because of limited resolution, may overestimate wind speeds. We recommend that suchmore » simulations include artificial dissipation terms to control the Mach and Richardson numbers and to capture mechanical dissipation as heat.« less
On whether zero is in the global attractor of the 2D Navier–Stokes equations

The set of nonzero external forces for which the zero function is in the global attractor of the two-dimensional Navier–Stokes equations is shown to be meagre in a Frechet topology. A criterion in terms of a Taylor expansion in complex time is used to characterize the forces in this set. This leads to several relations between certain Gevrey subclasses of C∞ and a new upper bound for a Gevrey norm of solutions in the attractor, valid in the strip of analyticity in time.
THE EFFECT OF EXTREME CONFINEMENT ON THE NONLINEAR-OPTICAL RESPONSE OF QUANTUM WIRES

This work focuses on understanding the nonlinear-optical response of a 1-D quantum wire embedded in 2-D space when quantum-size effects in the transverse direction are minimized using an extremely weighted delta function potential. Our aim is to establish the fundamental basis for understanding the effect of geometry on the nonlinear-optical response of quantum loops that are formed into a network of quantum wires. It is shown that in the limit of full confinement, the sum rules are obeyed when the transverse infinite-energy continuum states are included. While the continuum states associated with the transverse wavefunction do not contribute to the nonlinear optical response, they are essential to preserving the validity of the sum rules. This work is a building block for future studies of nonlinear-optical enhancement of quantum graphs (which include loops and bent wires) based on their geometry. These properties are important in quantum mechanical modeling of any response function of quantum-confined systems, including the nonlinear-optical response of any system in which there is confinement in at least one dimension, such as nanowires, which provide confinement in two dimensions.
A New Approach to Developing Interactive Software Modules Through Graduate Education

Educational technology has attained significant importance as a mechanism for supporting experiential learning of science concepts. However, the growth of this mechanism is limited by the significant time and technical expertise needed to develop such products, particularly in specialized fields of science. We sought to test whether interactive, educational, online software modules can be developed effectively by students as a curriculum component of an advanced science course. We discuss a set of 15 such modules developed by Harvard University graduate students to demonstrate various concepts related to astronomy and physics. Their successful development of these modules demonstrates that online software tools for education and outreach on specialized topics can be produced while simultaneously fulfilling project-based learning objectives. We describe a set of technologies suitable for module development and present in detail four examples of modules developed by the students. We offer recommendations for incorporating educational software development within a graduate curriculum and conclude by discussing the relevance of this novel approach to new online learning environments like edX.
THE COSMIC ABUNDANCE OF CLASSICAL MILKY WAY SATELLITES

We study the abundance of satellites akin to the brightest, classical dwarf spheroidals around galaxies similar in magnitude and isolation to the Milky Way and M31 in the Sloan Digital Sky Survey. From a combination of photometric and spectroscopic redshifts, we bound the mean and the intrinsic scatter in the number of satellites down to 10 mag fainter than the Milky Way. Restricting to magnitudes brighter than Sagittarius, we show that the Milky Way is not a significant statistical outlier in its population of classical dwarf spheroidals. At fainter magnitudes, we find an upper limit of 13 on the mean number of satellites brighter than the Fornax dwarf spheroidal. Methods to improve these limits that utilize full photometric redshift distributions hold promise, but are currently limited by incompleteness at the very lowest redshifts. Theoretical models are left to explain why the majority of dark matter subhalos that orbit Milky-Way-like galaxies are inefficient at making galaxies at the luminosity scale of the brightest dwarf spheroidals, or why these subhalos predicted by {Lambda}CDM do not exist.
The Splitting of Branes on Orientifold Planes

Continuing the study in hep-th/0004092, we investigate a non-trivial string dynamical process related to orientifold planes, i.e., the splitting of physical NS-branes and D(p+2)-branes on orientifold Op-planes. Creation or annihilation of physical Dp-branes usually accompanies the splitting process. In the particular case p = 4, we use Seiberg-Witten curves as an independent method to check the results.
APOGEE Net: Improving the Derived Spectral Parameters for Young Stars through Deep Learning

Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for main sequence or evolved stars, where reliable synthetic spectra provide labels and data for training. Spectral models of young stellar objects (YSOs) and low mass main sequence (MS) stars are less well-matched to their empirical counterparts, however, posing barriers to previous approaches to classify spectra of such stars. In this work we generate labels for YSOs and low mass MS stars through their photometry. We then use these labels to train a deep convolutional neural network to predict log g, Teff, and Fe/H for stars with APOGEE spectra in the DR14 dataset. This "APOGEE Net" has produced reliable predictions of log g for YSOs, with uncertainties of within 0.1 dex and a good agreement with the structure indicated by pre-main sequence evolutionary tracks, and correlate well with independently derived stellar radii. These values will be useful for studying pre-main sequence stellar populations to accurately diagnose membership and ages.
Slow reflection and two-photon generation of microcavity exciton─polaritons

When photons in an optical cavity couple to a sharp electronic resonance, new states appear that are mixed states of photons and excitons, known as polaritons. The polaritons can be viewed as photons with an effective mass and with repulsive interactions. In the studies reported here, we have resonantly injected polaritons into a cavity and tracked them in time and space over millimeter distances as they experience a force due to a gradient of cavity width. Their motion is well described by the equations of motion for a moving mass under a constant force, that is, a parabolic trajectory. This can be called “slow reflection,” as the photons gradually decelerate, turn around, and go back the other direction. From these measurements we can accurately measure the lifetime of the polaritons in our samples to be 180±10 ps, over 1 order of magnitude longer than previous works. This corresponds to a cavity leakage time of 135 ps and a cavity Q of 320,000. Additionally, we have shown the same effect with polaritons generated by direct two-photon excitation of the polariton states, allowing the possibility of modulation of two-photon absorption by a polariton condensate.
Partial Field Opening and Current Sheet Formation in the Disk Magnetosphere

In this paper I analyze the process of formation of thin current structures in the magnetosphere of a conducting accretion disk in response to the field-line twisting brought about by the rotation of the disk relative to the central star. I consider an axisymmetric force-free magnetically linked star-disk configuration and investigate the expansion of the poloidal field lines and partial field-line opening caused by the differential rotation between the star and a nonuniformly rotating disk. I present a simple analytical model that describes the asymptotic behavior of the field in the strong-expansion limit. I demonstrate the existence of a finite (of order 1 rad) critical twist angle, beyond which the poloidal field starts inflating very rapidly. If the relative star-disk twist is enhanced locally, in some finite part of the disk (which may be the case for a Keplerian disk that extends inward significantly closer to the central star than the corotation radius), then, as the twist is increased by a finite amount, the field approaches a partially open configuration, with some field lines going out to infinity. Simultaneous with this partial field opening, a very thin, radially extended current layer forms, thus laying out a way toward reconnection in the disk magnetosphere. Reconnection, in turn, leads to a very interesting scenario for a quasi-periodic behavior of magnetically linked star-disk systems with successive cycles of field inflation, opening, and reconnection.
Material design of indium-based compounds: Possible candidates for charge, valence, and bond disproportionation and superconductivity

We design and investigate the physical properties of new indium compounds $A\mathrm{In}{X}_{3}$ ($A$ = alkali metals, $X$ = F or Cl). We find nine new In-based materials in their ground state that are thermodynamically stable but are not reported in ICSD (Inorganic Crystal Structure Database). We also discuss several metastable structures. This new series of materials display multiple valences, charge and bond disproportionation, and dimerization. The most common valence of In is 3+. We also find two rare alternatives, one has ${\mathrm{In}}^{2+}$ with In-In dimerization and the other shows valence disproportionation to ${\mathrm{In}}^{1+}$ and ${\mathrm{In}}^{3+}$ with bond disproportionation. We study the possibility of superconductivity in these new In compounds and find that ${\mathrm{CsInF}}_{3}$ has a transition temperature about 24 K with sufficient hole doping and pressure.
An Argument in Favor of Strong Scaling for Deep Neural Networks with Small Datasets

In recent years, with the popularization of deep learning frameworks and large datasets, researchers have started parallelizing their models in order to train faster. This is crucially important, because they typically explore many hyperparameters in order to find the best ones for their applications. This process is time consuming and, consequently, speeding up training improves productivity. One approach to parallelize deep learning models followed by many researchers is based on weak scaling. The minibatches increase in size as new GPUs are added to the system. In addition, new learning rates schedules have been proposed to fix optimization issues that occur with large minibatch sizes. In this paper, however, we show that the recommendations provided by recent work do not apply to models that lack large datasets. In fact, we argument in favor of using strong scaling for achieving reliable performance in such cases. We evaluated our approach with up to 32 GPUs and show that weak scaling not only does not have the same accuracy as the sequential model, it also fails to converge most of time. Meanwhile, strong scaling has good scalability while having exactly the same accuracy of a sequential implementation.
DETECTION OF FAST-MOVING WAVES PROPAGATING OUTWARD ALONG SUNSPOTS’ RADIAL DIRECTION IN THE PHOTOSPHERE

Helioseismic and magnetohydrodynamic waves are abundant in and above sunspots. Through cross-correlating oscillation signals in the photosphere observed by the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, we reconstruct how waves propagate away from virtual wave sources located inside a sunspot. In addition to the usual helioseismic wave, a fast-moving wave is detected traveling along the sunspot’s radial direction from the umbra to about 15 Mm beyond the sunspot boundary. The wave has a frequency range of 2.5–4.0 mHz with a phase velocity of 45.3 km s{sup −1}, substantially faster than the typical speeds of Alfven and magnetoacoustic waves in the photosphere. The observed phenomenon is consistent with a scenario of that a magnetoacoustic wave is excited at approximately 5 Mm beneath the sunspot. Its wavefront travels to and sweeps across the photosphere with a speed higher than the local magnetoacoustic speed. The fast-moving wave, if truly excited beneath the sunspot’s surface, will help open a new window for studying the internal structure and dynamics of sunspots.
Comparison of transport map generated by heat flow interpolation and the optimal transport Brenier map

This paper shows that the non-expansive transport map constructed by Y.-H. Kim and E. Milman using heat flow interpolation is in general different from the optimal transport Brenier map.