cssi/SciSciGPT-SciSciNet huggingface dataset:
Investigation of Phase Distortion on Perceived Speech Quality for Hearing-Impaired Listeners.

Phase serves as a critical component of speech that influences the quality and intelligibility. Current speech enhancement algorithms are beginning to address phase distortions, but the algorithms focus on normal-hearing (NH) listeners. It is not clear whether phase enhancement is beneficial for hearing-impaired (HI) listeners. We investigated the influence of phase distortion on speech quality through a listening study, in which NH and HI listeners provided speech-quality ratings using the MUSHRA procedure. In one set of conditions, the speech was mixed with babble noise at 4 different signal-to-noise ratios (SNRs) from -5 to 10 dB. In another set of conditions, the SNR was fixed at 10 dB and the noisy speech was presented in a simulated reverberant room with T60s ranging from 100 to 1000 ms. The speech level was kept at 65 dB SPL for NH listeners and amplification was applied for HI listeners to ensure audibility. Ideal ratio masking (IRM) was used to simulate speech enhancement. Two objective metrics (i.e., PESQ and HASQI) were utilized to compare subjective and objective ratings. Results indicate that phase distortion has a negative impact on perceived quality for both groups and PESQ is more closely correlated with human ratings.
Homological stability for configuration spaces of manifolds

Let C n (M) be the configuration space of n distinct ordered points in M. We prove that if M is any connected orientable manifold (closed or open), the homology groups H i (C n (M);ℚ) are representation stable in the sense of Church and Farb ( arXiv:1008.1368 ). Applying this to the trivial representation, we obtain as a corollary that the unordered configuration space B n (M) satisfies classical homological stability: for each i, H i (B n (M);ℚ)≈H i (B n+1(M);ℚ) for n>i. This improves on results of McDuff, Segal, and others for open manifolds. Applied to closed manifolds, this provides natural examples where rational homological stability holds even though integral homological stability fails. To prove the main theorem, we introduce the notion of monotonicity for a sequence of S n -representations, which is of independent interest. Monotonicity provides a new mechanism for proving representation stability using spectral sequences. The key technical point in the main theorem is that certain sequences of induced representations are monotone.
Solving All-Pairs Shortest-Paths Problem in Large Graphs Using Apache Spark

Algorithms for computing All-Pairs Shortest-Paths (APSP) are critical building blocks underlying many practical applications. The standard sequential algorithms, such as Floyd-Warshall and Johnson, quickly become infeasible for large input graphs, necessitating parallel approaches. In this work, we propose, implement and thoroughly analyse different strategies for APSP on distributed memory clusters with Apache Spark. Our solvers are designed for large undirected weighted graphs, and differ in complexity and degree of reliance on techniques outside of pure Spark API. We demonstrate that the best performing solver is able to handle APSP problems with over 200,000 vertices on a 1024-core cluster. However, it requires auxiliary shared persistent storage to compensate for missing Spark functionality.
Twisted duality for embedded graphs

We consider two operations on an edge of an embedded graph (or equivalently a ribbon graph): giving a half-twist to the edge and taking the partial dual with respect to the edge. These two operations give rise to an action of S3 jE(G)j , the ribbon group, on G. The action of the ribbon group on embedded graphs extends the concepts of duality, partial duality and Petrie duality. We show that this ribbon group action gives a complete characterization of duality in that if G is any cellularly embedded graph with medial graph Gm, then the orbit of G under the group action is precisely the set of all graphs with medial graphs isomorphic (as abstract graphs) to Gm. We provide characterizations of special sets of twisted duals, such as the partial duals, of embedded graphs in terms of medial graphs and we show how dierent kinds of graph isomorphism give rise to these various notions of duality. The ribbon group action then leads to a deeper understanding of the properties of, and relationships among, various graph polynomials via the generalized transition polynomial which interacts naturally with the ribbon group action. ribbon group action on the set of embedded graphs by taking duals and twisting individual edges. The ribbon group action completes the classical concept of duality for embedded graphs, and we exploit this group action to unravel and explore connections between embedded graphs and their medial graphs, and to determine implications of these new relations. The ribbon group action allows us to: classify the twisted duals of an embedded graph; characterize all graphs with isomorphic medial graphs under various kinds of isomorphism; extend the concept of a Tait graph to all 4-regular graphs; and to determine new properties of polynomials of embedded graphs. There are many investigations into planar and abstract graphs that have not yet been fully extended to the context of graphs embedded in surfaces. Consider for example the classical relations among the medial graphs and the duals of plane graphs. Suppose that G is a plane graph, G is its dual, and Gm is its medial graph. The medial graph of G is exactly the medial graph of G, i.e. (G )m = Gm, where \=" denotes equality as plane graphs. In fact, the connection between geometric duals and medial graphs is a little stronger than this. The two graphs G and G are the only plane graphs that have Gm as their plane medial graphs, that is,
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this letter, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model - a deep, multimodal convolutional network - predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at 1) estimating grasp adjustment outcomes, 2) selecting efficient grasp adjustments for quick grasping, and 3) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.
Surface Gravities for 228 M, L, and T Dwarfs in the NIRSPEC Brown Dwarf Spectroscopic Survey

We combine 131 new medium-resolution (R ~ 2000) J-band spectra of M, L, and T dwarfs from the Keck NIRSPEC Brown Dwarf Spectroscopic Survey (BDSS) with 97 previously published BDSS spectra to study surface-gravity-sensitive indices for 228 low-mass stars and brown dwarfs spanning spectral types M5–T9. Specifically, we use an established set of spectral indices to determine surface gravity classifications for all of the M6–L7 objects in our sample by measuring the equivalent widths (EW) of the K I lines at 1.1692, 1.1778, and 1.2529 μm, and the 1.2 μm FeH_J absorption index. Our results are consistent with previous surface gravity measurements, showing a distinct double peak—at ~L5 and T5—in K I EW as a function of spectral type. We analyze the K I EWs of 73 objects of known ages and find a linear trend between log(Age) and EW. From this relationship, we assign age ranges to the very low gravity, intermediate gravity, and field gravity designations for spectral types M6–L0. Interestingly, the ages probed by these designations remain broad, change with spectral type, and depend on the gravity-sensitive index used. Gravity designations are useful indicators of the possibility of youth, but current data sets cannot be used to provide a precise age estimate.
E-commerce Recommendation with Weighted Expected Utility

Different from shopping at retail stores, consumers on e-commerce platforms usually cannot touch or try products before purchasing, which means that they have to make decisions when they are uncertain about the outcome (e.g., satisfaction level) of purchasing a product. To study people's preferences with regard to choices that have uncertain outcomes, economics researchers have proposed the hypothesis of Expected Utility (EU) that models the subject value associated with an individual's choice as the statistical expectations of that individual's valuations of the outcomes of this choice. Despite its success in studies of game theory and decision theory, the effectiveness of EU, however, is mostly unknown in e-commerce recommendation systems. Previous research on e-commerce recommendation interprets the utility of purchase decisions either as a function of the consumed quantity of the product or as the gain of sellers/buyers in the monetary sense. As most consumers just purchase one unit of a product at a time and most alternatives have similar prices, such modeling of purchase utility is likely to be inaccurate in practice. In this paper, we interpret purchase utility as the satisfaction level a consumer gets from a product and propose a recommendation framework using EU to model consumers' behavioral patterns. We assume that consumer estimates the expected utilities of all the alternatives and choose products with maximum expected utility for each purchase. To deal with the potential psychological biases of each consumer, we introduce the usage of Probability Weight Function (PWF) and design our algorithm based on Weighted Expected Utility (WEU). Empirical study on real-world e-commerce datasets shows that our proposed ranking-based recommendation framework achieves statistically significant improvement against both classical Collaborative Filtering/Latent Factor Models and state-of-the-art deep models in top-K recommendation.
SEMIPARAMETRIC TIME TO EVENT MODELS IN THE PRESENCE OF ERROR-PRONE, SELF-REPORTED OUTCOMES-WITH APPLICATION TO THE WOMEN'S HEALTH INITIATIVE.

The onset of several silent, chronic diseases such as diabetes can be detected only through diagnostic tests. Due to cost considerations, self-reported outcomes are routinely collected in lieu of expensive diagnostic tests in large-scale prospective investigations such as the Women’s Health Initiative. However, self-reported outcomes are subject to imperfect sensitivity and specificity. Using a semiparametric likelihood-based approach, we present time to event models to estimate the association of one or more covariates with a error-prone, self-reported outcome. We present simulation studies to assess the effect of error in self-reported outcomes with regard to bias in the estimation of the regression parameter of interest. We apply the proposed methods to prospective data from 152,830 women enrolled in the Women’s Health Initiative to evaluate the effect of statin use with the risk of incident diabetes mellitus among postmenopausal women. The current analysis is based on follow-up through 2010, with a median duration of follow-up of 12.1 years. The methods proposed in this paper are readily implemented using our freely available R software package icensmis, which is available at the Comprehensive R Archive Network (CRAN) website.
Multitask learning of vegetation biochemistry from hyperspectral data

Statistical models have been successful in accurately estimating the biochemical contents of vegetation from the reflectance spectra. However, their performance deteriorates when there is a scarcity of sizable amount of ground truth data for modeling the complex nonlinear relationship occurring between the spectrum and the biochemical quantity. We propose a novel Gaussian process based multitask learning method for improving the prediction of a biochemical through the transfer of knowledge from the learned models for predicting related biochemicals. This method is most advantageous when there are few ground truth data for the biochemical of interest, but plenty of ground truth data for related biochemicals. The proposed multitask Gaussian process hypothesizes that the inter-relationship between the biochemical quantities is better modeled by using a combination of two or more covariance functions and inter-task correlation matrices. In the experiments, our method outperformed the current methods on two real-world datasets.
Sobolev Mappings: Lipschitz Density is not an Isometric Invariant of the Target

If M is a compact smooth manifold and X is a compact metric space, then the Sobolev space W 1,p (M,X) is defined through an isometric embedding of X into a Banach space. We prove that the answer to the question whether Lipschitz mappings Lip (M,X) are dense in W 1,p (M,X) may depend on the isometric embedding of the target.
Bounding the Costs of Quantum Simulation of Many-Body Physics in Real Space

We present a quantum algorithm for simulating the dynamics of a first-quantized Hamiltonian in real space based on the truncated Taylor series algorithm. We avoid the possibility of singularities by applying various cutoffs to the system and using a high-order finite difference approximation to the kinetic energy operator. We find that our algorithm can simulate $\eta$ interacting particles using a number of calculations of the pairwise interactions that scales, for a fixed spatial grid spacing, as $\tilde{O}(\eta^2)$, versus the $\tilde{O}(\eta^5)$ time required by previous methods (assuming the number of orbitals is proportional to $\eta$), and scales super-polynomially better with the error tolerance than algorithms based on the Lie-Trotter-Suzuki product formula. Finally, we analyze discretization errors that arise from the spatial grid and show that under some circumstances these errors can remove the exponential speedups typically afforded by quantum simulation.
Different Power-Law Indices in the Frequency Distributions of Flares with and without Coronal Mass Ejections

We investigated the frequency distributions of flares with and without coronal mass ejections (CMEs) as a function of flare parameters (peak flux, fluence, and duration of soft X-ray flares). We used CMEs observed by the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO) mission and soft X-ray flares (C3.2 and above) observed by the Geostationary Operational Environmental Satellite (GOES) during 1996-2005. We found that the distributions obey a power law of the form dN/dX ∝ X-α, where X is a flare parameter and dN is the number of events recorded within the interval [X, X + dX]. For the flares with (without) CMEs, we obtained the power-law index α = 1.98 ± 0.05 (α = 2.52 ± 0.03) for the peak flux, α = 1.79 ± 0.05 (α = 2.47 ± 0.11) for the fluence, and α = 2.49 ± 0.11 (α = 3.22 ± 0.15) for the duration. The power-law indices for flares without CMEs are steeper than those for flares with CMEs. The larger power-law index for flares without CMEs supports the possibility that nanoflares contribute to coronal heating.
Statistics of an Unstable Barotropic Jet from a Cumulant Expansion

Low-order equal-time statistics of a barotropic flow on a rotating sphere are investigated. The flow is driven by linear relaxation toward an unstable zonal jet. For relatively short relaxation times, the flow is dominated by critical-layer waves. For sufficiently long relaxation times, the flow is turbulent. Statistics obtained from a second-order cumulant expansion are compared to those accumulated in direct numerical simulations, revealing the strengths and limitations of the expansion for different relaxation times.
DAVIES CRITICAL POINT AND TUNNELING

From the point of view of tunneling, the physical meaning of the Davies critical point of a second-order phase transition in the black hole thermodynamics is clarified. At the critical point, the nonthermal contribution vanishes so that the black hole radiation is entirely thermal. It separates two phases: one with radiation enhanced by the nonthermal contribution, the other suppressed by the nonthermal contribution. We show this in both charged and rotating black holes. The phase transition is also analyzed in the cases in which emissions of charges and angular momenta are incorporated.
Discovering Potential Correlations via Hypercontractivity

Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest. While existing correlation measures are suitable for discovering average correlation, they fail to discover hidden or potential correlations. To bridge this gap, (i) we postulate a set of natural axioms that we expect a measure of potential correlation to satisfy; (ii) we show that the rate of information bottleneck, i.e., the hypercontractivity coefficient, satisfies all the proposed axioms; (iii) we provide a novel estimator to estimate the hypercontractivity coefficient from samples; and (iv) we provide numerical experiments demonstrating that this proposed estimator discovers potential correlations among various indicators of WHO datasets, is robust in discovering gene interactions from gene expression time series data, and is statistically more powerful than the estimators for other correlation measures in binary hypothesis testing of canonical examples of potential correlations.
Modeling Photoionized Turbulent Material in the Circumgalactic Medium

The circumgalactic medium (CGM) of nearby star-forming galaxies shows clear indications of O vi absorption accompanied by little to no N v absorption. This unusual spectral signature, accompanied by absorption from lower ionization state species whose columns vary by orders of magnitude along different sight lines, indicates that the CGM must be viewed as a dynamic, multiphase medium, such as occurs in the presence of turbulence. To explore this possibility, we carry out a series of chemodynamical simulations of a isotropic turbulent media, using the MAIHEM package. The simulations assume a metallicity of 0.3 Z ⊙ and a redshift zero metagalatic UV background, and they track ionizations, recombinations, and species-by-species radiative cooling for a wide range of elements. We find that turbulence with a one-dimensional (1D) velocity dispersion of σ 1D ≈ 60 km s−1 replicates many of the observed features within the CGM, such as clumping of low ionization-state ions and the existence of O vi at moderate ionization parameters. However, unlike observations, N v often arises in our simulations with derived column densities of a similar magnitude to those of O vi. While higher values of σ 1D lead to a thermal runaway in our isotropic simulations, this would not be the case in stratified media, and thus we speculate that more complex models of the turbulence may well match the absence of N v in the CGM of star-forming galaxies.
Realizable ranks of joins and intersections of subgroups in free groups

The famous Hanna Neumann Conjecture (now the Friedman–Mineyev theorem) gives an upper bound for the ranks of the intersection of arbitrary subgroups H and K of a non-abelian free group. It is an in...
Multi-Scale Jacobi Method for Anderson Localization

A new KAM-style proof of Anderson localization is obtained. A sequence of local rotations is dened, such that o-diagonal matrix elements of the Hamiltonian are driven rapidly to zero. This leads to the rst proof via multi-scale analysis of exponential decay of the eigenfunction correlator (this implies strong dynamical localization). The method has been used in recent work on many-body localization [25].
Dual-carrier Floquet circulator with time-modulated optical resonators

Spatiotemporal modulation has shown great promise as a strong time-reversal symmetry breaking mechanism that enables integrated nonreciprocal devices and topological materials at optical frequencies. However, ideal circulator and isolator performance has relied on spatial symmetry or momentum matching between modulation and optical modes. The resulting systems have been challenging to experimentally realize, due to the prohibitively complex and lossy biasing networks and tight fabrication tolerances that maintain the desired rotational and mirror symmetries. In this work, we propose a microresonator Floquet circulator that leverages the previously untapped degrees of freedom of the modulation, through waveforms with strong harmonic components. The Floquet circulator response exhibits ideal on-resonance isolation and supports broadband forward transmission with no trade-off in insertion loss. We present a numerical demonstration in an on-chip photonic crystal platform with just two modulated resonators req...
Predicting Fluid Intelligence of Children Using T1-Weighted MR Images and a StackNet

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of 82.42 on the combined training and validation set with 10-fold cross-validation. The proposed StackNet achieves a mean squared error of 94.25 on the testing data. The source code is available on GitHub (https://github.com/UCSB-VRL/ABCD-MICCAI2019).
Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning

Machine learning applications often require hyperparameter tuning. The hyperparameters usually drive both the efficiency of the model training process and the resulting model quality. For hyperparameter tuning, machine learning algorithms are complex black-boxes. This creates a class of challenging optimization problems, whose objective functions tend to be nonsmooth, discontinuous, unpredictably varying in computational expense, and include continuous, categorical, and/or integer variables. Further, function evaluations can fail for a variety of reasons including numerical difficulties or hardware failures. Additionally, not all hyperparameter value combinations are compatible, which creates so called hidden constraints. Robust and efficient optimization algorithms are needed for hyperparameter tuning. In this paper we present an automated parallel derivative-free optimization framework called Autotune , which combines a number of specialized sampling and search methods that are very effective in tuning machine learning models despite these challenges. Autotune provides significantly improved models over using default hyperparameter settings with minimal user interaction on real-world applications. Given the inherent expense of training numerous candidate models, we demonstrate the effectiveness of Autotune's search methods and the efficient distributed and parallel paradigms for training and tuning models, and also discuss the resource trade-offs associated with the ability to both distribute the training process and parallelize the tuning process.
Exoplanet Atmosphere Forecast: Observers Should Expect Spectroscopic Transmission Features to be Muted to 33%

To ensure robust constraints are placed on exoplanet atmospheric transmission spectra, future observations need to obtain high signal-to-noise ratio (SNR) measurements assuming smaller amplitude molecular signatures than those of clear solar metallicity atmospheres. Analyzing 37 exoplanet transmission spectra we find clear solar molecular features are measured in <7% of cases.
Action and index spectra and periodic orbits in Hamiltonian dynamics

The paper focuses on the connection between the existence of infinitely many periodic orbits for a Hamiltonian system and the behavior of its action or index spectrum under iterations. We use the action and index spectra to show that any Hamiltonian diffeomorphism of a closed, rational manifold with zero first Chern class has infinitely many periodic orbits and that, for a general rational manifold, the number of geometrically distinct periodic orbits is bounded from below by the ratio of the minimal Chern number and half of the dimension. These generalizations of the Conley conjecture follow from another result proved here asserting that a Hamiltonian diffeomorphism with a symplectically degenerate maximum on a closed rational manifold has infinitely many periodic orbits. We also show that for a broad class of manifolds and/or Hamiltonian diffeomorphisms the minimal action-index gap remains bounded for some infinite sequence of iterations and, as a consequence, whenever a Hamiltonian diffeomorphism has finitely many periodic orbits, the actions and mean indices of these orbits must satisfy a certain relation. Furthermore, for Hamiltonian diffeomorphisms of CP n with exactly nC1 periodic orbits a stronger result holds. Namely, for such a Hamiltonian
Macros to conduct tests of multimodality in SAS

ABSTRACTThe Dip Test of Unimodality and Silverman's Critical Bandwidth Test are two popular tests to determine if an unknown density contains more than one mode. While the tests can be easily run in R, they are not included in SAS software. We provide implementations of the Dip Test and Silverman Test as macros in the SAS software, capitalizing on the capability of SAS to execute R code internally. Descriptions of the macro parameters, installation steps, and sample macro calls are provided, along with an appendix for troubleshooting. We illustrate the use of the macros on data simulated from one or more Gaussian distributions as well as on the famous iris dataset.
Compress-and-Forward Scheme for Relay Networks: Backword Decoding and Connection to Bisubmodular Flows

In this paper, a compress-and-forward scheme with backward decoding is presented for the unicast wireless relay network. The encoding at the source and relay is a generalization of the noisy network coding (NNC) scheme. While it achieves the same reliable data rate as NNC scheme, the backward decoding allows for a better decoding complexity as compared with the joint decoding of the NNC scheme. Characterizing the layered decoding scheme is shown to be equivalent to characterizing an information flow for the wireless network. A node-flow for a graph with bisubmodular capacity constraints is presented and a max-flow min-cut theorem is presented. This generalizes many well-known results of flows over capacity constrained graphs studied in computer science literature. The results for the unicast relay network are generalized to the network with multiple sources with independent messages intended for a single destination.
Semilinear Mixed Problems on Hilbert Complexes and Their Numerical Approximation

Arnold, Falk, and Winther recently showed (Bull. Am. Math. Soc. 47:281–354, 2010) that linear, mixed variational problems, and their numerical approximation by mixed finite element methods, can be studied using the powerful, abstract language of Hilbert complexes. In another recent article (arXiv: 1005.4455), we extended the Arnold–Falk–Winther framework by analyzing variational crimes (a la Strang) on Hilbert complexes. In particular, this gave a treatment of finite element exterior calculus on manifolds, generalizing techniques from surface finite element methods and recovering earlier a priori estimates for the Laplace–Beltrami operator on 2- and 3-surfaces, due to Dziuk (Lecture Notes in Math., vol. 1357:142–155, 1988) and later Demlow (SIAM J. Numer. Anal. 47:805–827, 2009), as special cases. In the present article, we extend the Hilbert complex framework in a second distinct direction: to the study of semilinear mixed problems. We do this, first, by introducing an operator-theoretic reformulation of the linear mixed problem, so that the semilinear problem can be expressed as an abstract Hammerstein equation. This allows us to obtain, for semilinear problems, a priori solution estimates and error estimates that reduce to the Arnold–Falk–Winther results in the linear case. We also consider the impact of variational crimes, extending the results of our previous article to these semilinear problems. As an immediate application, this new framework allows for mixed finite element methods to be applied to semilinear problems on surfaces.
Preference learning along multiple criteria: A game-theoretic perspective

The literature on ranking from ordinal data is vast, and there are several ways to aggregate overall preferences from pairwise comparisons between objects. In particular, it is well known that any Nash equilibrium of the zero sum game induced by the preference matrix defines a natural solution concept (winning distribution over objects) known as a von Neumann winner. Many real-world problems, however, are inevitably multi-criteria, with different pairwise preferences governing the different criteria. In this work, we generalize the notion of a von Neumann winner to the multi-criteria setting by taking inspiration from Blackwell's approachability. Our framework allows for non-linear aggregation of preferences across criteria, and generalizes the linearization-based approach from multi-objective optimization. From a theoretical standpoint, we show that the Blackwell winner of a multi-criteria problem instance can be computed as the solution to a convex optimization problem. Furthermore, given random samples of pairwise comparisons, we show that a simple plug-in estimator achieves near-optimal minimax sample complexity. Finally, we showcase the practical utility of our framework in a user study on autonomous driving, where we find that the Blackwell winner outperforms the von Neumann winner for the overall preferences.
Consideration of learning orientations as an application of achievement goals in evaluating life science majors in introductory physics

A proposed method for measuring students' learning orientations through a short self-report instrument.
Assignments to sheaves of pseudometric spaces

An assignment to a sheaf is the choice of a local section from each open set in the sheaf's base space, without regard to how these local sections are related to one another. This article explains that the consistency radius -- which quantifies the agreement between overlapping local sections in the assignment -- is a continuous map. When thresholded, the consistency radius produces the consistency filtration, which is a filtration of open covers. This article shows that the consistency filtration is a functor that transforms the structure of the sheaf and assignment into a nested set of covers in a structure-preserving way. Furthermore, this article shows that consistency filtration is robust to perturbations, establishing its validity for arbitrarily thresholded, noisy data.
Central quantile subspace

Quantile regression (QR) is becoming increasingly popular due to its relevance in many scientific investigations. There is a great amount of work about linear and nonlinear QR models. Specifically, nonparametric estimation of the conditional quantiles received particular attention, due to its model flexibility. However, nonparametric QR techniques are limited in the number of covariates. Dimension reduction offers a solution to this problem by considering low-dimensional smoothing without specifying any parametric or nonparametric regression relation. The existing dimension reduction techniques focus on the entire conditional distribution. We, on the other hand, turn our attention to dimension reduction techniques for conditional quantiles and introduce a new method for reducing the dimension of the predictor $$\mathbf {X}$$. The novelty of this paper is threefold. We start by considering a single index quantile regression model, which assumes that the conditional quantile depends on $$\mathbf {X}$$ through a single linear combination of the predictors, then extend to a multi-index quantile regression model, and finally, generalize the proposed methodology to any statistical functional of the conditional distribution. The performance of the methodology is demonstrated through simulation examples and real data applications. Our results suggest that this method has a good finite sample performance and often outperforms the existing methods.
On two rationality conjectures for cubic fourfolds

Motivated by the question of rationality of cubic fourfolds, we show that a cubic X has an associated K3 surface in the sense of Hassett if and only if the variety F of lines on X is birational to a moduli space of sheaves on a K3 surface, but that having F birational to Hilb^2(K3) is more restrictive. We compare the loci in the moduli space of cubics where each condition is satisfied.
RELATIVE PHOTOMETRY OF HAT-P-1b OCCULTATIONS

We present Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph observations of two occultations of the transiting exoplanet HAT-P-1b. By measuring the planet to star flux ratio near opposition, we constrain the geometric albedo of the planet, which is strongly linked to its atmospheric temperature gradient. An advantage of HAT-P-1 as a target is its binary companion ADS 16402 A, which provides an excellent photometric reference, simplifying the usual steps in removing instrumental artifacts from HST time-series photometry. We find that without this reference star, we would need to detrend the lightcurve with the time of the exposures as well as the first three powers of HST orbital phase, and this would introduce a strong bias in the results for the albedo. However, with this reference star, we only need to detrend the data with the time of the exposures to achieve the same per-point scatter, therefore we can avoid most of the bias associated with detrending. Our final result is a 2{sigma} upper limit of 0.64 for the geometric albedo of HAT-P-1b between 577 and 947 nm.
The OPTX Project. I. The Flux and Redshift Catalogs for the CLANS, CLASXS, and CDF-N Fields

We present the redshift catalogs for the X-ray sources detected in the Chandra Deep Field-North (CDF-N), the Chandra Large Area Synoptic X-ray Survey (CLASXS), and the Chandra Lockman Area North Survey (CLANS). The catalogs for the CDF-N and CLASXS fields include redshifts from previous work, while the redshifts for the CLANS field are all new. For fluxes above 10−14 ergs cm−2 s−1 (2-8 keV) we have redshifts for 76% of the sources. We extend the redshift information for the full sample using photometric redshifts. The goal of the OPTX Project is to use these three surveys, which are among the most spectroscopically complete surveys to date, to analyze the effect of spectral type on the shape and evolution of the X-ray luminosity functions and to compare the optical spectral types with the X-ray spectral properties. We also present the CLANS X-ray catalog. The nine ACIS-I fields cover a solid angle of ~0.6 deg2 and reach fluxes of 7 × 10−16 ergs cm−2 s−1 (0.5-2 keV) and 3.5 × 10−15 ergs cm−2 s−1 (2-8 keV). We find a total of 761 X-ray point sources. In addition, we present the optical and infrared photometric catalog for the CLANS X-ray sources, as well as updated optical and infrared photometric catalogs for the X-ray sources in the CLASXS and CDF-N fields. The CLANS and CLASXS surveys bridge the gap between the ultradeep pencil-beam surveys, such as the CDFs, and the shallower, very large-area surveys. As a result, they probe the X-ray sources that contribute the bulk of the 2-8 keV X-ray background and cover the flux range of the observed break in the log N–log S distribution. We construct differential number counts for each individual field and for the full sample.
PROFESSOR C. N. YANG AND STATISTICAL MECHANICS

Professor Chen Ning Yang has made seminal and influential contributions in many different areas in theoretical physics. This talk focuses on his contributions in statistical mechanics, a field in which Professor Yang has held a continual interest for over sixty years. His Master's thesis was on the theory of binary alloys with multi-site interactions, some 30 years before others studied the problem. Likewise, his other works opened the door and led to subsequent developments in many areas of modern day statistical mechanics and mathematical physics. He made seminal contributions in a wide array of topics, ranging from the fundamental theory of phase transitions, the Ising model, Heisenberg spin chains, lattice models, and the Yang–Baxter equation, to the emergence of Yangian in quantum groups. These topics and their ramifications will be discussed in this talk.
On spectral radii of unraveled balls

Abstract Given a graph G, the unraveled ball of radius r centered at a vertex v is the ball of radius r centered at v in the universal cover of G. We prove a lower bound on the maximum spectral radius of unraveled balls of fixed radius, and we show, among other things, that if the average degree of G after deleting any ball of radius r is at least d then its second largest eigenvalue is at least 2 d − 1 cos ⁡ ( π r + 1 ) .
The ATLAS High Level Trigger Region of Interest Builder

This article describes the design, testing and production of the ATLAS Region of Interest Builder (RoIB). This device acts as an interface between the Level 1 trigger and the high level trigger (HLT) farm for the ATLAS LHC detector. It distributes all of the Level 1 data for a subset of events to a small number of (16 or less) individual commodity processors. These processors in turn provide this information to the HLT. This allows the HLT to use the Level 1 information to narrow data requests to areas of the detector where Level 1 has identified interesting objects.
On the Boundedness of Effective Potentials Arising from String Compactifications

We study effective potentials coming from compactifications of string theory. We show that, under mild assumptions, such potentials are bounded from below in four dimensions, giving an affirmative answer to a conjecture proposed by the second author in Douglas (JHEP 3:71, 2010). We also derive some sufficient conditions for the existence of critical points. All proofs and mathematical hypotheses are discussed in the context of their relevance to the physics of the problem.
Measurements of UHECR Mass Composition by Telescope Array

Telescope Array (TA) has recently published results of nearly nine years of X max observations providing its highest statistics measurement of ultra high energy cosmic ray (UHECR) mass composition to date for energies exceeding 1018.2 eV. This analysis measured agreement of observed data with results expected for four different single elements. Instead of relying only on the first and second moments of X max distributions, we employ a morphological test of agreement between data and Monte Carlo to allow for systematic uncertainties in data and in current UHECR hadronic models. Results of this latest analysis and implications of UHECR composition observed by TA are presented. TA can utilize different analysis methods to understand composition as both a crosscheck on results and as a tool to understand systematics affecting X max measurements. The different analysis efforts utilizing fluorescence detector stereo, surface detector and fluorescence detector hybrid, and surface detector-only, currently underway at TA performed to understand composition are also discussed.
General aspects of heterotic string compactifications on stacks and gerbes

In this paper we work out some basic results concerning heterotic string compactifications on stacks and, in particular, gerbes. A heterotic string compactification on a gerbe can be understood as, simultaneously, both a compactification on a space with a restriction on nonperturbative sectors, and also, a gauge theory in which a subgroup of the gauge group acts trivially on the massless matter. Gerbes admit more bundles than corresponding spaces, which suggests they are potentially a rich playground for heterotic string compactifications. After we give a general characterization of heterotic strings on stacks, we specialize to gerbes, and consider three different classes of ‘building blocks’ of gerbe compactifications. We argue that heterotic string compactifications on one class is equivalent to compactification of the same heterotic string on a disjoint union of spaces, compactification on another class is dual to compactifications of other heterotic strings on spaces, and compactification on the third class is not perturbatively consistent, so that we do not in fact recover a broad array of new heterotic compactifications, just combinations of existing ones. In appendices we explain how to compute massless spectra of heterotic string compactifications on stacks, derive some new necessary conditions for a heterotic string on a stack or orbifold to be well-defined, and also review some basic properties of bundles on gerbes.
Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions

We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient’s possible future medical conditions given the patient’s current and past history of reported conditions. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as “condition 1 and condition 2 → condition 3”) from a large set of candidate rules. Because this method “borrows strength” using the conditions of many similar patients, it is able to provide predictions specialized to any given patient, even when little information about the patient’s history of conditions is available.
Optimal control of electromagnetic field using metallic nanoclusters

The dielectric properties of metallic nanoclusters in the presence of an applied electromagnetic field are investigated using the non-local linear response theory. In the quantum limit we find a nontrivial dependence of the induced field and charge distributions on the spatial separation between the clusters and on the frequency of the driving field. Using a genetic algorithm, these quantum functionalities are exploited to custom-design sub-wavelength lenses with a frequency-controlled switching capability.
Spin Seebeck effect and thermal spin galvanic effect in Ni80Fe20/p-Si bilayers

The development of spintronics and spin-caloritronics devices needs efficient generation, detection, and manipulation of spin current. The thermal spin current from the spin-Seebeck effect has been reported to be more energy efficient than the electrical spin injection methods. However, spin detection has been the one of the bottlenecks since metals with large spin-orbit coupling is an essential requirement. In this work, we report an efficient thermal generation and interfacial detection of spin current. We measured a spin-Seebeck effect in Ni80Fe20 (25 nm)/p-Si (50 nm) (polycrystalline) bilayers without a heavy metal spin detector. p-Si, having a centrosymmetric crystal structure, has insignificant intrinsic spin-orbit coupling, leading to negligible spin-charge conversion. We report a giant inverse spin-Hall effect, essential for the detection of spin-Seebeck effects, in the Ni80Fe20/p-Si bilayer structure, which originates from Rashba spin orbit coupling due to structure inversion asymmetry at the int...
PERMUTATION CLASSES OF EVERY GROWTH RATE ABOVE 2.48188

We prove that there are permutation classes (hereditary properties of permutations) of every growth rate (Stanley–Wilf limit) at least λ≈2.48187, the unique real root of x5−2x4−2x2−2x−1, thereby establishing a conjecture of Albert and Linton.
T-duality and the case of the disappearing brane

When an NS5-brane crosses a D6-brane, a D4-brane is created stretching between them by the Hanany-Witten effect. However, the T-dual situation involves a Kaluza-Klein monopole crossing a D5-brane and should not result in brane creation. Thus, the newly created D4-brane disappears when T-duality is applied. The T-duality is in a direction transverse to the D4-brane so that one would naively have expected the creation of a D5-brane in the T-dual picture. This situation is investigated via the corresponding supergravity solutions and the tension with the naive result is resolved. Along the way, a differential form version of the supergravity T-duality relations is developed and some comments are made concerning the flux-expulsion properties of D6-branes.
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization

Formality style transfer is the task of converting informal sentences to grammatically-correct formal sentences, which can be used to improve performance of many downstream NLP tasks. In this work, we propose a semi-supervised formality style transfer model that utilizes a language model-based discriminator to maximize the likelihood of the output sentence being formal, which allows us to use maximization of token-level conditional probabilities for training. We further propose to maximize mutual information between source and target styles as our training objective instead of maximizing the regular likelihood that often leads to repetitive and trivial generated responses. Experiments showed that our model outperformed previous state-of-the-art baselines significantly in terms of both automated metrics and human judgement. We further generalized our model to unsupervised text style transfer task, and achieved significant improvements on two benchmark sentiment style transfer datasets.
The Reliability of the Low-latency Estimation of Binary Neutron Star Chirp Mass

The LIGO and Virgo Collaborations currently conduct searches for gravitational waves from compact binary coalescences in real-time. For promising candidate events, a sky map and distance estimation are released in low-latency, to facilitate their electromagnetic follow-up. Currently, no information is released about the masses of the compact objects. Recently, Margalit and Metzger (2019) have suggested that knowledge of the chirp mass of the detected binary neutron stars could be useful to prioritize the electromagnetic follow-up effort, and have urged the LIGO-Virgo collaboration to release chirp mass information in low-latency. One might worry that low-latency searches for compact binaries make simplifying assumptions that could introduce biases in the mass parameters: neutron stars are treated as point particles with dimensionless spins below $0.05$ and perfectly aligned with the orbital angular momentum. Furthermore, the template bank used to search for them has a finite resolution. In this paper we show that none of these limitations can introduce chirp mass biases larger than $\sim 10^{-3}~M_\odot$. Even the total mass is usually accurately estimated, with biases smaller than 6%. The mass ratio and effective inspiral spins, on the other hand, can suffer from more severe biases.