cssi/SciSciGPT-SciSciNet huggingface dataset:
Enhancing cycling stability of tungsten oxide supercapacitor electrodes via a boron cluster-based molecular cross-linking approach

We report our discovery of utilizing perhydroxylated dodecaborate clusters ([B12(OH)12]2−) as a molecular cross-linker to generate a hybrid tungsten oxide material. The reaction of [NnBu4]2[B12(OH)12] with WCl6, followed by subsequent annealing of the product at 500 °C in air successfully produces a tungsten oxide material cross-linked with B12-based clusters. The comprehensive structural study of the produced hybrid material confirms a cross-linked network of intact boron-rich clusters and tungsten oxides. We further demonstrate how these robust B12-based clusters in the resulting hybrid tungsten oxide material can effectively preserve the specific capacitance up to 4000 cycles and reduce the charge transfer resistance as well as the response time compared to that of pristine tungsten oxide. Ultimately, this work highlights a promising capability of boron-rich clusters in hybrid metal oxides to obtain fast and stable supercapacitors with high capacitance.
Educational Gender Gaps.

Cross-country studies reveal two consistent gender gaps in education—underachievement in school by boys and low rates of participation in STEM studies by girls. Recent economics research has shown the importance of social influences on women's STEM avoidance, but male low achievement has been less-studied and tends to be attributed to behavior problems and deficient non-cognitive skills. I revisit the determinants of the gender gap in U.S. educational attainment with a relatively-advantaged sample of young men and women and find that school behavior and measured skills are not very important drivers of gender differences, particularly in the transition to college. Educational aspirations, on the other hand, are strongly predictive of educational gaps and the gender difference in aspirations cannot be explained, even with rich adolescent data that includes parental expectations and school achievement indicators. These results suggest that gender identity concerns may influence (and damage) the educational prospects of boys as well as girls through norms of masculinity that discourage academic achievement.
Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply reinforcement learning to quantum hypothesis testing, where one designs measurements that can distinguish between multiple quantum states $\left. {\left\{ {{\rho _j}} \right\}} \right|_{j = 1}^m$ while minimizing the error probability. Although the Helstrom measurement is known to be optimal when there are m=2 states, the general problem of finding a minimal-error measurement is challenging. Additionally, in the case where the candidate states correspond to a quantum system with many qubit subsystems, implementing the optimal measurement on the entire system may be impractical. In this work, we develop locally-adaptive measurement strategies that are experimentally feasible in the sense that only one quantum subsystem is measured in each round. RLNN is used to find the optimal measurement protocol for arbitrary sets of tensor product quantum states. Numerical results for the network performance are shown. In special cases, the neural network testing-policy achieves the same probability of success as the optimal collective measurement.
Verifiable state machines: Proofs that untrusted services operate correctly

This article describes recent progress in realizing verifiable state machines, a primitive that enables untrusted services to provide cryptographic proofs that they operate correctly. Applications of this primitive range from proving the correct operation of distributed and concurrent cloud services to reducing blockchain transaction costs by leveraging inexpensive off-chain computation without trust.
Non-archimedean entire curves in closed subvarieties of semi-abelian varieties

We prove a non-archimedean analogue of the fact that a closed subvariety of a semi-abelian variety is hyperbolic modulo its special locus, and thereby generalize a result of Cherry.
On Inference of Network Topology and Confirmation Bias in Cyber-Social Networks

This article studies topology inference, from agent states, of a directed cyber-social network with opinion spreading dynamics model that explicitly takes confirmation bias into account. The cyber-social network comprises a set of partially connected directed network of agents at the social level, and a set of information sources at the cyber layer. The necessary and sufficient conditions for the existence of exact inference solution are characterized. A method for exact inference, when it is possible, of entire network topology as well as confirmation bias model parameters is proposed for the case where the bias mentioned earlier follows a piece-wise linear model. The particular case of no confirmation bias is analyzed in detail. For the setting where the model of confirmation bias is unknown, an algorithm that approximates the network topology, building on the exact inference method, is presented. This algorithm can exactly infer the weighted communication from the neighbors to the non-followers of information sources. Numerical simulations demonstrate the effectiveness of the proposed methods for different scenarios.
A stacked approach for chained equations multiple imputation incorporating the substantive model

Multiple imputation by chained equations (MICE) has emerged as a popular approach for handling missing data. A central challenge for applying MICE is determining how to incorporate outcome information into covariate imputation models, particularly for complicated outcomes. Often, we have a particular analysis model in mind, and we would like to ensure congeniality between the imputation and analysis models. We propose a novel strategy for directly incorporating the analysis model into the handling of missing data. In our proposed approach, multiple imputations of missing covariates are obtained without using outcome information. We then utilize the strategy of imputation stacking, where multiple imputations are stacked on top of each other to create a large data set. The analysis model is then incorporated through weights. Instead of applying Rubin's combining rules, we obtain parameter estimates by fitting a weighted version of the analysis model on the stacked data set. We propose a novel estimator for obtaining standard errors for this stacked and weighted analysis. Our estimator is based on the observed data information principle in Louis' work and can be applied for analyzing stacked multiple imputations more generally. Our approach for analyzing stacked multiple imputations is the first method that can be easily applied (using R package StackImpute) for a wide variety of standard analysis models and missing data settings.
Image propagation through extended MVKS phase turbulence under variable turbulence inner scale

Image propagation through turbulence is examined using Hufnagel-Valley model for different turbulent path distances. Performance is assessed via cross-correlation (CC) products for variable turbulence inner scale which is shown to have a significant impact on the recovery.
The Heart Failure Readmission Intervention by Variable Early Follow-up (THRIVE) Study: A Pragmatic Randomized Trial.

Background: In-person clinic follow-up within 7 days after discharge from a heart failure hospitalization is associated with lower 30-day readmission. However, health systems and patients may find ...
Fourier transform as a triangular matrix

Let V be a finite dimensional vector space over the field with two elements with a given nondegenerate symplectic form. Let [V] be the vector space of complex valued functions on V and let [V]_Z be the subgroup of [V] consisting of integer valued functions. We show that there exists a Z-basis of [V]_Z consisting of characteristic functions of certain explicit isotropic subspaces of V such that the matrix of the Fourier transform from [V] to [V] with respect to this basis is triangular. We show that this is a special case of a result which holds for any two-sided cell in a Weyl group.
Coronavirus disease 2019 (COVID-19) in two pediatric patients with kidney disease on chronic immunosuppression: A case series

Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). While children appear to experience less severe disease than adults, those with underlying conditions such as kidney disease may be more susceptible to infection. Limited data are present for children with kidney disease, and there are limited prior reports of pediatric hemodialysis patients with COVID-19. This report describes the mild clinical disease course of COVID-19 in two pediatric patients with chronic kidney disease, one on hemodialysis and both on chronic immunosuppression. We review treatment in these patients, as well as our measures to reduce transmission among our hemodialysis patients and staff.
Predictive Health Information and Employment Discrimination under the ADA and GINA.

New technologies, including genomics and precision medicine, are expanding the range of predictive health information. Employers interested in assessing the future health risks of prospective or current employees can gain access to information in an individual’s clinical record or can use algorithms to make their own predictions. When individuals have sub-clinical markers of future serious impairments, neither of the two main laws prohibiting discrimination in employment based on health status, the Americans with Disabilities Act (ADA) nor the Genetic Information Nondiscrimination Act (GINA) applies. The ADA does not apply to physical or mental impairments that are not currently a substantial limitation of a major life activity. GINA does not apply to conditions that have “manifested,” including physical changes detectable by a trained professional. Both statutes should be amended to prohibit forms of discrimination that were not contemplated when the legislation was enacted.
The two-color Soergel calculus

We give a diagrammatic presentation for the category of Soergel bimodules for the dihedral group .
The maximum relaxation time of a random walk

Abstract We show the minimum spectral gap of the normalized Laplacian over all simple, connected graphs on n vertices is ( 1 + o ( 1 ) ) 54 n 3 . This minimum is achieved asymptotically by a double kite graph. Consequently, this leads to sharp upper bounds for the maximum relaxation time of a random walk, settling a conjecture of Aldous and Fill. We also improve an eigenvalue–diameter inequality by giving a new lower bound for the spectral gap of the normalized Laplacian. This eigenvalue lower bound is asymptotically best possible.
Inclusive prompt photon production in electron-nucleus scattering at small x

We compute the differential cross-section for inclusive prompt photon production in deeply inelastic scattering (DIS) of electrons on nuclei at small x in the framework of the Color Glass Condensate (CGC) effective theory. The leading order (LO) computation in this framework resums leading logarithms in x as well as power corrections to all orders in Q , 2 /Q2, where Qs,A(x) is the nuclear saturation scale. This LO result is proportional to universal dipole and quadrupole Wilson line correlators in the nucleus. In the soft photon limit, the Low-Burnett-Kroll theorem allows us to recover existing results on inclusive DIS dijet production. The k⊥ and collinearly factorized expressions for prompt photon production in DIS are also recovered in a leading twist approximation to our result. In the latter case, our result corresponds to the dominant next-to-leading order (NLO) perturbative QCD contribution at small x. We next discuss the computation of the NLO corrections to inclusive prompt photon production in the CGC framework. In particular, we emphasize the advantages for higher order computations in inclusive photon production, and for fully inclusive DIS, arising from the simple momentum space structure of the dressed quark and gluon “shock wave” propagators in the “wrong” light cone gauge A− = 0 for a nucleus moving with P + → ∞.
Energy and linear and angular momenta in simple electromagnetic systems

We present examples of simple electromagnetic systems in which energy, linear momentum, and angular momentum exhibit interesting behavior. The systems are sufficiently simple to allow exact solutions of Maxwell’s equations in conjunction with the electrodynamic laws of force, torque, energy, and momentum. In all the cases examined, conservation of energy and momentum is confirmed.
Relationships between Characteristics of the Line-of-sight Magnetic Field and Solar Flare Forecasts

We analyze the relationship between the flare X-ray peak flux, and characteristics of the polarity inversion line (PIL) and active regions (ARs), derived from line-of-sight (LOS) magnetograms. The PIL detection algorithm based on a magnetogram segmentation procedure is applied for each AR with 1 hr cadence. The PIL and AR characteristics are associated with the AR flare history and divided into flaring and nonflaring cases. Effectiveness of the derived characteristics for flare forecasting is determined by the number of nonflaring cases separated from flaring cases by a certain threshold, and by their Fisher ranking score. The Support Vector Machine (SVM) classifier trained only on the PIL characteristics is used for the flare prediction. We have obtained the following results: (1) the PIL characteristics are more effective than global characteristics of ARs, (2) the highest True Skill Statistics (TSS) values of 0.76 ± 0.03 for ≥M1.0 flares and 0.84 ± 0.07 for ≥X1.0 flares are obtained using the "Sigmoid" SVM kernel, (3) the TSS scores obtained using only the LOS magnetograms are slightly lower than the scores obtained using vector magnetograms, but significantly better than current expert-based predictions, (4) for prediction of ≥M1.0 class flares 74.4% of all cases, and 91.2% for ≥X1.0 class, can be pre-classified as negative with no significant effect on the results, (5) the inclusion of global AR characteristics does not improve the forecast. The study confirms the unique role of the PIL region characteristics in the flare initiation process, and demonstrates possibilities of flare forecasting using only the LOS magnetograms.
Stability and Coalescence of Massive Twin Binaries

Massive stars are usually found in binaries, and binaries with periods less than 10 days may have a preference for near equal component masses (?twins?). In this paper we investigate the evolution of massive twin binaries all the way to contact and the possibility that these systems can be progenitors of double neutron star binaries. The small orbital separations of observed double neutron star binaries suggest that the progenitor systems underwent a common envelope phase at least once during their evolution. Bethe & Brown proposed that massive binary twins will undergo a common envelope evolution while both components are ascending the red giant branch (RGB) or asymptotic giant branch (AGB) simultaneously, also known as double-core evolution. Using models generated from the stellar evolution code EZ (evolve zero-age main sequence), we determine the range of mass ratios resulting in a contact binary with both components simultaneously ascending the RGB or AGB as a function of the difference in birth times, ??. We find that, even for a generous ?? = 5 Myr, the minimum mass ratio for an primary and increases for larger mass primaries. We use a smoothed particle hydrodynamics code, StarSmasher, to study specifically the evolution of q = 1 common envelope systems as a function of initial component mass, age, and orbital separation. We also consider a q = 0.997 system to test the effect of relaxing the constraint of strictly identical components. We find the dynamical stability limit, the largest orbital separation where the binary becomes dynamically unstable, as a function of the component mass and age. Finally, we calculate the efficiency of ejecting matter during the inspiral phase to extrapolate the properties of the remnant binary from our numerical results, assuming the common envelope is completely ejected. We find that for the nominal core masses, there is a minimum orbital separation for a given component mass such that the helium cores survive common envelope evolution in a tightly bound binary and are viable progenitors for double neutron stars.
A spatiospectral localization approach to estimating potential fields on the surface of a sphere from noisy, incomplete data taken at satellite altitudes

Satellites mapping the spatial variations of the gravitational or magnetic fields of the Earth or other planets ideally fly on polar orbits, uniformly covering the entire globe. Thus, potential fields on the sphere are usually expressed in spherical harmonics, basis functions with global support. For various reasons, however, inclined orbits are favorable. These leave a "polar gap": an antipodal pair of axisymmetric polar caps without any data coverage, typically smaller than 10° in diameter for terrestrial gravitational problems, but 20° or more in some planetary magnetic configurations. The estimation of spherical harmonic field coefficients from an incompletely sampled sphere is prone to error, since the spherical harmonics are not orthogonal over the partial domain of the cut sphere. Although approaches based on wavelets have gained in popularity in the last decade, we present a method for localized spherical analysis that is firmly rooted in spherical harmonics. We construct a basis of bandlimited spherical functions that have the majority of their energy concentrated in a subdomain of the unit sphere by solving Slepian's (1960) concentration problem in spherical geometry, and use them for the geodetic problem at hand. Most of this work has been published by us elsewhere. Here, we highlight the connection of the "spherical Slepian basis" to wavelets by showing their asymptotic self-similarity, and focus on the computational considerations of calculating concentrated basis functions on irregularly shaped domains.
A Statistical Detection of Gamma-Ray Emission from Galaxy Clusters: Implications for the Gamma-Ray Background and Structure Formation

The origin of the diffuse extragalactic high-energy gamma-ray background (EGRB) filling the universe remains unknown. The spectrum of this extragalactic radiation, as measured by EGRET on board the Compton Gamma Ray Observatory, is well fitted by a power law across nearly four decades in energy, from 30 MeV to 100 GeV. It has been estimated that not more than a quarter of the diffuse gamma-ray background could be due to unresolved point sources. Recent studies have suggested that much of the diffuse background could originate from the upscatter of cosmic microwave background photons by relativistic electrons produced by shock waves in the intergalactic medium during large-scale structure formation. In this work, we search for evidence of gamma-ray emission associated with galaxy clusters by cross-correlating high Galactic latitude EGRET data with Abell clusters. Our results indicate a possible association of emission with clusters at a � 3 � level. For a subset of the 447 richest (R � 2) clusters, the mean surface brightness excess is 1:2 � 10 � 6 photons cm � 2 s � 1 sr � 1 (>100 MeV), corresponding to a typical nonthermal bolometric luminosity of L� � 1 � 10 44 ergs s � 1 . Extrapolating this measurement and assuming no evolution, we conservatively estimate that � 1%–10% of the EGRB could originate from clusters with z < 1. For this cluster population, the predicted nonthermal luminosity is in excellent agreement with our measurement, suggesting that the clusters have experienced mass accretion within the last 10 9 yr. If correct, then future gamma-ray missions, such as the Gamma-Ray Large Area Space Telescope, should be able to directly detect nearby galaxy clusters. Subject headings: galaxies: clusters: general — gamma rays: observations — large-scale structure of universe
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment

Pairwise alignment of DNA sequencing data is a ubiquitous task in bioinformatics and typically represents a heavy computational burden. State-of-the-art approaches to speed up this task use hashing to identify short segments (k-mers) that are shared by pairs of reads, which can then be used to estimate alignment scores. However, when the number of reads is large, accurately estimating alignment scores for all pairs is still very costly. Moreover, in practice, one is only interested in identifying pairs of reads with large alignment scores. In this work, we propose a new approach to pairwise alignment estimation based on two key new ingredients. The first ingredient is to cast the problem of pairwise alignment estimation under a general framework of rank-one crowdsourcing models, where the workers’ responses correspond to k-mer hash collisions. These models can be accurately solved via a spectral decomposition of the response matrix. The second ingredient is to utilise a multi-armed bandit algorithm to adaptively refine this spectral estimator only for read pairs that are likely to have large alignments. The resulting algorithm iteratively performs a spectral decomposition of the response matrix for adaptively chosen subsets of the read pairs.
Schmidt's game, fractals, and orbits of toral endomorphisms

Given an integer matrix M ∈GL n (ℝ) and a point y ∈ℝ n /ℤ n , consider the set S. G. Dani showed in 1988 that whenever M is semisimple and y ∈ℚ n /ℤ n , the set has full Hausdorff dimension. In this paper we strengthen this result, extending it to arbitrary M ∈GL n (ℝ)∩M n × n (ℤ) and y ∈ℝ n /ℤ n , and in fact replacing the sequence of powers of M by any lacunary sequence of (not necessarily integer) m × n matrices. Furthermore, we show that sets of the form and their generalizations always intersect with ‘sufficiently regular’ fractal subsets of ℝ n . As an application, we give an alternative proof of a recent result [M. Einsiedler and J. Tseng. Badly approximable systems of affine forms, fractals, and Schmidt games. Preprint , arXiv:0912.2445] on badly approximable systems of affine forms.
Online Neural Connectivity Estimation with Noisy Group Testing

One of the primary goals of systems neuroscience is to relate the structure of neural circuits to their function, yet patterns of connectivity are difficult to establish when recording from large populations in behaving organisms. Many previous approaches have attempted to estimate functional connectivity between neurons using statistical modeling of observational data, but these approaches rely heavily on parametric assumptions and are purely correlational. Recently, however, holographic photostimulation techniques have made it possible to precisely target selected ensembles of neurons, offering the possibility of establishing direct causal links. A naive method for inferring functional connections is to stimulate each individual neuron multiple times and observe the responses of cells in the local network, but this approach scales poorly with the number of neurons. Here, we propose a method based on noisy group testing that drastically increases the efficiency of this process in sparse networks. By stimulating small ensembles of neurons, we show that it is possible to recover binarized network connectivity with a number of tests that grows only logarithmically with population size under minimal statistical assumptions. Moreover, we prove that our approach, which reduces to an efficiently solvable convex optimization problem, is equivalent to Variational Bayesian inference on the binary connection weights, and we derive rigorous bounds on the posterior marginals. This allows us to extend our method to the streaming setting, where continuously updated posteriors allow for optional stopping, and we demonstrate the feasibility of inferring connectivity for networks of up to tens of thousands of neurons online.
Local velocity variations for a drop moving through an orifice: Effects of edge geometry and surface wettability

Velocity fields determined within and surrounding a drop moving through an orifice reveal the relative importance of local deformation, fluid rotation, and dissipation in the surrounding fluid as well as the coupling between fluid inertia and contact-line motion.
Comments on flipped SU(5) (and F-theory)

We study the impact of nonrenormalizable operators in flipped SU(5) that can generate a large μ term, R-parity violation, and rapid proton decay. While our motivation is to determine whether F-theory can naturally realize flipped SU(5), this analysis is general and leads to a characterization of symmetries capable of controlling such operators and should be independent of F-theory. We then discuss some specific implications for F-theory model building, where a significant μ problem is unavoidable. Finally, we mention previously noted difficulties associated to engineering GUT-Higgs fields in F-theory, suggest a direct engineering of SU(5) × U(1) χ as an alternative, and present a sample construction of this type.
Rho1 activation recapitulates early gastrulation events in the ventral, but not dorsal, epithelium of Drosophila embryos

Ventral furrow formation, the first step in Drosophila gastrulation, is a well-studied example of tissue morphogenesis. Rho1 is highly active in a subset of ventral cells and is required for this morphogenetic event. However, it is unclear whether spatially patterned Rho1 activity alone is sufficient to recapitulate all aspects of this morphogenetic event, including anisotropic apical constriction and coordinated cell movements. Here, using an optogenetic probe that rapidly and robustly activates Rho1 in Drosophila tissues, we show that Rho1 activity induces ectopic deformations in the dorsal and ventral epithelia of Drosophila embryos. These perturbations reveal substantial differences in how ventral and dorsal cells, both within and outside the zone of Rho1 activation, respond to spatially and temporally identical patterns of Rho1 activation. Our results demonstrate that an asymmetric zone of Rho1 activity is not sufficient to recapitulate ventral furrow formation and reveal that additional, ventral-specific factors contribute to the cell- and tissue-level behaviors that emerge during ventral furrow formation.
Origins of Hot Jupiters

Hot Jupiters were the first exoplanets to be discovered around main sequence stars and astonished us with their close-in orbits. They are a prime example of how exoplanets have challenged our textb...
Layered palladates and their relation to nickelates and cuprates

We explore the layered palladium oxides ${\mathrm{La}}_{2}{\mathrm{PdO}}_{4}$, ${\mathrm{LaPdO}}_{2}$, and ${\mathrm{La}}_{4}{\mathrm{Pd}}_{3}{\mathrm{O}}_{8}$ via ab initio calculations. ${\mathrm{La}}_{2}{\mathrm{PdO}}_{4}$, being low spin ${d}^{8}$, is quite different from its high spin nickel analog. Hypothetical ${\mathrm{LaPdO}}_{2}$, despite its ${d}^{9}$ configuration, has a paramagnetic electronic structure very different from cuprates. On the other hand, the hypothetical trilayer compound ${\mathrm{La}}_{4}{\mathrm{Pd}}_{3}{\mathrm{O}}_{8}$ (${d}^{8.67}$) is more promising in that its paramagnetic electronic structure is very similar to that of overdoped cuprates. But even in the ${d}^{9}$ limit (achieved by partial substitution of La with a 4+ ion), we find that an antiferromagnetic insulating state cannot be stabilized due to the less correlated nature of Pd ions. Therefore, this material, if it could be synthesized, would provide an ideal platform for testing the validity of magnetic theories for high-temperature superconductivity.
Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model

Multi-target multi-camera tracking (MTMCT), i.e., tracking multiple targets across multiple cameras, is a crucial technique for smart city applications. In this paper, we propose an effective and reliable MTMCT framework for vehicles, which consists of a traffic-aware single camera tracking (TSCT) algorithm, a trajectory-based camera link model (CLM) for vehicle re-identification (ReID), and a hierarchical clustering algorithm to obtain the cross camera vehicle trajectories. First, the TSCT, which jointly considers vehicle appearance, geometric features, and some common traffic scenarios, is proposed to track the vehicles in each camera separately. Second, the trajectory-based CLM is adopted to facilitate the relationship between each pair of adjacently connected cameras and add spatio-temporal constraints for the subsequent vehicle ReID with temporal attention. Third, the hierarchical clustering algorithm is used to merge the vehicle trajectories among all the cameras to obtain the final MTMCT results. Our proposed MTMCT is evaluated on the CityFlow dataset and achieves a new state-of-the-art performance with IDF1 of 74.93%.
Quantifying the Economic Case for Electric Semi-Trucks

There has been considerable interest in the electrification of freight transport; however, there is uncertainty in the technological and economic competitiveness due to the size and the substantial costs associated with large battery packs. Here we analyze the trade-off between the initial investment and the operating costs to compare a Class 8 diesel semi-truck and an electric truck with a range of 500 miles. We define the payback period to be the time span required for the operational cost savings to break even with the initial price differential. For an initial price differential of ∼US$100 000, we estimate a baseline payback period of 3.24 ± 1.46 years. We identify four targets in the 2020—2030 time frame, each of which needs to be met in order to ensure a payback period of about ∼5 years: (i) an optimized vehicle design with a drag coefficient of 0.4 ± 0.04 for lowering the pack size and meeting the payload demands, (ii) pack price of <US$150/kWh, (iii) a price of electricity <US$0.2/kWh with a peak ...
Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness

Machine learning models are increasingly being used in important decision-making software such as approving bank loans, recommending criminal sentencing, hiring employees, and so on. It is important to ensure the fairness of these models so that no discrimination is made based on protected attribute (e.g., race, sex, age) while decision making. Algorithms have been developed to measure unfairness and mitigate them to a certain extent. In this paper, we have focused on the empirical evaluation of fairness and mitigations on real-world machine learning models. We have created a benchmark of 40 top-rated models from Kaggle used for 5 different tasks, and then using a comprehensive set of fairness metrics, evaluated their fairness. Then, we have applied 7 mitigation techniques on these models and analyzed the fairness, mitigation results, and impacts on performance. We have found that some model optimization techniques result in inducing unfairness in the models. On the other hand, although there are some fairness control mechanisms in machine learning libraries, they are not documented. The mitigation algorithm also exhibit common patterns such as mitigation in the post-processing is often costly (in terms of performance) and mitigation in the pre-processing stage is preferred in most cases. We have also presented different trade-off choices of fairness mitigation decisions. Our study suggests future research directions to reduce the gap between theoretical fairness aware algorithms and the software engineering methods to leverage them in practice.
An analysis of carrier dynamics in methylammonium lead triiodide perovskite solar cells using cross correlation noise spectroscopy

Using cross correlation current noise spectroscopy, we have investigated carrier dynamics in methylammonium lead triiodide solar cells. This method provides space selectivity for devices with a planar multi-layered structure, effectively amplifying current noise contributions coming from the most resistive element of the stack. In the studied solar cells, we observe near full-scale shot noise, indicating the dominance of noise generation by a single source, likely the interface between the perovskite and the spiro-organic 2,2′,7,7′-tetrakis(N,N-di-p-methoxyphenyl-amine)9,9′-spirobifluorene hole-transport layer. We argue that the strong 1/f noise term has contributions from both the perovskite layer and interfaces. It displays a non-ideal dependence on photocurrent, S ∝ I 1.4 (instead of usual S ∝ I 2), which is likely due to current-induced halide migration. Finally, we observe generation–recombination noise. We argue that this contribution is due to bimolecular recombination in the perovskite bulk absorption layer. Extrapolating our results, we estimate that at standard 1 sun illumination, the electron–hole recombination time is 5 μs.
The Impact of Stellar Abundance Variations on Stellar Habitable Zone Evolution

The high-quality spectra required for radial velocity planet searches are well suited to providing abundances for a wide array of elements in large samples of stars. Abundance ratios of the most common elements relative to Fe are observed to vary by more than a factor of two in planet host candidates. This level of variation has a substantial impact on the evolution of the host star and the extent of its habitable zone. We present stellar models of 1 M ☉ stars with custom compositions representing the full range of these non-solar abundance ratios. We find that the effects derived from variation over the observed range of [O/Fe] has a particularly dramatic effect. Habitability lifetimes for some classes of orbits can vary by gigayears for the observed range in [O/Fe].
Split-gate cavity coupler for silicon circuit quantum electrodynamics

Coherent charge-photon and spin-photon coupling has recently been achieved in silicon double quantum dots (DQDs). Here, we demonstrate a versatile split-gate cavity-coupler that allows more than one DQD to be coupled to the same microwave cavity. Measurements of the cavity transmission as a function of level detuning yield a charge cavity coupling rate of g c / 2 π = 58 MHz, a charge decoherence rate of γ c / 2 π = 36 MHz, and a cavity decay rate of κ / 2 π = 1.2 MHz. The charge cavity coupling rate is in good agreement with device simulations. Our coupling technique can be extended to enable simultaneous coupling of multiple DQDs to the same cavity mode, opening the door to long-range coupling of semiconductor qubits using microwave frequency photons.
K-user interference channels: General outer bound and sum-capacity for certain Gaussian channels

This paper derives an outer bound on the capacity region of a general memoryless interference channel with an arbitrary number of users. The derived bound is the first known outer bound region valid for any memoryless channel besides the cut-set bound, which is loose even in the two-user case. In Gaussian noise, classes of channels for which the proposed bound gives the sum-rate capacity are identified, including degraded channels and a class of Z-channels.
Deep Learning the Morphology of Dark Matter Substructure

Strong gravitational lensing is a promising probe of the substructure of dark matter halos. Deep learning methods have the potential to accurately identify images containing substructure, and differentiate WIMP dark matter from other well motivated models, including vortex substructure of dark matter condensates and superfluids. This is crucial in future efforts to identify the true nature of dark matter. We implement, for the first time, a classification approach to identifying dark matter substructure based on simulated strong lensing images with different substructure. Utilizing convolutional neural networks trained on sets of simulated images, we demonstrate the feasibility of deep neural networks to reliably distinguish among different types of dark matter substructure. With thousands of strong lensing images anticipated with the coming launch of LSST, we expect that supervised and unsupervised deep learning models will play a crucial role in determining the nature of dark matter.
Topological Transition on the Conformal Manifold

Despite great successes in the study of gapped phases, a comprehensive understanding of the gapless phases and their transitions is still under developments. In this paper, we study a general phenomenon in the space of (1+1)$d$ critical phases with fermionic degrees of freedom described by a continuous family of conformal field theories (CFT), a.k.a. the conformal manifold. Along a one-dimensional locus on the conformal manifold, there can be a transition point, across which the fermionic CFTs on the two sides differ by stacking an invertible fermionic topological order (IFTO), point-by-point along the locus. At every point on the conformal manifold, the order and disorder operators have power-law two-point functions, but their critical exponents cross over with each other at the transition point, where stacking the IFTO leaves the fermionic CFT unchanged. We call this continuous transition on the fermionic conformal manifold a topological transition. By gauging the fermion parity, the IFTO stacking becomes a Kramers-Wannier duality between the corresponding bosonic CFTs. Both the IFTO stacking and the Kramers-Wannier duality are induced by the electromagnetic duality of the (2+1)$d$ $\mathbb{Z}_2$ topological order. We provide several examples of topological transitions, including the familiar Luttinger model of spinless fermions (i.e. the $c=1$ massless Dirac fermion with the Thirring interaction), and a new class of $c=2$ examples describing $U(1)\times SU(2)$-protected gapless phases.
Phase Transitions in the Two-Dimensional Ising Model from the Microcanonical Perspective

The continuous ferromagnetic-paramagnetic phase transition in the two-dimensional Ising model has already been excessively studied by conventional canonical statistical analysis in the past. We use the recently developed generalized microcanonical inflection-point analysis method to investigate the least-sensitive inflection points of the microcanonical entropy and its derivatives to identify transition signals. Surprisingly, this method reveals that there are potentially two additional transitions for the Ising system besides the critical transition.
Self-organized oscillations of Leidenfrost drops

In the Leidenfrost effect, a thin layer of evaporated vapour forms between a liquid and a hot solid. The complex interactions between the solid, liquid and vapour phases can lead to rich dynamics even in a single Leidenfrost drop. Here we investigate the self-organized oscillations of Leidenfrost drops that are excited by a constant flow of evaporated vapour beneath the drop. We show that for small Leidenfrost drops, the frequency of a recently reported ‘breathing mode’ (Caswell, Phys. Rev. E, vol. 90, 2014, 013014) can be explained by a simple balance of gravitational and surface tension forces. For large Leidenfrost drops, azimuthal star-shaped oscillations are observed. Our previous work showed how the coupling between the rapid evaporated vapour flow and the vapour–liquid interface excites the star-shaped oscillations (Ma et al., Phys. Rev. Fluids, vol. 2, 2017, 031602). In our experiments, star-shaped oscillation modes of are observed in different liquids, and the number of observed modes depends sensitively on the viscosity of the liquid. Here we expand on this work by directly comparing the oscillations with theoretical predictions, as well as show how the oscillations are initiated by a parametric forcing mechanism through pressure oscillations in the vapour layer. The pressure oscillations are driven by the capillary waves of a characteristic wavelength beneath the drop. These capillary waves can be generated by a large shear stress at the liquid–vapour interface due to the rapid flow of evaporated vapour. We also explore potential effects of thermal convection in the liquid. Although the measured Rayleigh number is significantly larger than the critical Rayleigh number, the frequency (wavelength) of the oscillations depends only on the capillary length of the liquid, and is independent of the drop radius and substrate temperature. Thus convection seems to play a minor role in Leidenfrost drop oscillations, which are mostly hydrodynamic in origin.
Improving Grounded Natural Language Understanding through Human-Robot Dialog

Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically—continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.
RADIATION-HYDRODYNAMIC SIMULATIONS OF THE FORMATION OF ORION-LIKE STAR CLUSTERS. I. IMPLICATIONS FOR THE ORIGIN OF THE INITIAL MASS FUNCTION

One model for the origin of typical Galactic star clusters such as the Orion Nebula Cluster (ONC) is that they form via the rapid, efficient collapse of a bound gas clump within a larger, gravitationally unbound giant molecular cloud. However, simulations in support of this scenario have thus far not included the radiation feedback produced by the stars; radiative simulations have been limited to significantly smaller or lower-density regions. Here we use the ORION AMR code to conduct the first ever radiation-hydrodynamic simulations of the global collapse scenario for the formation of an ONC-like cluster. We show that radiative feedback has a dramatic effect on the evolution: once the first ~10%-20% of the gas mass is incorporated into stars, their radiative feedback raises the gas temperature high enough to suppress any further fragmentation. However, gas continues to accrete onto existing stars, and, as a result, the stellar mass distribution becomes increasingly top-heavy, eventually rendering it incompatible with the observed initial mass function (IMF). Systematic variation in the location of the IMF peak as star formation proceeds is incompatible with the observed invariance of the IMF between star clusters, unless some unknown mechanism synchronizes the IMFs in different clusters by ensuring that star formation is always truncated when the IMF peak reaches a particular value. We therefore conclude that the global collapse scenario, at least in its simplest form, is not compatible with the observed stellar IMF. We speculate that processes that slow down star formation, and thus reduce the accretion luminosity, may be able to resolve the problem.
Improved Bounded-Strength Decoupling Schemes for Local Hamiltonians

We address the task of switching off the Hamiltonian of a system by removing all internal and system-environment couplings. We propose dynamical decoupling schemes that use only bounded-strength controls for quantum many-body systems with local system Hamiltonians and local environmental couplings. To do so, we introduce the combinatorial concept of balanced-cycle orthogonal arrays (BOAs) and show how to construct them from classical error-correcting codes. The derived decoupling schemes may be useful as a primitive for more complex schemes, e.g., for Hamiltonian simulation. For the case of $n$ qubits and a two-local Hamiltonian, the length of the resulting decoupling scheme scales as $O(n \log n)$ , improving over the previously best-known schemes that scaled quadratically with $n$ . More generally, using BOAs constructed from families of Bose-Chaudhuri-Hocquenghem (BCH) codes, we show that bounded-strength decoupling for any $\ell $ -local Hamiltonian, where $\ell {\geq } 2$ , can be achieved using decoupling schemes of length at most $O(n^{\ell -1} \log n)$ .
Joint Energy and SINR Coverage in Spatially Clustered RF-Powered IoT Network

Owing to the ubiquitous availability of radio-frequency (RF) signals, RF energy harvesting is emerging as an appealing solution for powering Internet-of-Things (IoT) devices. In this paper, we model and analyze an IoT network which harvests RF energy and receives information from the same wireless network. In order to enable this operation, each time slot is partitioned into charging and information reception phases. For this setup, we characterize two performance metrics: 1) energy coverage and 2) joint signal-to-interference-plus-noise and energy coverage. The analysis is performed using a realistic spatial model that captures the spatial coupling between the locations of the IoT devices and the nodes of the wireless network (referred, henceforth, as the IoT gateways), which is often ignored in the literature. In particular, we model the locations of the IoT devices using a Poisson cluster process and assume that some of the clusters have IoT gateways (GWs) deployed at their centers while the other GWs are deployed independently of the IoT devices. The level of coupling can be controlled by tuning the fraction of total GWs that are deployed at the cluster centers. Due to the inherent intractability of computing the distribution of shot noise process for this setup, we propose two accurate approximations, using which the aforementioned metrics are characterized. Multiple system design insights are drawn from our results. For instance, we demonstrate the existence of optimal slot partitioning that maximizes the system throughput. In addition, we explore the effect of the level of coupling between the locations of the IoT devices and the GWs on this optimal slot partitioning. Particularly, our results reveal that the optimal value of time duration for the charging phase increases as the level of coupling decreases.
The density profile of the dark matter halo of ngc 4605

We have obtained ~100 pc resolution CO and ~60 pc resolution Hα observations of the dwarf spiral galaxy NGC 4605. We use them to derive a high-resolution rotation curve and study the central density profile of NGC 4605's dark matter halo. We find that these observations do not agree with the predictions of most high-resolution cold dark matter calculations. We investigate two extreme cases: (1) NGC 4605 has a maximal exponential disk, which we model using K-band observations and remove to study the structure of its dark matter halo, and (2) NGC 4605 is dark matter-dominated, and its disk is dynamically negligible. Because the mass-to-light ratio of the maximal disk is already very low, we favor the first solution, which indicates the halo has one component with a density profile ρ ∝ r-0.65 out to R ~ 2.8 kpc. In the second case, the rotation curve requires the presence of two components: a small (~600 pc) core surrounded by a much steeper ρ ∝ r-1.1 halo. Removal of intermediate (submaximal) disks does not ameliorate the discrepancy between the predictions and the observations.