cssi/SciSciGPT-SciSciNet huggingface dataset:
Automatic microscopic cell counting by use of deeply-supervised density regression model

Accurately counting cells in microscopic images is important for medical diagnoses and biological studies, but manual cell counting is very tedious, time-consuming, and prone to subjective errors, and automatic counting can be less accurate than desired. To improve the accuracy of automatic cell counting, we propose here a novel method that employs deeply-supervised density regression. A fully convolutional neural network (FCNN) serves as the primary FCNN for density map regression. Innovatively, a set of auxiliary FCNNs are employed to provide additional supervision for learning the intermediate layers of the primary CNN to improve network performance. In addition, the primary CNN is designed as a concatenating framework to integrate multi-scale features through shortcut connections in the network, which improves the granularity of the features extracted from the intermediate CNN layers and further supports the final density map estimation. The experimental results on immunofluorescent images of human embryonic stem cells demonstrate the superior performance of the proposed method over other state-of-the-art methods.
Fundamental limits to high-contrast wavefront control

The current generation of ground-based coronagraphic instruments uses deformable mirrors to correct for phase errors and to improve contrast levels at small angular separations. Improving these techniques, several space and ground based instruments are currently developed using two deformable mirrors to correct for both phase and amplitude errors. However, as wavefront control techniques improve, more complex telescope pupil geometries (support structures, segmentation) will soon be a limiting factor for these next generation coronagraphic instruments. In this paper we discuss fundamental limits associated with wavefront control with deformable mirrors in high contrast coronagraph. We start with an analytic prescription of wavefront errors, along with their wavelength dependence, and propagate them through coronagraph models. We then consider a few wavefront control architectures, number of deformable mirrors and their placement in the optical train of the instrument, and algorithms that can be used to cancel the starlight scattered by these wavefront errors over a finite bandpass. For each configuration we derive the residual contrast as a function of bandwidth and of the properties of the incoming wavefront. This result has consequences when setting the wavefront requirements, along with the wavefront control architecture of future high contrast instrument both from the ground and from space. In particular we show that these limits can severely affect the effective Outer Working Angle that can be achieved by a given coronagraph instrument.
Timing by Stellar Pulsations as an Exoplanet Discovery Method

The stable oscillations of pulsating stars can serve as accurate timepieces, which may be monitored for the influence of exoplanets. An external companion gravitationally tugs the host star, causing periodic changes in pulsation arrival times. This method is most sensitive to detecting substellar companions around the hottest pulsating stars, especially compact remnants like white dwarfs and hot subdwarfs, as well as delta Scuti variables (A stars). However, it is applicable to any pulsating star with sufficiently stable oscillations. Care must be taken to ensure that the changes in pulsation arrival times are not caused by intrinsic stellar variability; an external, light-travel-time effect from an exoplanet identically affects all pulsation modes. With more long-baseline photometric campaigns coming online, this method is yielding new detections of substellar companions.
Geo‐text data and data‐driven geospatial semantics

Many datasets nowadays contain links between geographic locations and natural language texts. These links can be geotags, such as geotagged tweets or geotagged Wikipedia pages, in which location coordinates are explicitly attached to texts. These links can also be place mentions, such as those in news articles, travel blogs, or historical archives, in which texts are implicitly connected to the mentioned places. This kind of data is referred to as geo-text data. The availability of large amounts of geo-text data brings both challenges and opportunities. On the one hand, it is challenging to automatically process this kind of data due to the unstructured texts and the complex spatial footprints of some places. On the other hand, geo-text data offers unique research opportunities through the rich information contained in texts and the special links between texts and geography. As a result, geo-text data facilitates various studies especially those in data-driven geospatial semantics. This paper discusses geo-text data and related concepts. With a focus on data-driven research, this paper systematically reviews a large number of studies that have discovered multiple types of knowledge from geo-text data. Based on the literature review, a generalized workflow is extracted and key challenges for future work are discussed.
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation

We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is to (1) generate training data automatically in the target language by augmenting machine-translated datasets with local entities scraped from public websites, (2) add a few-shot boost of human-translated sentences and train a novel XLMR-LSTM semantic parser, and (3) test the model on natural utterances curated using human translators. We assess the effectiveness of our approach by extending the current capabilities of Schema2QA, a system for English Question Answering (QA) on the open web, to 10 new languages for the restaurants and hotels domains. Our model achieves an overall test accuracy ranging between 61% and 69% for the hotels domain and between 64% and 78% for restaurants domain, which compares favorably to 69% and 80% obtained for English parser trained on gold English data and a few examples from validation set. We show our approach outperforms the previous state-of-the-art methodology by more than 30% for hotels and 40% for restaurants with localized ontologies for the subset of languages tested. Our methodology enables any software developer to add a new language capability to a QA system for a new domain, leveraging machine translation, in less than 24 hours. Our code is released open-source.
Cascade Model for Planetesimal Formation by Turbulent Clustering

We use a newly developed cascade model of turbulent concentration of particles in protoplanetary nebulae to calculate several properties of interest to the formation of primitive planetesimals and to the meteorite record. The model follows, and corrects, calculations of the primary planetesimal Initial Mass Function (IMF) by Cuzzi et al. (2010), in which an incorrect cascade model was used. Here we use the model of Hartlep et al. (2017), which has been validated against several published numerical simulations of particle concentration in turbulence. We find that, for a range of nebula and particle properties, planetesimals may be "born big", formed as sandpiles with diameters in the 10-100 km range, directly from freely floating particles. The IMFs have a modal nature, with a well-defined peak rather than a powerlaw size dependence. Predictions for the inner and outer nebula behave similarly in these regards, and observations of inner and outer nebula primitive bodies support such modal IMFs. Also, we present predictions of local particle concentrations on several lengthscales in which particles "commonly" find themselves, which have significance for meteoritical observations of the redox state and isotopic fractionation in regions of chondrule formation. An important difference between these results, and those of Cuzzi et al. (2010), is that particle growth-by-sticking must proceed to at least the 1-few cm radius range for the IMF and meteoritical properties to be most plausibly satisfied. That is, as far as the inner nebula goes, the predominant "particles" must be aggregates of chondrules (or chondrule-size precursors) rather than individual chondrules themselves.
Thermalization near Integrability in a Dipolar Quantum Newton's Cradle

Experiments involving a magnetic quantum Newton's cradle provide insights into how interacting quantum particles achieve thermal equilibrium.
Simultaneous Nearest Neighbor Search

Motivated by applications in computer vision and databases, we introduce and study the Simultaneous Nearest Neighbor Search (SNN) problem. Given a set of data points, the goal of SNN is to design a data structure that, given a collection of queries, finds a collection of close points that are compatible with each other. Formally, we are given k query points Q=q_1,...,q_k, and a compatibility graph G with vertices in Q, and the goal is to return data points p_1,...,p_k that minimize (i) the weighted sum of the distances from q_i to p_i and (ii) the weighted sum, over all edges (i,j) in the compatibility graph G, of the distances between p_i and p_j. The problem has several applications in computer vision and databases, where one wants to return a set of *consistent* answers to multiple related queries. Furthermore, it generalizes several well-studied computational problems, including Nearest Neighbor Search, Aggregate Nearest Neighbor Search and the 0-extension problem. In this paper we propose and analyze the following general two-step method for designing efficient data structures for SNN. In the first step, for each query point q_i we find its (approximate) nearest neighbor point p'_i; this can be done efficiently using existing approximate nearest neighbor structures. In the second step, we solve an off-line optimization problem over sets q_1,...,q_k and p'_1,...,p'_k; this can be done efficiently given that k is much smaller than n. Even though p'_1,...,p'_k might not constitute the optimal answers to queries q_1,...,q_k, we show that, for the unweighted case, the resulting algorithm satisfies a O(log k/log log k)-approximation guarantee. Furthermore, we show that the approximation factor can be in fact reduced to a constant for compatibility graphs frequently occurring in practice, e.g., 2D grids, 3D grids or planar graphs. Finally, we validate our theoretical results by preliminary experiments. In particular, we show that the empirical approximation factor provided by the above approach is very close to 1.
Fringe Science: Defringing CCD Images with Neon Lamp Flat Fields

Fringing in CCD images is troublesome from the aspect of photometric quality and image flatness in the final reduced product. Additionally, defringing during calibration requires the inefficient use of time during the night to collect and produce a "supersky" fringe frame. The fringe pattern observed in a CCD image for a given near-IR filter is dominated by small thickness variations across the detector, with a second-order effect caused by the wavelength extent of the emission lines within the bandpass that produce the interference pattern. We show that essentially any set of emission lines that generally match the wavelength coverage of the night-sky emission lines within a bandpass will produce an identical fringe pattern. We present an easy, inexpensive, and efficient method that uses a neon lamp as a flat-field source and produces high-S/N fringe frames to use for defringing an image during the calibration process.
Extending Item Response Theory to Online Homework.

Lyman Briggs College and Department of Physics and Astronomy,Michigan State University, East Lansing, MI 48824, USA(Dated: January 9, 2014)Item Response Theory becomes an increasingly important tool when analyzing \Big Data" gath-ered from online educational venues. However, the mechanism was originally developed in traditionalexam settings, and several of its assumptions are infringed upon when deployed in the online realm.For a large enrollment physics course for scientists and engineers, the study compares outcomesfrom IRT analyses of exam and homework data, and then proceeds to investigate the e ects of eachconfounding factor introduced in the online realm. It is found that IRT yields the correct trends forlearner ability and meaningful item parameters, yet overall agreement with exam data is moderate.It is also found that learner ability and item discrimination is over wide ranges robust with respectto model assumptions and introduced noise, less so than item diculty.
Fundamental limits on quantum dynamics based on entropy change

It is well known in the realm of quantum mechanics and information theory that the entropy is non-decreasing for the class of unital physical processes. However, in general, the entropy does not exhibit monotonic behavior. This has restricted the use of entropy change in characterizing evolution processes. Recently, a lower bound on the entropy change was provided in the work of Buscemi, Das, and Wilde [Phys. Rev. A 93(6), 062314 (2016)]. We explore the limit that this bound places on the physical evolution of a quantum system and discuss how these limits can be used as witnesses to characterize quantum dynamics. In particular, we derive a lower limit on the rate of entropy change for memoryless quantum dynamics, and we argue that it provides a witness of non-unitality. This limit on the rate of entropy change leads to definitions of several witnesses for testing memory effects in quantum dynamics. Furthermore, from the aforementioned lower bound on entropy change, we obtain a measure of non-unitarity for...
Probing Oort Cloud and Local Interstellar Medium Properties via Dust Produced in Cometary Collisions

The Oort cloud remains one of the most poorly explored regions of the solar system. We propose that its properties can be constrained by studying a population of dust grains produced in collisions of comets in the outer solar system. We explore the dynamics of μm-sized grains outside the heliosphere (beyond ~250 AU), which are predominantly affected by the magnetic field of the interstellar medium (ISM) flow past the Sun. We derive analytic models for the production and motion of small particles as a function of their birth location in the cloud and calculate the particle flux and velocity distribution in the inner solar system. These models are verified by direct numerical simulations. We show that grains originating in the Oort cloud have a unique distribution of arrival directions, which should easily distinguish them from both interplanetary and interstellar dust populations. We also demonstrate that the distribution of particle arrival velocities is uniquely determined by the mass distribution and dust production rate in the cloud. Cometary collisions within the cloud produce a flux of μm-sized grains in the inner solar system of up to several m–2 yr–1. The next generation dust detectors may be sensitive enough to detect and constrain this dust population, which will illuminate the Oort cloud's properties. We also show that the recently detected mysterious population of large (μm-sized) unbound particles, which seems to arrive with the ISM flow, is unlikely to be generated by collisions of comets in the Oort cloud.
Distributed State Estimation Using Intermittently Connected Robot Networks

This paper considers the problem of distributed state estimation (DSE) using multirobot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines: first, communication schedules that ensure that the network is intermittently connected, and second, sampling-based motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first DSE framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.
Derivations and Observations of Prominence Bulk Motions and Mass

In this chapter we review observations and techniques for measuring both bulk flows in prominences and prominence mass. Measuring these quantities is essential to development and testing of models discussed throughout this book. Prominence flows are complex and various, ranging from the relatively linear flows along prominence spines to the complex, turbulent patterns exhibited by hedgerow prominences. Techniques for measuring flows include time slice and optical flow techniques used for motions in the plane of the sky and the use of spectral line profiles to determine Doppler velocities along the line of sight. Prominence mass measurement is chiefly done via continuum absorption measurements, but mass has also been estimated using cloud modeling and white light measurements.
A rarefaction-tracking method for hyperbolic conservation laws

A numerical method for scalar conservation laws in one space dimension is presented. The solution is approximated by local similarity solutions. While many commonly used approaches are based on shocks, the presented method uses rarefaction and compression waves. The solution is represented by particles that carry function values and move according to the method of characteristics. Between two neighboring particles, an interpolation is defined by an analytical similarity solution of the conservation law. An interaction of particles represents a collision of characteristics. The resulting shock is resolved by merging particles so that the total area under the function is conserved. The method is variation diminishing; nevertheless, it has no numerical dissipation away from shocks. Although shocks are not explicitly tracked, they can be located accurately. Numerical examples are presented, and specific applications and extensions of the approach outlined.
Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach

Abstract High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is important to investigate the thermodynamics and elucidate the evolution of various chemical phases. In this work, we introduce a data-driven approach to construct the effective Hamiltonian and study the thermodynamics of HEAs through canonical Monte Carlo simulation. The main characteristic of our method is to use pairwise interactions between atoms as features and systematically improve the representativeness of the dataset using samples from Monte Carlo simulation. We find this method produces highly robust and accurate effective Hamiltonians that give less than 0.1 mRy test error for all the three refractory HEAs: MoNbTaW, MoNbTaVW, and MoNbTaTiW. Using replica exchange to speed up the MC simulation, we calculated the specific heats and short-range order parameters in a wide range of temperatures. For all the studied materials, we find there are two major order-disorder transitions occurring respectively at T 1 and T 2 , where T 1 is near room temperature but T 2 is much higher. We further demonstrate that the transition at T 1 is caused by W and Nb while the one at T 2 is caused by the other elements. By comparing with experiments, the results provide insight into the role of chemical ordering in the strength and ductility of HEAs.
Strichartz estimates for the wave equation on manifolds with boundary

We prove certain mixed-norm Strichartz estimates on manifolds with boundary. Using them we are able to prove new results for the critical and subcritical wave equation in 4-dimensions with Dirichlet or Neumann boundary conditions. We obtain global existence in the subcritical case, as well as global existence for the critical equation with small data. We also can use our Strichartz estimates to prove scattering results for the critical wave equation with Dirichlet boundary conditions in 3-dimensions.
Scaling and universality in extremal black hole perturbations

We show that the emergent near-horizon conformal symmetry of extremal black holes gives rise to universal behavior in perturbing fields, both near and far from the black hole horizon. The scale-invariance of the near-horizon region entails power law time-dependence with three universal features: (1) the decay off the horizon is always precisely twice as fast as the decay on the horizon; (2) the special rates of 1/t off the horizon and $$ 1/\sqrt{v} $$ on the horizon commonly occur; and (3) sufficiently high-order transverse derivatives grow on the horizon (Aretakis instability). The results are simply understood in terms of near-horizon (AdS2) holography. We first show how the general features follow from symmetry alone and then go on to present the detailed universal behavior of scalar, electromagnetic, and gravitational perturbations of d-dimensional electrovacuum black holes.
More is the Same; Phase Transitions and Mean Field Theories

This paper is the first in a series that will look at the theory of phase transitions from the perspectives of physics and the philosophy of science. The series will consider a group of related concepts derived from condensed matter and statistical physics. The key technical ideas go under the names of “singularity”, “order parameter”, “mean field theory”, “variational method”, “correlation length”, “universality class”, “scale changes”, and “renormalization”. The first four of these will be considered here. In a less technical vein, the question here is how can matter, ordinary matter, support a diversity of forms. We see this diversity each time we observe ice in contact with liquid water or see water vapor (steam) come up from a pot of heated water. Different phases can be qualitatively different in that walking on ice is well within human capacity, but walking on liquid water is proverbially forbidden to ordinary humans. These differences have been apparent to humankind for millennia, but only brought within the domain of scientific understanding since the 1880s. A phase transition is a change from one behavior to another. A first order phase transition involves a discontinuous jump in some statistical variable. The discontinuous property is called the order parameter. Each phase transition has its own order parameter. The possible order parameters range over a tremendous variety of physical properties. These properties include the density of a liquid-gas transition, the magnetization in a ferromagnet, the size of a connected cluster in a percolation transition, and a condensate wave function in a superfluid or superconductor. A continuous transition occurs when the discontinuity in the jump approaches zero. This article is about statistical mechanics and the development of mean field theory as a basis for a partial understanding of phase transition phenomena. Much of the material in this review was first prepared for the Royal Netherlands Academy of Arts and Sciences in 2006. It has appeared in draft form on the authors’ web site ( http://jfi.uchicago.edu/~leop/ ) since then. The title of this article is a hommage to Philip Anderson and his essay “More is Different” (Sci. New Ser. 177(4047):393–396, 1972; N.-P. Ong and R. Bhatt (eds.) More is Different: Fifty Years of Condensed Matter Physics, Princeton Series in Physics, Princeton University Press, 2001) which describes how new concepts, not applicable in ordinary classical or quantum mechanics, can arise from the consideration of aggregates of large numbers of particles. Since phase transitions only occur in systems with an infinite number of degrees of freedom, such transitions are a prime example of Anderson’s thesis.
Uniform integrability of the OLS estimators, and the convergence of their moments

The problem of convergence of moments of a sequence of random variables to the moments of its asymptotic distribution is important in many applications. These include the determination of the optimal training sample size in the cross-validation estimation of the generalization error of computer algorithms, and in the construction of graphical methods for studying dependence patterns between two biomarkers. In this paper, we prove the uniform integrability of the ordinary least squares estimators of a linear regression model, under suitable assumptions on the design matrix and the moments of the errors. Further, we prove the convergence of the moments of the estimators to the corresponding moments of their asymptotic distribution, and study the rate of the moment convergence. The canonical central limit theorem corresponds to the simplest linear regression model. We investigate the rate of the moment convergence in canonical central limit theorem proving a sharp improvement of von Bahr’s (Ann Math Stat 36:808–818, 1965) theorem.
THE CONNECTION BETWEEN DIFFUSE LIGHT AND INTRACLUSTER PLANETARY NEBULAE IN THE VIRGO CLUSTER

We compare the distribution of diffuse intracluster light (ICL) detected in the Virgo Cluster via broadband imaging with that inferred from searches for intracluster planetary nebulae (IPNe). We find a rough correspondence on large scales (~100 kpc) between the two, but with very large scatter (~1.3 mag arcsec–2). On smaller scales (1-10 kpc), the presence or absence of correlation is clearly dependent on the underlying surface brightness. On these scales, we find a correlation in regions of higher surface brightness (μV 27) which are dominated by the halos of large galaxies such as M87, M86, and M84. In those cases, we are likely tracing PNe associated with galaxies rather than true IPNe. In true intracluster fields, at lower surface brightness, the correlation between luminosity and IPN candidates is much weaker. While a correlation between broadband light and IPNe is expected based on stellar populations, a variety of statistical, physical, and methodological effects can act to wash out this correlation and explain the lack of a strong correlation at lower surface brightness found here. A significant complication comes from the stochastic nature of the PN population combined with contamination of the IPN catalogs due to photometric errors and background emission line objects. If we attribute the lack of a strong observed correlation solely to the effects of contamination, our Monte Carlo analysis shows that our results are mostly consistent with a "IPNe-follow-light" model if the IPN catalogs are contaminated by ~40%. Further complications arise from the line-of-sight depth of the Virgo and uncertainty in the stellar populations of the ICL, both of which may contribute to the slight systematic differences seen between the IPN-inferred surface brightnesses and those derived from our deep surface photometry.
Prospective Performance of Graphene HEB for Ultrasensitive Detection of Sub-mm Radiation

Noise equivalent power and time constant of a submillimeter wave hot-electron bolometer (HEB) made from monolayer graphene are analyzed using the lowest electron-phonon thermal conductance data reported to date. Frequency-domain multiplexed Johnson Noise Thermometry (JNT) is used for the detector readout. Planar microantennas or waveguides can provide efficient coupling of the graphene microdevice to radiation. The results show that the graphene HEB detector can be radiation background limited at very low level corresponding to the photon noise on a space telescope with cryogenically cooled mirror. Beside the high sensitivity, absence of a hard power saturation limit, higher operating temperature, and the ability to read 1,000s of elements with a single broadband amplifier will be the advantages of such a detector.
Microscopic Theory of the Thermodynamic Properties of Sr$_3$Ru$_2$O$_7$

The thermodynamic properties of the bilayer ruthenate compound Sr3Ru2O7 at very low temperatures are investigated by using a tight-binding model yielding the realistic band structure combined with the on-site interactions treated at the mean-field level. We find that both the total density of states at the Fermi energy and the entropy exhibit a sudden increase near the critical magnetic field for the nematic phase, echoing the experimental findings. A new mechanism to explain the anisotropic transport properties is proposed based on scatterings at the anisotropic domain boundaries. Our results suggest that extra cares are necessary to isolate the contributions due to the quantum criticality from the band structure singularity in Sr3Ru2O7.
Detection of knots and a cabling formula for A-polynomials

We say that a given knot J ⊂ S^3 is detected by its knot Floer homology and AA–polynomial if whenever a knot K ⊂ S^3 has the same knot Floer homology and the same A–polynomial as J, then K=J. In this paper we show that every torus knot T(p,q) is detected by its knot Floer homology and A–polynomial. We also give a one-parameter family of infinitely many hyperbolic knots in S^3 each of which is detected by its knot Floer homology and AA–polynomial. In addition we give a cabling formula for the AA–polynomials of cabled knots in S^3, which is of independent interest. In particular we give explicitly the AA–polynomials of iterated torus knots.
Sparse Maximum-Entropy Random Graphs with a Given Power-Law Degree Distribution

Even though power-law or close-to-power-law degree distributions are ubiquitously observed in a great variety of large real networks, the mathematically satisfactory treatment of random power-law graphs satisfying basic statistical requirements of realism is still lacking. These requirements are: sparsity, exchangeability, projectivity, and unbiasedness. The last requirement states that entropy of the graph ensemble must be maximized under the degree distribution constraints. Here we prove that the hypersoft configuration model, belonging to the class of random graphs with latent hyperparameters, also known as inhomogeneous random graphs or W-random graphs, is an ensemble of random power-law graphs that are sparse, unbiased, and either exchangeable or projective. The proof of their unbiasedness relies on generalized graphons, and on mapping the problem of maximization of the normalized Gibbs entropy of a random graph ensemble, to the graphon entropy maximization problem, showing that the two entropies converge to each other in the large-graph limit.
Daughters mimic sterile neutrinos (almost!) perfectly

Since only recently, cosmological observations are sensitive to hot dark matter (HDM) admixtures with sub-eV mass, mhdmeff < eV, that are not fully-thermalised, Δ Neff < 1. We argue that their almost automatic interpretation as a sterile neutrino species is neither from theoretical nor practical parsimony principles preferred over HDM formed by decay products (daughters) of an out-of-equilibrium particle decay. While daughters mimic sterile neutrinos in Neff and mhdmeff, there are opportunities to assess this possibility in likelihood analyses. Connecting cosmological parameters and moments of momentum distribution functions, we show that—also in the case of mass-degenerate daughters with indistinguishable main physical effects—the mimicry breaks down when the next moment, the skewness, is considered. Predicted differences of order one in the root-mean-squares of absolute momenta are too small for current sensitivities.
PrivacyFL: A Simulator for Privacy-Preserving and Secure Federated Learning

Federated learning is a technique that enables distributed clients to collaboratively learn a shared machine learning model without sharing their training data. This reduces data privacy risks, however, privacy concerns still exist since it is possible to leak information about the training dataset from the trained model's weights or parameters. Therefore, it is important to develop federated learning algorithms that train highly accurate models in a privacy-preserving manner. Setting up a federated learning environment, especially with security and privacy guarantees, is a time-consuming process with numerous configurations and parameters that can be manipulated. In order to help clients ensure that collaboration is feasible and to check that it improves their model accuracy, a real-world simulator for privacy-preserving and secure federated learning is required. In this paper, we introduce PrivacyFL, which is an extensible, easily configurable, and scalable simulator for federated learning environments. Its key features include latency simulation, robustness to client departure/failure, support for both centralized (with one or more servers) and decentralized (serverless) learning, and configurable privacy and security mechanisms based on differential privacy and secure multiparty computation (MPC). In this paper, we motivate our research, describe the architecture of the simulator and associated protocols, and discuss its evaluation in numerous scenarios that highlight its wide range of functionality and its advantages. Our paper addresses a significant real-world problem: checking the feasibility of participating in a federated learning environment under a variety of circumstances. It also has a strong practical impact because organizations such as hospitals, banks, and research institutes, which have large amounts of sensitive data and would like to collaborate, would greatly benefit from having a system that enables them to do so in a privacy-preserving and secure manner.
Stage-wise Conservative Linear Bandits

We study stage-wise conservative linear stochastic bandits: an instance of bandit optimization, which accounts for (unknown) safety constraints that appear in applications such as online advertising and medical trials. At each stage, the learner must choose actions that not only maximize cumulative reward across the entire time horizon but further satisfy a linear baseline constraint that takes the form of a lower bound on the instantaneous reward. For this problem, we present two novel algorithms, stage-wise conservative linear Thompson Sampling (SCLTS) and stage-wise conservative linear UCB (SCLUCB), that respect the baseline constraints and enjoy probabilistic regret bounds of order O(\sqrt{T} \log^{3/2}T) and O(\sqrt{T} \log T), respectively. Notably, the proposed algorithms can be adjusted with only minor modifications to tackle different problem variations, such as constraints with bandit-feedback, or an unknown sequence of baseline actions. We discuss these and other improvements over the state-of-the-art. For instance, compared to existing solutions, we show that SCLTS plays the (non-optimal) baseline action at most O(\log{T}) times (compared to O(\sqrt{T})). Finally, we make connections to another studied form of safety constraints that takes the form of an upper bound on the instantaneous reward. While this incurs additional complexity to the learning process as the optimal action is not guaranteed to belong to the safe set at each round, we show that SCLUCB can properly adjust in this setting via a simple modification.
New Keck observations of lithium in very metal-poor stars

Lithium abundances have been determined in more than 100 metal-poor halo stars both in the field and in clusters. From these data we find trends of Li with both temperature and metallicity and a real dispersion in Li abundances in the Spite Li plateau. We attribute this dispersion primarily to Li depletion (presumably due to extra mixing induced by stellar rotation) and to Galactic chemical evolution. We derive a primordial Li of 2.44 $\,{\pm}\,$ 0.18 for A(Li) $_p$ = log N(Li/H) + 12.00. This agrees with the Li abundances predicted by the WMAP results. For stars cooler than the Li plateau we have evidence that Li depletion sets in at hotter temperatures for the higher metallicity stars than for the low-metal stars. This is the opposite sense of predictions from stellar models. The smooth transition of the Li content from the Li plateau stars to the cool stars adds weight to the inference of Li depletion in the plateau stars.
Limits on Clouds and Hazes for the TRAPPIST-1 Planets

The TRAPPIST-1 planetary system is an excellent candidate for study of the evolution and habitability of M-dwarf planets. Transmission spectroscopy observations performed with the Hubble Space Telescope (HST) suggest the innermost five planets do not possess clear hydrogen atmospheres. Here we reassess these conclusions with recently updated mass constraints and expand the analysis to include limits on metallicity, cloud top pressure, and the strength of haze scattering. We connect recent laboratory results of particle size and production rate for exoplanet hazes to a one-dimensional atmospheric model for TRAPPIST-1 transmission spectra. Doing so, we obtain a physically-based estimate of haze scattering cross sections. We find haze scattering cross sections on the order of 1e-26 to 1e-19 cm squared are needed in hydrogen-rich atmospheres for TRAPPIST-1 d, e, and f to match the HST data. For TRAPPIST-1 g, we cannot rule out a clear hydrogen-rich atmosphere. We also modeled the effects an opaque cloud deck and substantial heavy element content have on the transmission spectra. We determine that hydrogen-rich atmospheres with high altitude clouds, at pressures of 12mbar and lower, are consistent with the HST observations for TRAPPIST-1 d and e. For TRAPPIST-1 f and g, we cannot rule out clear hydrogen-rich cases to high confidence. We demonstrate that metallicities of at least 60xsolar with tropospheric (0.1 bar) clouds agree with observations. Additionally, we provide estimates of the precision necessary for future observations to disentangle degeneracies in cloud top pressure and metallicity. Our results suggest secondary, volatile-rich atmospheres for the outer TRAPPIST-1 planets d, e, and f.
Tree homology and a conjecture of Levine

In his study of the group of homology cylinders, J Levine [23] made the conjecture that a certain group homomorphism 0 W T ! D 0 is an isomorphism. Both T and D 0 are defined combinatorially using trivalent trees and have strong connections to a variety of topological settings, including the mapping class group, homology cylinders, finite type invariants, Whitney tower intersection theory and the homology of Out.Fn/. In this paper, we confirm Levine’s conjecture by applying discrete Morse theory to certain subcomplexes of a Kontsevich-type graph complex. These are chain complexes generated by trees, and we identify particular homology groups of them with the domain T and range D 0 of Levine’s map. The isomorphism 0 is a key to classifying the structure of links up to grope and Whitney tower concordance, as explained in [6; 5]. In this paper and [3] we apply our result to confirm and improve upon Levine’s conjectured relation between two filtrations of the group of homology cylinders. 57M27, 57M25; 57N10
Theory of Exceptional Points of Degeneracy in Uniform Coupled Waveguides and Balance of Gain and Loss

We present a transmission line theory of exceptional points of degeneracy (EPD) in coupled-mode guiding structures, i.e., a theory that illustrates the characteristics of coupled electromagnetic modes under a special dispersion degeneracy condition, yet unexplored in the contest of gain and loss. We show that coupled transmission lines (CTLs) at radio frequencies having gain (active devices) and loss (e.g., material, radiation) balance exhibit EPDs. We demonstrate the concept of parity-time ( $\mathcal {PT}$ )-symmetry in uniform CTLs that involve symmetric gain and loss and how this condition is associated with a second-order EPD. Furthermore, we also demonstrate that $\mathcal {PT}$ -symmetry is not a necessary condition for realizing EPDs, and indeed, we show that EPD is also obtained with asymmetric distributions of gain and loss in uniform CTLs. We further propose potential applications of the EPDs in designing leaky-wave antennas with the capability of beam and directivity control as well as enhanced sensitivity. Operating near such special degeneracy conditions leads to potential performance enhancement in a variety of microwave and optical resonators, antennas, and devices such as distributed oscillators, including lasers, amplifiers, radiating oscillators, pulse compressors, and $Q$ -switching sensors.
Federated Accelerated Stochastic Gradient Descent

We propose Federated Accelerated Stochastic Gradient Descent (FedAc), a principled acceleration of Federated Averaging (FedAvg, also known as Local SGD) for distributed optimization. FedAc is the first provable acceleration of FedAvg that improves convergence speed and communication efficiency on various types of convex functions. For example, for strongly convex and smooth functions, when using $M$ workers, the previous state-of-the-art FedAvg analysis can achieve a linear speedup in $M$ if given $M$ rounds of synchronization, whereas FedAc only requires $M^{\frac{1}{3}}$ rounds. Moreover, we prove stronger guarantees for FedAc when the objectives are third-order smooth. Our technique is based on a potential-based perturbed iterate analysis, a novel stability analysis of generalized accelerated SGD, and a strategic tradeoff between acceleration and stability.
Concentration phenomena for neutronic multigroup diffusion in random environments

Abstract We study the asymptotic behavior of the principal eigenvalue of a weakly coupled, cooperative linear elliptic system in a stationary ergodic heterogeneous medium. The system arises as the so-called multigroup diffusion model for neutron flux in nuclear reactor cores, the principal eigenvalue determining the criticality of the reactor in a stationary state. Such systems have been well studied in recent years in the periodic setting, and the purpose of this work is to obtain results in random media. Our approach connects the linear eigenvalue problem to a system of quasilinear viscous Hamilton–Jacobi equations. By homogenizing the latter, we characterize the asymptotic behavior of the eigenvalue of the linear problem and exhibit some concentration behavior of the eigenfunctions.
THE FAR-ULTRAVIOLET “CONTINUUM” IN PROTOPLANETARY DISK SYSTEMS. I. ELECTRON-IMPACT H2 AND ACCRETION SHOCKS*

We present deep spectroscopic observations of the classical T Tauri stars DF Tau and V4046 Sgr in order to better characterize two important sources of far-ultraviolet continuum emission in protoplanetary disks. These new Hubble Space Telescope-Cosmic Origins Spectrograph observations reveal a combination of line and continuum emission from collisionally excited H2 and emission from accretion shocks. H2 is the dominant emission in the 1400 A λ 1650 A band spectrum of V4046 Sgr, while an accretion continuum contributes strongly across the far-ultraviolet spectrum of DF Tau. We compare the spectrum of V4046 Sgr to models of electron-impact-induced H2 emission to constrain the physical properties of the emitting region, after making corrections for attenuation within the disk. We find reasonable agreement with the broad spectral characteristics of the H2 model, implying N(H2) ~ 1018 cm–2, T(H2) = 3000+1000 –500 K, and a characteristic electron energy in the range of ~50-100 eV. We propose that self-absorption and hydrocarbons provide the dominant attenuation for H2 line photons originating within the disk. For both DF Tau and V4046 Sgr, we find that a linear fit to the far-UV data can reproduce near-UV/optical accretion spectra. We discuss outstanding issues concerning how these processes operate in protostellar/protoplanetary disks, including the effective temperature and absolute strength of the radiation field in low-mass protoplanetary environments. We find that the 912-2000 A continuum in low-mass systems has an effective temperature of ~104 K with fluxes 105-107 times the interstellar level at 1 AU.
THE STAR-FORMING GALAXY CONTRIBUTION TO THE COSMIC MeV AND GeV GAMMA-RAY BACKGROUND

Star-forming galaxies could be major contributors to the cosmic GeV γ-ray background, and they are expected to be MeV-dim because of the 'pion bump' falling off below ∼100 MeV. However, there are very few observations of galaxies in the MeV range and other emission processes could be present. We investigate the MeV background from star-forming galaxies by running one-zone models of cosmic ray populations, including inverse Compton and bremsstrahlung, as well as nuclear lines (including {sup 26}Al), emission from core-collapse supernovae, and positron annihilation emission, in addition to the pionic emission. We use the Milky Way and M82 as templates of normal and starburst galaxies and compare our models to radio and GeV-TeV γ-ray data. We find that (1) higher gas densities in high-z normal galaxies lead to a strong pion bump, (2) starbursts may have significant MeV emission if their magnetic field strengths are low, and (3) cascades can contribute to the MeV emission of starbursts if they emit mainly hadronic γ-rays. Our fiducial model predicts that most of the unresolved GeV background is from star-forming galaxies, but this prediction is uncertain by an order of magnitude. About ∼2% of the claimed 1 MeV background is diffuse emission frommore » star-forming galaxies; we place a firm upper limit of ≲ 10% based on the spectral shape of the background. The star formation contribution is constrained to be small because its spectrum is peaked, while the observed background is steeply falling with energy through the MeV-GeV range.« less
A Simple Scaling Analysis of X-Ray Emission and Absorption in Hot-Star Winds

We present a simple analysis of X-ray emission and absorption for hot-star winds, designed to explore the natural scalings of the observed X-ray luminosity with wind and stellar properties. We show that an exospheric approximation, in which all of the emission above the optical depth unity radius escapes the wind, reproduces very well the formal solution for radiation transport through a spherically symmetric wind. Using this approximation we find that the X-ray luminosity LX scales naturally with the wind density parameter /v∞, obtaining LX ~ (/v∞)2 for optically thin winds, and LX ~ (/v∞)1+s for optically thick winds with an X-ray filling factor that varies in radius as f ~ rs. These scalings with wind density contrast with the commonly inferred empirical scalings of X-ray luminosity LX with bolometric luminosity LBol. The empirically derived linear scaling of LX ~ LBol for thick winds can, however, be reproduced through a delicate cancellation of emission and absorption, if one assumes modest radial fall-off in the X-ray filling factor (s ≈ -0.25 or s ≈ -0.4, depending on details of the secondary scaling of wind density with luminosity). We also explore the nature of the X-ray spectral energy distribution in the context of this model and find that the spectrum is divided into a soft, optically thick part and a hard, optically thin part. Finally, we conclude that the energy-dependent emissivity must have a high-energy cutoff, corresponding to the maximum shock energy, in order to reproduce the general trends seen in X-ray spectral energy distributions of hot stars.
Effective field theory for bound state reflection

Abstract Elastic quantum bound-state reflection from a hard-wall boundary provides direct information regarding the structure and compressibility of quantum bound states. We discuss elastic quantum bound-state reflection and derive a general theory for elastic reflection of shallow dimers from hard-wall surfaces using effective field theory. We show that there is a small expansion parameter for analytic calculations of the reflection scattering length. We present a calculation up to second order in the effective Hamiltonian in one, two, and three dimensions. We also provide numerical lattice results for all three cases as a comparison with our effective field theory results. Finally, we provide an analysis of the compressibility of the alpha particle confined to a cubic lattice with vanishing Dirichlet boundaries.