cssi/SciSciGPT-SciSciNet huggingface dataset:
Versatile mixed methods for the incompressible Navier–Stokes equations

Abstract In the spirit of the “Principle of Equipresence” introduced by Truesdell & Toupin, The Classical Field Theories (1960), we use the full version of the viscous stress tensor ν ∇ u + ∇ u T − 2 3 ∇ ⋅ u I which was originally derived for compressible flows, instead of the classical incompressible stress tensor ν ∇ u . (Note that, here ν is the dynamic viscosity coefficient, and u is the velocity field.) In our approach, the divergence-free constraint for the viscous stress term is not enforced ahead of discretization. Instead, our formulation allows the scheme itself to “choose” a consistent way to interpret the divergence-free constraint: i.e., the divergence-free constraint is interpreted (or enforced) in a consistent fashion in both the mass conservation equation and the stress tensor term (in the momentum equation). Furthermore, our approach preserves the original symmetrical properties of the stress tensor, e.g. its rotational invariance, and it remains physically correct in the context of compressible flows. As a result, our approach facilitates versatility and code reuse. In this paper, we introduce our approach and establish some important mathematical properties for the resulting class of finite element schemes. More precisely, for general mixed methods, which are not necessarily pointwise divergence-free, we establish the existence of a new norm induced by the full, viscous bilinear form. Thereafter, we prove the coercivity of the viscous bilinear form and the semi-coercivity of a convective trilinear form. In addition, we demonstrate L2-stability of the discrete velocity fields for the general class of methods and (by deduction) the H(div)-conforming methods. Finally, we run some numerical experiments to illustrate the behavior of the versatile mixed methods, and we make careful comparisons with a conventional H(div)-conforming scheme.
State and Local Government Employment in the COVID-19 Crisis

Abstract Local governments are facing large losses in revenues and increased expenditures because of the COVID-19 crisis. We document a causal relationship between fiscal pressures induced by COVID-19 and the layoffs of state and local government workers. States that depend more on sales tax as a source of revenue laid off significantly more workers than other states. The CARES Act’s provision of $150 billion in aid to state and local governments reduced the fiscal pressures they faced. Exploiting a kink in the formula for allocation of funding across states, we estimate that without this funding state and local governments would have laid off an additional 401,000 workers in April 2020, 40 percent more than realized. State rainy day fund balances limit the sensitivity of employment to these revenue shocks, revealing that balanced budget requirements for state and local governments increase the procyclicality of public service provision.
Capacity-achieving Polar-based LDGM Codes with Crowdsourcing Applications

In this paper we study codes with sparse generator matrices. More specifically, codes with a certain constraint on the weight of all the columns in the generator matrix are considered. The end result is the following. For any binary-input memoryless symmetric (BMS) channel and any e> 2e∗, where ${\varepsilon ^{\ast}} = \frac{1}{6} - \frac{5}{3}\log \frac{4}{3} \approx 0.085$, we show an explicit sequence of capacity-achieving codes with all the column weights of the generator matrix upper bounded by (log N)1+ e, where N is the code block length. The constructions are based on polar codes. Applications to crowdsourcing are also shown.
Cadherin clusters stabilized by a combination of specific and nonspecific cis-interactions

We demonstrate a combined experimental and computational approach for the quantitative characterization of lateral interactions between membrane-associated proteins. In particular, weak, lateral (cis) interactions between E-cadherin extracellular domains tethered to supported lipid bilayers, were studied using a combination of dynamic single-molecule Forster Resonance Energy Transfer (FRET) and kinetic Monte Carlo (kMC) simulations. Cadherins are intercellular adhesion proteins that assemble into clusters at cell-cell contacts through cis- and trans- (adhesive) interactions. A detailed and quantitative understanding of cis-clustering has been hindered by a lack of experimental approaches capable of detecting and quantifying lateral interactions between proteins on membranes. Here single-molecule intermolecular FRET measurements of wild-type E-cadherin and cis-interaction mutants combined with simulations demonstrate that both nonspecific and specific cis-interactions contribute to lateral clustering on lipid bilayers. Moreover, the intermolecular binding and dissociation rate constants are quantitatively and independently determined, demonstrating an approach that is generalizable for other interacting proteins.
Resilient critical infrastructure: Bayesian network analysis and contract-Based optimization

Abstract Instilling resilience in critical infrastructure (CI) such as dams or power grids is a major challenge for tomorrow’s cities and communities. Resilience, here, pertains to a CI’s ability to adapt or rapidly recover from disruptive events. In this paper, the problem of optimizing and managing the resilience of CIs is studied. In particular, a comprehensive two-fold framework is proposed to improve CI resilience by considering both the individual CIs and their collective contribution to an entire system of multiple CIs. To this end, a novel analytical resilience index is proposed to measure the effect of each CI’s physical components on its probability of failure. In particular, a Markov chain defining each CI’s performance state and a Bayesian network modeling the probability of failure are introduced to infer each CI’s resilience index. Then, to maximize the resilience of a system of CIs, a novel approach for allocating resources, such as drones or maintenance personnel, is proposed. In particular, a comprehensive resource allocation framework, based on the tools of contract theory, is proposed enabling the system operator to optimally allocate resources, such as, redundant components or monitoring devices to each individual CI based on its economic contribution to the entire system. The optimal solution of the contract-based resilience resource allocation problem is analytically derived using dynamic programming. The proposed framework is then evaluated using a case study pertaining to hydropower dams and their interdependence to the power grid. Simulation results, within the case study, show that the system operator can economically benefit from allocating the resources while dams have a 60% average improvement over their initial resilience indices.
Beamspace Channel Estimation for Massive MIMO mmWave Systems: Algorithm and VLSI Design

Millimeter-wave (mmWave) communication in combination with massive multiuser multiple-input multiple-output (MU-MIMO) enables high-bandwidth data transmission to multiple users in the same time-frequency resource. The strong path loss of wave propagation at such high frequencies necessitates accurate channel state information to ensure reliable data transmission. We propose a novel channel estimation algorithm called BEAmspace CHannel EStimation (BEACHES), which leverages the fact that wave propagation at mmWave frequencies is predominantly directional. BEACHES adaptively denoises the channel vectors in the beamspace domain using an adaptive shrinkage procedure that relies on Stein’s unbiased risk estimator (SURE). Simulation results for line-of-sight (LoS) and non-LoS mmWave channels reveal that BEACHES performs on par with state-of-the-art channel estimation methods while requiring orders-of-magnitude lower complexity. To demonstrate the effectiveness of BEACHES in practice, we develop a very large-scale integration (VLSI) architecture and provide field-programmable gate array (FPGA) implementation results. Our results show that adaptive channel denoising can be performed at high throughput and in a hardware-friendly manner for massive MU-MIMO mmWave systems with hundreds of antennas.
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect

We study the effect of persistence of engagement on learning in a stochastic multi-armed bandit setting. In advertising and recommendation systems, repetition effect includes a wear-in period, where the user's propensity to reward the platform via a click or purchase depends on how frequently they see the recommendation in the recent past. It also includes a counteracting wear-out period, where the user's propensity to respond positively is dampened if the recommendation was shown too many times recently. Priming effect can be naturally modelled as a temporal constraint on the strategy space, since the reward for the current action depends on historical actions taken by the platform. We provide novel algorithms that achieves sublinear regret in time and the relevant wear-in/wear-out parameters. The effect of priming on the regret upper bound is also additive, and we get back a guarantee that matches popular algorithms such as the UCB1 and Thompson sampling when there is no priming effect. Our work complements recent work on modeling time varying rewards, delays and corruptions in bandits, and extends the usage of rich behavior models in sequential decision making settings.
Eccentricities and the stability of closely-spaced five-planet systems

Abstract Observations of exoplanets have revealed that systems with planets on closely-spaced orbits are common, which motivates the question “How closely can planets orbit to one another and still be dynamically-stable for very long times?”. To address this question, we investigate the stability of idealized planetary systems consisting of five planets, each equal in mass to the Earth, orbiting a one solar mass star. All planets orbit in the same plane and in the same direction, and the planets are uniformly spaced in units of mutual Hill Sphere radii. Most of the systems that we integrate begin with one or more planets on eccentric orbits, with eccentricities e as large as e = 0 . 05 being considered. For a given initial orbital separation, larger initial eccentricity of a single planet generally leads to shorter system lifetime, with little systematic dependence of which planet is initially on an eccentric orbit. The approximate trend of instability times increasing exponentially with initial orbital separation of the planets found previously for planets with initially circular orbits is also present for systems with initially eccentric orbits. Mean motion resonances also tend to destabilize these systems, although the reductions in system lifetimes are not as large as for initially circular orbits. Systems with all planets having initial e = 0 . 05 and aligned periapse angles typically survive far longer than systems with the same spacing in initial semi-major axis and one planet with e = 0 . 05 , but they have slightly shorter lifetimes than those with planets initially on circular orbits.
Deep Hashing for Secure Multimodal Biometrics

When compared to unimodal systems, multimodal biometric systems have several advantages, including lower error rate, higher accuracy, and larger population coverage. However, multimodal systems have an increased demand for integrity and privacy because they must store multiple biometric traits associated with each user. In this paper, we present a deep learning framework for feature-level fusion that generates a secure multimodal template from each user’s face and iris biometrics. We integrate a deep hashing (binarization) technique into the fusion architecture to generate a robust binary multimodal shared latent representation. Further, we employ a hybrid secure architecture by combining cancelable biometrics with secure sketch techniques and integrate it with a deep hashing framework, which makes it computationally prohibitive to forge a combination of multiple biometrics that passes the authentication. The efficacy of the proposed approach is shown using a multimodal database of face and iris and it is observed that the matching performance is improved due to the fusion of multiple biometrics. Furthermore, the proposed approach also provides cancelability and unlinkability of the templates along with improved privacy of the biometric data. Additionally, we also test the proposed hashing function for an image retrieval application using a benchmark dataset. The main goal of this paper is to develop a method for integrating multimodal fusion, deep hashing, and biometric security, with an emphasis on structural data from modalities like face and iris. The proposed approach is in no way a general biometrics security framework that can be applied to all biometrics modalities, as further research is needed to extend the proposed framework to other unconstrained biometric modalities.
Koopman Operator Applications in Signalized Traffic Systems

This paper proposes Koopman operator theory and the related algorithm dynamical mode decomposition (DMD) for analysis and control of signalized traffic flow networks. DMD provides a model-free approach for representing complex oscillatory dynamics from measured data, and we study its application to several problems in signalized traffic. We first study a single signalized intersection, and we propose applying this method to infer traffic signal control parameters such as phase timing directly from traffic flow data. Next, we propose using the oscillatory modes of the Koopman operator, approximated with DMD, for early identification of unstable queue growth that has the potential to cause cascading congestion. Then we demonstrate how DMD can be coupled with knowledge of the traffic signal control status to determine traffic signal control parameters that are able to reduce queue lengths. Lastly, we demonstrate that DMD allows for determining the structure and the strength of interactions in a network of signalized intersections. All examples are demonstrated using a case study network instrumented with high resolution traffic flow sensors.
Correlation functions: Getting into shape

The ability to measure characteristics of source shapes using nonidentical particle correlations is discussed. Both strong-interaction induced and Coulomb induced correlations are shown to provide sensitivity to source shapes. By decomposing correlation functions with spherical or Cartesian harmonics, details of the shapes can be especially well isolated.
The cosmic-ray population of the galactic central molecular zone

The conditions in the Galactic Center are often compared with those in starburst systems, which contain higher supernova rates, stronger magnetic fields, more intense radiation fields, and larger amounts of dense molecular gas than in our own Galactic disk. Interactions between such an augmented interstellar medium and cosmic rays result in brighter radio and γ-ray emission. Here, we test how well the comparisons between the Galactic Center and starburst galaxies hold by applying a model for cosmic-ray interactions to the Galactic Center to predict the resulting γ-ray emission. The model only partially explains the observed γ-ray and radio emission. The model for the γ-ray spectrum agrees with the data at TeV energies but not at GeV energies. Additionally, as the fits of the model to the radio and γ-ray spectra require significant differences in the optimal wind speed and magnetic field strength, we find that the single-zone model alone cannot account for the observed emission from the Galactic Center. Our model is improved by including a soft, additional cosmic-ray population. We assess such a cosmic-ray population and its potential sources and find that a cosmic-ray electron spectrum is energetically favored over a cosmic-ray proton spectrum.
What is odd about binary Parseval frames

This paper examines the construction and properties of binary Parseval frames. We address two questions: When does a binary Parseval frame have a complementary Parseval frame? Which binary symmetric idempotent matrices are Gram matrices of binary Parseval frames? In contrast to the case of real or complex Parseval frames, the answer to these questions is not always affirmative. The key to our understanding comes from an algorithm that constructs binary orthonormal sequences that span a given subspace, whenever possible. Special regard is given to binary frames whose Gram matrices are circulants.
Median and mode in first passage under restart

This paper proposes a new analysis to explore the advantages of restarting to the optimization of the median and mode of a generic first passage-time density. The results allow the design of restart protocols for acceleration of randomized computer algorithms and can be of use in chemical synthesis applications.
Gravitational instability of finite isothermal spheres in general relativity. Analogy with neutron stars

We investigate the effects of relativity on the gravitational instability of finite isothermal gaseous spheres. In the first part of the paper, we treat the gravitational field within the framework of Newtonian mechanics but we allow the speed of the particles to be close to the velocity of light so that special relativity must be taken into account. In the second part of the paper, we study the full general relativistic problem for a gas described by an equation of state such that the pressure is proportional to the energy density (“isothermal” distribution). For , this equation of state describes the core of neutron stars. The mass-density diagram displays some damped oscillations and there exists a critical value of mass-energy above which no equilibrium state is possible. We show analytically that the mass peaks are associated with new modes of instability. These results are strikingly similar to those obtained by Antonov (1962) and Lynden-Bell & Wood (1968) for a classical isothermal gas. Our study completes the analogy between isothermal spheres and neutron stars investigated by Yabushita (1974).
From SYM perturbation theory to closed strings in matrix theory

For the purpose of better understanding the AdS/CFT correspondence it is useful to have a description of the theory for all values of the 't Hooft coupling, and for all N. We discuss such a description in the framework of Matrix theory for SYM on D4-branes, which is given in terms of quantum mechanics on the moduli space of solutions of the Nahm equations. This description reduces to both SYM perturbation theory and to closed string perturbation theory, each in its appropriate regime of validity, suggesting a way of directly relating the variables in the two descriptions. For example, it shows explicitly how holes in the world-sheets of the 't Hooft expansion close to give closed surfaces.
Formation of Quantum Phase Slip Pairs in Superconducting Nanowires

For quantum computing to be practical, the effects of decoherence on quantum information must be minimized. Discovery of a regime in which transitions that conserve parity are much more likely to occur than those that do not opens the door to parity-based information processing proposals to protect quantum information.
RESONANCE BROADENING AND HEATING OF CHARGED PARTICLES IN MAGNETOHYDRODYNAMIC TURBULENCE

The heating, acceleration, and pitch-angle scattering of charged particles by magnetohydrodynamic (MHD) turbulence are important in a wide range of astrophysical environments, including the solar wind, accreting black holes, and galaxy clusters. We simulate the interaction of high-gyrofrequency test particles with fully dynamical simulations of subsonic MHD turbulence, focusing on the parameter regime with β ~ 1, where β is the ratio of gas to magnetic pressure. We use the simulation results to calibrate analytical expressions for test particle velocity-space diffusion coefficients and provide simple fits that can be used in other work. The test particle velocity diffusion in our simulations is due to a combination of two processes: interactions between particles and magnetic compressions in the turbulence (as in linear transit-time damping; TTD) and what we refer to as Fermi Type-B (FTB) interactions, in which charged particles moving on field lines may be thought of as beads sliding along moving wires. We show that test particle heating rates are consistent with a TTD resonance that is broadened according to a decorrelation prescription that is Gaussian in time (but inconsistent with Lorentzian broadening due to an exponential decorrelation function, a prescription widely used in the literature). TTD dominates the heating for vs vA (e.g., electrons), where vs is the thermal speed of species s and vA is the Alfven speed, while FTB dominates for vs vA (e.g., minor ions). Proton heating rates for β ~ 1 are comparable to the turbulent cascade rate. Finally, we show that velocity diffusion of collisionless, large gyrofrequency particles due to large-scale MHD turbulence does not produce a power-law distribution function.
Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling

For a base station that supports cellular communications in sub-6 GHz LTE and millimeter (mmWave) bands, we propose a supervised machine learning algorithm to improve the success rate in the handover between the two radio frequencies using sub-6 GHz and mmWave prior channel measurements within a temporal window. The main contributions of our paper are: 1) introduce partially blind handovers, 2) employ machine learning to perform handover success predictions from sub-6 GHz to mmWave frequencies, and 3) show that this machine learning based algorithm combined with partially blind handovers can improve the handover success rate in a realistic network setup of colocated cells. Simulation results show improvement in handover success rates for our proposed algorithm compared to standard handover algorithms.
The Structure of the DoAr 25 Circumstellar Disk

We present high spatial resolution ( 0.3'' ≈ 40 AU) Submillimeter Array observations of the 865 μm continuum emission from the circumstellar disk around the young star DoAr 25. Despite its bright millimeter emission, this source exhibits only a comparatively small infrared excess and low accretion rate, suggesting that the material and structural properties of the inner disk may be in an advanced state of evolution. A simple model of the physical conditions in the disk is derived from the submillimeter visibilities and the complete spectral energy distribution using a Monte Carlo radiative transfer code. For the standard assumption of a homogeneous grain size distribution at all disk radii, the results indicate a shallow surface density profile, Σ ∝ r−p with p ≈ 0.34, significantly less steep than a steady-state accretion disk (p = 1) or the often adopted minimum mass solar nebula (p = 1.5). Even though the total mass of material is large (Md ≈ 0.10 M☉), the densities inferred in the inner disk for such a model may be too low to facilitate any mode of planet formation. However, alternative models with steeper density gradients (p ≈ 1) can explain the observations equally well if substantial grain growth in the planet formation region (r ≤ 40 AU) has occurred. We discuss these data in the context of such models with dust properties that vary with radius and highlight their implications for understanding disk evolution and the early stages of planet formation.
The origin of the distinction between microscopic formulas for stress and Cauchy stress

Stress is calculated routinely in atomistic simulations. The widely used microscopic stress formulas derived from classical or quantum mechanics, however, are distinct from the concept of Cauchy stress, i.e., the true mechanical stress. This work examines various atomistic formulations for local stress and their inconsistencies. Using standard mathematic theorems and the law of mechanics, we show that Cauchy stress results unambiguously from the definition of internal force density, thereby removing the long-standing confusion about the atomistic basis of the fundamental property of Cauchy stress and leading to a new atomistic formula for local stress The new atomistic stress formula has clear physical meaning and well-defined values, satisfies the conservation law, and is fully consistent with the concept of Cauchy stress.
A Modular Quintic Calabi-Yau Threefold of Level 55

In this note we search the parameter space of Horrocks–Mumford quintic threefolds and locate a Calabi–Yau threefold that is modular, in the sense that the L-function of its middle-dimensional cohomology is associated with a classical modular form of weight 4 and level 55. School of Mathematics, Aras Na Laoi, University College Cork, Cork, Ireland e-mail: e.lee@ucc.ie edward.lee@post.harvard.edu Received by the editors March 18, 2009. Published electronically March 4, 2011. This material is based upon works supported by the Science Foundation Ireland under Grant No. MATF634. AMS subject classification: 14J15, 11F23, 14J32, 11G40.
Einstein's Equations and a Cosmology with Finite Matter

We discuss various space–time metrics which are compatible with Einstein's equations and a previously suggested cosmology with a finite total mass.1 In this alternative cosmology, the matter density was postulated to be a spatial delta function at the time of the big bang thereafter diffusing outward with constant total mass. This proposal explores a departure from standard assumptions that the big bang occurred everywhere at once or was just one of an infinite number of previous and later transitions.
Differential Privacy as a Mutual Information Constraint

Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between e-differential privacy and (e,δ)-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.
Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy

Given the rapid advances in unmanned aerial vehicles, or drones, and increasing need to monitor at a city level, one of the current research gaps is how to systematically deploy drones over multiple periods. We propose a real-time data-driven approach: we formulate the first deterministic arc-inventory routing problem and derive its stochastic dynamic policy. The policy is expected to be of greatest value in scenarios where uncertainty is highest and costliest, such as city monitoring during major events. The Bellman equation for an approximation of the proposed inventory routing policy is formulated as a selective vehicle routing problem. We propose an approximate dynamic programming algorithm based on Least Squares Monte Carlo simulation to find that policy. The algorithm has been modified so that the least squares dependent variable is defined to be the “expected stock out cost upon the next replenishment”. The new algorithm is tested on 30 simulated instances of real time trajectories over 5 time periods of the selective vehicle routing problem to evaluate the proposed policy and algorithm. Computational results on the selected instances show that the algorithm on average outperforms the myopic policy by 23–28%, depending on the parametric design. Further tests are conducted on classic benchmark arc routing problem instances. The 11-link instance gdb19 (Golden et al., 1983) is expanded into a sequential 15-period stochastic dynamic example and used to demonstrate why a naive static multi-period deployment plan would not be effective in real networks.
Sparse Nonparametric Graphical Models

We present some nonparametric methods for graphical modeling. In the discrete case, where the data are binary or drawn from a finite alphabet, Markov random fields are already essentially nonparametric, since the cliques can take only a finite number of values. Continuous data are different. The Gaussian graphical model is the standard parametric model for continuous data, but it makes distributional assumptions that are often unrealistic. We discuss two approaches to building more flexible graphical models. One allows arbitrary graphs and a nonparametric extension of the Gaussian; the other uses kernel density estimation and restricts the graphs to trees and forests. Examples of both methods are presented. We also discuss possible future research directions for nonparametric graphical modeling.
Optimal Auctions vs. Anonymous Pricing: Beyond Linear Utility

The revenue optimal mechanism for selling a single item to agents with independent but non-identically distributed values is complex for agents with linear utility (Myerson,1981) and has no closed-form characterization for agents with non-linear utility (cf. Alaei et al., 2012). Nonetheless, for linear utility agents satisfying a natural regularity property, Alaei et al. (2018) showed that simply posting an anonymous price is an e-approximation. We give a parameterization of the regularity property that extends to agents with non-linear utility and show that the approximation bound of anonymous pricing for regular agents approximately extends to agents that satisfy this approximate regularity property. We apply this approximation framework to prove that anonymous pricing is a constant approximation to the revenue optimal single-item auction for agents with public-budget utility, private-budget utility, and (a special case of) risk-averse utility.
RADIAL TRENDS IN IMF-SENSITIVE ABSORPTION FEATURES IN TWO EARLY-TYPE GALAXIES: EVIDENCE FOR ABUNDANCE-DRIVEN GRADIENTS

We have observed two massive early-type galaxies with Keck/LRIS and measured radial gradients in the strengths of stellar absorption features from 4000-5500 u and 8000-10,000 u We present spatially resolved measurements of the dwarf-sensitive spectral indices NaI (8190 u and Wing-Ford FeH (9915 u as well as indices for species of H, C2, CN, Mg, Ca, TiO, and Fe. Our measurements show a metallicity gradient in both objects, and Mg/Fe consistent with uniform α-enhancement, matching widely observed trends for massive early-type galaxies. The NaI index and the CN1 index at 4160 u exhibit significantly steeper gradients, with a break at r ∼ 0.1reff (r ∼ 300 pc). Inside this radius NaI and CN1 increase sharply toward the galaxy center, relative to other indices. We interpret this trend as a rapid central rise in [Na/Fe] and [N/Fe]. In contrast, the FeH index exhibits a marginal decrease toward the galaxy center, relative to Fe. Our investigation is among the first to track FeH as a function of radius, and to demonstrate discrepant behavior between NaI and FeH. We suggest that the shallow gradient in FeH and steep, broken NaI profile reflect unique abundance patterns rather than a gradient in the stellar initial mass function.
Self-Improving Algorithms

We investigate ways in which an algorithm can improve its expected performance by fine-tuning itself automatically with respect to an unknown input distribution $\mathcal{D}$. We assume here that $\mathcal{D}$ is of product type. More precisely, suppose that we need to process a sequence $I_1,I_2,\ldots$ of inputs $I=(x_1,x_2,\ldots,x_n)$ of some fixed length $n$, where each $x_i$ is drawn independently from some arbitrary, unknown distribution $\mathcal{D}_i$. The goal is to design an algorithm for these inputs so that eventually the expected running time will be optimal for the input distribution $\mathcal{D}=\prod_i\mathcal{D}_i$. We give such self-improving algorithms for two problems: (i) sorting a sequence of numbers and (ii) computing the Delaunay triangulation of a planar point set. Both algorithms achieve optimal expected limiting complexity. The algorithms begin with a training phase during which they collect information about the input distribution, followed by a stationary regime in which the algorithms settle to their optimized incarnations.
Orbital Motion of Young Binaries in Ophiuchus and Upper Centaurus–Lupus

We present measurements of the orbital positions and flux ratios of 17 binary and triple systems in the Ophiuchus star forming region and the Upper Centaurus-Lupus cluster based on adaptive optics imaging at the Keck Observatory. We report the detection of visual companions in MML 50 and MML 53 for the first time, as well as the possible detection of a third component in WSB 21. For six systems in our sample, our measurements provide a second orbital position following their initial discoveries over a decade ago. For eight systems with sufficient orbital coverage, we analyze the range of orbital solutions that fit the data. Ultimately, these observations will help provide the groundwork toward measuring precise masses for these pre-main sequence stars and understanding the distribution of orbital parameters in young multiple systems.
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition

Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.
Periodic points of Hamiltonian surface diffeomorphisms

The main result of this paper is that every non-trivial Hamiltonian dieomor- phism of a closed oriented surface of genus at least one has periodic points of arbitrarily high period. The same result is true for S 2 provided the dif- feomorphism has at least three xed points. In addition we show that up to isotopy relative to its xed point set, every orientation preserving dieomor- phism F : S! S of a closed orientable surface has a normal form. If the xed point set is nite this is just the Thurston normal form.
Signature of Planetary Mergers on Stellar Spins

One of the predictions of high eccentricity planetary migration is that many planets will end up plunging into their host stars. We investigate the consequence of planetary mergers on their stellar hosts' spin-period. Energy and angular momentum conservation yield that a planet consumption by a star will spin-up the star. We find that our calculations align with the observed bifurcation in the stellar spin-period in young clusters. For example, after a Sun-like star has eaten a Jupiter-mass planet it will spin up by ~60% (i.e., spin-period is reduced by ~60%), causing an apparent gap in the stellar spin period, between stars that consumed a planet and those that did not. The spun-up star will later spin down due to magnetic braking, consistent with the disappearance of this bifurcation in clusters (>300Myr). The agreement between the calculations presented here, and the observed spin-period color diagram of stars in young clusters provides circumstantial evidence that planetary accretion onto their host stars is a generic feature of planetary-system evolution.
Configurable Electrostatically Doped High Performance Bilayer Graphene Tunnel FET

A bilayer graphene-based electrostatically doped tunnel field-effect transistor (BED-TFET) is proposed. Unlike graphene nanoribbon TFETs in which the edge states deteriorate the OFF-state performance, BED-TFETs operate based on bandgaps induced by vertical electric fields in the source, channel, and drain regions without any chemical doping. The performance of the transistor is evaluated by self-consistent quantum transport simulations. This device has several advantages: 1) ultra-low power $(V_{\mathrm {DD}}=0.1$ V); 2) high performance ( ${I}_{\mathrm{ ON}} / {I}_{\mathrm{ OFF}}$ >104); 3) steep subthreshold swing (SS<10mv/dec); and 4) electrically configurable between N-TFET and P-TFET post fabrication. The operation principle of the BED-TFET and its performance sensitivity to the device design parameters are presented.
The immediate environments of two Herbig Be stars: MWC 1080 and HD 259431

Deep mid-infrared (10-20 ?m) images with sub-arcsecond resolution were obtained for two Herbig Be stars, MWC?1080 and HD?259431, to probe their immediate environments. Our goal is to understand the origin of the diffuse nebulosities observed around these two very young objects. By analyzing our new mid-IR images and comparing them to published data at other wavelengths, we demonstrate that the well-extended emission around MWC?1080 traces neither a disk nor an envelope, but rather the surfaces of a cavity created by the outflow from MWC?1080A, the primary star of the MWC?1080 system. In the N-band images, the filamentary nebulosities trace the hourglass-shaped gas cavity wall out to ~0.15?pc. This scenario reconciles the properties of the MWC?1080 system revealed by a wide range of observations. Our finding confirms that the environment around MWC?1080, where a small cluster is forming, is strongly affected by the outflow from the central Herbig Be star. Similarities observed between the two subjects of this study suggest that the filamentary emission around HD?259431 may also arise from a similar outflow cavity structure.
Interferometric Visibility of a Scintillating Source: Statistics at the Nyquist Limit

We derive the distribution of interferometric visibility for a source exhibiting strong diffractive scintillation, with particular attention to spectral resolution at or near the Nyquist limit. We also account for arbitrary temporal averaging, intrinsic variability within the averaging time, and the possibility of spatially extended source emission. We demonstrate that the interplay between scintillation and self-noise induces several remarkable features, such as a broad "skirt" in the visibility distribution. Our results facilitate the interpretation of interferometric observations of pulsars at meter and decimeter wavelengths.
Polarizable Mean-Field Model of Water for Biological Simulations with Amber and Charmm force fields

Although a great number of computational models of water are available today, the majority of current biological simulations are done with simple models, such as TIP3P and SPC, developed almost thirty years ago and only slightly modified since then. The reason is that the non-polarizable force fields that are mostly used to describe proteins and other biological molecules are incompatible with more sophisticated modern polarizable models of water. The issue is electronic polarizability: in liquid state, in protein, and in vacuum the water molecule is polarized differently, and therefore has different properties; thus the only way to describe all these different media with the same model is to use a polarizable water model. However, to be compatible with the force field of the rest of the system, e.g. a protein, the latter should be polarizable as well. Here we describe a novel model of water that is in effect polarizable, and yet compatible with the standard non-polarizable force fields such as AMBER, CHARMM, GROMOS, OPLS, etc. Thus the model resolves the outstanding problem of incompatibility.
Dynamically warped theory space and collective supersymmetry breaking

We study deconstructed gauge theories in which a warp factor emerges dynamically. We present nonsupersymmetric models in which the potential for the link fields has translational invariance, broken only by boundary effects that trigger an exponential profile of vacuum expectation values. The spectrum of physical states deviates exponentially from that of the continuum for large masses; we discuss the effects of such exponential towers on gauge coupling unification. We also present a supersymmetric example in which a warp factor is driven by Fayet-Iliopoulos terms. The model is peculiar in that it possesses a global supersymmetry that remains unbroken despite nonvanishing D-terms. Inclusion of gravity and/or additional messenger fields leads to the collective breaking of supersymmetry and to unusual phenomenology.
Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing

How cognitive neural systems process information is largely unknown, in part because of how difficult it is to accurately follow the flow of information from sensors via neurons to actuators. Measuring the flow of information is different from measuring correlations between firing neurons, for which several measures are available, foremost among them the Shannon information, which is an undirected measure. Several information-theoretic notions of "directed information" have been used to successfully detect the flow of information in some systems, in particular in the neuroscience community. However, recent work has shown that directed information measures such as transfer entropy can sometimes inadequately estimate information flow, or even fail to identify manifest directed influences, especially if neurons contribute in a cryptographic manner to influence the effector neuron. Because it is unclear how often such cryptic influences emerge in cognitive systems, the usefulness of transfer entropy measures to reconstruct information flow is unknown. Here, we test how often cryptographic logic emerges in an evolutionary process that generates artificial neural circuits for two fundamental cognitive tasks (motion detection and sound localization). Besides counting the frequency of problematic logic gates, we also test whether transfer entropy applied to an activity time-series recorded from behaving digital brains can infer information flow, compared to a ground-truth model of direct influence constructed from connectivity and circuit logic. Our results suggest that transfer entropy will sometimes fail to infer directed information when it exists, and sometimes suggest a causal connection when there is none. However, the extent of incorrect inference strongly depends on the cognitive task considered. These results emphasize the importance of understanding the fundamental logic processes that contribute to information flow in cognitive processing, and quantifying their relevance in any given nervous system.
Trick or Heat?: Manipulating Critical Temperature-Based Control Systems Using Rectification Attacks

Temperature sensing and control systems are widely used in the closed-loop control of critical processes such as maintaining the thermal stability of patients, or in alarm systems for detecting temperature-related hazards. However, the security of these systems has yet to be completely explored, leaving potential attack surfaces that can be exploited to take control over critical systems. In this paper we investigate the reliability of temperature-based control systems from a security and safety perspective. We show how unexpected consequences and safety risks can be induced by physical-level attacks on analog temperature sensing components. For instance, we demonstrate that an adversary could remotely manipulate the temperature sensor measurements of an infant incubator to cause potential safety issues, without tampering with the victim system or triggering automatic temperature alarms. This attack exploits the unintended rectification effect that can be induced in operational and instrumentation amplifiers to control the sensor output, tricking the internal control loop of the victim system to heat up or cool down. Furthermore, we show how the exploit of this hardware-level vulnerability could affect different classes of analog sensors that share similar signal conditioning processes. Our experimental results indicate that conventional defenses commonly deployed in these systems are not sufficient to mitigate the threat, so we propose a prototype design of a low-cost anomaly detector for critical applications to ensure the integrity of temperature sensor signals.
KINETIC ENERGY DRIVEN SUPERCONDUCTIVITY, THE ORIGIN OF THE MEISSNER EFFECT, AND THE REDUCTIONIST FRONTIER

Is superconductivity associated with a lowering or an increase of the kinetic energy of the charge carriers? Conventional BCS theory predicts that the kinetic energy of carriers increases in the transition from the normal to the superconducting state. However, substantial experimental evidence obtained in recent years indicates that in at least some superconductors the opposite occurs. Motivated in part by these experiments many novel mechanisms of superconductivity have recently been proposed where the transition to superconductivity is associated with a lowering of the kinetic energy of the carriers. However none of these proposed unconventional mechanisms explores the fundamental reason for kinetic energy lowering nor its wider implications. Here I propose that kinetic energy lowering is at the root of the Meissner effect, the most fundamental property of superconductors. The physics can be understood at the level of a single electron atom: kinetic energy lowering and enhanced diamagnetic susceptibility are intimately connected. We propose that this connection extends to superconductors because they are, in a very real sense, "giant atoms". According to the theory of hole superconductivity, superconductors expel negative charge from their interior driven by kinetic energy lowering and in the process expel any magnetic field lines present in their interior. Associated with this we predict the existence of a macroscopic electric field in the interior of superconductors and the existence of macroscopic quantum zero-point motion in the form of a spin current in the ground state of superconductors (spin Meissner effect). In turn, the understanding of the role of kinetic energy lowering in superconductivity suggests a new way to understand the fundamental origin of kinetic energy lowering in quantum mechanics quite generally. This provides a new understanding of "quantum pressure", the stability of matter and the origin of fermion anticommutation relations, it leads to the prediction that spin currents exist in the ground state of aromatic ring molecules, and that the electron wavefunction is double-valued, requiring a reformulation of conventional quantum mechanics.