cssi/SciSciGPT-SciSciNet huggingface dataset:
$E_{ {\gamma }}$ -Resolvability

The conventional channel resolvability refers to the minimum rate needed for an input process to approximate the channel output distribution in total variation distance. In this paper, we study $E_{\gamma }$ -resolvability, in which total variation is replaced by the more general $E_{\gamma }$ distance. A general one-shot achievability bound for the precision of such an approximation is developed. Let $Q_{\sf X|U}$ be a random transformation, $n$ be an integer, and $E\in (0,+\infty )$ . We show that in the asymptotic setting where $\gamma =\exp (nE)$ , a (nonnegative) randomness rate above $\inf _{Q_{\sf U}: D(Q_{\sf X}\|{\pi }_{\sf X})\le E} \{D(Q_{\sf X}\|{\pi }_{\sf X})+I(Q_{\sf U},Q_{\sf X|U})-E\}$ is sufficient to approximate the output distribution ${\pi }_{\sf X}^{\otimes n}$ using the channel $Q_{\sf X|U}^{\otimes n}$ , where $Q_{\sf U}\to Q_{\sf X|U}\to Q_{\sf X}$ , and is also necessary in the case of finite $\mathcal {U}$ and $\mathcal {X}$ . In particular, a randomness rate of $\inf _{Q_{\sf U}}I(Q_{\sf U},Q_{\sf X|U})-E$ is always sufficient. We also study the convergence of the approximation error under the high-probability criteria in the case of random codebooks. Moreover, by developing simple bounds relating $E_{\gamma }$ and other distance measures, we are able to determine the exact linear growth rate of the approximation errors measured in relative entropy and smooth Renyi divergences for a fixed-input randomness rate. The new resolvability result is then used to derive: 1) a one-shot upper bound on the probability of excess distortion in lossy compression, which is exponentially tight in the i.i.d. setting; 2) a one-shot version of the mutual covering lemma; and 3) a lower bound on the size of the eavesdropper list to include the actual message and a lower bound on the eavesdropper false-alarm probability in the wiretap channel problem, which is (asymptotically) ensemble-tight.
Exotic Elliptic Algebras

This paper examines a general method for producing twists of a comodule algebra by tensoring it with a torsor then taking co-invariants. We examine the properties that pass from the original algebra to the twisted algebra and vice versa. We then examine the special case where the algebra is a 4-dimensional Sklyanin algebra viewed as a comodule algebra over the Hopf algebra of functions on the non-cyclic group of order 4 with the torsor being the 2x2 matrix algebra. The twisted algebra is an "exotic elliptic algebra". We show that the twisted algebra has many of the good properties that the Sklyanin algebra has, and that it has some new properties that make it quite unusual by comparison.
Keplerian dynamics on the heisenberg group and elsewhere

Posing Kepler’s problem of motion around a fixed “sun” requires the geometric mechanician to choose a metric and a Laplacian. The metric provides the kinetic energy. The fundamental solution to the Laplacian (with delta source at the “sun”) provides the potential energy. Posing Kepler’s three laws (with input from Galileo) requires symmetry conditions. The metric space must be homogeneous, isotropic, and admit dilations. Any Riemannian manifold enjoying these three symmetry properties is Euclidean. So if we want a semblance of Kepler’s three laws to hold but also want to leave the Euclidean realm, we are forced out of the realm of Riemannian geometries. The Heisenberg group (a subRiemannian geometry) and lattices provide the simplest examples of metric spaces enjoying a semblance of all three of the Keplerian symmetries. We report success in posing, and solving, the Kepler problem on the Heisenberg group. We report failures in posing the Kepler problem on the rank two lattice and partial success in solving the problem on the integers. We pose a number of questions.
A New Primal–Dual Algorithm for Minimizing the Sum of Three Functions with a Linear Operator

In this paper, we propose a new primal–dual algorithm for minimizing $$f({\mathbf {x}})+g({\mathbf {x}})+h({\mathbf {A}}{\mathbf {x}})$$ , where f, g, and h are proper lower semi-continuous convex functions, f is differentiable with a Lipschitz continuous gradient, and $${\mathbf {A}}$$ is a bounded linear operator. The proposed algorithm has some famous primal–dual algorithms for minimizing the sum of two functions as special cases. E.g., it reduces to the Chambolle–Pock algorithm when $$f=0$$ and the proximal alternating predictor–corrector when $$g=0$$ . For the general convex case, we prove the convergence of this new algorithm in terms of the distance to a fixed point by showing that the iteration is a nonexpansive operator. In addition, we prove the O(1 / k) ergodic convergence rate in the primal–dual gap. With additional assumptions, we derive the linear convergence rate in terms of the distance to the fixed point. Comparing to other primal–dual algorithms for solving the same problem, this algorithm extends the range of acceptable parameters to ensure its convergence and has a smaller per-iteration cost. The numerical experiments show the efficiency of this algorithm.
Basmajian-type identities and Hausdorff dimension of limit sets

In this paper, we study Basmajian-type series identities on holomorphic families of Cantor sets associated to one-dimensional complex dynamical systems. We show that the series is absolutely summable if and only if the Hausdorff dimension of the Cantor set is strictly less than one. Throughout the domain of convergence, these identities can be analytically continued and they exhibit non-trivial monodromy.
Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions

Resolving a conjecture of Abbe, Bandeira, and Hall, the authors have recently shown that the semidefinite programming (SDP) relaxation of the maximum likelihood estimator achieves the sharp threshold for exactly recovering the community structure under the binary stochastic block model (SBM) of two equal-sized clusters. The same was shown for the case of a single cluster and outliers. Extending the proof techniques, in this paper, it is shown that SDP relaxations also achieve the sharp recovery threshold in the following cases: 1) binary SBM with two clusters of sizes proportional to network size but not necessarily equal; 2) SBM with a fixed number of equal-sized clusters; and 3) binary censored block model with the background graph being Erdős–Renyi. Furthermore, a sufficient condition is given for an SDP procedure to achieve exact recovery for the general case of a fixed number of clusters plus outliers. These results demonstrate the versatility of SDP relaxation as a simple, general purpose, computationally feasible methodology for community detection.
Essential dimension of the spin groups in characteristic 2

We determine the essential dimension of the spin group Spin(n) as an algebraic group over a field of characteristic 2, for n at least 15. In this range, the essential dimension is the same as in characteristic not 2. In particular, it is exponential in n. This is surprising in that the essential dimension of the orthogonal groups is smaller in characteristic 2. We also find the essential dimension of Spin(n) in characteristic 2 for n at most 10.
Disaster-resilient control plane design and mapping in software-defined networks

Communication networks, such as core optical networks, heavily depend on their physical infrastructure, and hence they are vulnerable to man-made disasters, such as Electromagnetic Pulse (EMP) or Weapons of Mass Destruction (WMD) attacks, as well as to natural disasters. Large-scale disasters may cause huge data loss and connectivity disruption in these networks. As our dependence on network services increases, the need for novel survivability methods to mitigate the effects of disasters on communication networks becomes a major concern. Software-Defined Networking (SDN), by centralizing control logic and separating it from physical equipment, facilitates network programmability and opens up new ways to design disaster-resilient networks. On the other hand, to fully exploit the potential of SDN, along with data-plane survivability, we also need to design the control plane to be resilient enough to survive network failures caused by disasters. Several distributed SDN controller architectures have been proposed to mitigate the risks of overload and failure, but they are optimized for limited faults without addressing the extent of large-scale disaster failures. For disaster resiliency of the control plane, we propose to design it as a virtual network, which can be solved using Virtual Network Mapping techniques. We select appropriate mapping of the controllers over the physical network such that the connectivity among the controllers (controller-to-controller) and between the switches to the controllers (switch-to-controllers) is not compromised by physical infrastructure failures caused by disasters. We formally model this disaster-aware control-plane design and mapping problem, and demonstrate a significant reduction in the disruption of controller-to-controller and switch-to-controller communication channels using our approach.
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system

Reinforcement learning has emerged as a promising methodology for training robot controllers. However, most results have been limited to simulation due to the need for a large number of samples and the lack of automated-yet-safe data collection methods. Model-based reinforcement learning methods provide an avenue to circumvent these challenges, but the traditional concern has been the mismatch between the simulator and the real world. Here, we show that control policies learned in simulation can successfully transfer to a physical system, composed of three Phantom robots pushing an object to various desired target positions. We use a modified form of the natural policy gradient algorithm for learning, applied to a carefully identified simulation model. The resulting policies, trained entirely in simulation, work well on the physical system without additional training. In addition, we show that training with an ensemble of models makes the learned policies more robust to modeling errors, thus compensating for difficulties in system identification. The results are illustrated in the accompanying video.
Space–Time Physical-Layer Network Coding

A space–time physical-layer network coding (ST-PNC) method is presented for information exchange among multiple users over fully connected multiway relay networks. The method involves two steps: 1) side-information learning and 2) space–time relay transmission. In the first step, different sets of users are scheduled to send signals over networks, and the remaining users and relays overhear the transmitted signals, thereby learning the interference patterns. In the second step, multiple relays cooperatively send out linear combinations of signals received in the previous phase using space–time precoding so that all users efficiently exploit their side information in the form of 1) what they sent and 2) what they overheard in decoding. This coding concept is illustrated through two simple network examples. It is shown that ST-PNC improves the sum of degrees of freedom (sum-DoF) of the network compared to existing interference management methods. With ST-PNC, the sum-DoF of a general multiway relay network without channel knowledge at the users is characterized in terms of relevant system parameters, chiefly the number of users, the number of relays, and the number of antennas at relays. A major implication of the derived results is that efficiently harnessing both transmitted and overheard signals as side information brings significant performance improvements to fully connected multiway relay networks.
The discriminant invariant of Cantor group actions

Abstract In this work, we investigate the dynamical and geometric properties of weak solenoids, as part of the development of a “calculus of group chains” associated to Cantor minimal actions. The study of the properties of group chains was initiated in the works of McCord [23] and Fokkink and Oversteegen [14] , to study the problem of determining which weak solenoids are homogeneous continua. We develop an alternative condition for the homogeneity in terms of the Ellis semigroup of the action, then investigate the relationship between non-homogeneity of a weak solenoid and its discriminant invariant, which we introduce in this work. A key part of our study is the construction of new examples that illustrate various subtle properties of group chains that correspond to geometric properties of non-homogeneous weak solenoids.
Independent sets in random graphs from the weighted second moment method

We prove new lower bounds on the likely size of the maximum independent set in a random graph with a given constant average degree. Our method is a weighted version of the second moment method, where we give each independent set a weight based on the total degree of its vertices.
Halo Substructure and the Power Spectrum

We present a semianalytic model to investigate the merger history, destruction rate, and survival probability of substructure in hierarchically formed dark matter halos and use it to study the substructure content of halos as a function of input primordial power spectrum. For a standard cold dark matter "concordance" cosmology (ΛCDM; n = 1, σ8 = 0.95) we successfully reproduce the subhalo velocity function and radial distribution profile seen in N-body simulations and determine that the rate of merging and disruption peaks ~10-12 Gyr in the past for Milky Way-like halos, while surviving substructures are typically accreted within the last ~0-8 Gyr. We explore power spectra with normalizations and spectral "tilts" spanning the ranges σ8 1-0.65 and n 1-0.8, and include a "running-index" model with dn/d ln k = -0.03 similar to the best-fit model discussed in the first-year Wilkinson Microwave Anisotropy Probe (WMAP) report. We investigate spectra with truncated small-scale power, including a broken-scale inflation model and three warm dark matter cases with mW = 0.75-3.0 keV. We find that the mass fraction in substructure is relatively insensitive to the tilt and overall normalization of the primordial power spectrum. All of the CDM-type models yield projected substructure mass fractions that are consistent with, but on the low side, of published estimates from strong lens systems: f9 = 0.4%-1.5% (64th percentile) for subhalos smaller than 109 M☉ within projected cylinders of radius r < 10 kpc. Truncated models produce significantly smaller fractions, f9 = 0.02%-0.2% for mW 1 keV, and are disfavored by lensing estimates. This suggests that lensing and similar probes can provide a robust test of the CDM paradigm and a powerful constraint on broken-scale inflation/warm particle masses, including masses larger than the ~1 keV upper limits of previous studies. We compare our predicted subhalo velocity functions with the dwarf satellite population of the Milky Way. Assuming that dwarfs have isotropic velocity dispersions, we find that the standard n = 1 model overpredicts the number of Milky Way satellites at Vmax 35 km s-1, as expected. Models with less small-scale power do better because subhalos are less concentrated and the mapping between observed velocity dispersion and halo Vmax is significantly altered. The running-index model, or a fixed tilt with σ8 ~ 0.75, can account for the local dwarfs without the need for differential feedback (for Vmax 20 km s-1); however, these comparisons depend sensitively on the assumption of isotropic velocities in satellite galaxies.
Sensor Deployment With Limited Communication Range in Homogeneous and Heterogeneous Wireless Sensor Networks

We study the heterogeneous wireless sensor networks (WSNs) and propose the necessary condition of the optimal sensor deployment. Similar to that in homogeneous WSNs, the necessary condition implies that every sensor node location should coincide with the centroid of its own optimal sensing region. Moreover, we discuss the dynamic sensor deployment in both the homogeneous and the heterogeneous WSNs with limited communication range for the sensor nodes. The purpose of sensor deployment is to improve sensing performance, reflected by distortion and coverage. We model the sensor deployment problem as a source coding problem with distortion reflecting sensing accuracy. However, when the communication range is limited, a WSN is divided into several disconnected sub-graphs under certain conditions as we will discuss in this paper. In such a scenario, neither the conventional distortion nor the coverage represents the sensing performance as the collected data in disconnected sub-graphs cannot be communicated with the access point. By defining an appropriate sensing performance measure, we propose a Restrained Lloyd (RL) algorithm and a deterministic annealing (DA) algorithm to optimize sensor deployment in both the homogeneous and heterogeneous WSNs. Our simulation results show that both the DA and the RL algorithms outperform the existing algorithms when communication range is limited.
VRGym: a virtual testbed for physical and interactive AI

We propose VRGym, a virtual reality (VR) testbed for realistic human-robot interaction. Different from existing toolkits and VR environments, the VRGym emphasizes on building and training both physical and interactive agents for robotics, machine learning, and cognitive science. VRGym leverages mechanisms that can generate diverse 3D scenes with high realism through physics-based simulation. We demonstrate that VRGym is able to (i) collect human interactions and fine manipulations, (ii) accommodate various robots with a ROS bridge, (iii) support experiments for human-robot interaction, and (iv) provide toolkits for training the state-of-the-art machine learning algorithms. We hope VRGym can help to advance general-purpose robotics and machine learning agents, as well as assisting human studies in the field of cognitive science.1
An Asymptotic Comparison of Two Time-Homogeneous PAM Models

Both Wick-Ito-Skorokhod and Stratonovich interpretations of the parabolic Anderson model (PAM) lead to solutions that are real analytic as functions of the noise intensity e, and, in the limit e->0, the difference between the two solutions is of order e^2 and is non-random.
Stochastic feedback control of systems with unknown nonlinear dynamics

This paper studies the stochastic optimal control problem for systems with unknown dynamics. First, an open-loop deterministic trajectory optimization problem is solved without knowing the explicit form of the dynamical system. Next, a Linear Quadratic Gaussian (LQG) controller is designed for the nominal trajectory-dependent linearized system, such that under a small noise assumption, the actual states remain close to the optimal trajectory. The trajectory-dependent linearized system is identified using input-output experimental data consisting of the impulse responses of the nominal system. A computational example is given to illustrate the performance of the proposed approach.
Rigidity theorems for submetries in positive curvature

Abstract We derive general structure and rigidity theorems for submetries f : M → X , where M is a complete Riemannian manifold with sectional curvature sec M ≥ 1 . When applied to a non-trivial Riemannian submersion, it follows that diam X ≤ π / 2 . In case of equality, there is a Riemannian submersion S → M from a unit sphere, and as a consequence, f is known up to metric congruence. A similar rigidity theorem also holds in the general context of Riemannian foliations.
Color Confinement, Hadron Dynamics, and Hadron Spectroscopy from Light-Front Holography and Superconformal Algebra

The QCD light-front Hamiltonian equation derived from quantization at fixed LF time provides a causal, frame-independent method for computing hadron spectroscopy as well as dynamical observables such as structure functions, transverse momentum distributions, and distribution amplitudes. The QCD Lagrangian with zero quark mass has no explicit mass scale. de Alfaro, Fubini, and Furlan (dAFF) have made an important observation that a mass scale can appear in the equations of motion without affecting the conformal invariance of the action if one adds a term to the Hamiltonian proportional to the dilatation operator or the special conformal operator. If one applies the dAFF procedure to the QCD light-front Hamiltonian, it leads to a color-confining potential for mesons, where is the LF radial variable conjugate to the invariant mass squared. The same result, including spin terms, is obtained using light-front holography, the duality between light-front dynamics and , if one modifies the action by the dilaton in the fifth dimension . When one generalizes this procedure using superconformal algebra, the resulting light-front eigensolutions provide a unified Regge spectroscopy of meson, baryon, and tetraquarks, including remarkable supersymmetric relations between the masses of mesons and baryons and a universal Regge slope. The pion eigenstate has zero mass at The superconformal relations also can be extended to heavy-light quark mesons and baryons. This approach also leads to insights into the physics underlying hadronization at the amplitude level. I will also discuss the remarkable features of the Poincare invariant, causal vacuum defined by light-front quantization and its impact on the interpretation of the cosmological constant. AdS/QCD also predicts the analytic form of the nonperturbative running coupling . The mass scale underlying hadron masses can be connected to the parameter in the QCD running coupling by matching the nonperturbative dynamics to the perturbative QCD regime. The result is an effective coupling defined at all momenta. One obtains empirically viable predictions for spacelike and timelike hadronic form factors, structure functions, distribution amplitudes, and transverse momentum distributions. Finally, I address the interesting question of whether the momentum sum rule is valid for nuclear structure functions.
Directed Chain Stochastic Differential Equations

Abstract We propose a particle system of diffusion processes coupled through a chain-like network structure described by an infinite-dimensional, nonlinear stochastic differential equation of McKean–Vlasov type. It has both (i) a local chain interaction and (ii) a mean-field interaction. It can be approximated by a limit of finite particle systems, as the number of particles goes to infinity. Due to the local chain interaction, propagation of chaos does not necessarily hold. Furthermore, we exhibit a dichotomy of presence or absence of mean-field interaction, and we discuss the problem of detecting its presence from the observation of a single component process.
On convergence criteria for the coupled flow of Li–Yuan–Zhang

A one-parameter family of coupled flows depending on a parameter $$\kappa >0$$ is introduced which reduces when $$\kappa =1$$ to the coupled flow of a metric $$\omega $$ with a (1, 1)-form $$\alpha $$ due recently to Y. Li, Y. Yuan, and Y. Zhang. It is shown in particular that, for $$\kappa \not =1$$ , estimates for derivatives of all orders would follow from $$C^0$$ estimates for $$\omega $$ and $$\alpha $$ . Together with the monotonicity of suitably adapted energy functionals, this can be applied to establish the convergence of the flow in some situations, including on Riemann surfaces. Very little is known as yet about the monotonicity and convergence of flows in presence of couplings, and conditions such as $$\kappa \not =1$$ seem new and may be useful in the future.
Thermodynamic Limit for Directed Polymers and Stationary Solutions of the Burgers Equation

The first goal of this paper is to prove multiple asymptotic results for a time-discrete and space-continuous polymer model of a random walk in a random potential. These results include: existence of deterministic free energy density in the infinite volume limit for every fixed asymptotic slope; concentration inequalities for free energy implying a bound on its fluctuation exponent; straightness estimates implying a bound on the transversal fluctuation exponent. The culmination of this program is almost sure existence and uniqueness of polymer measures on one-sided infinite paths with given endpoint and slope, and interpretation of these infinite-volume Gibbs measures as thermodynamic limits. Moreover, we prove that marginals of polymer measures with the same slope and different endpoints are asymptotic to each other. The second goal of the paper is to develop ergodic theory of the Burgers equation with positive viscosity and random kick forcing on the real line without any compactness assumptions. Namely, we prove a One Force -- One Solution principle, using the infinite volume polymer measures to construct a family of stationary global solutions for this system, and proving that each of those solutions is a one-point pullback attractor on the initial conditions with the same spatial average. This provides a natural extension of the same program realized for the inviscid Burgers equation with the help of action minimizers that can be viewed as zero temperature limits of polymer measures.
Faster Evaluation of Subtraction Games

Subtraction games are played with one or more heaps of tokens, with players taking turns removing from a single heap a number of tokens belonging to a specified subtraction set; the last player to move wins. We describe how to compute the set of winning heap sizes in single-heap subtraction games (for an input consisting of the subtraction set and maximum heap size n), in time O~(n), where the O~ elides logarithmic factors. For multi-heap games, the optimal game play is determined by the nim-value of each heap; we describe how to compute the nim-values of all heaps of size up to n in time O~(mn), where m is the maximum nim-value occurring among these heap sizes. These time bounds improve naive dynamic programming algorithms with time O(n|S|), because m <=|S| for all such games. We apply these results to the game of subtract-a-square, whose set of winning positions is a maximal square-difference-free set of a type studied in number theory in connection with the Furstenberg-Sarkozy theorem. We provide experimental evidence that, for this game, the set of winning positions has a density comparable to that of the densest known square-difference-free sets, and has a modular structure related to the known constructions for these dense sets. Additionally, this game's nim-values are (experimentally) significantly smaller than the size of its subtraction set, implying that our algorithm achieves a polynomial speedup over dynamic programming.
Neumann Li-Yau gradient estimate under integral Ricci curvature bounds

We prove a Li-Yau gradient estimate for positive solutions to the heat equation, with Neumann boundary conditions, on a compact Riemannian submanifold with boundary ${\bf M}^n\subseteq {\bf N}^n$, satisfying the integral Ricci curvature assumption: \begin{equation} D^2 \sup_{x\in {\bf N}} \left( \oint_{B(x,D)} |Ric^-|^p dy \right)^{\frac{1}{p}} n/2$, where $diam({\bf M})\leq D$. The boundary of ${\bf M}$ is not necessarily convex, but it needs to satisfy the interior rolling $R-$ball condition.
Free probability for pairs of faces III: 2-Variables bi-free partial S- and T-transforms

Abstract We introduce two 2-variables transforms: the partial bi-free S -transform and the partial bi-free T -transform. These transforms are the analogues for the bi-multiplicative and, respectively, for the additive–multiplicative bi-free convolution of the 2-variables partial bi-free R -transform in our previous paper in this series.
Regularity of minimal submanifolds and mean curvature flows with a common free boundary

Let N be a smooth (n + l)-dimensional Riemannian manifold. We show that if V is an area-stationary union of three or more C1,μ n-dimensional submanifolds-with-boundary Mk⊂N with a common boundary Γ...
Evidence accumulation and change rate inference in dynamic environments

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.
Training GANs with Optimism

We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games. WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics. Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs. We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynamics converges to an equilibrium, in contrast to GD dynamics which are bound to cycle. We also portray the huge qualitative difference between GD and OMD dynamics with toy examples, even when GD is modified with many adaptations proposed in the recent literature, such as gradient penalty or momentum. We apply OMD WGAN training to a bioinformatics problem of generating DNA sequences. We observe that models trained with OMD achieve consistently smaller KL divergence with respect to the true underlying distribution, than models trained with GD variants. Finally, we introduce a new algorithm, Optimistic Adam, which is an optimistic variant of Adam. We apply it to WGAN training on CIFAR10 and observe improved performance in terms of inception score as compared to Adam.
On the shape of capillarity droplets in a container

We provide a quantitative description of global minimizers of the Gauss free energy for a liquid droplet bounded in a container in the small volume regime.
2,2′-Dipyridylamine as Heterogeneous Organic Molecular Electrocatalyst for Two-Electron Oxygen Reduction Reaction in Acid Media

Continuous production of hydrogen peroxide (H2O2) through the two-electron oxygen reduction reaction (2e-ORR) in distributed electrochemical cells offers important advantages for point-of-use water...
Determinants of Attendance in Major League Soccer

This study examines the determinants of Major League Soccer team attendance during the league’s recent era of growth. Estimates indicate that regular-season on-field performance is positively associated with attendance, but the returns to success are diminishing. Positive novelty effects are identified for newer teams and soccer-specific stadiums but not for stadium age. Income is positively associated with attendance, indicating that MLS matches are a normal good. Population size, the Hispanic share of population, the presence of other major-league franchises, and the number of designated players on a team do not appear to be strong determinants of seasonal attendance.
A Theory of Ethical Accounting and Its Implications for Hypocrisy in Organizations

Management scholars have typically regarded the widespread instances of hypocrisy across business, religious, and political institutions to be motivated and strategic. We suggest, however, that hyp...
Small subalgebras of polynomial rings and Stillman’s Conjecture

We show that in a polynomial ring $R$ in $N$ variables over an algebraically closed field $K$ of arbitrary characteristic, any $K$-subalgebra of $R$ generated over $K$ by at most $n$ forms of degree at most $d$ is contained in a $K$-subalgebra of $R$ generated by $B \leq {}^\eta\mathcal{B}(n,d)$ forms $G_1,..., G_B$ of degree $\leq d$, where ${}^\eta\mathcal{B}(n,d)$ does not depend on $N$ or $K$, such that these forms are a regular sequence and such that for any ideal $J$ generated by forms that are in the $K$-span of $G_1, ..., G_B$, the ring $R/J$ satisfies the Serre condition $R_\eta$. These results imply a conjecture of M. Stillman asserting that the projective dimension of an $n$-generator ideal $I$ of $R$ whose generators are forms of degree $\leq d$ is bounded independent of $N$. We also show that there is a primary decomposition of $I$ such that all numerical invariants of the decomposition (e.g., the number of primary components and the degrees and numbers of generators of all of the prime and primary ideals occurring) are bounded independent of $N$.
An improved model for gas-liquid flow pattern prediction based on machine learning

Abstract The determination of flow patterns is a fundamental problem in two-phase flow analysis, and an accurate model for gas-liquid flow pattern prediction is critical for any multiphase flow characterization as the model is used in many applications in petroleum engineering. We developed a new model based on machine learning techniques via dimensionally analyzing more than 8000 laboratory multi-phase flow tests. As shown in the test results, the flow pattern is affected by fluid properties, in-situ flow rates of liquid and gas, flow conduit geometry and mechanical properties. Applying hydraulic fundamentals and dimensional analysis, three upscaling numbers are developed to reduce the number of freedom dimensions. These dimensionless variables are easy to use for upscaling and have physical meanings. Machine learning techniques on the dimensionless variables significantly improved their predictive accuracy. Until now the best matching on these laboratory data was approximately 80% using the most recently developed semi-analytical models. The quality of the matching is improved to 90% or greater on the experimental data using machine learning techniques.
Energy-Aware Digital Signatures for Embedded Medical Devices

Authentication is vital for the Internet of Things (IoT)applications involving sensitive data (e.g., medical and financial systems). Digital signatures offer scalable authentication with non-repudiation and public verifiability, which are necessary for auditing and dispute resolution in such IoT applications. However, digital signatures have been shown to be highly costly for low-end IoT devices, especially when embedded devices (e.g., medical implants)must operate without a battery replacement for a long time. We propose an Energy-aware Signature for Embedded Medical devices (ESEM)that achieves near-optimal signer efficiency. ESEM signature generation does not require any costly operations (e.g., elliptic curve (EC)scalar multiplication/addition), but only a small constant-number of pseudo-random function calls, additions, and a single modular multiplication. ESEM has the smallest signature size among its EC-based counterparts with an identical private key size. We achieve this by eliminating the use of the ephemeral public key (i.e, commitment)in Schnorr-type signatures from the signing via a distributed construction at the verifier without interaction with the signer while permitting a constant-size public key. We proved that ESEM is secure (in random oracle model), and fully implemented it on an 8-bit AVR microcontroller that is commonly used in medical devices. Our experiments showed that ESEM achieves $8.4\times$ higher energy efficiency over its closest counterpart while offering a smaller signature and code size. Hence, ESEM can be suitable for deployment on resource limited embedded devices in IoT. We open-sourced our software for public testing and wide-adoption.
Domain decomposition and multiscale mortar mixed finite element methods for linear elasticity with weak stress symmetry

Two non-overlapping domain decomposition methods are presented for the mixed finite element formulation of linear elasticity with weakly enforced stress symmetry. The methods utilize either displacement or normal stress Lagrange multiplier to impose interface continuity of normal stress or displacement, respectively. By eliminating the interior subdomain variables, the global problem is reduced to an interface problem, which is then solved by an iterative procedure. The condition number of the resulting algebraic interface problem is analyzed for both methods. A multiscale mortar mixed finite element method for the problem of interest on non-matching multiblock grids is also studied. It uses a coarse scale mortar finite element space on the non-matching interfaces to approximate the trace of the displacement and impose weakly the continuity of normal stress. A priori error analysis is performed. It is shown that, with appropriate choice of the mortar space, optimal convergence on the fine scale is obtained for the stress, displacement, and rotation, as well as some superconvergence for the displacement. Computational results are presented in confirmation of the theory of all proposed methods.
Generating Personalized Recipes from Historical User Preferences.

Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user’s historical preferences. We attend on technique- and recipe-level representations of a user’s previously consumed recipes, fusing these ‘user-aware’ representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model’s ability to generate plausible and personalized recipes compared to non-personalized baselines.
Large-Scale Multilingual Speech Recognition With A Streaming End-To-End Model

A method (400) of transcribing speech using a multilingual end-to-end (E2E) speech recognition model (115) includes receiving audio data (110) for an utterance (106) spoken in a particular native language, obtaining a language vector (115) identifying the particular language, and processing, using the multilingual E2E speech recognition model, the language vector and acoustic features (117) derived from the audio data to generate a transcription (1:20) for the utterance. The multilingual E2E speech recognition model includes a plurality of language-specific adaptor modules (300) that include one or more adaptor modules specific to the particular native language and one or more other adaptor modules specific to at least one other native language different than the particular native language. The method also includes providing the transcription for output.
Effect of nicotine on biofilm formation of Streptococcus mutans isolates from smoking and non-smoking subjects.

Objectives: To investigate effects of nicotine on biofilm formation of Streptococcus mutans isolates from oral washes of smoker and non-smoker human subjects. Materials and methods: This study was conducted using 60 S. mutans isolates with three S. mutans isolates collected from oral washes of ten smoking subjects and ten from non-smoking subjects. Biofilm was formed by culturing each S. mutans strain (10 μl) in 190 μl of TSB supplemented with 1% sucrose (TSBS) containing 0, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, and 32.0 mg/ml of nicotine for 24 h in 5% CO2 at 37°C in 96 well microtiter plates. The absorbance values of biofilm were measured at 490 nm in a microplate spectrophotometer. Results: There was a significant effect (p-value < 0.05) of nicotine concentrations and smoking on the growth of biofilm, planktonic cells, and total absorbance, for all strains of S. mutans. Isolates from smokers had significantly more biofilm at 0-16 mg/ml of nicotine compared to those from non-smokers (p-value < 0.0001). Conclusion: S. mutans smoker isolates are more affected by high nicotine concentrations than non-smoker isolates. Clinical Relevance: The use of nicotine products increases the growth of S. mutans and may place tobacco users at risk for dental decay.
Chagas Disease Serological Test Performance in U.S. Blood Donor Specimens

ABSTRACT Chagas disease affects an estimated 300,000 individuals in the United States. Diagnosis in the chronic phase requires positive results from two different IgG serological tests. Three enzyme-linked immunosorbent assays (ELISAs) (Hemagen, Ortho, and Wiener) and one rapid test (InBios) are FDA cleared, but comparative data in U.S. populations are sparse. We evaluated 500 seropositive and 300 seronegative blood donor plasma samples. Country of birth was known for 255 seropositive specimens, which were grouped into regions as follows: Mexico (n = 94), Central America (n = 88), and South America (n = 73). Specimens were tested by the four FDA-cleared IgG serological assays. Test performance was evaluated by two comparators and latent class analysis. InBios had the highest sensitivity (97.4% to 99.3%) but the lowest specificity (87.5% to 92.3%). Hemagen had the lowest sensitivity (88.0% to 92.0%) but high specificity (99.0% to 100.0%). The level of sensitivity was intermediate for Ortho (92.4% to 96.5%) and Wiener (94.0% to 97.1%); both had high specificity (98.8% to 100.0% and 96.7% to 99.3%, respectively). The levels of antibody reactivity and clinical sensitivity were lowest in donors from Mexico, intermediate in those from Central America, and highest in those from South America. Our findings provide an initial evidence base to improve laboratory diagnosis of Chagas disease in the United States. The best current testing algorithm would employ a high-sensitivity screening test followed by a high-specificity confirmatory test.
Understanding the decision to screen for lung cancer or not: A qualitative analysis

Background Although new screening programmes with low-dose computed tomography (LDCT) for lung cancer have been implemented throughout the United States, screening uptake remains low and screening-eligible persons' decisions to screen or not remain poorly understood. Objective To describe how current and former long-term smokers explain their decisions regarding participation in lung cancer screening. Design Phone interviews using a semi-structured interview guide were conducted to ask screening-eligible persons to describe their decisions regarding screening with LDCT. The interviews were transcribed and analysed with conventional content analytic techniques. Setting and participants A subsample of 40 participants (20 who had screened and 20 who had not) were drawn from the sample of a survey study whose participants were recruited by Facebook targeted advertisements. Results The sample was divided into the following five groups based on their decisions regarding lung cancer screening participation: Group 1: no intention to be screened, Group 2: no deliberate consideration but somewhat open to being screened, Group 3: deliberate consideration but no definitive decision to be screened, Group 4: intention to be screened and Group 5: had been screened. Reasons for screening participation decisions are described for each group. Across groups, data revealed that screening-eligible persons have a number of misconceptions regarding LDCT, including that a scan is needed only if one is symptomatic or has not had a chest x-ray. A physician recommendation was a key influence on decisions to screen. Discussion and conclusions Education initiatives aimed at providers and long-term smokers regarding LDCT is needed. Quality patient/provider communication is most likely to improve screening rates.
A signal modeling framework for integrated design of sensor networks

Summary form only given. Information processing, communication and routing are intimately coupled in sensor networks and the sensor signal characteristics in space and time directly impact these vital network functions. In this paper, we propose a signal modeling framework for sensor measurements that exposes the interaction between space-time signal sampling, distributed signal processing, and communication and routing of information through the network. We assume that the signal in a region of interest is a bandlimited stationary Gaussian field in spatial and temporal dimensions. The proposed model is based on the notion of spatial coherence regions that are dictated by Nyquist sampling in the spatial dimensions: the signals remain strongly correlated within each coherence region and vary approximately independently between different coherence regions. This simple approximate model has several implications for the design of sensor networks. First, it imposes a structure on distributed signal processing algorithms that is naturally suited to the communication constraints: high-bandwidth (feature-level) information exchange is limited to spatially local nodes within each coherence region, whereas low-bandwidth (decision-level) information exchange is sufficient between farther nodes in different coherence regions. Second, it enables simple bounds on the rate at which information is generated by any sub-region in the network. Third, it suggests natural communication strategies for transporting information from the coherence regions to a destination node or region and enables estimates of the corresponding link capacities.
P-partition generating function equivalence of naturally labeled posets

Abstract The P-partition generating function of a (naturally labeled) poset P is a quasisymmetric function enumerating order-preserving maps from P to Z + . Using the Hopf algebra of posets, we give necessary conditions for two posets to have the same generating function. In particular, we show that they must have the same number of antichains of each size, as well as the same shape (as defined by Greene). We also discuss which shapes guarantee uniqueness of the P-partition generating function and give a method of constructing pairs of non-isomorphic posets with the same generating function.
Mid-infrared semiconductor laser based trace gas sensor technologies: Recent advances and applications

Recent advances in the development of ultra-sensitive sensor technology based on mid - infrared semiconductor lasers for the detection of trace gas species and their application in industrial process control and environmental monitoring will be reported.
DeepBinaryMask: Learning a binary mask for video compressive sensing

Abstract In this paper, we propose an encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing. In video compressive sensing one frame is acquired using a set of coded masks (sensing matrix) from which a number of video frames, equal to the number of coded masks, is reconstructed. The proposed framework is an end-to-end model where the sensing matrix is trained along with the video reconstruction. The encoder maps a video block to compressive measurements by learning the binary elements of the sensing matrix. The decoder is trained to map the measurements from a video patch back to a video block via several hidden layers of a Multi-Layer Perceptron network. The predicted video blocks are stacked together to recover the unknown video sequence. The reconstruction performance is found to improve when using the trained sensing mask from the network as compared to other mask designs such as random, across a wide variety of compressive sensing reconstruction algorithms. Finally, our analysis and discussion offers insights into understanding the characteristics of the trained mask designs that lead to the improved reconstruction quality.
Light-tree establishment for optical multicasting in Flexible Optical WDM (FWDM) networks

Multicast-enabled FWDM network architecture is introduced and a naturally-inspired algorithm is proposed for the light-tree establishment problem. The algorithm improves spectral efficiency by at least 22% and 17% compared to fixed and flexible-grid solutions respectively.