cssi/SciSciGPT-SciSciNet huggingface dataset:
Capturing Complementarity in Set Functions by Going Beyond Submodularity/Subadditivity.

We introduce two new “degree of complementarity” measures: supermodular width and superadditive width. Both are formulated based on natural witnesses of complementarity. We show that both measures are robust by proving that they, respectively, characterize the gap of monotone set functions from being submodular and subadditive. Thus, they define two new hierarchies over monotone set functions, which we will refer to as Supermodular Width (SMW) hierarchy and Superadditive Width (SAW) hierarchy, with foundations — i.e. level 0 of the hierarchies — resting exactly on submodular and subadditive functions, respectively. We present a comprehensive comparative analysis of the SMW hierarchy and the Supermodular Degree (SD) hierarchy, defined by Feige and Izsak. We prove that the SMW hierarchy is strictly more expressive than the SD hierarchy: Every monotone set function of supermodular degree d has supermodular width at most d, and there exists a supermodular-width-1 function over a ground set of m elements whose supermodular degree is m − 1. We show that previous results regarding approximation guarantees for welfare and constrained maximization as well as regarding the Price of Anarchy (PoA) of simple auctions can be extended without any loss from the supermodular degree to the supermodular width. We also establish almost matching information-theoretical lower bounds for these two well-studied fundamental maximization problems over set functions. The combination of these approximation and hardness results illustrate that the SMW hierarchy provides not only a natural notion of complementarity, but also an accurate characterization of “near submodularity” needed for maximization approximation. While SD and SMW hierarchies support nontrivial bounds on the PoA of simple auctions, we show that our SAW hierarchy seems to capture more intrinsic properties needed to realize the efficiency of simple auctions. So far, the SAW hierarchy provides the best dependency for the PoA of Singlebid Auction, and is nearly as competitive as the Maximum over Positive Hypergraphs (MPH) hierarchy for Simultaneous Item First Price Auction (SIA). We also provide almost tight lower bounds for the PoA of both auctions with respect to the SAW hierarchy.
The Galois theory of the lemniscate

Abstract This article studies the Galois groups that arise from division points of the lemniscate. We compute these Galois groups two ways: first, by class field theory, and second, by proving the irreducibility of lemnatomic polynomials, which are analogs of cyclotomic polynomials. We also discuss Abelʼs theorem on the lemniscate and explain how lemnatomic polynomials relate to Chebyshev polynomials.
Error Bounds, Quadratic Growth, and Linear Convergence of Proximal Methods

The proximal gradient algorithm for minimizing the sum of a smooth and nonsmooth convex function often converges linearly even without strong convexity. One common reason is that a multiple of the step length at each iteration may linearly bound the “error”—the distance to the solution set. We explain the observed linear convergence intuitively by proving the equivalence of such an error bound to a natural quadratic growth condition. Our approach generalizes to linear and quadratic convergence analysis for proximal methods (of Gauss-Newton type) for minimizing compositions of nonsmooth functions with smooth mappings. We observe incidentally that short step-lengths in the algorithm indicate near-stationarity, suggesting a reliable termination criterion.
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces

It has been a long-standing problem to efficiently learn a halfspace using as few labels as possible in the presence of noise. In this work, we propose an efficient Perceptron-based algorithm for actively learning homogeneous halfspaces under the uniform distribution over the unit sphere. Under the bounded noise condition~\cite{MN06}, where each label is flipped with probability at most $\eta < \frac 1 2$, our algorithm achieves a near-optimal label complexity of $\tilde{O}\left(\frac{d}{(1-2\eta)^2}\ln\frac{1}{\epsilon}\right)$ in time $\tilde{O}\left(\frac{d^2}{\epsilon(1-2\eta)^3}\right)$. Under the adversarial noise condition~\cite{ABL14, KLS09, KKMS08}, where at most a $\tilde \Omega(\epsilon)$ fraction of labels can be flipped, our algorithm achieves a near-optimal label complexity of $\tilde{O}\left(d\ln\frac{1}{\epsilon}\right)$ in time $\tilde{O}\left(\frac{d^2}{\epsilon}\right)$. Furthermore, we show that our active learning algorithm can be converted to an efficient passive learning algorithm that has near-optimal sample complexities with respect to $\epsilon$ and $d$.
Data-Driven Low-Dimensional Modeling and Uncertainty Quantification for Airfoil Icing

The formation and accretion of ice on the leading edge of an airfoil can be detrimental to aerodynamic performance. Furthermore, the geometric shape of leading edge ice profiles can vary significantly depending on a wide range of physical parameters, which can translate into a wide variability in aerodynamic performance. The purpose of this work is to explore the variability in airfoil aerodynamic performance that results from variability in leading edge ice shape profile. First, we demonstrate how to identify a low-dimensional set of parameters that governs ice shape from a database of ice shapes using Proper Orthogonal Decomposition (POD). Then, we investigate the effects of uncertainty in the POD coefficients. This is done by building a global response surface surrogate using Polynomial Chaos Expansions (PCE). To construct this surrogate efficiently, we use adaptive sparse grid sampling of the POD parameter space. We then analyze the data from a statistical standpoint.
Kronecker coefficients and noncommutative super Schur functions

Abstract The theory of noncommutative Schur functions can be used to obtain positive combinatorial formulae for the Schur expansion of various classes of symmetric functions, as shown by Fomin and Greene [11] . We develop a theory of noncommutative super Schur functions and use it to prove a positive combinatorial rule for the Kronecker coefficients g λ μ ν where one of the partitions is a hook, recovering previous results of the two authors [7] , [22] . This method also gives a precise connection between this rule and a heuristic for Kronecker coefficients first investigated by Lascoux [19] .
Odd behavior in the coefficients of reciprocals of binary power series

Let &#x1d49c; be a finite subset of ℕ including 0 and f&#x1d49c;(n) be the number of ways to write n =∑i=0∞ϵ i2i, where ϵi ∈&#x1d49c;. The sequence (f&#x1d49c;(n)) mod 2 is always periodic. Small examples suggest that the number of odd f&#x1d49c;(n)’s in a period is at most 1 plus the number of even f&#x1d49c;(n)’s in a period. We find strong larger counterexamples and give four families of sets (&#x1d49c;m) with |&#x1d49c;m| = 4 such that the proportion of odd f&#x1d49c;m(n)’s goes to 1 as m →∞.
Dualities between cellular sheaves and cosheaves

Abstract This paper affirms a conjecture of MacPherson—that the derived category of cellular sheaves is equivalent to the derived category of cellular cosheaves. We give a self-contained treatment of cellular sheaves and cosheaves and note that certain classical dualities give rise to an exchange of sheaves with cosheaves. Following a result of Pitts that states that cosheaves are cocontinuous functors on the category of sheaves, we use the derived equivalence provided here to gain a novel description of compactly-supported sheaf cohomology.
Minimax filter: learning to preserve privacy from inference attacks

Preserving privacy of continuous and/or high-dimensional data such as images, videos and audios, can be challenging with syntactic anonymization methods which are designed for discrete attributes. Differentially privacy, which uses a more rigorous definition of privacy loss, has shown more success in sanitizing continuous data. However, both syntactic and differential privacy are susceptible to inference attacks, i.e., an adversary can accurately infer sensitive attributes from sanitized data. The paper proposes a novel filter-based mechanism which preserves privacy of continuous and high-dimensional attributes against inference attacks. Finding the optimal utility-privacy tradeoff is formulated as a min-diff-max optimization problem. The paper provides an ERM-like analysis of the generalization error and also a practical algorithm to perform minimax optimization. In addition, the paper proposes a noisy minimax filter which combines minimax filter and differentially-private mechanism. Advantages of the method over purely noisy mechanisms is explained and demonstrated with examples. Experiments with several real-world tasks including facial expression classification, speech emotion classification, and activity classification from motion, show that the minimax filter can simultaneously achieve similar or higher target task accuracy and lower inference accuracy, often significantly lower than previous methods.
An ACL2 Mechanization of an Axiomatic Framework for Weak Memory.

Proving the correctness of programs written for multiple processors is a challenging problem, due in no small part to the weaker memory guarantees afforded by most modern architectures. In particular, the existence of store buffers means that the programmer can no longer assume that writes to different locations become visible to all processors in the same order. However, all practical architectures do provide a collection of weaker guarantees about memory consistency across processors, which enable the programmer to write provably correct programs in spite of a lack of full sequential consistency. In this work, we present a mechanization in the ACL2 theorem prover of an axiomatic weak memory model (introduced by Alglave et al.). In the process, we provide a new proof of an established theorem involving these axioms.
Higher Order Nonlinear Complementary Filtering on Lie Groups

Nonlinear observer design for systems whose state-space evolves on Lie groups is considered. The proposed method is similar to previously developed nonlinear observers in that it involves propagating the state estimate using a process model and corrects the propagated state estimate using an innovation term on the tangent space of the Lie group. In the proposed method, the innovation term is constructed by passing the gradient of an invariant cost function, resolved in a basis of the tangent space, through a linear time-invariant system. The introduction of the linear system completes the extension of linear complementary filters to nonlinear Lie group observers by allowing higher order filtering. In practice, the proposed method allows for greater design freedom and, with the appropriate selection of the linear filter, the ability to filter bias and noise over specific bandwidths. A disturbance observer that accounts for constant and harmonic disturbances in group velocity measurements is also considered. Local asymptotic stability about the desired equilibrium point is demonstrated. A numerical example that demonstrates the desirable properties of the observer is presented in the context of pose estimation.
Raman spectroscopy of diesel and gasoline engine-out soot using different laser power

Abstract We studied engine-out soot samples collected from a heavy-duty direct-injection diesel engine and port-fuel injection gasoline spark-ignition engine. The two types of soot samples were characterized using Raman spectroscopy with different laser powers. A Matlab program using least-square-method with trust-region-reflective algorithm was developed for curve fitting. A DOE (design of experiments) method was used to avoid local convergence. The method was used for two-band fitting and three-band fitting. The fitting results were used to determine the intensity ratio of D (for “Defect” or “Disorder”) and G (for “Graphite”) Raman bands. It is found that high laser power may cause oxidation of soot sample, which gives higher D/G intensity ratio. Diesel soot has consistently higher amorphous/graphitic carbon ratio, and thus higher oxidation reactivity, compared to gasoline soot, which is reflected by the higher D/G intensity ratio in Raman spectra measured under the same laser power.
Unitary dilation of freely independent contractions

Inspired by the Sz.-Nagy-Foias dilation theorem we show that $n$ freely independent contractions dilate to $n$ freely independent unitaries.
A Central Limit Theorem for linear random fields

Abstract A Central Limit Theorem is proved for linear random fields when sums are taken over union of finitely many disjoint rectangles. The approach does not rely upon the use of Beveridge–Nelson decomposition and the conditions needed are similar in nature to those given by Ibragimov for linear processes. When specializing this result to the case when sums are being taken over rectangles, a complete analogue of the Ibragimov result is obtained for random fields with a lot of uniformity.
Graph-Based Deep-Tree Recursive Neural Network (DTRNN) for Text Classification

A novel graph-to-tree conversion mechanism called the deep-tree generation (DTG) algorithm is first proposed to predict text data represented by graphs. The DTG method can generate a richer and more accurate representation for nodes (or vertices) in graphs. It adds flexibility in exploring the vertex neighborhood information to better reflect the second order proximity and homophily equivalence in a graph. Then, a Deep-Tree Recursive Neural Network (DTRNN) method is presented and used to classify vertices that contains text data in graphs. To demonstrate the effectiveness of the DTRNN method, we apply it to three real-world graph datasets and show that the DTRNN method outperforms several state-of-the-art benchmarking methods.
Universal geometric cluster algebras from surfaces

A universal geometric cluster algebra over an exchange matrix B is a universal object in the category of geometric cluster algebras over B related by coefficient specializations. (Following an earlier paper on universal geometric cluster algebras, we broaden the definition of geometric cluster algebras relative to the definition originally given Fomin and Zelevinsky.) The universal objects are closely related to a fan F_B called the mutation fan for B. In this paper, we consider universal geometric cluster algebras and mutation fans for cluster algebras arising from marked surfaces. We identify two crucial properties of marked surfaces: The Curve Separation Property and the Null Tangle Property. The latter property implies the former. We prove the Curve Separation Property for all marked surfaces except once-punctured surfaces without boundary components, and as a result we obtain a construction of the rational part of F_B for these surfaces. We prove the Null Tangle Property for a smaller family of surfaces, and use it to construct universal geometric coefficients for these surfaces.
Stochastic MPC design for a two-component granulation process

We address the issue of control of a stochastic two-component granulation process in pharmaceutical applications through using Stochastic Model Predictive Control (SMPC) and model reduction to obtain the desired particle distribution. We first use the method of moments to reduce the governing integro-differential equation down to a nonlinear ordinary differential equation (ODE). This reduced-order model is employed in the SMPC formulation. The probabilistic constraints in this formulation keep the variance of particles' drug concentration in an admissible range. To solve the resulting stochastic optimization problem, we first employ polynomial chaos expansion to obtain the Probability Distribution Function (PDF) of the future state variables using the uncertain variables' distributions. As a result, the original stochastic optimization problem for a particulate system is converted to a deterministic dynamic optimization. This approximation lessens the computation burden of the controller and makes its real time application possible.
One‐way infinite 2‐walks in planar graphs

We prove that every 3-connected 2-indivisible infinite planar graph has a 1-way infinite 2-walk. (A graph is 2-indivisible if deleting finitely many vertices leaves at most one infinite component, and a 2-walk is a spanning walk using every vertex at most twice.) This improves a result of Timar, which assumed local finiteness. Our proofs use Tutte subgraphs, and allow us to also provide other results when the graph is bipartite or an infinite analog of a triangulation: then the prism over the graph has a spanning 1-way infinite path.
Thermodynamically Favorable Computation via Tile Self-assembly

The recently introduced Thermodynamic Binding Networks (TBN) model was developed with the purpose of studying self-assembling systems by focusing on their thermodynamically favorable final states, and ignoring the kinetic pathways through which they evolve. The model was intentionally developed to abstract away not only the notion of time, but also the constraints of geometry. Collections of monomers with binding domains which allow them to form polymers via complementary bonds are analyzed to determine their final, stable configurations, which are those which maximize the number of bonds formed (i.e. enthalpy) and the number of independent components (i.e. entropy). In this paper, we first develop a definition of what it means for a TBN to perform a computation, and then present a set of constructions which are capable of performing computations by simulating the behaviors of space-bounded Turing machines and boolean circuits. In contrast to previous TBN results, these constructions are robust to great variability in the counts of monomers existing in the systems and the numbers of polymers that form in parallel. Although the Turing machine simulating TBNs are inefficient in terms of the numbers of unique monomer types required, as compared to algorithmic self-assembling systems in the abstract Tile Assembly Model (aTAM), we then show that a general strategy of porting those aTAM system designs to TBNs produces TBNs which incorrectly simulate computations. Finally, we present a refinement of the TBN model which we call the Geometric Thermodynamic Binding Networks (GTBN) model in which monomers are defined with rigid geometries and form rigid bonds. Utilizing the constraints imposed by geometry, we then provide a GTBN construction capable of simulating Turing machines as efficiently as in the aTAM.
Geodesic period integrals of eigenfunctions on Riemannian surfaces and the Gauss–Bonnet Theorem

We use the Gauss-Bonnet theorem and the triangle comparison theorems of Rauch and Toponogov to show that on compact Riemann surfaces of negative curvature period integrals of eigenfunctions $e_\lambda$ over geodesics go to zero at the rate of $O((\log\lambda)^{-1/2})$ if $\lambda$ are their frequencies. As discussed in \cite{CSPer}, no such result is possible in the constant curvature case if the curvature is $\ge0$. Notwithstanding, we also show that these bounds for period integrals are valid provided that integrals of the curvature over all geodesic balls of radius $r\le 1$ are pinched from above by $-\delta r^N$ for some fixed $N$ and $\delta>0$. This allows, for instance, the curvature to be nonpositive and to vanish of finite order at a finite number of isolated points. Naturally, the above results also hold for the appropriate type of quasi-modes.
Annihilation of torsion in homology of finite m-AQ quandles

It is a classical result in reduced homology of finite groups that the order of a group annihilates its homology. Similarly, we have proved that the torsion subgroup of rack and quandle homology of a finite quasigroup quandle is annihilated by its order. However, it does not hold for connected quandles in general. In this paper, we define an m-almost quasigroup (m-AQ) quandle which is a generalization of a quasigroup quandle and study annihilation of torsion in its rack and quandle homology groups.
Mordell-Lang plus Bogomolov

We formulate a conjecture for semiabelian varieties A over number fields that includes both the Mordell-Lang conjecture (now proven) and the Bogomolov conjecture. We prove the "Mordellic" (finitely generated) part of the conjecture when A is isogenous to the product of an abelian variety and a torus. The proof makes use of the Mordell-Lang conjecture, the Bogomolov conjecture, and an equidistribution theorem.
The Square Root Velocity Framework for Curves in a Homogeneous Space

In this paper we study the shape space of curves with values in a homogeneous space M = G/K, where G is a Lie group and K is a compact Lie subgroup. We generalize the square root velocity framework to obtain a reparametrization invariant metric on the space of curves in M. By identifying curves in M with their horizontal lifts in G, geodesics then can be computed. We can also mod out by reparametrizations and by rigid motions of M. In each of these quotient spaces, we can compute Karcher means, geodesics, and perform principal component analysis. We present numerical examples including the analysis of a set of hurricane paths.
Probabilizing parking functions

Abstract We explore the link between combinatorics and probability generated by the question “What does a random parking function look like?” This gives rise to novel probabilistic interpretations of some elegant, known generating functions. It leads to new combinatorics: how many parking functions begin with i? We classify features (e.g., the full descent pattern) of parking functions that have exactly the same distribution among parking functions as among all functions. Finally, we develop the link between parking functions and Brownian excursion theory to give examples where the two ensembles differ.
Bounded Point Evaluations For Certain Polynomial And Rational Modules

Let $K$ be a compact subset of the complex plane $\mathbb C.$ Let $P(K)$ and $R(K)$ be the closures in $C(K)$ of analytic polynomials and rational functions with poles off $K,$ respectively. Let $A(K) \subset C(K)$ be the algebra of functions that are analytic in the interior of $K$. For $1\le t <\infty,$ let $P^t(1, \phi_1,...,\phi_N,K)$ be the closure of $P(K)+P(K)\phi_1+...+P(K)\phi_N$ in $L^t(dA|_K),$ where $dA|_K$ is the area measure restricted to $K$ and $\phi_1,...,\phi_N\in L^t(dA|_K).$ Let $HP(\phi_1,...,\phi_N,K)$ be the closure of $P(K)\phi_1+...+P(K)\phi_N +R(K)$ in $C(K),$ where $\phi_1,...,\phi_N\in C(K).$ In this paper, we prove if $R(K)\ne C(K),$ then there exists an analytic bounded point evaluation for both $P^t(1, \phi_1,...,\phi_N,K)$ and $HP(\phi_1,...,\phi_N,K)$ for certain smooth functions $\phi_1,...,\phi_N,$ in particular, for $\bar z,\bar z^2,...,\bar z^N.$ We show that $A(K)\subset HP(\bar z,\bar z^2,...,\bar z^N,K)$ if and only if $R(K) = A(K).$ In particular, $C(K) \ne HP(\bar z,\bar z^2,...,\bar z^N,K)$ unless $R(K) = C(K).$ We also give an example of $K$ showing the results are not valid if we replace $\bar z^n$ by certain $\phi_n,$ that is, there exist $K$ and a function $\phi\in A(K)$ such that $R(K) \ne A(K),$ but $A(K) = HP (\phi ,K).$
Nowhere-harmonic colorings of graphs

Proper vertex colorings of a graph are related to its boundary map, also called its signed vertex-edge incidence matrix. The vertex Laplacian of a graph, a natural extension of the boundary map, leads us to introduce nowhere-harmonic colorings and analogues of the chromatic polynomial and Stanley's theorem relating negative evaluations of the chromatic polynomial to acyclic orientations. Further, we discuss some examples demonstrating that nowhere-harmonic colorings are more complicated from an enumerative perspective than proper colorings.
On the component number of links from plane graphs

A short, elementary proof is given of the result that the number of components of a link arising from a medial graph M(Γ) by resolving vertices is equal to the nullity of the mod-2 Laplacian matrix of Γ.
Geometrically convergent sequences of upper and lower bounds on the Wallis ratio and related expressions

Sequences of algebraic upper and lower bounds on the Wallis ratio ( x + 1)/( x + 1 ) are given with the relative errors that converge to 0 geometrically and uniformly on any interval of the form (x0,1) for x0 > 1 ; moreover, the relative and absolute errors converge to 0 as x ! 1. These conclusions are based on corresponding results for the digamma function := 0 /. Relations with other relevant results are discussed, as well as the corresponding computational aspects. This work was motivated by studies of exact bounds involving the Student probability distribution. AMS 2000 subject classifications: Primary 33B15, 26D07, 26D15, 41A17; secondary 33F05, 65D20, 60E15, 62E15, 62E17. Keywords and phrases: Gamma function, digamma function, Wallis ra- tio, upper bounds, lower bounds, exact bounds, inequalities in approxima- tion, Student's distribution, Student's statistic, probability inequalities.
An endpoint version of uniform Sobolev inequalities

We prove an endpoint version of the uniform Sobolev inequalities in Kenig-Ruiz-Sogge [8]. It was known that strong type inequalities no longer hold at the endpoints; however, we show that restricted weak type inequalities hold there, which imply the earlier classical result by real interpolation. The key ingredient in our proof is a type of interpolation first introduced by Bourgain [2]. We also prove restricted weak type Stein-Tomas restriction inequalities on some parts of the boundary of a pentagon, which completely characterizes the range of exponents for which the inequalities hold.
A refined upper bound for the hyperbolic volume of alternating links and the colored Jones polynomial

We give a rened upper bound for the hyperbolic volume of an alternating link in terms of the rst three and the last three coecients of its colored Jones polynomial.
Model theoretic properties of the Urysohn sphere

Abstract We characterize model theoretic properties of the Urysohn sphere as a metric structure in continuous logic. In particular, our first main result shows that the theory of the Urysohn sphere is SOP n for all n ≥ 3 , but does not have the fully finite strong order property. Our second main result is a geometric characterization of dividing independence in the theory of the Urysohn sphere. We further show that this characterization satisfies the extension axiom, and so forking and dividing are the same for complete types. Our results require continuous analogs of several tools and notions in classification theory. While many of these results are undoubtedly known to researchers in the field, they have not previously appeared in publication. Therefore, we include a full exposition of these results for general continuous theories.
Deep recurrent NMF for speech separation by unfolding iterative thresholding

In this paper, we propose a novel recurrent neural network architecture for speech separation. This architecture is constructed by unfolding the iterations of a sequential iterative soft-thresholding algorithm (ISTA) that solves the optimization problem for sparse nonnegative matrix factorization (NMF) of spectrograms. We name this network architecture deep recurrent NMF (DR-NMF). The proposed DR-NMF network has three distinct advantages. First, DR-NMF provides better interpretability than other deep architectures, since the weights correspond to NMF model parameters, even after training. This interpretability also provides principled initializations that enable faster training and convergence to better solutions compared to conventional random initialization. Second, like many deep networks, DR-NMF is an order of magnitude faster at test time than NMF, since computation of the network output only requires evaluating a few layers at each time step. Third, when a limited amount of training data is available, DR-NMF exhibits stronger generalization and separation performance compared to sparse NMF and state-of-the-art long-short term memory (LSTM) networks. When a large amount of training data is available, DR-NMF achieves lower yet competitive separation performance compared to LSTM networks.
Implementation and analysis of QUIC for MQTT

Abstract Transport and security protocols are essential to ensure reliable and secure communication between two parties. For IoT applications, these protocols must be lightweight, since IoT devices are usually resource constrained. Unfortunately, the existing transport and security protocols – namely TCP/TLS and UDP/DTLS – fall short in terms of connection overhead, latency, and connection migration when used in IoT applications. In this paper, after studying the root causes of these shortcomings, we show how utilizing QUIC in IoT scenarios results in a higher performance. Based on these observations, and given the popularity of MQTT as an IoT application layer protocol, we integrate MQTT with QUIC. By presenting the main APIs and functions developed, we explain how connection establishment and message exchange functionalities work. We evaluate the performance of MQTTw/QUIC versus MQTTw/TCP using wired, wireless, and long-distance testbeds. Our results show that MQTTw/QUIC reduces connection overhead in terms of the number of packets exchanged with the broker by up to 56%. In addition, by eliminating half-open connections, MQTTw/QUIC reduces processor and memory usage by up to 83% and 50%, respectively. Furthermore, by removing the head-of-line blocking problem, delivery latency is reduced by up to 55%. We also show that the throughput drops experienced by MQTTw/QUIC when a connection migration happens is considerably lower than that of MQTTw/TCP.
Automated migration of hierarchical data to relational tables using programming-by-example

While many applications export data in hierarchical formats like XML and JSON, it is often necessary to convert such hierarchical documents to a relational representation. This paper presents a novel programming-by-example approach, and its implementation in a tool called Mitra, for automatically migrating tree-structured documents to relational tables. We have evaluated the proposed technique using two sets of experiments. In the first experiment, we used Mitra to automate 98 data transformation tasks collected from StackOverflow. Our method can generate the desired program for 94% of these benchmarks with an average synthesis time of 3.8 seconds. In the second experiment, we used Mitra to generate programs that can convert real-world XML and JSON datasets to full-fledged relational databases. Our evaluation shows that Mitra can automate the desired transformation for all datasets.
Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient

The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work. The primary objective is to develop suitable CNN architecture for variable flow conditions and object geometry, in addition to identifying a sufficient data preparation process. Multiple CNN structures were trained to learn the lift coefficients of the airfoils with a variety of shapes in multiple flow Mach numbers, Reynolds numbers, and diverse angles of attack. This is conducted to illustrate the concept of the technique. A multi-layered perceptron (MLP) is also used for the training sets. The MLP results are compared with that of the CNN results. The newly proposed meta-modeling concept has been found to be comparable with the MLP in learning capability; and more importantly, our CNN model exhibits a competitive prediction accuracy with minimal constraints in a geometric representation.
Competitive Erosion is Conformally Invariant

We study a graph-theoretic model of interface dynamics called competitive erosion. Each vertex of the graph is occupied by a particle, which can be either red or blue. New red and blue particles are emitted alternately from their respective bases and perform random walk. On encountering a particle of the opposite color they remove it and occupy its position. We consider competitive erosion on discretizations of ‘smooth’, planar, simply connected, domains. The main result of this article shows that at stationarity, with high probability, the blue and the red regions are separated by the level curves of the Green function, with Neumann boundary conditions, which are orthogonal circular arcs on the disc and hyperbolic geodesics on a general, simply connected domain. This establishes conformal invariance of the model.
Structural Design Using Laplacian Shells

We introduce a method to design lightweight shell objects that are structurally robust under the external forces they may experience during use. Given an input 3D model and a general description of the external forces, our algorithm generates a structurally-sound minimum weight shell object. Our approach works by altering the local shell thickness repeatedly based on the stresses that develop inside the object. A key issue in shell design is that large thickness values might result in self-intersections on the inner boundary creating a significant computational challenge during optimization. To address this, we propose a shape parametrization based on the solution to the Laplace's equation that guarantees smooth and intersection-free shell boundaries. Combined with our gradient-free optimization algorithm, our method provides a practical solution to the structural design of hollow objects with a single inner cavity. We demonstrate our method on a variety of problems with arbitrary 3D models under complex force configurations and validate its performance with physical experiments.
Hemodynamic Effects of Weight Loss in Obesity: A Systematic Review and Meta-Analysis

Objectives We aimed to explore whether weight loss may improve central hemodynamics in obesity Hypothesis Hemodynamic abnormalities in obese HFpEF patients are correlated with the amount of excess body mass, suggesting a possible causal relationship. Methods We systematically searched relevant databases from inception to May 2018, without language restriction. Studies reporting invasive hemodynamic measures prior to and following therapeutic weight loss interventions in patients with obesity but no clinically-overt HF were extracted. Results A total of 9 studies were identified, providing data for 111 patients. Six studies tested dietary intervention and 3 studies tested bariatric surgery. Over a median duration of 9.7 months [range 0.75 to 23], median weight loss of 43 kg [range 10-58] was associated with significant reductions in heart rate [-9 bpm, 95% CI -12 to -6, p Conclusion Therapeutic weight loss in obese patients without HF is associated with favorable hemodynamic effects that could improve symptoms in patients with obesity and HF. Randomized controlled trials evaluating strategies for weight loss in obesity-related HFpEF are needed.
Reduced Order Model Predictive Control For Setpoint Tracking

Despite the success of model predictive control (MPC), its application to high-dimensional systems, such as flexible structures and coupled fluid/rigid-body systems, remains a largely open challenge due to excessive computational complexity. A promising solution approach is to leverage reduced order models for designing the model predictive controller. In this paper we present a reduced order MPC scheme that enables setpoint tracking while robustly guaranteeing constraint satisfaction for linear, discrete, time-invariant systems. Setpoint tracking is enabled by designing the MPC cost function to account for the steady-state error between the full and reduced order models. Robust constraint satisfaction is accomplished by solving (offline) a set of linear programs to provide bounds on the errors due to bounded disturbances, state estimation, and model approximation. The approach is validated on a synthetic system as well as a high-dimensional linear model of a flexible rod, obtained using finite element methods.
Data flow languages

There are several computer system architectures which have the goal of exploiting parallelism—multiprocessors, vector machines and array processors. For each of these architectures there have been attempts to design compilers to optimize programs written in conventional languages (e.g. "vectorizing" compilers for the FORTRAN language). There have also been new language designs to facilitate using these systems, such as Concurrent PASCAL for multiprocessors,6 and languages that utilize the features of such systems directly, such as GLYPNIR for the Illiac IV array processor19 and various "vectorizing" dialects of FORTRAN. These languages almost always make the multiprocessor, vector, or array properties of the computer visible to the programmer—that is, they are actually vehicles whereby the programmer helps the compiler uncover parallelism. Many of these languages or dialects are "unnatural" in that they closely reflect the behavior of the system for which they were designed, rather than reflecting the way programmers think about problem solutions.
The Trumpiest Trump? Identifying a Subject’s Most Characteristic Tweets

The sequence of documents produced by any given author varies in style and content, but some documents are more typical or representative of the source than others. We quantify the extent to which a given short text is characteristic of a specific person, using a dataset of tweets from fifteen celebrities. Such analysis is useful for generating excerpts of high-volume Twitter profiles, and understanding how representativeness relates to tweet popularity. We first consider the related task of binary author detection (is x the author of text T?), and report a test accuracy of 90.37% for the best of five approaches to this problem. We then use these models to compute characterization scores among all of an author’s texts. A user study shows human evaluators agree with our characterization model for all 15 celebrities in our dataset, each with p-value < 0.05. We use these classifiers to show surprisingly strong correlations between characterization scores and the popularity of the associated texts. Indeed, we demonstrate a statistically significant correlation between this score and tweet popularity (likes/replies/retweets) for 13 of the 15 celebrities in our study.
A Cost-Effective Rapid-Cycling Synchrotron

The present Fermilab proton Booster is an early example of a rapid-cycling synchrotron (RCS). Built in 1960s, it features a design in which the combined-function dipole magnets serve as vacuum cham...
On Inferring Additive and Replacing Horizontal Gene Transfers Through Phylogenetic Reconciliation

Horizontal gene transfer is one of the most important mechanisms for microbial evolution and adaptation. It is well known that horizontal gene transfer can be either "additive" or "replacing" depending on whether the transferred gene adds itself as a new gene in the recipient genome or replaces an existing homologous gene. Yet, all existing phylogenetic techniques for the inference of horizontal gene transfer assume either that all transfers are additive or that all transfers are replacing. This limitation not only affects the applicability and accuracy of these methods but also makes it difficult to distinguish between additive and replacing transfers. Here, we address this important problem by formalizing a phylogenetic reconciliation framework that simultaneously models both additive and replacing transfer events. Specifically, we (1) introduce the DTRL reconciliation framework that explicitly models both additive and replacing transfer events, along with gene duplications and losses, (2) prove that the underlying computational problem is NP-hard, (3) perform the first experimental study to assess the impact of replacing transfer events on the accuracy of the traditional DTL reconciliation model (which assumes that all transfers are additive) and demonstrate that traditional DTL reconciliation remains highly robust to the presence of replacing transfers, (4) propose a simple heuristic algorithm for DTRL reconciliation based on classifying transfer events inferred through DTL reconciliation as being replacing or additive, and (5) evaluate the classification accuracy of the heuristic under a range of evolutionary conditions. Thus, this work lays the methodological and algorithmic foundations for estimating DTRL reconciliations and distinguishing between additive and replacing transfers. An implementation of our heuristic for DTRL reconciliation is freely available open-source as part of the RANGER-DTL software package from https://compbio.engr.uconn.edu/software/ranger-dtl/.
Dealing with State Estimation in Fractional-Order Systems under Artifacts

Fractional-order dynamical systems are used to describe processes that exhibit long-term memory with power-law dependence. Notable examples include complex neurophysiological signals such as electroencephalogram (EEG) and blood-oxygen-level dependent (BOLD) signals. When analyzing different neurophysiological signals and other signals with a different origin (for example, biological systems), we often find the presence of artifacts, that is, recorded activity due to external causes having origins outside the system of interest. In this paper, we consider the problem of estimating the states of a discrete-time fractional-order dynamical system when there are artifacts present in some of the sensor measurements. Specifically, we provide necessary and sufficient conditions that ensure we can retrieve the system states even in the presence of artifacts. We provide a state estimation algorithm that can estimate the states of the system in the presence of artifacts. Finally, we present illustrative examples of our main results using real EEG data.
Reduced-order Aggregate Dynamical Model for Wind Farms

This paper presents an aggregate reduced-order model for a wind farm composed of identical parallel-connected Type-3 wind turbines. The model for individual turbines includes mechanical dynamics (arising from the turbine and doubly fed induction generator) and electrical dynamics (arising from the rotor-side and grid-side converters and associated filters). The proposed aggregate wind-farm model is structure preserving, in the sense that the parameters of the model are derived by scaling corresponding ones from the individual turbines. The aggregate model hence maps to an equivalent-albeit fictitious–wind turbine that captures the dynamics corresponding to the entire wind farm. The reduced-order model has obvious computational advantages, but more importantly, the presented analysis rigorously formalizes parametric scalings for aggregate wind-turbine models that have been applied with limited justification in prior works. Exhaustive numerical simulations validate the accuracy and computational benefits of the proposed reduced-order model.
Supervised Classifiers for Audio Impairments with Noisy Labels.

Voice-over-Internet-Protocol (VoIP) calls are prone to various speech impairments due to environmental and network conditions resulting in bad user experience. A reliable audio impairment classifier helps to identify the cause for bad audio quality. The user feedback after the call can act as the ground truth labels for training a supervised classifier on a large audio dataset. However, the labels are noisy as most of the users lack the expertise to precisely articulate the impairment in the perceived speech. In this paper, we analyze the effects of massive noise in labels in training dense networks and Convolutional Neural Networks (CNN) using engineered features, spectrograms and raw audio samples as inputs. We demonstrate that CNN can generalize better on the training data with a large number of noisy labels and gives remarkably higher test performance. The classifiers were trained both on randomly generated label noise and the label noise introduced by human errors. We also show that training with noisy labels requires a significant increase in the training dataset size, which is in proportion to the amount of noise in the labels.
Structural Evidence of Photoisomerization Pathways in Fluorescent Proteins

Double-bond photoisomerization in molecules such as the green fluorescent protein (GFP) chromophore can occur either via a volume-demanding one-bond-flip pathway or via a volume-conserving hula-twist pathway. Understanding the factors that determine the pathway of photoisomerization would inform the rational design of photoswitchable GFPs as improved tools for super-resolution microscopy. In this communication, we reveal the photoisomerization pathway of a photoswitchable GFP, rsEGFP2, by solving crystal structures of cis and trans rsEGFP2 containing a monochlorinated chromophore. The position of the chlorine substituent in the trans state breaks the symmetry of the phenolate ring of the chromophore and allows us to distinguish the two pathways. Surprisingly, we find that the pathway depends on the arrangement of protein monomers within the crystal lattice: in a looser packing, the one-bond-flip occurs, whereas, in a tighter packing (7% smaller unit cell size), the hula-twist occurs.
Whose voices count in biodiversity conservation? Ecological democracy in biodiversity offsetting, REDD+, and rewilding

Ecological democracy seeks environmentally sustainable ends through broad, active democratic participation. What happens when laws fostering participation in environmental decision-making and biodi...
Maternal overprotection in childhood is associated with amygdala reactivity and structural connectivity in adulthood

Recently, we reported that variability in early-life caregiving experiences maps onto individual differences in threat-related brain function. Here, we extend this work to provide further evidence that subtle variability in specific features of early caregiving shapes structural and functional connectivity between the amygdala and medial prefrontal cortex (mPFC) in a cohort of 312 young adult volunteers. Multiple regression analyses revealed that participants who reported higher maternal overprotection exhibited increased amygdala reactivity to explicit signals of interpersonal threat but not implicit signals of broad environmental threat. While amygdala functional connectivity with regulatory regions of the mPFC was not significantly associated with maternal overprotection, participants who reported higher maternal overprotection exhibited relatively decreased structural integrity of the uncinate fasciculus (UF), a white matter tract connecting these same brain regions. There were no significant associations between structural or functional brain measures and either maternal or paternal care ratings. These findings suggest that an overprotective maternal parenting style during childhood is associated with later functional and structural alterations of brain regions involved in generating and regulating responses to threat.