cssi/SciSciGPT-SciSciNet huggingface dataset:
Trust exploitation and attention competition: A game theoretical model

The proliferation of Social Network Sites (SNSs) has greatly reformed the way of information dissemination, but also provided a new venue for hosts with impure motivations to disseminate malicious information. Social trust is the basis for information dissemination in SNSs. Malicious nodes judiciously and dynamically make the balance between maintaining its social trust and selfishly maximizing its malicious gain over a long time-span. Studying the optimal response strategies for each malicious node could assist to design the best system maneuver so as to achieve the targeted level of overall malicious activities. In this paper, we propose an interaction-based social trust model, and formulate the maximization of long-term malicious gains of multiple competing nodes as a non-cooperative differential game. Through rigorous analysis, optimal response strategies are identified and the best system maneuver mechanism is presented. Extensive numerical studies further verify the analytical results.
Analyzing the Behavior of Visual Question Answering Models

Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze two models, one each from two major classes of VQA models -- with-attention and without-attention and show the similarities and differences in the behavior of these models. We also analyze the winning entry of the VQA Challenge 2016. Our behavior analysis reveals that despite recent progress, today's VQA models are "myopic" (tend to fail on sufficiently novel instances), often "jump to conclusions" (converge on a predicted answer after 'listening' to just half the question), and are "stubborn" (do not change their answers across images).
PEXSI-$\Sigma$: A Green's function embedding method for Kohn-Sham density functional theory

In this paper, we propose a new Green's function embedding method called PEXSI-$\Sigma$ for describing complex systems within the Kohn-Sham density functional theory (KSDFT) framework, after revisiting the physics literature of Green's function embedding methods from a numerical linear algebra perspective. The PEXSI-$\Sigma$ method approximates the density matrix using a set of nearly optimally chosen Green's functions evaluated at complex frequencies. For each Green's function, the complex boundary conditions are described by a self energy matrix $\Sigma$ constructed from a physical reference Green's function, which can be computed relatively easily. In the linear regime, such treatment of the boundary condition can be numerically exact. The support of the $\Sigma$ matrix is restricted to degrees of freedom near the boundary of computational domain, and can be interpreted as a frequency dependent surface potential. This makes it possible to perform KSDFT calculations with $\mathcal{O}(N^2)$ computational complexity, where $N$ is the number of atoms within the computational domain. Green's function embedding methods are also naturally compatible with atomistic Green's function methods for relaxing the atomic configuration outside the computational domain. As a proof of concept, we demonstrate the accuracy of the PEXSI-$\Sigma$ method for graphene with divacancy and dislocation dipole type of defects using the DFTB+ software package.
Binary operations for homotopy groups with coefficients

We define and study binary operations for homotopy groups with coefficients, and give conditions to prove that certain binary operations are the homomorphic image of the generalized Whitehead product. This allows carrying over properties of the generalized Whitehead product to these operations. We discuss two classes of binary operations, i.e., the Whitehead products and the torsion products. We also introduce a new class of operations called Ext operations and determine some of its properties. Then we compare the torsion product to the Whitehead product in a special case, and prove that the smash product of two Moore spaces has the homotopy type of a wedge of two Moore spaces.
Almost Positive Curvature on an Irreducible Compact Rank 2 Symmetric Space

A Riemannian manifold is said to be almost positively curved if the sets of points for which all $2$-planes have positive sectional curvature is open and dense. We show that the Grassmannian of oriented $2$-planes in $\mathbb{R}^7$ admits a metric of almost positive curvature, giving the first example of an almost positively curved metric on an irreducible compact symmetric space of rank greater than $1$. The construction and verification rely on the Lie group $\mathbf{G}_2$ and the octonions, so do not obviously generalize to any other Grassmannians.
Integrability of Rough Almost Complex Structures

We extend the Newlander-Nirenberg theorem to manifolds with almost complex structures that have somewhat less than Lipschitz regularity. We also discuss the regularity of local holomorphic coordinates in the integrable case, with particular attention to Lipschitz almost complex structures.
A new subadditivity formula for test ideals

Abstract We exhibit a new subadditivity formula for test ideals on singular varieties using an argument similar to [9] and [16] . Any subadditivity formula for singular varieties must have a correction term that measures the singularities of that variety. Whereas earlier subadditivity formulas accomplished this by multiplying by the Jacobian ideal, our approach is to use the formalism of Cartier algebras [1] . We also show that our subadditivity containment is sharper than ones shown previously in [32] and [11] . The first of these results follows from a Noether normalization technique due to Hochster and Huneke. The second of these results is obtained using ideas from [33] and [11] to show that the adjoint ideal J X ( A , Z ) reduces mod p to Takagi's adjoint test ideal, even when the ambient space is singular, provided that A is regular at the generic point of X. One difficulty of using this new subadditivity formula in practice is the computational complexity of computing its correction term. Thus, we discuss a combinatorial construction of the relevant Cartier algebra in the toric setting.
Center manifolds for a class of degenerate evolution equations and existence of small-amplitude kinetic shocks

Abstract We construct center manifolds for a class of degenerate evolution equations including the steady Boltzmann equation and related kinetic models, establishing in the process existence and behavior of small-amplitude kinetic shock and boundary layers. Notably, for Boltzmann's equation, we show that elements of the center manifold decay in velocity at near-Maxwellian rate, in accord with the formal Chapman–Enskog picture of near-equilibrium flow as evolution along the manifold of Maxwellian states, or Grad moment approximation via Hermite polynomials in velocity. Our analysis is from a classical dynamical systems point of view, with a number of interesting modifications to accommodate ill-posedness of the underlying evolution equation.
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units

This paper presents a general framework for norm-based capacity control for $L_{p,q}$ weight normalized deep neural networks. We establish the upper bound on the Rademacher complexities of this family. With an $L_{p,q}$ normalization where $q\le p^*$ and $1/p+1/p^{*}=1$, we discuss properties of a width-independent capacity control, which only depends on the depth by a square root term. We further analyze the approximation properties of $L_{p,q}$ weight normalized deep neural networks. In particular, for an $L_{1,\infty}$ weight normalized network, the approximation error can be controlled by the $L_1$ norm of the output layer, and the corresponding generalization error only depends on the architecture by the square root of the depth.
Spectral Mackey functors and equivariant algebraic K-theory (I)

Abstract Spectral Mackey functors are homotopy-coherent versions of ordinary Mackey functors as defined by Dress. We show that they can be described as excisive functors on a suitable ∞-category, and we use this to show that universal examples of these objects are given by algebraic K-theory. More importantly, we introduce the unfurling of certain families of Waldhausen ∞-categories bound together with suitable adjoint pairs of functors; this construction completely solves the homotopy coherence problem that arises when one wishes to study the algebraic K-theory of such objects as spectral Mackey functors. Finally, we employ this technology to introduce fully functorial versions of A-theory, upside-down A-theory, and the algebraic K-theory of derived stacks. We use this to give what we think is the first general construction of π 1 et -equivariant algebraic K-theory for profinite etale fundamental groups. This is key to our approach to the “Mackey functor case” of a sequence of conjectures of Gunnar Carlsson.
On the Optimality of Treating Interference as Noise: Compound Interference Networks

In a $K$ -user Gaussian interference channel, it has been shown by Geng et al. that if for each user, the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in decibel scale), then power control and treating interference as noise (TIN) is optimal from the perspective of generalized degrees of freedom (GDoF) and achieves the entire channel capacity region to within a constant gap. In this paper, we generalize the optimality of TIN to compound networks. We show that for a $K$ -user compound Gaussian interference channel, if in every possible state for each receiver, the channel always satisfies the TIN-optimality condition identified by Geng et al. , then the GDoF region of the compound channel is the intersection of the GDoF regions of all possible network realizations, which is achievable by power control and TIN. Furthermore, we demonstrate that for a general $K$ -user compound interference channel, regardless of the number of states of each receiver, we can always construct a counterpart $K$ -user regular interference channel that has the same TIN region as the original compound channel. The regular interference channel has only one state for each receiver, which may be different from all of the original states. Solving the GDoF-based power control problem for the compound channel is equivalent to solving the same problem in its regular counterpart. Exploring the power control problem further we develop a centralized power control scheme for $K$ -user compound interference channels, to achieve all the Pareto optimal GDoF tuples. Finally, based on this scheme, we devise an iterative power control algorithm which requires at most $K$ updates to obtain the globally optimal power allocation for any feasible GDoF tuple.
One-Bit OFDM Receivers via Deep Learning

This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), we design a novel generative supervised deep neural network that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver—specifically, an autoencoder—jointly learns a precoder and decoder for data symbol detection. Since quantization prevents end-to-end training, we propose a two-step sequential training policy for this model. With synthetic data, our deep learning-based channel estimation can outperform least squares channel estimation for unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data detection, our proposed design achieves lower bit error rate (BER) in fading than unquantized OFDM at average SNRs up to 10 dB.
The large graph limit of a stochastic epidemic model on a dynamic multilayer network.

We consider a Markovian SIR-type (Susceptible → Infected → Recovered) stochastic epidemic process with multiple modes of transmission on a contact network. The network is given by a random graph fo...
Composing inference algorithms as program transformations

Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm. We reduce this effort by generating inference procedures from models automatically. We make this code generation modular by decomposing inference algorithms into reusable program-to-program transformations. These transformations perform exact inference as well as generate probabilistic programs that compute expectations, densities, and MCMC samples. The resulting inference procedures are about as accurate and fast as other probabilistic programming systems on real-world problems.
Optimal status updating for an energy harvesting sensor with a noisy channel

Consider an energy harvesting sensor continuously monitors a system and sends time-stamped status update to a destination. The destination keeps track of the system status through the received updates. Under the energy causality constraint at the sensor, our objective is to design an optimal online status updating policy to minimize the long-term average Age of Information (AoI) at the destination. We focus on the scenario where the the channel between the source and the destination is noisy, and each transmitted update may fail independently with a constant probability. We assume there is no channel state information or transmission feedback available to the sensor. We prove that within a broadly defined class of online policies, the best-effort uniform updating policy, which was shown to be optimal when the channel is perfect, is still optimal in the presence of update failures. Our proof relies on tools from Martingale processes, and the construction of a sequence of virtual policies.
Learning coverage functions and private release of marginals

We study the problem of approximating and learning coverage functions. A function c : 2 → R is a coverage function, if there exists a universe U with non-negative weights w(u) for each u ∈ U and subsets A1, A2, . . . , An of U such that c(S) = ∑ u∈∪i∈SAi w(u). Alternatively, coverage functions can be described as non-negative linear combinations of monotone disjunctions. They are a natural subclass of submodular functions and arise in a number of applications. We give an algorithm that for any γ, δ > 0, given random and uniform examples of an unknown coverage function c, finds a function h that approximates c within factor 1 + γ on all but δ-fraction of the points in time poly(n, 1/γ, 1/δ). This is the first fully-polynomial algorithm for learning an interesting class of functions in the demanding PMAC model of Balcan and Harvey (2012). Our algorithms are based on several new structural properties of coverage functions. Using the results in (Feldman and Kothari, 2014), we also show that coverage functions are learnable agnostically with excess `1-error over all product and symmetric distributions in time n . In contrast, we show that, without assumptions on the distribution, learning coverage functions is at least as hard as learning polynomial-size disjoint DNF formulas, a class of functions for which the best known algorithm runs in time 2 ) (Klivans and Servedio, 2004). As an application of our learning results, we give simple differentially-private algorithms for releasing monotone conjunction counting queries with low average error. In particular, for any k ≤ n, we obtain private release of k-way marginals with average error ᾱ in time n.
A Three-Operator Splitting Scheme and its Optimization Applications

Operator-splitting methods convert optimization and inclusion problems into fixed-point equations; when applied to convex optimization and monotone inclusion problems, the equations given by operator-splitting methods are often easy to solve by standard techniques. The hard part of this conversion, then, is to design nicely behaved fixed-point equations. In this paper, we design a new, and thus far, the only nicely behaved fixed-point equation for solving monotone inclusions with three operators; the equation employs resolvent and forward operators, one at a time, in succession. We show that our new equation extends the Douglas-Rachford and forward-backward equations; we prove that standard methods for solving the equation converge; and we give two accelerated methods for solving the equation.
Unstable modes in projection-based reduced-order models: How many can there be, and what do they tell you?

Abstract Projection methods provide an appealing way to construct reduced-order models of large-scale linear dynamical systems: they are intuitively motivated and fairly easy to compute. Unfortunately, the resulting reduced models need not inherit the stability of the original system. How many unstable modes can these reduced models have? This note investigates this question, using theory originally motivated by iterative methods for linear algebraic systems and eigenvalue problems, and illustrating the theory with a number of small examples. From these results follow rigorous upper bounds on the number of unstable modes in reduced models generated via orthogonal projection, for both continuous- and discrete-time systems. Can anything be learned from the unstable modes in reduced-order models? Several examples illustrate how such instability can helpfully signal transient growth in the original system.
Uncertainty quantification in non-equilibrium molecular dynamics simulations of thermal transport

Abstract Bulk thermal conductivity estimates based on predictions from non-equilibrium molecular dynamics (NEMD) using the so-called direct method are known to be severely under-predicted since finite simulation length-scales are unable to mimic bulk transport. Moreover, subjecting the system to a temperature gradient by means of thermostatting tends to impact phonon transport adversely. Additionally, NEMD predictions are tightly coupled with the choice of the inter-atomic potential and the underlying values associated with its parameters. In the case of silicon (Si), nominal estimates of the Stillinger-Weber (SW) potential parameters are largely based on a constrained regression approach aimed at agreement with experimental data while ensuring structural stability. However, this approach has its shortcomings and it may not be ideal to use the same set of parameters to study a wide variety of Si-based systems subjected to different thermodynamic conditions. In this study, NEMD simulations are performed on a Si bar to investigate the impact of bar-length, and the applied temperature gradient on the discrepancy between predictions and the available measurement for bulk thermal conductivity at 300 K by constructing statistical response surfaces at different temperatures. The approach helps quantify the discrepancy, observed to be largely dependent on the system-size, with minimal computational effort. A computationally efficient approach based on derivative-based sensitivity measures to construct a reduced-order polynomial chaos surrogate for NEMD predictions is also presented. The surrogate is used to perform parametric sensitivity analysis, forward propagation of the uncertainty, and calibration of the important SW potential parameters in a Bayesian setting. It is found that only two (out of seven) parameters contribute significantly to the uncertainty in bulk thermal conductivity estimates for Si.
Cosmic Mass Spectrometer

Abstract We argue that if ultrahigh-energy ( E ≳ 10 10 GeV ) cosmic rays are heavy nuclei (as indicated by existing data), then the pointing of cosmic rays to their nearest extragalactic sources is expected for 10 10.6 ≲ E / GeV ≲ 10 11 . This is because for a nucleus of charge Ze and baryon number A, the bending of the cosmic ray decreases as Z / E with rising energy, so that pointing to nearby sources becomes possible in this particular energy range. In addition, the maximum energy of acceleration capability of the sources grows linearly in Z, while the energy loss per distance traveled decreases with increasing A. Each of these two points tend to favor heavy nuclei at the highest energies. The traditional bi-dimensional analyses, which simultaneously reproduce Auger data on the spectrum and nuclear composition, may not be capable of incorporating the relative importance of all these phenomena. In this paper we propose a multi-dimensional reconstruction of the individual emission spectra (in E, direction, and cross-correlation with nearby putative sources) to study the hypothesis that primaries are heavy nuclei subject to GZK photo-disintegration, and to determine the nature of the extragalactic sources. More specifically, we propose to combine information on nuclear composition and arrival direction to associate a potential clustering of events with a 3-dimensional position in the sky. Actually, both the source distance and maximum emission energy can be obtained through a multi-parameter likelihood analysis to accommodate the observed nuclear composition of each individual event in the cluster. We show that one can track the level of GZK interactions on an statistical basis by comparing the maximum energy at the source of each cluster. We also show that nucleus-emitting-sources exhibit a cepa stratis structure on Earth which could be pealed off by future space-missions, such as POEMMA. Finally, we demonstrate that metal-rich starburst galaxies are highly-plausible candidate sources, and we use them as an explicit example of our proposed multi-dimensional analysis.
Relational theories with null values and non-herbrand stable models

Generalized relational theories with null values in the sense of Reiter are first-order theories that provide a semantics for relational databases with incomplete information. In this paper we show that any such theory can be turned into an equivalent logic program, so that models of the theory can be generated using computational methods of answer set programming. As a step towards this goal, we develop a general method for calculating stable models under the domain closure assumption but without the unique name assumption.
G-torsors over a Dedekind scheme

We prove the equivalence of three “points of view” on the notion of a G-torsor when the base scheme is a Dedekind scheme, generalizing known results when the base is a field. The two main tools that we generalize are Chevalley’s theorem on semi-invariants (cf. Borel (1991)[1, II.5.1]) and a Tannakian description of G-torsors given by Nori and Saavedra (cf. Nori (1976)[8, Sec. 2] and Saavedra Rivano (1972) [11, II.4.2]). As an application, we show that the fibered category of G-torsors on a regular proper curve over a field k is an Artin stack locally of finite presentation over k.
Robust Neural Machine Translation with Doubly Adversarial Inputs

Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input. We propose an approach to improving the robustness of NMT models, which consists of two parts: (1) attack the translation model with adversarial source examples; (2) defend the translation model with adversarial target inputs to improve its robustness against the adversarial source inputs. For the generation of adversarial inputs, we propose a gradient-based method to craft adversarial examples informed by the translation loss over the clean inputs. Experimental results on Chinese-English and English-German translation tasks demonstrate that our approach achieves significant improvements (2.8 and 1.6 BLEU points) over Transformer on standard clean benchmarks as well as exhibiting higher robustness on noisy data.
Gorenstein complexes and recollements from cotorsion pairs

We describe a general correspondence between injective (resp. projective) recollements of triangulated categories and injective (resp. projective) cotorsion pairs. This provides a model category description of these recollement situations. Our applications focus on displaying several recollements that glue together various full subcategories of K(R), the homotopy category of chain complexes of modules over a general ring R. When R is (left) Noetherian, these recollements involve complexes built from the Gorenstein injective modules. When R is a (left) coherent ring for which all flat modules have finite projective dimension we obtain the duals. These results extend to a general ring R by replacing the Gorenstein modules with the Gorenstein AC-modules introduced in [9]. We also see that in any abelian category with enough injectives, the Gorenstein injective objects enjoy a maximality property in that they contain every other class making up the right half of an injective cotorsion pair.
Sutured Khovanov homology distinguishes braids from other tangles

We show that the sutured Khovanov homology of a balanced tangle in the product sutured manifold D x I has rank 1 if and only if the tangle is isotopic to a braid.
Comparison of Metric Spectral Gaps

Let $A=(a_{ij})\in M_n(\R)$ be an $n$ by $n$ symmetric stochastic matrix. For $p\in [1,\infty)$ and a metric space $(X,d_X)$, let $\gamma(A,d_X^p)$ be the infimum over those $\gamma\in (0,\infty]$ for which every $x_1,...,x_n\in X$ satisfy $$ \frac{1}{n^2} \sum_{i=1}^n\sum_{j=1}^n d_X(x_i,x_j)^p\le \frac{\gamma}{n}\sum_{i=1}^n\sum_{j=1}^n a_{ij} d_X(x_i,x_j)^p. $$ Thus $\gamma(A,d_X^p)$ measures the magnitude of the {\em nonlinear spectral gap} of the matrix $A$ with respect to the kernel $d_X^p:X\times X\to [0,\infty)$. We study pairs of metric spaces $(X,d_X)$ and $(Y,d_Y)$ for which there exists $\Psi:(0,\infty)\to (0,\infty)$ such that $\gamma(A,d_X^p)\le \Psi(\gamma(A,d_Y^p))$ for every symmetric stochastic $A\in M_n(\R)$ with $\gamma(A,d_Y^p)<\infty$. When $\Psi$ is linear a complete geometric characterization is obtained. Our estimates on nonlinear spectral gaps yield new embeddability results as well as new nonembeddability results. For example, it is shown that if $n\in \N$ and $p\in (2,\infty)$ then for every $f_1,...,f_n\in L_p$ there exist $x_1,...,x_n\in L_2$ such that {equation}\label{eq:p factor} \forall\, i,j\in \{1,...,n\},\quad \|x_i-x_j\|_2\lesssim p\|f_i-f_j\|_p, {equation} and $$ \sum_{i=1}^n\sum_{j=1}^n \|x_i-x_j\|_2^2=\sum_{i=1}^n\sum_{j=1}^n \|f_i-f_j\|_p^2. $$ This statement is impossible for $p\in [1,2)$, and the asymptotic dependence on $p$ in \eqref{eq:p factor} is sharp. We also obtain the best known lower bound on the $L_p$ distortion of Ramanujan graphs, improving over the work of Matou\v{s}ek. Links to Bourgain--Milman--Wolfson type and a conjectural nonlinear Maurey--Pisier theorem are studied.
Adversarial Task Assignment

The problem of assigning tasks to workers is of long-standing fundamental importance. Examples of this include the classical problem of assigning computing tasks to nodes in a distributed computing environment, assigning jobs to robots, and crowdsourcing. Extensive research into this problem generally addresses important issues such as uncertainty and incentives. However, the problem of adversarial tampering with the task assignment process has not received as much attention. We are concerned with a particular adversarial setting where an attacker may target a set of workers in order to prevent the tasks assigned to these workers from being completed. When all tasks are homogeneous, we provide an efficient algorithm for computing the optimal assignment. When tasks are heterogeneous, we show that the adversarial assignment problem is NP-Hard, and present an algorithm for solving it approximately. Our theoretical results are accompanied by extensive experiments showing the effectiveness of our algorithms.
Beilinson-Hodge cycles on semiabelian varieties

Beilinson conjectured that all rational cycles of type (q,q) on the qth cohomology of a smooth complex algebraic variety should come from motivic cohomology. The purpose of this note is to prove this when the variety is a semiabelian variety or a product of curves. The proof is based on the study of invariants under the Mumford-Tate group.
Robust image sentiment analysis using progressively trained and domain transferred deep networks

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.
Sequences of embedded minimal disks whose curvatures blow up on a prescribed subset of a line

For any prescribed closed subset of a line segment in Euclidean 3-space, we construct a sequence of minimal disks that are properly embedded in an open solid cylinder around the segment and that have curvatures blowing up precisely at the points of the closed set.
Competitively chasing convex bodies

Let F be a family of sets in some metric space. In the F-chasing problem, an online algorithm observes a request sequence of sets in F and responds (online) by giving a sequence of points in these sets. The movement cost is the distance between consecutive such points. The competitive ratio is the worst case ratio (over request sequences) between the total movement of the online algorithm and the smallest movement one could have achieved by knowing in advance the request sequence. The family F is said to be chaseable if there exists an online algorithm with finite competitive ratio. In 1991, Linial and Friedman conjectured that the family of convex sets in Euclidean space is chaseable. We prove this conjecture.
A note on axial symmetries

This note describes how to characterize and normalize an axial Killing field on a general Riemannian geometry or four-dimensional Lorentzian geometry. No global assumptions are necessary, such as that the orbits of the Killing field all have period 2π. Rather, any Killing field that vanishes at at least one point necessarily has the expected global properties.
Order statistics and probabilistic robust control

Abstract Order statistics theory is applied in this paper to probabilistic robust control theory to compute the minimum sample size needed to come up with a reliable estimate of an uncertain quantity under continuity assumption of the related probability distribution. Also, the concept of distribution-free tolerance intervals is applied to estimate the range of an uncertain quantity and extract the information about its distribution. To overcome the limitations imposed by the continuity assumption in the existing order statistics theory, we have derived a cumulative distribution function of the order statistics without the continuity assumption and developed an inequality showing that this distribution has an upper bound which equals to the corresponding distribution when the continuity assumption is satisfied. By applying this inequality, we investigate the minimum computational effort needed to come up with an reliable estimate for the upper bound (or the lower bound) and the range of a quantity. We also give conditions, which are much weaker than the absolute continuity assumption, for the existence of such minimum sample size. Furthermore, the issue of making tradeoff between performance level and risk is addressed and a guideline for making this kind of tradeoff is established. This guideline can be applied in general without continuity assumption.
Ad-nilpotent ideals and equivalence relations

Abstract In this paper we study ad-nilpotent ideals of a complex simple Lie algebra g and their connections with affine Weyl groups and nilpotent orbits. We define a left equivalence relation for ad-nilpotent ideals based on their normalizer and generators, and prove that the equivalence relation corresponds to Lusztig's star operation for simply laced algebraic group G.
Stability phenomena in the topology of moduli spaces

The recent proof by Madsen and Weiss of Mumford's conjecture on the stable cohomology of moduli spaces of Riemann surfaces, was a dramatic example of an important stability theorem about the topology of moduli spaces. In this article we give a survey of families of classifying spaces and moduli spaces where "stability phenomena" occur in their topologies. Such stability theorems have been proved in many situations in the history of topology and geometry, and the payoff has often been quite remarkable. In this paper we discuss classical stability theorems such as the Freudenthal suspension theorem, Bott periodicity, and Whitney's embedding theorems. We then discuss more modern examples such as those involving configuration spaces of points in manifolds, holomorphic curves in complex manifolds, gauge theoretic moduli spaces, the stable topology of general linear groups, and pseudoisotopies of manifolds. We then discuss the stability theorems regarding the moduli spaces of Riemann surfaces: Harer's stability theorem on the cohomology of moduli space, and the Madsen-Weiss theorem, which proves a generalization of Mumford's conjecture. We also describe Galatius's recent theorem on the stable cohomology of automorphisms of free groups. We end by speculating on the existence of general conditions in which one might expect these stability phenomena to occur.
Covers of the projective line and the moduli space of quadratic differentials

Consider the Hurwitz space parameterizing covers of \({\mathbb{P}^1}\) branched at four points. We study its intersection with divisor classes on the moduli space of curves. As applications, we calculate the slope of Teichmuller curves parameterizing square-tiled cyclic covers. In addition, we come up with a relation among the slope of Teichmuller curves, the sum of Lyapunov exponents and the Siegel–Veech constant for the moduli space of quadratic differentials, which yields information for the effective cone of the moduli space of curves.
A compactness result for energy-minimizing harmonic maps with rough domain metric

In 1996, Shi generalized the epsilon-regularity theorem of Schoen and Uhlenbeck to energy-minimizing harmonic maps from a domain equipped with a bounded measurable Riemannian metric. In the present work we prove a compactness result for such energy-minimizing maps. As an application, we combine our result with Shi's theorem to give an improved bound on the Hausdorff dimension of the singular set, assuming that the map has bounded energy at all scales. This last assumption can be removed when the target manifold is simply-connected.
Predictive Control for Energy Management in Ship Power Systems Under High-Power Ramp Rate Loads

Electrical weapons and combat systems integrated into ships create challenges for their power systems. The main challenge is operation under high-power ramp rate loads, such as rail-guns and radar systems. When operated, these load devices may exceed the ships generators in terms of power ramp rate, which may drive the system to instability. Thus, electric ships require integration of energy storage devices in coordination with the power generators to maintain the power balance between distributed resources and load devices. In order to support the generators by using energy storage systems, an energy management scheme must be deployed to ensure load demand is met. This paper proposes and implements an energy management scheme based on model predictive control to optimize the coordination between the energy storage and the power generators under high-power ramp rate conditions. The simulation and experimental results validate the proposed technique in a reduced scale, notional electric ship power system.
Quantum algorithms and circuits for scientific computing

Quantum algorithms for scientific computing require modules implementing fundamental functions, such as the square root, the logarithm, and others. We require algorithms that have a well-controlled numerical error, that are uniformly scalable and reversible (unitary), and that can be implemented efficiently. We present quantum algorithms and circuits for computing the square root, the natural logarithm, and arbitrary fractional powers. We provide performance guarantees in terms of their worst-case accuracy and cost. We further illustrate their performance by providing tests comparing them to the respective floating point implementations found in widely used numerical software.
The Role of Schema Matching in Large Enterprises

the principal use case for schema matching research has been as a precursor for code generation, i.e., constructing mappings between schema elements with the end goal of data transfer. In this paper, we argue that schema matching plays valuable roles independent of mapping construction, especially as schemata grow to industrial scales. Specifically, in large enterprises human decision makers and planners are often the immediate consumer of information derived from schema matchers, instead of schema mapping tools. We list a set of real application areas illustrating this role for schema matching, and then present our experiences tackling a customer problem in one of these areas. We describe the matcher used, where the tool was effective, where it fell short, and our lessons learned about how well current schema matching technology is suited for use in large enterprises. Finally, we suggest a new agenda for schema matching research based on these experiences.
Tests for injectivity of modules over commutative rings

It is proved that a module M over a commutative noetherian ring R is injective if \(\mathrm {Ext}_{R}^{i}((R/{\mathfrak p})_{\mathfrak p},M)=0\) holds for every \(i\geqslant 1\) and every prime ideal \(\mathfrak {p}\) in R. This leads to the following characterization of injective modules: If F is faithfully flat, then a module M such that \({\text {Hom}}_R(F,M)\) is injective and \({\text {Ext}}^i_R(F,M)=0\) for all \(i\geqslant 1\) is injective. A limited version of this characterization is also proved for certain non-noetherian rings.
UAV Air-to-Ground Channel Characterization for mmWave Systems

Communication at mmWave bands carries critical importance for 5G wireless networks. In this paper, we study the characterization of mmWave air-to-ground (AG) channels for unmanned aerial vehicle (UAV) communications. In particular, we use ray tracing simulations using Remcom Wireless InSite software to study the behavior of AG mmWave bands at two different frequencies: 28 GHz and 60 GHz. Received signal strength (RSS) and root mean square delay spread (RMS-DS) of multipath components (MPCs) are analyzed for different UAV heights considering four different environments: urban, suburban, rural, and over sea. It is observed that the RSS mostly follows the two ray propagation model along the UAV flight path for higher altitudes. This two ray propagation model is affected by the presence of high rise scatterers in urban scenario. Moreover, we present details of a universal serial radio peripheral (USRP) based channel sounder that can be used for AG channel measurements for mmWave (60 GHz) UAV communications.
Rademacher&#8211;Carlitz polynomials

We introduce and study the \emph{Rademacher-Carlitz polynomial} \[ \RC(u, v, s, t, a, b) := \sum_{k = \lceil s \rceil}^{\lceil s \rceil + b - 1} u^{\fl{\frac{ka + t}{b}}} v^k \] where $a, b \in \Z_{>0}$, $s, t \in \R$, and $u$ and $v$ are variables. These polynomials generalize and unify various Dedekind-like sums and polynomials; most naturally, one may view $\RC(u, v, s, t, a, b)$ as a polynomial analogue (in the sense of Carlitz) of the \emph{Dedekind-Rademacher sum} \[ \r_t(a,b) := \sum_{k=0}^{b-1}\left(\left(\frac{ka+t}{b} \right)\right) \left(\left(\frac{k}{b} \right)\right), \] which appears in various number-theoretic, combinatorial, geometric, and computational contexts. Our results come in three flavors: we prove a reciprocity theorem for Rademacher-Carlitz polynomials, we show how they are the only nontrivial ingredients of integer-point transforms \[ \sigma(x,y):=\sum_{(j,k) \in \mathcal{P}\cap \Z^2} x^j y^k \] of any rational polyhedron $\mathcal{P}$, and we derive a novel reciprocity theorem for Dedekind-Rademacher sums, which follows naturally from our setup.
Autofocals: evaluating gaze-contingent eyeglasses for presbyopes

Presbyopia, the loss of accommodation due to the stiffening of the crystalline lens in the eye, affects nearly 20% of the population worldwide. Traditional forms of presbyopia correction use fixed focal elements that inherently trade off field of view or stereo vision for a greater range of distances at which the wearer can see clearly. However, none of these offer the same natural refocusing enjoyed in youth. In this work, we built a new type of presbyopia correction, dubbed "autofocal," which externally mimics the natural accommodation response of the eye by combining data from eye trackers and a depth sensor, and then automatically drives focus-tunable lenses. We evaluated autofocals against progressives and mono-vision in a user study; compared to these traditional corrections, autofocals maintain better visual acuity at all tested distances, allow for faster and more accurate visual task performance, and are easier to refocus with for a majority of wearers.
International Travel for Living Donor Kidney Donation: A Proposal for Focused Screening of Vulnerable Groups

As the gap between organ donors and patients on the recipient waiting list grows, residents of the United States who are in need of kidney transplantation occasionally contract with living donors from outside the United States. Those donors then travel to the United States to undergo living donor kidney donation at US transplant centers. This practice is not limited to the United States and occurs with some regularity around the world. However, there is very little written about this practice from the perspective of the US transplant system, and there is little in the way of guidance (either legal or ethical) to assist centers that accommodate it in distinguishing between ethically permissible travel for transplant and what could potentially be human trafficking for organ removal. This article will present an ethical analysis of travel for organ donation with particular attention to lessons that can be drawn from living donor donation in other countries. This inquiry is particularly germane because Organ Procurement and Transplantation Network has promulgated guidelines with respect to obligations owed to living donors, but those guidelines appear to assume that the donor is a US resident. The critical question then is whether or to what extent those guidelines are applicable to the instant scenario in which the living donor is a nonresident. In addition, this article addresses several critical ethical concerns implicated by the often vulnerable populations from which donors are drawn. Finally, this article proposes that focused inquiry by transplant centers is necessary when donors are nonresidents.
Rules of Thumb and Attention Elasticities: Evidence from Under- and Overreaction to Taxes

This paper tests costly attention models of consumers’ misreaction to opaque taxes. We report an online shopping experiment that involves shrouded sales taxes that are exogenously varied within consumer over time. Some consumers systematically underreact to sales taxes while others systematically overreact, but higher stakes decrease both under- and overreaction. This is consistent with consumers using heterogeneous rules of thumb to compute the opaque tax when the stakes are low, but using costly mental effort at higher stakes. The results allow us to differentiate between various theories of limited attention. We also develop novel econometric techniques for quantifying individual differences.
Semi-Supervised Learning Via Feedforward-Designed Convolutional Neural Networks

A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work. One unique property of FF-CNNs is that no backpropagation is used in model parameters determination. Since unlabeled data may not always enhance semi-supervised learning [1], we define an effective quality score and use it to select a subset of unlabeled data in the training process. We conduct experiments on the MNIST, SVHN, and CIFAR-10 datasets, and show that the proposed semi-supervised FF-CNN solution outperforms the CNN trained by backpropagation (BP-CNN) when the amount of labeled data is reduced. Furthermore, we develop an ensemble system that combines the output decision vectors of different semi-supervised FF-CNNs to boost classification accuracy. The ensemble systems can achieve further performance gains on all three benchmarking datasets.
Object Recognition Under Multifarious Conditions: A Reliability Analysis and a Feature Similarity-Based Performance Estimation

In this paper, we investigate the reliability of online recognition platforms, Amazon Rekognition and Microsoft Azure, with respect to changes in background, acquisition device, and object orientation. We focus on platforms that are commonly used by the public to better understand their real-world performances. To assess the variation in recognition performance, we perform a controlled experiment by changing the acquisition conditions one at a time. We use three smartphones, one DSLR, and one webcam to capture side views and overhead views of objects in a living room, an office, and photo studio setups. Moreover, we introduce a framework to estimate the recognition performance with respect to backgrounds and orientations. In this framework, we utilize both handcrafted features based on color, texture, and shape characteristics and data-driven features obtained from deep neural networks. Experimental results show that deep learning- based image representations can estimate the recognition performance variation with a Spearman’s rank-order correlation of 0.94 under multifarious acquisition conditions.
Occupant-Location-Catered Control of IoT-Enabled Building HVAC Systems

This article studies about the catered controls for building heating-ventilation-air-conditioning (HVAC) systems, which react to the changing location of an occupant. A Markov jump-linear system model is developed which captures the building’s thermal processes, HVAC system, occupant movement, and proposed proportional-integral-derivative feedback. A statistical analysis of the closed-loop dynamics is used to confirm moment boundedness and tune the control gains to improve temperature regularity with limited actuation, in the face of disturbances. The control strategy is demonstrated in an example.