cssi/SciSciGPT-SciSciNet huggingface dataset:
Blowing Up Finitely Supported Complete Ideals in a Regular Local Ring

Let I be a finitely supported complete m-primary ideal of a regular local ring (R,m). We consider singularities of the projective models ProjR[It] and ProjR[It]‾ over Spec R, where R[It]‾ denotes the integral closure of the Rees algebra R[It]. A theorem of Lipman implies that the ideal I has a unique factorization as a ⁎-product of special ⁎-simple complete ideals with possibly negative exponents for some of the factors. If ProjR[It]‾ is regular, we prove that ProjR[It]‾ is the regular model obtained by blowing up the finite set of base points of I. Extending work of Lipman and Huneke–Sally in dimension 2, we prove that every local ring S on ProjR[It]‾ that is a unique factorization domain is regular. Moreover, if dim⁡S≥2 and S dominates R, then S is an infinitely near point to R, that is, S is obtained from R by a finite sequence of local quadratic transforms.
Convergence and quasi-optimality of adaptive finite element methods for harmonic forms

Numerical computation of harmonic forms (typically called harmonic fields in three space dimensions) arises in various areas, including computer graphics and computational electromagnetics. The finite element exterior calculus framework also relies extensively on accurate computation of harmonic forms. In this work we study the convergence properties of adaptive finite element methods (AFEM) for computing harmonic forms. We show that a properly defined AFEM is contractive and achieves optimal convergence rate beginning from any initial conforming mesh. This result is contrasted with related AFEM convergence results for elliptic eigenvalue problems, where the initial mesh must be sufficiently fine in order for AFEM to achieve any provable convergence rate.
Acoustic-to-word model without OOV

Recently, the acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion was shown as a natural end-to-end model directly targeting words as output units. However, this type of word-based CTC model suffers from the out-of-vocabulary (OOV) issue as it can only model limited number of words in the output layer and maps all the remaining words into an OOV output node. Therefore, such word-based CTC model can only recognize the frequent words modeled by the network output nodes. It also cannot easily handle the hot-words which emerge after the model is trained. In this study, we improve the acoustic-to-word model with a hybrid CTC model which can predict both words and characters at the same time. With a shared-hidden-layer structure and modular design, the alignments of words generated from the word-based CTC and the character-based CTC are synchronized. Whenever the acoustic-to-word model emits an OOV token, we back off that OOV segment to the word output generated from the character-based CTC, hence solving the OOV or hot-words issue. Evaluated on a Microsoft Cortana voice assistant task, the proposed model can reduce the errors introduced by the OOV output token in the acoustic-to-word model by 30%.
Listen to Your Face: Inferring Facial Action Units from Audio Channel

Extensive efforts have been devoted to recognizing facial action units (AUs). However, it is still challenging to recognize AUs from spontaneous facial displays especially when they are accompanied by speech. Different from all prior work that utilized visual observations for facial AU recognition, this paper presents a novel approach that recognizes speech-related AUs exclusively from audio signals based on the fact that facial activities are highly correlated with voice during speech. Specifically, dynamic and physiological relationships between AUs and phonemes are modeled through a continuous time Bayesian network (CTBN); then AU recognition is performed by probabilistic inference via the CTBN model. A pilot audiovisual AU-coded database has been constructed to evaluate the proposed audio-based AU recognition framework. The database consists of a “clean” subset with frontal and neutral faces and a challenging subset collected with large head movements and occlusions. Experimental results on this database show that the proposed CTBN model achieves promising recognition performance for 7 speech-related AUs and outperforms both the state-of-the-art visual-based and audio-based methods especially for those AUs that are activated at low intensities or “hardly visible” in the visual channel. The improvement is more impressive on the challenging subset, where the visual-based approaches suffer significantly.
Imitating Latent Policies from Observation

In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations. We introduce a method that characterizes the causal effects of latent actions on observations while simultaneously predicting their likelihood. We then outline an action alignment procedure that leverages a small amount of environment interactions to determine a mapping between the latent and real-world actions. We show that this corrected labeling can be used for imitating the observed behavior, even though no expert actions are given. We evaluate our approach within classic control environments and a platform game and demonstrate that it performs better than standard approaches. Code for this work is available at this https URL.
Brascamp-Lieb inequality and its reverse: An information theoretic view

We generalize a result by Carlen and Cordero-Erausquin on the equivalence between the Brascamp-Lieb inequality and the subadditivity of relative entropy by allowing for random transformations (a broadcast channel). This leads to a unified perspective on several functional inequalities that have been gaining popularity in the context of proving impossibility results. We demonstrate that the information theoretic dual of the Brascamp-Lieb inequality is a convenient setting for proving properties such as data processing, tensorization, convexity and Gaussian optimality. Consequences of the latter include an extension of the Brascamp-Lieb inequality allowing for Gaussian random transformations, the determination of the multivariate Wyner common information for Gaussian sources, and a multivariate version of Nelson's hypercontractivity theorem. Finally we present an information theoretic characterization of a reverse Brascamp-Lieb inequality involving a random transformation (a multiple access channel).
Least squares policy iteration with instrumental variables vs. direct policy search: comparison against optimal benchmarks using energy storage

This article studies least-squares approximate policy iteration (API) methods with parametrized value-function approximation. We study several variations of the policy evaluation phase, namely, Bel...
Spectral methods from tensor networks

A tensor network is a diagram that specifies a way to ``multiply'' a collection of tensors together to produce another tensor (or matrix). Many existing algorithms for tensor problems (such as tensor decomposition and tensor PCA), although they are not presented this way, can be viewed as spectral methods on matrices built from simple tensor networks. In this work we leverage the full power of this abstraction to design new algorithms for certain continuous tensor decomposition problems. An important and challenging family of tensor problems comes from orbit recovery, a class of inference problems involving group actions (inspired by applications such as cryo-electron microscopy). Orbit recovery problems over finite groups can often be solved via standard tensor methods. However, for infinite groups, no general algorithms are known. We give a new spectral algorithm based on tensor networks for one such problem: continuous multi-reference alignment over the infinite group SO(2). Our algorithm extends to the more general heterogeneous case.
Hook3 is a scaffold for the opposite-polarity microtubule-based motors cytoplasmic dynein-1 and KIF1C

The unidirectional and opposite-polarity microtubule-based motors, dynein and kinesin, drive long-distance intracellular cargo transport. Cellular observations suggest that opposite-polarity motors may be coupled. We recently identified an interaction between the cytoplasmic dynein-1 activating adaptor Hook3 and the kinesin-3 KIF1C. Here, using in vitro reconstitutions with purified components, we show that KIF1C and dynein/dynactin can exist in a complex scaffolded by Hook3. Full-length Hook3 binds to and activates dynein/dynactin motility. Hook3 also binds to a short region in the "tail" of KIF1C, but unlike dynein/dynactin, this interaction does not activate KIF1C. Hook3 scaffolding allows dynein to transport KIF1C toward the microtubule minus end, and KIF1C to transport dynein toward the microtubule plus end. In cells, KIF1C can recruit Hook3 to the cell periphery, although the cellular role of the complex containing both motors remains unknown. We propose that Hook3's ability to scaffold dynein/dynactin and KIF1C may regulate bidirectional motility, promote motor recycling, or sequester the pool of available dynein/dynactin activating adaptors.
A Pseudoline Counterexample to the Strong Dirac Conjecture

We demonstrate an infinite family of pseudoline arrangements, in which an arrangement of $n$ pseudolines has no member incident to more than $4n/9$ points of intersection. This shows the "Strong Dirac" conjecture to be false for pseudolines. We also raise a number of open problems relating to possible differences between the structure of incidences between points and lines versus the structure of incidences between points and pseudolines.
Nonfibered knots and representation shifts

A conjecture of [13] states that a knot is nonfibered if and only if its infinite cyclic cover has uncountably many finite covers. We prove the conjecture for a class of knots that includes all knots of genus 1, using techniques from symbolic dynamics.
Hierarchically hyperbolic groups are determined by their Morse boundaries

We generalize a result of Paulin on the Gromov boundary of hyperbolic groups to the Morse boundary of top-heavy hierarchically hyperbolic spaces admitting cocompact group actions by isometries. Namely we show that if the Morse boundaries of two such spaces each contain at least three points, then the spaces are quasi-isometric if and only if there exists a homeomorphism between their Morse boundaries such that the map and its inverse are 2-stable, quasi-mobius. Our result extends a recent result of Charney–Murray, who prove such a classification for CAT(0) groups, and is new for mapping class groups and the fundamental groups of 3-manifolds without Nil or Sol components.
Exact bounds on the closeness between the Student and standard normal distributions

Upper bounds on the Kolmogorov distance (and, equivalently in this case, on the total variation distance) between the Student distribu- tion with p degrees of freedom (SDp) and the standard normal distribution are obtained. These bounds are in a certain sense best possible, and the corresponding relative errors are small even for moderate values of p. The same bounds hold on the closeness between SDp and SDq with q > p. AMS 2000 subject classifications: Primary 62E17; secondary 60E15, 62E20, 62E15. Keywords and phrases: Student's distribution, standard normal distri- bution, Kolmogorov distance, total variation distance, probability inequal- ities.
On the Distortion of Voting with Multiple Representative Candidates

We study positional voting rules when candidates and voters are embedded in a common metric space, and cardinal preferences are naturally given by distances in the metric space. In a positional voting rule, each candidate receives a score from each ballot based on the ballot's rank order; the candidate with the highest total score wins the election. The cost of a candidate is his sum of distances to all voters, and the distortion of an election is the ratio between the cost of the elected candidate and the cost of the optimum candidate. We consider the case when candidates are representative of the population, in the sense that they are drawn i.i.d. from the population of the voters, and analyze the expected distortion of positional voting rules. Our main result is a clean and tight characterization of positional voting rules that have constant expected distortion (independent of the number of candidates and the metric space). Our characterization result immediately implies constant expected distortion for Borda Count and elections in which each voter approves a constant fraction of all candidates. On the other hand, we obtain super-constant expected distortion for Plurality, Veto, and approving a constant number of candidates. These results contrast with previous results on voting with metric preferences: When the candidates are chosen adversarially, all of the preceding voting rules have distortion linear in the number of candidates or voters. Thus, the model of representative candidates allows us to distinguish voting rules which seem equally bad in the worst case.
Nonlinear dynamics of a solid-state laser with injection

We analyze the dynamics of a solid-state laser driven by an injected sinusoidal field. For this type of laser, the cavity round-trip time is much shorter than its fluorescence time, yielding a dimensionless ratio of time scales $\ensuremath{\sigma}\ensuremath{\ll}1.$ Analytical criteria are derived for the existence, stability, and bifurcations of phase-locked states. We find three distinct unlocking mechanisms. First, if the dimensionless detuning \ensuremath{\Delta} and injection strength $k$ are small in the sense that $k=O(\ensuremath{\Delta})\ensuremath{\ll}{\ensuremath{\sigma}}^{1/2},$ unlocking occurs by a saddle-node infinite-period bifurcation. This is the classic unlocking mechanism governed by the Adler equation: after unlocking occurs, the phases of the drive and the laser drift apart monotonically. The second mechanism occurs if the detuning and the drive strength are large: $k=O(\ensuremath{\Delta})\ensuremath{\gg}{\ensuremath{\sigma}}^{1/2}.$ In this regime, unlocking is caused instead by a supercritical Hopf bifurcation, leading first to phase trapping and only then to phase drift as the drive is decreased. The third and most interesting mechanism occurs in the distinguished intermediate regime $k,$ $\ensuremath{\Delta}=O({\ensuremath{\sigma}}^{1/2}).$ Here the system exhibits complicated, but nonchaotic, behavior. Furthermore, as the drive decreases below the unlocking threshold, numerical simulations predict a self-similar sequence of bifurcations, the details of which are not yet understood.
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions

We present a new approach to sample from generic binary distributions, based on an exact Hamiltonian Monte Carlo algorithm applied to a piecewise continuous augmentation of the binary distribution of interest. An extension of this idea to distributions over mixtures of binary and possibly-truncated Gaussian or exponential variables allows us to sample from posteriors of linear and probit regression models with spike-and-slab priors and truncated parameters. We illustrate the advantages of these algorithms in several examples in which they outperform the Metropolis or Gibbs samplers.
Exponentially fast parameter estimation in networks using distributed dual averaging

In this paper we present an optimization-based view of distributed parameter estimation and observational social learning in networks. Agents receive a sequence of random, independent and identically distributed (i.i.d.) signals, each of which individually may not be informative about the underlying true state, but the signals together are globally informative enough to make the true state identifiable. Using an optimization-based characterization of Bayesian learning as proximal stochastic gradient descent (with Kullback-Leibler divergence from a prior as a proximal function), we show how to efficiently use a distributed, online variant of Nesterov's dual averaging method to solve the estimation with purely local information. When the true state is globally identifiable, and the network is connected, we prove that agents eventually learn the true parameter using a randomized gossip scheme. We demonstrate that with high probability the convergence is exponentially fast with a rate dependent on the KL divergence of observations under the true state from observations under the second likeliest state. Furthermore, our work also highlights the possibility of learning under continuous adaptation of network which is a consequence of employing constant, unit stepsize for the algorithm.
On Kirchberg's embedding problem

Kirchberg's Embedding Problem (KEP) asks whether every separable C ⁎ algebra embeds into an ultrapower of the Cuntz algebra O 2 O 2 . In this paper, we use model theory to show that this conjecture is equivalent to a local approximate nuclearity condition that we call the existence of good nuclear witnesses . In order to prove this result, we study general properties of existentially closed C ⁎ algebras. Along the way, we establish a connection between existentially closed C ⁎ algebras, the weak expectation property of Lance, and the local lifting property of Kirchberg. The paper concludes with a discussion of the model theory of O 2 O 2 . Several results in this last section are proven using some technical results concerning tubular embeddings, a notion first introduced by Jung for studying embeddings of tracial von Neumann algebras into the ultrapower of the hyperfinite II 1 factor.
The Unit Bar Visibility Number of a Graph

A t-unit-bar representation of a graph G is an assignment of sets of at most t horizontal unit-length segments in the plane to the vertices of G so that (1) all of the segments are pairwise nonintersecting, and (2) two vertices x and y are adjacent if and only if there is a vertical channel of positive width connecting a segment assigned to x and a segment assigned to y that intersects no other segment. The unit bar visibility number of a graph G, denoted ub(G), is the minimum t such that G has a t-unit-bar visibility representation. Our results include a linear time algorithm that determines ub(T ) when T is a tree, bounds on ub(Km,n) that determine ub(Km,n) asymptotically when n and m are asymptotically equal, and bounds on ub(Kn) that determine ub(Kn) exactly when n ≡ 1, 2 (mod 6). Submitted: September 2015 Reviewed: December 2015 Revised: February 2016 Accepted: March 2016 Final: March 2016 Published: April 2016 Article type: Regular paper Communicated by: W. S. Evans Research supported by REU in Extremal Graph Theory and Dynamical Systems – NSF award
Cycle doubling, merging, and renormalization in the tangent family

In this paper we study the transition to chaos for the restriction to the real and imaginary axes of the tangent family $\{ T_t(z)=i t\tan z\}_{0< t\leq \pi}$. Because tangent maps have no critical points but have an essential singularity at infinity and two symmetric asymptotic values, there are new phenomena: as $t$ increases we find single instances of "period quadrupling", "period splitting" and standard "period doubling"; there follows a general pattern of "period merging" where two attracting cycles of period $2^n$ "merge" into one attracting cycle of period $2^{n+1}$, and "cycle doubling" where an attracting cycle of period $2^{n+1}$ "becomes" two attracting cycles of the same period. We use renormalization to prove the existence of these bifurcation parameters. The uniqueness of the cycle doubling and cycle merging parameters is quite subtle and requires a new approach. To prove the cycle doubling and merging parameters are, indeed, unique, we apply the concept of "holomorphic motions" to our context. In addition, we prove that there is an "infinitely renormalizable" tangent map $T_{t_\infty}$. It has no attracting or parabolic cycles. Instead, it has a strange attractor contained in the real and imaginary axes which is forward invariant and minimal under $T^2_{t_\infty}$. The intersection of this strange attractor with the real line consists of two binary Cantor sets and the intersection with the imaginary line is totally disconnected, perfect and unbounded.
No Smooth Julia Sets for Polynomial Diffeomorphisms of \({\mathbb C}^2\) with Positive Entropy

For any polynomial diffeomorphism f of \({\mathbb C}^2\) with positive entropy, the Julia set of f is never \(C^1\) smooth as a manifold-with-boundary.
Asymptotics of scalar waves on long-range asymptotically Minkowski spaces

Abstract We show the existence of the full compound asymptotics of solutions to the scalar wave equation on long-range non-trapping Lorentzian manifolds modeled on the radial compactification of Minkowski space. In particular, we show that there is a joint asymptotic expansion at null and timelike infinity for forward solutions of the inhomogeneous equation. In two appendices we show how these results apply to certain spacetimes whose null infinity is modeled on that of the Kerr family. In these cases the leading order logarithmic term in our asymptotic expansions at null infinity is shown to be nonzero.
A McLean Theorem for the moduli space of Lie solutions to mass transport equations

Abstract On compact manifolds which are not simply connected, we prove the existence of “fake” solutions to the optimal transportation problem. These maps preserve volume and arise as the exponential of a closed 1-form, hence appear geometrically like optimal transport maps. The set of such solutions forms a manifold with dimension given by the first Betti number of the manifold. In the process, we prove a Hodge–Helmholtz decomposition for vector fields. The ideas are motivated by the analogies between special Lagrangian submanifolds and solutions to optimal transport problems.
The $K$-theory of assemblers

Abstract In this paper we introduce the notion of an assembler, which formally encodes “cutting and pasting” data. An assembler has an associated K-theory spectrum, in which π 0 is the free abelian group of objects of the assembler modulo the cutting and pasting relations, and in which the higher homotopy groups encode further geometric invariants. The goal of this paper is to prove structural theorems about this K-theory spectrum, including analogs of Quillen's localization and devissage theorems. We demonstrate the uses of these theorems by analyzing the assembler associated to the Grothendieck ring of varieties and the assembler associated to scissors congruence groups of polytopes.
Declarative Ajax Web Applications through SQL++ on a Unified Application State

Implementing even a conceptually simple web application requires an inordinate amount of time. FORWARD addresses three problems that reduce developer productivity: (a) Impedance mismatch across the multiple languages used at different tiers of the application architecture. (b) Distributed data access across the multiple data sources of the application (SQL database, user input of the browser page, session data in the application server, etc). (c) Asynchronous, incremental modification of the pages, as performed by Ajax actions. FORWARD belongs to a novel family of web application frameworks that attack impedance mismatch by offering a single unifying language. FORWARD's language is SQL++, a minimally extended SQL. FORWARD's architecture is based on two novel cornerstones: (a) A Unified Application State (UAS), which is a virtual database over the multiple data sources. The UAS is accessed via distributed SQL++ queries, therefore resolving the distributed data access problem. (b) Declarative page specifications, which treat the data displayed by pages as rendered SQL++ page queries. The resulting pages are automatically incrementally modified by FORWARD. User input on the page becomes part of the UAS. We show that SQL++ captures the semi-structured nature of web pages and subsumes the data models of two important data sources of the UAS: SQL databases and JavaScript components. We show that simple markup is sufficient for creating Ajax displays and for modeling user input on the page as UAS data sources. Finally, we discuss the page specification syntax and semantics that are needed in order to avoid race conditions and conflicts between the user input and the automated Ajax page modifications. FORWARD has been used in the development of eight commercial and academic applications. An alpha-release web-based IDE (itself built in FORWARD) enables development in the cloud.
A Convex Optimization Framework for Constrained Concurrent Motion Control of a Hybrid Redundant Surgical System

We present a constrained motion control framework for a redundant surgical system designed for minimally invasive treatment of pelvic osteolysis. The framework comprises a kinematics model of a six Degrees-of-Freedom (DoF) robotic arm integrated with a one DoF continuum manipulator as well as a novel convex optimization redundancy resolution controller. To resolve the redundancy resolution problem, formulated as a constrained $\ell_{2}$ -regularized quadratic minimization, we study and evaluate the potential use of an optimally tuned alternating direction method of multipliers (ADMM) algorithm. To this end, we prove global convergence of the algorithm at linear rate and propose expressions for the involved parameters resulting in a fast convergence. Simulations on the robotic system verified our analytical derivations and showed the capability and robustness of the ADMM algorithm in constrained motion control of our redundant surgical system.
Identifying student communities in blended courses.

Blended courses have become the norm in post-secondary education. Universities use large-scale learning management systems to manage class content. Instructors deliver readings, lectures, and office hours online; students use intelligent tutors, web forums, and online submission systems; and classes communicate via web forums. These online tools allow students to form new social networks or bring social relationships online. They also allow us to collect data on students' social relationships. In this paper we report on our research on community formation in blended courses based on online forum interactions. We found that it was possible to group students into communities using standard community detection algorithms via their posts and reply structure and that the students' grades are significantly correlated with their closest peers.
Crisis Communication Patterns in Social Media during Hurricane Sandy

Hurricane Sandy was one of the deadliest and costliest of hurricanes of the past few decades. Many states experienced significant power outage; however, many people used social media to communicate...
Adaptive MPC with Chance Constraints for FIR Systems

This paper proposes an adaptive stochastic Model Predictive Control (MPC) strategy for stable linear time invariant systems in the presence of bounded disturbances. We consider multi-input multi-output systems that can be expressed by a finite impulse response model, whose parameters we estimate using a linear Recursive Least Squares algorithm. Building on the work of [1], [2], our approach is able to handle hard input constraints and probabilistic output constraints. By using tools from distributionally robust optimization, we formulate our MPC design task as a convex optimization problem that can be solved using existing tools. Furthermore, we show that our adaptive stochastic MPC algorithm is persistently feasible. The efficacy of the developed algorithm is demonstrated in a numerical example and the results are compared with the adaptive robust MPC algorithm of [2].
Hierarchical variational models

Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. However, a central question remains: How to specify an expressive variational distribution that maintains efficient computation? To address this, we develop hierarchical variational models (HVMs). HVMs augment a variational approximation with a prior on its parameters, which allows it to capture complex structure for both discrete and continuous latent variables. The algorithm we develop is black box, can be used for any HVM, and has the same computational efficiency as the original approximation. We study HVMs on a variety of deep discrete latent variable models. HVMs generalize other expressive variational distributions and maintains higher fidelity to the posterior.
Electrostatic interpretation of zeros of orthogonal polynomials

We study the differential equation $ - (p(x) y')' + q(x) y' = \lambda y,$ where $p(x)$ is a polynomial of degree at most 2 and $q(x)$ is a polynomial of degree at most 1. This includes the classical Jacobi polynomials, Hermite polynomials, Legendre polynomials, Chebychev polynomials and Laguerre polynomials. We provide a general electrostatic interpretation of zeros of such polynomials: the set of real numbers $\left\{x_1, \dots, x_n\right\}$ satisfies $$ p(x_i) \sum_{k = 1 \atop k \neq i}^{n}{\frac{2}{x_k - x_i}} = q(x_i) - p'(x_i) \qquad \mbox{for all}~ 1\leq i \leq n$$ if and only if they are zeros of a polynomial solving the differential equation. We also derive a system of ODEs depending on $p(x),q(x)$ whose solutions converge to the zeros of the orthogonal polynomial at an exponential rate.
Locality-sensitive hashing for earthquake detection: a case study of scaling data-driven science

In this work, we report on a novel application of Locality Sensitive Hashing (LSH) to seismic data at scale. Based on the high waveform similarity between reoccurring earthquakes, our application identifies potential earthquakes by searching for similar time series segments via LSH. However, a straightforward implementation of this LSH-enabled application has difficulty scaling beyond 3 months of continuous time series data measured at a single seismic station. As a case study of a data-driven science workflow, we illustrate how domain knowledge can be incorporated into the workload to improve both the efficiency and result quality. We describe several end-to-end optimizations of the analysis pipeline from pre-processing to post-processing, which allow the application to scale to time series data measured at multiple seismic stations. Our optimizations enable an over 100× speedup in the end-to-end analysis pipeline. This improved scalability enabled seismologists to perform seismic analysis on more than ten years of continuous time series data from over ten seismic stations, and has directly enabled the discovery of 597 new earthquakes near the Diablo Canyon nuclear power plant in California and 6123 new earthquakes in New Zealand.
Systems of Brownian particles with asymmetric collisions

Nous etudions des systemes de particules browniennes sur l’axe reel qui interagissent de facon asymetrique en fonction de leurs temps locaux de collision. Nous prouvons l’existence et l’unicite au sens fort de tels processus et les identifions avec un systeme de particules constitue d’une famille de processus browniens ordonnes ou les coefficients de derive et de diffusion, ainsi que les temps locaux de collision entre les particules, dependent de leurs rangs. Ces systemes browniens peuvent etre compris comme des generalisations de processus stochastiques issus des marches boursiers et de la gestion de portefeuilles. Nous pouvons pallier certaines lacunes de ces modeles tout en conservant la possibilite d’effectuer des calculs explicites. Nous montrons aussi, qu’en plus de leur interet intrinseque, de tels systemes de particules browniennes apparaissent comme des limites universelles de processus de sauts sur un reseau avec des interactions locales. Une etape clef dans la preuve est l’analyse d’une generalisation des applications de Skorokhod qui inclut les temps locaux a l’intersection des faces de l’orthant positif. Le resultat generalise la convergence du processus d’exclusion simple totalement asymetrique (TASEP) vers sa version continue. Finalement, nous identifions parmi les systemes de particules browniennes ceux qui possedent une structure probabiliste determinantale.
Distances from points to planes

We prove that if $E \subset {\Bbb F}_q^d$, $d \ge 2$, $F \subset \operatorname{Graff}(d-1,d)$, the set of affine $d-1$-dimensional planes in ${\Bbb F}_q^d$, then $|\Delta(E,F)| \ge \frac{q}{2}$ if $|E||F|>q^{d+1}$, where $\Delta(E,F)$ the set of distances from points in $E$ to lines in $F$. In dimension three and higher this significantly improves the exponent obtained by Pham, Phuong, Sang, Vinh and Valculescu.
The linear profile decomposition for the Airy equation and the existence of maximizers for the Airy Strichartz inequality

We establish the linear profile decomposition for the Airy equation with complex or real initial data in L2. As an application, we obtain a dichotomy result on the existence of maximizers for the symmetric Airy Strichartz inequality.
Generating Appealing Brand Names

Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding its success or failure. In this work, we propose a computational method to generate appealing brand names based on the description of such entities. We use quantitative scores for readability, pronounceability, memorability and uniqueness of the generated names to rank order them. A set of diverse appealing names is recommended to the user for the brand naming task. Experimental results show that the names generated by our approach are more appealing than names which prior approaches and recruited humans could come up.
Investigative simulation: towards utilizing graph pattern matching for investigative search

This paper proposes the use of graph pattern matching for investigative graph search, which is the process of searching for and prioritizing persons of interest who may exhibit part or all of a pattern of suspicious behaviors or connections. While there are a variety of applications, our principal motivation is to aid law enforcement in the detection of homegrown violent extremists. We introduce investigative simulation, which consists of several necessary extensions to the existing dual simulation graph pattern matching scheme in order to make it appropriate for intelligence analysts and law enforcement officials. Specifically, we impose a categorical label structure on nodes consistent with the nature of indicators in investigations, as well as prune or complete search results to ensure sensibility and usefulness of partial matches to analysts. Lastly, we introduce a natural top-k ranking scheme that can help analysts prioritize investigative efforts. We demonstrate performance of investigative simulation on a real-world large dataset.
Denoising Flows on Trees

We study the estimation of flows on trees, a structured generalization of isotonic regression. A tree flow is defined recursively as a positive flow value into a node that is partitioned into an outgoing flow to the children nodes, with some amount of the flow possibly leaking outside. We study the behavior of the least squares estimator for flows, and the associated minimax lower bounds. We characterize the risk of the least squares estimator in two regimes. In the first regime, the diameter of the tree grows at most logarithmically with the number of nodes. In the second regime, the tree contains many long paths. The results are compared with known risk bounds for isotonic regression. In the many long paths regime, we find that the least squares estimator is not minimax rate optimal for flow estimation.
An FPT algorithm beating 2-approximation for k-cut

In the k-Cut problem, we are given an edge-weighted graph G and an integer k, and have to remove a set of edges with minimum total weight so that G has at least k connected components. Prior work on this problem gives, for all h ∈ [2, k], a (2 − h/k)-approximation algorithm for k-cut that runs in time nO(h). Hence to get a (2 − e-approximation algorithm for some absolute constant e, the best runtime using prior techniques is nO(ke). Moreover, it was recently shown that getting a (2−e)-approximation for general k is NP-hard, assuming the Small Set Expansion Hypothesis. If we use the size of the cut as the parameter, an FPT algorithm to find the exact k-Cut is known, but solving the k-Cut problem exactly is W[1]-hard if we parameterize only by the natural parameter of k. An immediate question is: can we approximate k-Cut better in FPT-time, using k as the parameter? We answer this question positively. We show that for some absolute constant e > 0, there exists a (2 − e)-approximation algorithm that runs in time 2O(k6) · O(n4). This is the first FPT algorithm that is parameterized only by k and strictly improves the 2-approximation.
Graph-Guided Banding of the Covariance Matrix

Regularization has become a primary tool for developing reliable estimators of the covariance matrix in high-dimensional settings. To curb the curse of dimensionality, numerous methods assume that ...
Measure rigidity for random dynamics on surfaces and related skew products

Given a surface $M$ and a Borel probability measure $\nu$ on the group of $C^2$-diffeomorphisms of $M$, we study $\nu$-stationary probability measures on $M$. We prove for hyperbolic stationary measures the following trichotomy: either the stable distributions are non-random, the measure is SRB, or the measure is supported on a finite set and is hence almost-surely invariant. In the proof of the above results, we study skew products with surface fibers over a measure preserving transformations equipped with a decreasing sub-$\sigma$-algebra $\hat {\mathcal F}$ and derive a related result. A number of applications of our main theorem are presented.
On the escape of CH4 from Pluto's atmosphere

We adapted a multispecies escape model, developed for close-in extrasolar planets, to calculate the escape rates of CH4 and N2 from Pluto. In the absence of escape, CH4 should overtake N2 as the dominant species below the exobase. The CH4 profile depends strongly on the escape rate, however, and the typical escape rates predicted for Pluto lead to a nearly constant mixing ratio of less than 1% below the exobase. In this case the CH4 escape rate is only 5–10% of the N2 escape rate. Observations of the CH4 profile by the New Horizons/ALICE spectrograph can constrain the CH4 escape rate and provide a unique test for escape models.
Pseudomonas aeruginosa Increases the Sensitivity of Biofilm-Grown Staphylococcus aureus to Membrane-Targeting Antiseptics and Antibiotics

Pseudomonas aeruginosa and Staphylococcus aureus often cause chronic, recalcitrant infections in large part due to their ability to form biofilms. The biofilm mode of growth enables these organisms to withstand antibacterial insults that would effectively eliminate their planktonic counterparts. We found that P. aeruginosa supernatant increased the sensitivity of S. aureus biofilms to multiple antimicrobial compounds, including fluoroquinolones and membrane-targeting antibacterial agents, including the antiseptic chloroxylenol. Treatment of S. aureus with the antiseptic chloroxylenol alone did not decrease biofilm cell viability; however, the combination of chloroxylenol and P. aeruginosa supernatant led to a 4-log reduction in S. aureus biofilm viability compared to exposure to chloroxylenol alone. We found that the P. aeruginosa-produced small molecule 2-n-heptyl-4-hydroxyquinoline N-oxide (HQNO) is responsible for the observed heightened sensitivity of S. aureus to chloroxylenol. Similarly, HQNO increased the susceptibility of S. aureus biofilms to other compounds, including both traditional and nontraditional antibiotics, which permeabilize bacterial membranes. Genetic and phenotypic studies support a model whereby HQNO causes an increase in S. aureus membrane fluidity, thereby improving the efficacy of membrane-targeting antiseptics and antibiotics. Importantly, our data show that P. aeruginosa exoproducts can enhance the ability of various antimicrobial agents to kill biofilm populations of S. aureus that are typically difficult to eradicate. Finally, our discovery that altering membrane fluidity shifts antimicrobial sensitivity profiles of bacterial biofilms may guide new approaches to target persistent infections, such as those commonly found in respiratory tract infections and in chronic wounds.IMPORTANCE The thick mucus in the airways of cystic fibrosis (CF) patients predisposes them to frequent, polymicrobial respiratory infections. Pseudomonas aeruginosa and Staphylococcus aureus are frequently coisolated from the airways of individuals with CF, as well as from diabetic foot ulcers and other wounds. Both organisms form biofilms, which are notoriously difficult to eradicate and promote chronic infection. In this study, we have shown that P. aeruginosa-secreted factors can increase the efficacy of compounds that alone have little or no bactericidal activity against S. aureus biofilms. In particular, we discovered that P. aeruginosa exoproducts can potentiate the antistaphylococcal activity of phenol-based antiseptics and other membrane-active drugs. Our findings illustrate that polymicrobial interactions can dramatically increase antibacterial efficacy in vitro and suggest that altering membrane physiology promotes the ability of certain drugs to kill bacterial biofilms-knowledge that may provide a path for the discovery of new biofilm-targeting antimicrobial strategies.
Recent Advances in Analysis and Design of Cyber-physical Systems using Passivity Indices

Analysis and resilient design of Cyber-physical Systems have greatly benefited from energy based concepts of passivity and dissipativity. Recently, there has been much research devoted to the use of passivity indices in different components of Cyber-physical systems. Passivity indices are measures of passivity, indicating how passive a system is or how far is it from being passive and generalize passivity based methods to systems that might not be passive. In this paper, we will review recent advances in the use of passivity indices in Cyber-physical systems. We will overview how the indices have been defined and applied to different components of Cyber-physical systems and how they are used in the resilient design of compositional Cvber-phvsical systems.
INSIDER DEMAND AND INDUSTRY TRENDS

We investigate the time-series patterns in industry return predictability conditioned on insider demand from 1996 to 2016. Current insider demand within an industry is positively associated with higher future industry returns. This relation is primarily driven by the buy side of insider trades and is more pronounced during periods of economic recession and high market volatility where uncertainty and information asymmetry are relatively high. Our results are consistent with the notion that corporate insiders have an informational advantage that can be used as an indicator for industry portfolio selection.
Monetary policy communication, policy slope, and the stock market

Abstract The slope factor is constructed from changes in federal funds futures of different horizons and predicts stock returns at the weekly frequency: faster policy easing positively predicts returns. It contains information about the speed of future monetary policy tightening and loosening, and predicts changes in interest rates and forecast revisions of professional forecasters. The tone of speeches by FOMC members correlates with the slope factor. The predictive power concentrates in times of high uncertainty in line with the pre-FOMC announcement drift. Our findings show the path of interest rates matters for asset prices, and monetary policy affects asset prices continuously.
Tax-motivated transfer mispricing in South Africa: Direct evidence using transaction data

Abstract This paper provides the first direct systematic evidence of profit shifting through transfer mispricing in a developing country. Using South African transaction-level customs data, I directly test for transfer price deviations from arm's-length pricing. I find that multinational firms in South Africa manipulate transfer prices in order to shift taxable profits to low-tax countries. The estimated tax loss relating to imported goods alone is 0.5% of corporate tax payments. My estimates do not support the common belief that transfer mispricing in South Africa is more severe than in advanced economies. I find that an OECD-recommended reform had no long-term impact on transfer mispricing but argue that the method used in this paper provides a cost-efficient way to curb transfer mispricing.
Fused density estimation: theory and methods

In this paper we introduce a method for nonparametric density estimation on geometric networks. We define fused density estimators as solutions to a total variation regularized maximum-likelihood density estimation problem. We provide theoretical support for fused density estimation by proving that the squared Hellinger rate of convergence for the estimator achieves the minimax bound over univariate densities of log-bounded variation. We reduce the original variational formulation in order to transform it into a tractable, finite-dimensional quadratic program. Because random variables on geometric networks are simple generalizations of the univariate case, this method also provides a useful tool for univariate density estimation. Lastly, we apply this method and assess its performance on examples in the univariate and geometric network setting. We compare the performance of different optimization techniques to solve the problem, and use these results to inform recommendations for the computation of fused density estimators.
List Decoding of Deletions Using Guess & Check Codes

Guess & Check (GC) codes are systematic binary codes that can correct multiple deletions, with high probability. GC codes have logarithmic redundancy in the length of the message k, and the encoding and decoding algorithms of these codes are deterministic and run in polynomial time for a constant number of deletions δ. The unique decoding properties of GC codes were examined in a previous work by the authors. In this paper, we investigate the list decoding performance of these codes. Namely, we study the average size and the maximum size of the list obtained by a GC decoder for a constant number of deletions δ. The theoretical results show that: (i) the average size of the list approaches 1 as k grows; and (ii) there exists an infinite sequence of GC codes indexed by k, whose maximum list size in upper bounded by a constant that is independent of k. We also provide numerical simulations on the list decoding performance of GC codes for multiple values of k and δ.
Searching heterogeneous personal digital traces

Digital traces of our lives are now constantly produced by various connected devices, internet services and interactions. Our actions result in a multitude of heterogeneous data objects, or traces, kept in various locations in the cloud or on local devices. Users have very few tools to organize, understand, and search the digital traces they produce. We propose a simple but flexible data model to aggregate, organize, and find personal information within a collection of a user's personal digital traces. Our model uses as basic dimensions the six questions: what, when, where, who, why, and how. These natural questions model universal aspects of a personal data collection and serve as unifying features of each personal data object, regardless of its source. We propose indexing and search techniques to aid users in searching for their past information in their unified personal digital data sets using our model. Experiments performed over real user data from a variety of data sources such as Facebook, Dropbox, and Gmail show that our approach significantly improves search accuracy when compared with traditional search tools.
Nonsplit module extensions over the one-sided inverse of $k[x]$

Let $R$ be the associative $k$-algebra generated by two elements $x$ and $y$ with defining relation $yx=1$. A complete description of simple modules over $R$ is obtained by using the results of Irving and Gerritzen. We examine the short exact sequence $0\rightarrow U\rightarrow E \rightarrow V\rightarrow 0$, where $U$ and $V$ are simple $R$-modules. It shows that nonsplit extension only occurs when both $U$ and $V$ are one-dimensional, or, under certain condition, $U$ is infinite-dimensional and $V$ is one-dimensional.