cssi/SciSciGPT-SciSciNet huggingface dataset:
Sampling without time: Recovering echoes of light via temporal phase retrieval

This paper considers the problem of sampling and reconstruction of a continuous-time sparse signal without assuming the knowledge of the sampling instants or the sampling rate. This topic has its roots in the problem of recovering multiple echoes of light from its low-pass filtered and auto-correlated, time-domain measurements. Our work is closely related to the topic of sparse phase retrieval and in this context, we discuss the advantage of phase-free measurements. While this problem is ill-posed, cues based on physical constraints allow for its appropriate regularization. We validate our theory with experiments based on customized, optical time-of-flight imaging sensors. What singles out our approach is that our sensing method allows for temporal phase retrieval as opposed to the usual case of spatial phase retrieval. Preliminary experiments and results demonstrate a compelling capability of our phaseretrieval based imaging device.
Disentangling by Factorising

We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon $\beta$-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs

In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.
Dehn coloring and the dimer model for knots

Fox coloring provides a combinatorial framework for studying dihedral representations of the knot group. The less well-known concept of Dehn coloring captures the same data. Recent work of Carter–Silver–Williams clarifies the relationship between the two focusing on how one transitions between Fox and Dehn colorings. In our work, we relate Dehn coloring to the dimer model for knots showing that Dehn coloring data is encoded by a certain weighted balanced overlaid Tait (BOT) graph. Using Kasteleyn theory, we provide graph theoretic methods for determining the structure of a knot’s coloring module. These constructions are closely related to Kauffman’s work on a state sum for the Alexander polynomial.
On solving L-SR1 trust-region subproblems

In this article, we consider solvers for large-scale trust-region subproblems when the quadratic model is defined by a limited-memory symmetric rank-one (L-SR1) quasi-Newton matrix. We propose a solver that exploits the compact representation of L-SR1 matrices. Our approach makes use of both an orthonormal basis for the eigenspace of the L-SR1 matrix and the Sherman---Morrison---Woodbury formula to compute global solutions to trust-region subproblems. To compute the optimal Lagrange multiplier for the trust-region constraint, we use Newton's method with a judicious initial guess that does not require safeguarding. A crucial property of this solver is that it is able to compute high-accuracy solutions even in the so-called hard case. Additionally, the optimal solution is determined directly by formula, not iteratively. Numerical experiments demonstrate the effectiveness of this solver.
(t, s)-RACKS AND THEIR LINK INVARIANTS

A (t, s)-rack is a rack structure defined on a module over the ring . We identify necessary and sufficient conditions for two (t, s)-racks to be isomorphic. We define enhancements of the rack counting invariant using the structure of (t, s)-racks and give some computations and examples. As an application, we use these enhanced invariants to obtain obstructions to knot ordering.
Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks

A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characterizing the structural diversity of common neighborhoods. Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks. We find that its positive effect on Facebook and negative effect on LinkedIn suggest different underlying networking needs in these networks. We also discover striking cases where diversity violates the principle of homophily---that is, where fewer mutual connections may lead to a higher tendency to link with each other. We then leverage structural diversity to develop a common neighborhood signature (CNS), which we apply to a large set of networks to uncover unique network superfamilies not discoverable by conventional methods. Our findings shed light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.
Comparison Results, Exit Time Moments, and Eigenvalues on Riemannian Manifolds with a Lower Ricci Curvature Bound

We study the relationship between the geometry of smoothly bounded domains in complete Riemannian manifolds and the associated sequence of $$L^1$$ -norms of exit time moments for Brownian motion. We establish bounds for Dirichlet eigenvalues and, for closed manifolds, we establish a comparison result for elements of the moment sequence using lower bounds on Ricci curvature.
Neural ideals and stimulus space visualization

Abstract A neural code C is a collection of binary vectors of a given length n that record the co-firing patterns of a set of neurons. Our focus is on neural codes arising from place cells, neurons that respond to geographic stimulus. In this setting, the stimulus space can be visualized as subset of R 2 covered by a collection U of convex sets such that the arrangement U forms an Euler diagram for C . There are some methods to determine whether such a convex realization U exists; however, these methods do not describe how to draw a realization. In this work, we look at the problem of algorithmically drawing Euler diagrams for neural codes using two polynomial ideals: the neural ideal, a pseudo-monomial ideal; and the neural toric ideal, a binomial ideal. In particular, we study how these objects are related to the theory of piercings in information visualization, and we show how minimal generating sets of the ideals reveal whether or not a code is 0, 1, or 2-inductively pierced.
Recent results in chiral effective field theory for the NN system

I describe recent progress towards a theory of the NN force which captures the consequences of QCD’s chiral symmetry and the pattern of its breaking, and is formulated as an expansion in a ratio of low and high mass scales, Mlo/Mhi. This “chiral effective field theory" of the NN system is a firm foundation for explorations of nuclear structure and reactions that are grounded in QCD’s low-energy symmetries. While calculations that use a χPT expansion for the NN potential have proven very successful, they can only be used with a narrow range of momentum-space cutoffs, which leaves the expansion parameter for observable quantities somewhat murky. Here we seek a truly systematic effective field theory for the NN amplitude, that is manifestly renormalizationgroup invariant at each order in a demonstrably perturbative expansion.
A Methodology to Evaluate Continuum-Scale Yield Surfaces Based on the Spatial Distributions of Yielding at the Crystal Scale

Using a correlation between local yielding and a multiaxial strength-to-stiffness parameter, the continuum-scale yield surface for a polyphase, polycrystalline solid is predicted. The predicted surface explicitly accounts for microstructure through the quantification of strength-to-stiffness based on a finite element model of a crystal-scale sample. The multiaxial strength-to-stiffness is evaluated from the elastic response of the sample and the restricted slip, single crystal yield surface. Macroscopic yielding is defined by the propagation of a yield band through the sample and is detected with the aid of a flood-fill algorithm. The methodology is demonstrated with the evaluation of a plane-stress yield surface for a dual-phase super-austenitic stainless steel.
Provision for Guaranteed Inertial Response in Diesel-Wind Systems via Model Reference Control

Frequency performance has been a crucial issue for islanded microgrids. On the one hand, most distributed energy resources are converter-interfaced and do not inherently respond to frequency variations. On the other hand, current inertia emulation approach cannot provide guaranteed response. In this paper, a model reference control based inertia emulation strategy is proposed for diesel-wind systems. Desired inertia can be precisely emulated through the proposed strategy. A typical frequency response model with parametric inertia is set to be the reference model. A measurement at a specific location delivers the information about the disturbance acting on the diesel-wind system to the reference model. The objective is for the speed of the diesel generator to track the reference so that the desired inertial response is realized. In addition, polytopic parameter uncertainty will be considered. The control strategy is configured in different ways according to different operating points. The parameters of the reference model are scheduled to ensure adequate frequency response under a predefined worst case. The controller is implemented in a nonlinear three-phase diesel-wind system fed microgrid using the Simulink software platform. The results show that exact synthetic inertia can be emulated and adequate frequency response is achieved.
Universal compression of power-law distributions

English words and the outputs of many other natural processes are well-known to follow a Zipf distribution. Yet this thoroughly-established property has never been shown to help compress or predict these important processes. We show that the expected redundancy of Zipf distributions of order α > 1 is roughly the 1/α power of the expected redundancy of unrestricted distributions. Hence for these orders, Zipf distributions can be better compressed and predicted than was previously known. Unlike the expected case, we show that worst-case redundancy is roughly the same for Zipf and for unrestricted distributions. Hence Zipf distributions have significantly different worst-case and expected redundancies, making them the first natural distribution class shown to have such a difference.
Higher weight on GL(3). I: The Eisenstein series

The purpose of this paper is to collect and make explicit the results of Langlands, Bump, Miyazaki and Manabe, Ishii and Oda for the $GL(3)$ Eisenstein series and Whittaker functions which are non-trivial on $SO(3,\mathbb{R})$. The final goal for the series of papers is a complete and completely explicit spectral expansion for $L^2(SL(3,\mathbb{Z})\backslash SL(3,\mathbb{R}))$ in the style of Duke, Friedlander and Iwaniec's paper on Artin L-functions. We derive a number of new results on the Whittaker functions and Eisenstein series, and give new, concrete proofs of the functional equations and spectral expansion in place of the general constructions of Langlands.
A complete hypergeometric point count formula for Dwork hypersurfaces

Abstract We extend our previous work on hypergeometric point count formulas by proving that we can express the number of points on families of Dwork hypersurfaces X λ d : x 1 d + x 2 d + … + x d d = d λ x 1 x 2 ⋯ x d over finite fields of order q ≡ 1 ( mod d ) in terms of Greene's finite field hypergeometric functions. We prove that when d is odd, the number of points can be expressed as a sum of hypergeometric functions plus ( q d − 1 − 1 ) / ( q − 1 ) and conjecture that this is also true when d is even. The proof rests on a result that equates certain Gauss sum expressions with finite field hypergeometric functions. Furthermore, we discuss the types of hypergeometric terms that appear in the point count formula and give an explicit formula for Dwork threefolds.
Recognizing Plans by Learning Embeddings from Observed Action Distributions

Automated video surveillance requires the recognition of agent plans from videos. One promising direction for plan recognition involves learning shallow action affinity models from plan traces. Extracting such traces from raw video involves uncertainty about the actions. One solution is to represent traces as sequences of action distributions. To use such a representation in approximate plan recognition, we need embeddings of these action distributions. To address this problem, we propose a distribution to vector (Distr2Vec) model, which learns embeddings of action distributions using KL-divergence as the loss function.
Linear Programming Approximations for Index Coding

Index coding, a source coding problem over broadcast channels, has been a subject of both theoretical and practical interests since its introduction (by Birk and Kol, 1998). In short, the problem can be defined as follows: there is an input $P \triangleq (p_{1}, {\dots }, p_{n})$ , a set of $n$ clients who each desire a single entry $p_{i}$ of the input, and a broadcaster whose goal is to send as few messages as possible to all clients so that each one can recover its desired entry. Additionally, each client has some predetermined “side information,” corresponding to the certain entries of the input $P$ , which we represent as the “side information graph” $ \mathcal {G}$ . The graph $ \mathcal {G}$ has a vertex $v_{i}$ for client $i$ and a directed edge $(v_{i}, v_{j})$ , indicating that client $i$ knows the $j$ th entry of the input. Given a fixed side information graph $ \mathcal {G}$ , we are interested in determining or approximating the “broadcast rate” of index coding on the graph, i.e., the least number of messages the broadcaster can transmit so that every client recovers its desired information. The complexity of determining this broadcast rate in the most general case is open, and the best-known approximations are barely better than the trivial $O(n)$ -approximation corresponding to sending each client their information directly without performing any coding. Using index coding schemes based on linear programs (LPs), we take a two-pronged approach to approximating the broadcast rate. First, extending earlier work on planar graphs, we focus on approximating the broadcast rate for special graph families, such as graphs with the small chromatic number and disk graphs. In certain cases, we are able to show that simple LP-based schemes give constant-factor approximations of the broadcast rate, which seem extremely difficult to obtain in the general case. Second, we provide several LP-based schemes for the general case, which are not constant-factor approximations, but which strictly improve on the best-known schemes. These can be viewed as both a strengthening of the constant-factor approximations proven for special graph families (as these schemes strictly improve on those which we prove are good approximations), as well as another tool that can be used either in practice or in future theoretical analyses.
Design and Development of Effective Transmission Mechanisms on a Tendon Driven Hand Orthosis for Stroke Patients

Tendon-driven hand orthoses have advantages over exoskeletons with respect to wearability and safety because of their low-profile design and ability to fit a range of patients without requiring custom joint alignment. However, no existing study on a wearable tendon-driven hand orthosis for stroke patients presents evidence that such devices can overcome spasticity given repeated use and fatigue, or discusses transmission efficiency. In this study, we propose two designs that provide effective force transmission by increasing moment arms around finger joints. We evaluate the designs with geometric models and experiment using a 3D-printed artificial finger to find force and joint angle characteristics of the suggested structures. We also perform clinical tests with stroke patients to demonstrate the feasibility of the designs. The testing supports the hypothesis that the proposed designs efficiently elicit extension of the digits in patients with spasticity as compared to existing baselines.
Efficient Redundancy Techniques for Latency Reduction in Cloud Systems

In cloud computing systems, assigning a task to multiple servers and waiting for the earliest copy to finish is an effective method to combat the variability in response time of individual servers and reduce latency. But adding redundancy may result in higher cost of computing resources, as well as an increase in queueing delay due to higher traffic load. This work helps in understanding when and how redundancy gives a cost-efficient reduction in latency. For a general task service time distribution, we compare different redundancy strategies in terms of the number of redundant tasks and the time when they are issued and canceled. We get the insight that the log-concavity of the task service time creates a dichotomy of when adding redundancy helps. If the service time distribution is log-convex (i.e., log of the tail probability is convex), then adding maximum redundancy reduces both latency and cost. And if it is log-concave (i.e., log of the tail probability is concave), then less redundancy, and early cancellation of redundant tasks is more effective. Using these insights, we design a general redundancy strategy that achieves a good latency-cost trade-off for an arbitrary service time distribution. This work also generalizes and extends some results in the analysis of fork-join queues.
The Method of Undetermined Generalization and Specialization Illustrated with Fred Galvin's Amazing Proof of the Dinitz Conjecture

(1996). The Method of Undetermined Generalization and Specialization. The American Mathematical Monthly: Vol. 103, No. 3, pp. 233-239.
Pólya sequences, Toeplitz kernels and gap theorems

Abstract A separated sequence Λ on the real line is called a Polya sequence if any entire function of zero exponential type bounded on Λ is constant. In this paper we solve the problem by Polya and Levinson that asks for a description of Polya sets. We also show that the Polya–Levinson problem is equivalent to a version of the so-called Beurling gap problem on Fourier transforms of measures. The solution is obtained via a recently developed approach based on the use of Toeplitz kernels and De Branges spaces of entire functions.
Web-Scale Responsive Visual Search at Bing

In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing. The system accommodates tens of billions of images in the index, with thousands of features for each image, and can respond in less than 200 ms. In order to overcome the challenges in relevance, latency, and scalability in such large scale of data, we employ a cascaded learning-to-rank framework based on various latest deep learning visual features, and deploy in a distributed heterogeneous computing platform. Quantitative and qualitative experiments show that our system is able to support various applications on Bing website and apps.
RTChoke: Efficient real-time traffic chokepoint detection and monitoring

We present a novel efficient adaptive sensing and monitoring solution for a system of mobile sensing devices that support traffic monitoring applications. We make a key observation that much of the variance in commute times arises at a few congestion hotspots, and a reliable estimate of congestion can be obtained by selectively monitoring congestion just at these hotspots. We design a smartphone application and a back-end system that automatically identifies and monitors congestion hotspots. The solution has low resource footprint in terms of both battery usage on the sensing devices and the network bytes used for uploading data. When a user is not inside any hotspot zone, adaptive sampling conserves battery power and reduces network usage, while ensuring that any new hotspots can be effectively identified. Our results show that our application consumes 40- 80% less energy than a periodic sampling system for different routes in our experiments, with similar accuracy of congestion information. The system can be used for a variety of applications such as automatic congestion alerts to users approaching hotspots, reliable end-to-end commute time estimates and effective alternate route suggestions.
A paraboloid fitting technique for calculating curvature from piecewise-linear interface reconstructions on 3D unstructured meshes

Abstract We present a novel method for calculating interface curvature on 3D unstructured meshes from piecewise-linear interface reconstructions typically generated in the volume of fluid method. Interface curvature is a necessary quantity to calculate in order to model surface tension driven flow. Curvature needs only to be computed in cells containing an interface. The approach requires a stencil containing only neighbors sharing a node with a target cell, and calculates curvature from a least-squares paraboloid fit to the interface reconstructions. This involves solving a 6 × 6 symmetric linear system in each mixed cell. We present verification tests where we calculate the curvature of a sphere, an ellipsoid, and a sinusoid in a 3D domain on regular Cartesian meshes, distorted hex meshes, and tetrahedral meshes. For both regular and unstructured meshes, we find in all cases the paraboloid fitting method for curvature to converge between first and second order with grid refinement.
Noncommutative motives in positive characteristic and their applications

Abstract Let k be a base field of positive characteristic. Making use of topological periodic cyclic homology, we start by proving that the category of noncommutative numerical motives over k is abelian semi-simple, as conjectured by Kontsevich. Then, we establish a far-reaching noncommutative generalization of the Weil conjectures, originally proven by Dwork and Grothendieck. In the same vein, we establish a far-reaching noncommutative generalization of the cohomological interpretations of the Hasse-Weil zeta function, originally proven by Hesselholt. As a third main result, we prove that the numerical Grothendieck group of every smooth proper dg category is a finitely generated free abelian group, as claimed (without proof) by Kuznetsov. Then, we introduce the noncommutative motivic Galois (super-)groups and, following an insight of Kontsevich, relate them to their classical commutative counterparts. Finally, we explain how the motivic measure induced by Berthelot's rigid cohomology can be recovered from the theory of noncommutative motives.
Weak solution for a class of fully nonlinear stochastic Hamilton–Jacobi–Bellman equations

Abstract This paper is concerned with a class of stochastic Hamilton–Jacobi–Bellman equations with controlled leading coefficients, which are fully nonlinear backward stochastic partial differential equations (BSPDEs for short). In order to formulate the weak solution for such kind of BSPDEs, a class of regular random parabolic potentials are introduced in the backward stochastic framework. The existence and uniqueness of weak solution is proved, and for the partially non-Markovian case, we obtain the associated gradient estimate. As a byproduct, the existence and uniqueness of solution for a class of degenerate reflected BSPDEs is discussed as well.
On finite representations of conformal algebras

Abstract We prove that a finite torsion-free associative conformal algebra has a finite faithful conformal representation. As a corollary, it is shown that one may join a conformal identity to such an algebra. Some examples are stated to demonstrate that a conformal identity cannot be joined to any torsion-free associative conformal algebra. In particular, there exist associative conformal algebras of linear growth and even locally finite ones that have no finite faithful representation. We also consider the problem of existence of a finite faithful representation for a torsion-free finite Lie conformal algebra (the analogue of Ado's Theorem). It turns out that the conformal analogue of the Poincare—Birkhoff—Witt Theorem would imply the Ado Theorem for finite Lie conformal algebras. We also prove that every torsion-free finite solvable Lie conformal algebra has a finite faithful representation.
Comparing Hecke eigenvalues of newforms

Given two distinct newforms with real Fourier coefficients, we show that the set of primes where the Hecke eigenvalues of one of them dominate the Hecke eigenvalues of the other has density $$\ge $$ 1/16. Furthermore, if the two newforms do not have complex multiplication, and neither is a quadratic twist of the other, we also prove a similar result for the squares of their Hecke eigenvalues.
The Status Gradient of Trends in Social Media

An active line of research has studied the detection and representation of trends in social media content. There is still relatively little understanding, however, of methods to characterize the early adopters of these trends: who picks up on these trends at different points in time, and what is their role in the system? We develop a framework for analyzing the population of users who participate in trending topics over the course of these topics' lifecycles. Central to our analysis is the notion of a "status gradient", describing how users of different activity levels adopt a trend at different points in time. Across multiple datasets, we find that this methodology reveals key differences in the nature of the early adopters in different domains.
Combinatorics of symmetric plabic graphs

A plabic graph is a planar bicolored graph embedded in a disk, which satisfies some combinatorial conditions. Postnikov's boundary measurement map takes the space of positive edge weights of a plabic graph $G$ to a positroid cell in some totally nonnegative Grassmannian. In this note, we investigate plabic graphs which are symmetric about a line of reflection, up to reversing the colors of vertices. These symmetric plabic graphs arise naturally in the study of total positivity for the Lagrangian Grassmannian. We characterize various combinatorial objects associated with symmetric plabic graphs, and describe the subset of a Grassmannian which can be realized by symmetric weightings of symmetric plabic graphs.
Counting Zeros of Harmonic Rational Functions and its Application to Gravitational Lensing

General Relativity gives that finitely many point masses be tween an observer and a light source create many images of the light source. Positions of these images are solutions of r(z) = ¯ where r(z) is a rational function. We study the number of solutions to p(z) = ¯ z and r(z) = ¯ where p(z) and r(z) are polynomials and rational functions, respectively. Upper and lower bounds were previously obtained by Khavinson-´ Swiatek, Khavinson-Neumann, and Petters. Between these bounds, we show that any number of simple zeros allowed by the Argument Principle occurs and nothing else occurs, off of a proper real algebraic set. If r(z) = ¯ z describes an n-point gravitational lens, we determine the possible numbers of generic images.
Free Functional Inequalities on the Circle

Abstract In this paper we deal with free functional inequalities on the circle. There are some interesting changes from their classical counterparts. For example, the free Poincare inequality has a slight change which seems to account for the lack of invariance under rotations of the base measure. Another instance is the modified Wasserstein distance on the circle which provides the tools for analyzing transportation, Log-Sobolev, and HWI inequalities. These new phenomena also indicate that they have classical counterparts, which does not seem to have been investigated thus far.
Amenability and Ramsey theory

The purpose of this article is to connect the notion of the amenability of a discrete group with a new form of structural Ramsey theory. The Ramsey theoretic reformulation of amenability constitutes a considerable weakening of the Folner criterion. As a by-product, it will be shown that in any non amenable group G, there is a subset E of G such that no finitely additive probability measure on G measures all translates of E equally.
Dynamic change-point detection using similarity networks

From a sequence of similarity networks, with edges representing certain similarity measures between nodes, we are interested in detecting a change-point which changes the statistical property of the networks. After the change, a subset of anomalous nodes which compares dissimilarly with the normal nodes. We study a simple sequential change detection procedure based on node-wise average similarity measures, and study its theoretical property. Simulation and real-data examples demonstrate such a simply stopping procedure has reasonably good performance. We further discuss the faulty sensor isolation (estimating anomalous nodes) using community detection.
Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure

Much of the past work in network analysis has focused on analyzing discrete graphs, where binary edges represent the “presence” or “absence” of a relationship. Since tradition al network measures (e.g., betweenness centrality) utilize a discrete link structure, complex systems must be transformed to this representation in order to investigate network prop erties. However, in many domains there may be uncertainty about the relationship structure and any uncertainty infor mation would be lost in translation to a discrete representati on. Uncertainty may arise in domains where there is moderating link information that cannot be easily observed, i.e., link s become inactive over time but may not be dropped or observed links may not always corresponds to a valid relationship. In order to represent and reason with these types of uncertainty, we move beyond the discrete graph framework and develop social network measures based on a probabilistic graph representation. More specifically, we develop measures of path length, betweenness centrality, and clustering coefficien t— one set based on sampling and one based on probabilistic paths. We evaluate our methods on three real-world networks from Enron, Facebook, and DBLP, showing that our proposed methods more accurately capture salient effects without being susceptible to local noise, and that the resulting analysis produces a better understanding of the graph structure and the uncertainty resulting from its change over time.
A Galois correspondence for compact group actions on C⁎-algebras

Abstract In this paper, we prove a Galois correspondence for compact group actions on C ⁎ -algebras in the presence of a commuting minimal action. Namely, we show that there is a one-to-one correspondence between the C ⁎ -subalgebras that are globally invariant under the compact action and the commuting minimal action, that in addition contain the fixed point algebra of the compact action and the closed, normal subgroups of the compact group.
Sequential Event Detection Using Multimodal Data in Nonstationary Environments

The problem of sequential detection of anomalies in multimodal data is considered. The objective is to observe physical sensor data from CCTV cameras, and social media data from Twitter and Instagram to detect anomalous behaviors or events. Data from each modality is transformed to discrete time count data by using an artificial neural network to obtain counts of objects in CCTV images and by counting the number of tweets or Instagram posts in a geographical area. The anomaly detection problem is then formulated as a problem of quickest detection of changes in count statistics. The quickest detection problem is then solved using the framework of partially observable Markov decision processes (POMDP), and structural results on the optimal policy are obtained. The resulting optimal policy is then applied to real multimodal data collected from New York City around a 5K race to detect the race. The count data both before and after the change is found to be nonstationary in nature. The proposed mathematical approach to this problem provides a framework for event detection in such nonstationary environments and across multiple data modalities.
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling

Access to word-sentiment associations is useful for many applications, including sentiment analysis, stance detection, and linguistic analysis. However, manually assigning fine-grained sentiment association scores to words has many challenges with respect to keeping annotations consistent. We apply the annotation technique of Best-Worst Scaling to obtain real-valued sentiment association scores for words and phrases in three different domains: general English, English Twitter, and Arabic Twitter. We show that on all three domains the ranking of words by sentiment remains remarkably consistent even when the annotation process is repeated with a different set of annotators. We also, for the first time, determine the minimum difference in sentiment association that is perceptible to native speakers of a language.
The Maximum Number of Complete Subgraphs of Fixed Size in a Graph with Given Maximum Degree

In this article, we make progress on a question related to one of Galvin that has attracted substantial attention recently. The question is that of determining among all graphs G with n vertices and , which has the most complete subgraphs of size t, for . The conjectured extremal graph is , where with . Gan et al. (Combin Probab Comput 24(3) (2015), 521–527) proved the conjecture when , and also reduced the general conjecture to the case . We prove the conjecture for and also establish a weaker form of the conjecture for all r.
Von Neumann entropy penalization and low-rank matrix estimation

We study a problem of estimation of a Hermitian nonnegatively definite matrix of unit trace (for instance, a density matrix of a quantum system) based on n i.i.d. measurements (X1,Y1),...,(Xn,Yn), where
Matrix Bundles and Operator Algebras Over a Finitely Bordered Riemann Surface

This note presents an analysis of a class of operator algebras constructed as cross-sectional algebras of flat holomorphic matrix bundles over a finitely bordered Riemann surface. These algebras are partly inspired by the bundle shifts of Abrahamse and Douglas. The first objective is to understand the boundary representations of the containing $$C^*$$ -algebra, i.e. Arveson’s noncommutative Choquet boundary for each of our operator algebras. The boundary representations of our operator algebras for their containing $$C^*$$ -algebras are calculated, and it is shown that they correspond to evaluations on the boundary of the Riemann surface. Secondly, we show that our algebras are Azumaya algebras, the algebraic analogues of n-homogeneous $$C^*$$ -algebras.
Retaining positive definiteness in thresholded matrices

Abstract Positive definite (p.d.) matrices arise naturally in many areas within mathematics and also feature extensively in scientific applications. In modern high-dimensional applications, a common approach to finding sparse positive definite matrices is to threshold their small off-diagonal elements. This thresholding, sometimes referred to as hard-thresholding, sets small elements to zero. Thresholding has the attractive property that the resulting matrices are sparse, and are thus easier to interpret and work with. In many applications, it is often required, and thus implicitly assumed, that thresholded matrices retain positive definiteness. In this paper we formally investigate the algebraic properties of p.d. matrices which are thresholded. We demonstrate that for positive definiteness to be preserved, the pattern of elements to be set to zero has to necessarily correspond to a graph which is a union of complete components. This result rigorously demonstrates that, except in special cases, positive definiteness can be easily lost. We then proceed to demonstrate that the class of diagonally dominant matrices is not maximal in terms of retaining positive definiteness when thresholded. Consequently, we derive characterizations of matrices which retain positive definiteness when thresholded with respect to important classes of graphs. In particular, we demonstrate that retaining positive definiteness upon thresholding is governed by complex algebraic conditions.
A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems.

Estimating the difficulty level of math word problems is an important task for many educational applications. Identification of relevant and irrelevant sentences in math word problems is an important step for calculating the difficulty levels of such problems. This paper addresses a novel application of text categorization to identify two types of sentences in mathematical word problems, namely relevant and irrelevant sentences. A novel joint probabilistic classification model is proposed to estimate the joint probability of classification decisions for all sentences of a math word problem by utilizing the correlation among all sentences along with the correlation between the question sentence and other sentences, and sentence text. The proposed model is compared with i) a SVM classifier which makes independent classification decisions for individual sentences by only using the sentence text and ii) a novel SVM classifier that considers the correlation between the question sentence and other sentences along with the sentence text. An extensive set of experiments demonstrates the effectiveness of the joint probabilistic classification model for identifying relevant and irrelevant sentences as well as the novel SVM classifier that utilizes the correlation between the question sentence and other sentences. Furthermore, empirical results and analysis show that i) it is highly beneficial not to remove stopwords and ii) utilizing part of speech tagging does not make a significant improvement although it has been shown to be effective for the related task of math word problem type classification.
Fourier transforms of powers of well-behaved 2D real analytic functions

This paper is a companion paper to [G4], where sharp estimates are proven for Fourier transforms of compactly supported functions built out of two-dimensional real-analytic functions. The theorems of [G4] are stated in a rather general form. In this paper, we expand on the results of [G4] and show that there is a class of "well-behaved" functions that contains a number of relevant examples for which such estimates can be explicitly described in terms of the Newton polygon of the function. We will further see that for a subclass of these functions, one can prove noticeably more precise estimates, again in an explicitly describable way.
Ocean dynamics of outer solar system satellites

Ocean worlds are prevalent in the solar system. Focusing on Enceladus, Titan, Europa, and Ganymede, I use rotating convection theory and numerical simulations to predict ocean currents and the potential for ice-ocean coupling. When the influence of rotation is relatively strong, the oceans have multiple zonal jets, axial convective motions, and most efficient heat transfer at high latitudes. This regime is most relevant to Enceladus and possibly to Titan, and may help explain their long-wavelength topography. For a more moderate rotational influence, fewer zonal jets form, Hadley-like circulation cells develop, and heat flux peaks near the equator. This regime is predicted for Europa, where it may help drive geologic activity via thermocompositional diapirism in the ice shell, and is possible for Titan. Weak rotational influence allows concentric zonal flows and overturning cells with no preferred orientation. Predictions for Ganymede's ocean span multiple regimes.
Effect of surface termination on the lattice thermal conductivity of monolayer Ti3C2Tz MXenes

Recently, two-dimensional transition metal carbides and nitrides (MXenes) have gained significant attention in electronics and electrochemical energy conversion and storage devices where the heat production significantly affects the safety and performance of these devices. In this paper, we have studied the thermal transport in monolayer T i 3 C 2 T z, the first and most studied MXene, using density functional theory and the phonon Boltzmann transport equation and quantified the effect of surface termination (bare, fluorine, and oxygen) on its lattice thermal conductivity. We found that the thermal conductivity of fluorine-terminated T i 3 C 2 T z (108 W/m K) is approximately one order of magnitude higher than its oxygen-terminated counterpart (11 W/m K). Our calculations reveal that the increased thermal conductivity for the fluorine-terminated structure is due to its enhanced specific heat and group velocity and diminished scattering rate of phonons.
Exploring a Cold Plasma-2d Black Hole Connection

Using a resonance nonlinear Schrodinger equation as a bridge, we explore a direct connection of cold plasma physics to two-dimensional black holes. Namely, we compute and diagonalize a metric attached to the propagation of magnetoacoustic waves in a cold plasma subject to a transverse magnetic field, and we construct an explicit change of variables by which this metric is transformed exactly to a Jackiw-Teitelboim black hole metric.
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation

The simplest and most widely applied method for guaranteeing differential privacy is to add instance-independent noise to a statistic of interest that is scaled to its global sensitivity. However, global sensitivity is a worst-case notion that is often too conservative for realized dataset instances. We provide methods for scaling noise in an instance-dependent way and demonstrate that they provide greater accuracy under average-case distributional assumptions. Specifically, we consider the basic problem of privately estimating the mean of a real distribution from i.i.d. samples. The standard empirical mean estimator can have arbitrarily-high global sensitivity. We propose the trimmed mean estimator, which interpolates between the mean and the median, as a way of attaining much lower sensitivity on average while losing very little in terms of statistical accuracy. To privately estimate the trimmed mean, we revisit the smooth sensitivity framework of Nissim, Raskhodnikova, and Smith (STOC 2007), which provides a framework for using instance-dependent sensitivity. We propose three new additive noise distributions which provide concentrated differential privacy when scaled to smooth sensitivity. We provide theoretical and experimental evidence showing that our noise distributions compare favorably to others in the literature, in particular, when applied to the mean estimation problem.
The turnover intentions and intentions to leave the country of foreign-born physicians in Finland: a cross-sectional questionnaire study.

A physician shortage is a worldwide problem and foreign-born physicians fill in the shortage of physicians in many developed countries. One problem that is associated with the physician shortage is increased physician turnover. Also, regarding foreign-born physicians, migration can be costly. The present study aimed to examine the turnover intentions and intentions to leave the country of foreign-born physicians. We examined how demographics, discrimination, language problems, perceived employment barriers, satisfaction with living in Finland, team climate, job satisfaction and patient-related stress were associated with these factors. The present study was a cross-sectional questionnaire study among 371 foreign-born physicians in Finland that were aged between 26 and 65 (65% women). Binary logistic regression analyses were conducted to examine the associations. Half of the respondents had turnover intentions and 14.5% had considered leaving the country. High satisfaction with living in Finland was associated with a lower likelihood of both turnover intentions and intentions to leave the country. High levels of discrimination and employment barriers were associated with a high likelihood of turnover intentions whereas good team climate was associated with a low likelihood of turnover intentions. High levels of language problems were associated with a high likelihood of intentions to leave the country. The present study showed the importance of satisfaction with living in the host country, the prevention of discrimination and employment barriers, language skills and a good team climate for the retention of foreign-born physicians in their current job and in the host country. Thus, to keep their foreign-born physicians, health care organisations should implement measures to tackle these challenges. Organisations could arrange, for example, diversity training, self-assessment, team reflections, leadership coaching and culturally-specific networks. Moreover, internships associated with the qualification process could be utilised better in order to give a thorough introduction to the host country’s health care environment and the possibilities for learning the language.
Identification of two new electronic transitions of TaF using intracavity laser spectroscopy

Abstract Several bands in two new electronic transitions of TaF have been recorded in high resolution using intracavity laser absorption spectroscopy (ILS). The (1,0), (0,0), and (0,1) bands of the [16.0]0+ – X 3Σ−(0+) transition and the (2,1), (1,0), (0,0), (0,1), and (1,2) bands of the [16.3]1 – X 3Σ−(0+) transition were observed. TaF was produced in the plasma discharge of a tantalum-lined copper hollow cathode with Ar [1 torr] as a sputter gas and trace of SF6 as a fluoride source. Trace H2 [1%] also was used as it was found to increase the intensity of the observed transitions. The transitions were analyzed using PGOPHER, and the obtained molecular parameters are presented and compared with a previous work.
How organic chemistry became one of UCLA's most popular classes

Organic chemistry has a bad reputation, despite having a tremendous impact on our everyday lives. It has remained a notorious "weed-out" class for decades-striking fear in the hearts of students-and has long been viewed as a gatekeeper course for those interested in pursuing a career in medicine or other health-related professions. This personal account examines the underlying teaching philosophies that transformed organic chemistry into one of the most popular classes on the UCLA campus. Special emphasis is placed on ways to increase engagement and help students feel connected. Educational initiatives, including organic chemistry music videos and various online resources created in partnerships with students, will be discussed. It is hoped that this account will stimulate ideas that transcend scientific disciplines all for the benefit of student education.