cssi/SciSciGPT-SciSciNet huggingface dataset:
Selective Rhenium-Catalyzed Oxidation of Secondary Alcohols with Methyl Sulfoxide in the Presence of Ethylene Glycol, a Convenient One-Pot Synthesis of Ketals

Secondary alcohols are oxidized preferentially by DMSO and the catalyst ReOCl3(PPh3)2 in the presence of ethylene glycol and refluxing toluene, producing the corresponding ketals. The reactions are rapid, and proceed in very good to excellent yields. The byproducts of the reaction, methyl sulfide and water, are easily removed. No epoxidation or other common side reactions were observed. This direct oxidative transformation of alcohols to the protected ketal derivatives should have broad synthetic applicability.
Total synthesis of spatane diterpenes: the tricyclic nucleus

A convergent, stereocontrolled construction of the cis,anti,cis-tricyclo[5.3.0.0 2,6 ]decane nucleus of spatane diterpenes was achieved by 2π+2π photocycloaddition of 2-cyclopenten-1-one, as an A-ring precursor, with a carbonyl-masked derivative of 6-methylbicyclo [2.2.1] hept-5-en-2-one as a temporarily bridged C-ring precursor
A multicomponent coupling sequence for direct access to substituted quinolines

A titanium-catalyzed three-component coupling reaction can be used to generate tautomers of N-aryl-1,3-diimines. Simple treatment of these products with acetic acid leads to cyclization forming quinoline derivatives in a one-pot procedure. The primary amines employed can be substituted anilines, aminonaphthalenes, or even heterocyclic amines, which leads to a variety of fused-ring heterocyclic frameworks. The one-pot yields varied from 25−71% for the 18 examples presented in this study.
Synthesis of C-glycosides of N-acetylglucosamine by direct alkylation of 2-amino-2-deoxy-2,3,4,6-tetra-O-acetyl glucopyranosyl chloride

Abstract The β-isomer of C-glycoside, ethyl 2-acetamino-2-deoxy-3,4,6-tri-O-acetyl-β-D-glucopyranosylacetate( 3 ) was prepared stereoselectively in two steps by reaction of 2-acetamino-2-deoxy-3,4,6-tri-O-acetyl-α-D-glucopyranosyl chloride( 4 ) with potassium diethylmalonate and 18-crown-6, followed by decarboxylation.
On the Conformation of Glycobilirubin

The first optically active glycine conjugate 1 of a bilirubin was prepared in several steps from ( S )-β-methylxanthobilirubic acid glycine conjugate 8 . The latter was synthesized by reaction of benzyl glycinate tosylate with the mixed anhydride formed in the reaction of ( S )-β-methylxanthobilirubic acid 6 with isobutyl chloroformate. Spectroscopic analysis of the circular dichroism spectra of 1 in various solvents, including aqueous buffer, indicate a conformational preference for the M -helical ridge-tile conformation, thus providing the first spectroscopic evidence on the conformation of glycobilirubins.
A Convergent Strategy for the Synthesis of β-Carba-galacto-disaccharides

A convergent strategy for the synthesis of β-carba-galacto-disaccharides is illustrated by the preparation of 1 and 4, from a central “glycone” component 22, and the corresponding “aglycone” segments, monosaccharide alcohols, 23a or 23b. The key step is the formation of the carbasugar ring via an oxocarbenium ion−enol ether cyclization
Polycentric Sovereignty: The Medieval Constitution, Governance Quality, and the Wealth of Nations

We extend the theory of market-preserving federalism by ascertaining under what conditions it is likely to prevail. We argue that political-economic systems characterized by polycentric sovereignty — a self-enforcing structure of political property rights that links governance rights with governance revenues, and affords holders of political property rights a check on potentially predatory behavior — will promote market-preserving federalism. We show how the estates system of Medieval Europe was an example of polycentric sovereignty. We also show how the 17th Amendment to the U.S. Constitution represented a degradation of polycentric sovereignty. We conclude by discussing the relationship between governance systems characterized by polycentric sovereignty and social wealth creation, arguing that the structure of political property rights in a governance system is an important characteristic that has hitherto been neglected.
High-throughput quantitative detection of basal autophagy and autophagic flux using image cytometry

Quantitative assessment of changes in macro-autophagy is often performed through manual quantification of the number of LC3B foci in immunofluorescence microscopy images. This method is highly laborious, subject to image-field selection and foci-counting bias, and is not sensitive for analyzing changes in basal autophagy. Alternative methods such as flow cytometry and transmission electron microscopy require highly specialized, expensive instruments and time-consuming sample preparation. Immunoblots are prone to exposure-related variations and noise that prevents accurate quantification. We report a high-throughput, inexpensive, reliable and objective method for studying basal level and flux changes in late-stage autophagy using image cytometry and acridine orange staining.
Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing.

Background: Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier. Methods and Results: Using pairs of deep neural netwo...
Learning recurrent dynamics in spiking networks

Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity in a network of excitatory and inhibitory neurons respecting Dale's law, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.
Revisiting Knowledge Transfer for Training Object Class Detectors

We propose to revisit knowledge transfer for training object detectors on target classes from weakly supervised training images, helped by a set of source classes with bounding-box annotations. We present a unified knowledge transfer framework based on training a single neural network multi-class object detector over all source classes, organized in a semantic hierarchy. This generates proposals with scores at multiple levels in the hierarchy, which we use to explore knowledge transfer over a broad range of generality, ranging from class-specific (bycicle to motorbike) to class-generic (objectness to any class). Experiments on the 200 object classes in the ILSVRC 2013 detection dataset show that our technique (1) leads to much better performance on the target classes (70.3% CorLoc, 36.9% mAP) than a weakly supervised baseline which uses manually engineered objectness [11] (50.5% CorLoc, 25.4% mAP). (2) delivers target object detectors reaching 80% of the mAP of their fully supervised counterparts. (3) outperforms the best reported transfer learning results on this dataset (+41% CorLoc and +3% mAP over [18, 46], +16.2% mAP over [32]). Moreover, we also carry out several across-dataset knowledge transfer experiments [27, 24, 35] and find that (4) our technique outperforms the weakly supervised baseline in all dataset pairs by 1.5 A— - 1.9A—, establishing its general applicability.
Improving textual network embedding with global attention via optimal transport

Constituting highly informative network embeddings is an essential tool for network analysis. It encodes network topology, along with other useful side information, into low dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network embedding problem, and present two novel strategies to improve over traditional attention mechanisms: (i) a content-aware sparse attention module based on optimal transport; and (ii) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.
Analytic $L$-functions: Definitions, theorems, and connections

$L$-functions can be viewed axiomatically, such as in the formulation due to Selberg, or they can be seen as arising from cuspidal automorphic representations of $\textrm{GL}(n)$, as first described by Langlands. Conjecturally these two descriptions of $L$-functions are the same, but it is not even clear that these are describing the same set of objects. We propose a collection of axioms that bridges the gap between the very general analytic axioms due to Selberg and the very particular and algebraic construction due to Langlands. Along the way we prove theorems about $L$-functions that satisfy our axioms and state conjectures that arise naturally from our axioms.
Fully Associative Patch-Based 1-to-N Matcher for Face Recognition

This paper focuses on improving face recognition performance by a patch-based 1-to-N signature matcher that learns correlations between different facial patches. A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed so that the local matching identity of each patch contributes to the global matching identities of all the patches. The proposed matcher consists of three steps. First, based on the signature, the local matching identity and the corresponding matching score of each patch are computed. Then, a fully associative weight matrix is learned to obtain the global matching identities and scores of all the patches. At last, the l1-regularized weighting is applied to combine the global matching identity of each patch and obtain a final matching identity. The proposed matcher has been integrated with the UR2D system for evaluation. The experimental results indicate that the proposed matcher achieves better performance than the current UR2D system. The Rank-1 accuracy is improved significantly by 3% and 0.55% on the UHDB31 dataset and the IJB-A dataset, respectively.
Traces of high powers of the Frobenius class in the hyperelliptic ensemble

The zeta function of a curve over a finite field may be expressed in terms of the characteristic polynomial of a unitary symplectic matrix, called the Frobenius class of the curve. We compute the expected value of the trace of the n-th power of the Frobenius class for an ensemble of hyperelliptic curves of genus g over a fixed finite field in the limit of large genus, and compare the results to the corresponding averages over the unitary symplectic group USp(2g). We are able to compute the averages for powers n almost up to 4g, finding agreement with the Random Matrix results except for small n and for n=2g. As an application we compute the one-level density of zeros of the zeta function of the curves, including lower-order terms, for test functions whose Fourier transform is supported in (-2,2). The results confirm in part a conjecture of Katz and Sarnak, that to leading order the low-lying zeros for this ensemble have symplectic statistics.
Graph pattern detection: hardness for all induced patterns and faster non-induced cycles

We consider the pattern detection problem in graphs: given a constant size pattern graph H and a host graph G, determine whether G contains a subgraph isomorphic to H. We present the following new improved upper and lower bounds: We prove that if a pattern H contains a k-clique subgraph, then detecting whether an n node host graph contains a not necessarily induced copy of H requires at least the time for detecting whether an n node graph contains a k-clique. The previous result of this nature required that H contains a k-clique which is disjoint from all other k-cliques of H. We show that if the famous Hadwiger conjecture from graph theory is true, then detecting whether an n node host graph contains a not necessarily induced copy of a pattern with chromatic number t requires at least the time for detecting whether an n node graph contains a t-clique. This implies that: (a) under Hadwiger’s conjecture for everyk-node pattern H, finding an induced copy of H requires at least the time of √k-clique detection and size ω(n√k/4) for any constant depth circuit, and (b) unconditionally, detecting an induced copy of a random G(k,p) pattern w.h.p. requires at least the time of Θ(k/logk)-clique detection, and hence also at least size nΩ(k/logk) for circuits of constant depth. We show that for every k, there exists a k-node pattern that contains a k−1-clique and that can be detected as an induced subgraph in n node graphs in the best known running time for k−1-Clique detection. Previously such a result was only known for infinitely many k. Finally, we consider the case when the pattern is a directed cycle on k nodes, and we would like to detect whether a directed m-edge graph G contains a k-Cycle as a not necessarily induced subgraph. We resolve a 14 year old conjecture of [Yuster-Zwick SODA’04] on the complexity of k-Cycle detection by giving a tight analysis of their k-Cycle algorithm. Our analysis improves the best bounds for k-Cycle detection in directed graphs, for all k>5.
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)

This work provides a simplified proof of the statistical minimax optimality of (iterate averaged) stochastic gradient descent (SGD), for the special case of least squares. This result is obtained by analyzing SGD as a stochastic process and by sharply characterizing the stationary covariance matrix of this process. The finite rate optimality characterization captures the constant factors and addresses model mis-specification.
The Bubble Skein Element and Applications

We study a certain skein element in the relative Kauffman bracket skein module of the disk with some marked points, and expand this element in terms of linearly independent elements of this module. This expansion is used to compute and study the head and the tail of the colored Jones polynomial and in particular we give a simple q-series for the tail of the knot 85. Furthermore, we use this expansion to obtain an easy determination of the theta coefficients.
Forbidden arithmetic progressions in permutations of subsets of the integers

Abstract Permutations of the positive integers avoiding arithmetic progressions of length 5 were constructed in Davis et al. (1977), implying the existence of permutations of the integers avoiding arithmetic progressions of length 7. We construct a permutation of the integers avoiding arithmetic progressions of length 6. We also prove a lower bound of 1 2 on the lower density of subsets of positive integers that can be permuted to avoid arithmetic progressions of length 4, sharpening the lower bound of 1 3 from LeSaulnier and Vijay (2011).
Feynman graph integrals and almost modular forms

We introduce a type of graph integrals on elliptic curves from the heat kernel. We show that such graph integrals have modular properties under the modular group $SL(2, \Z)$, and prove the polynomial nature of the anti-holomorphic dependence.
Diffusion approximations for load balancing mechanisms in cloud storage systems

In large storage systems, files are often coded across several servers to improve reliability and retrieval speed. We study load balancing under the batch sampling routeing scheme for a network of n servers storing a set of files using the maximum distance separable (MDS) code (cf. Li (2016)). Specifically, each file is stored in equally sized pieces across L servers such that any k pieces can reconstruct the original file. When a request for a file is received, the dispatcher routes the job into the k-shortest queues among the L for which the corresponding server contains a piece of the file being requested. We establish a law of large numbers and a central limit theorem as the system becomes large (i.e. n → ∞), for the setting where all interarrival and service times are exponentially distributed. For the central limit theorem, the limit process take values in l2, the space of square summable sequences. Due to the large size of such systems, a direct analysis of the n-server system is frequently intractable. The law of large numbers and diffusion approximations established in this work provide practical tools with which to perform such analysis. The power-of-d routeing scheme, also known as the supermarket model, is a special case of the model considered here.
Improved Algorithms for Collaborative PAC Learning

We study a recent model of collaborative PAC learning where $k$ players with $k$ different tasks collaborate to learn a single classifier that works for all tasks. Previous work showed that when there is a classifier that has very small error on all tasks, there is a collaborative algorithm that finds a single classifier for all tasks and has $O((\ln (k))^2)$ times the worst-case sample complexity for learning a single task. In this work, we design new algorithms for both the realizable and the non-realizable setting, having sample complexity only $O(\ln (k))$ times the worst-case sample complexity for learning a single task. The sample complexity upper bounds of our algorithms match previous lower bounds and in some range of parameters are even better than previous algorithms that are allowed to output different classifiers for different tasks.
Structured Signal Recovery From Non-Linear and Heavy-Tailed Measurements

We study high-dimensional signal recovery from non-linear measurements with design vectors having elliptically symmetric distribution. Special attention is devoted to the situation when the unknown signal belongs to a set of low statistical complexity, while both the measurements and the design vectors are heavy-tailed. We propose and analyze a new estimator that adapts to the structure of the problem, while being robust both to the possible model misspecification characterized by arbitrary non-linearity of the measurements as well as to data corruption modeled by the heavy-tailed distributions. Moreover, this estimator has low computational complexity. Our results are expressed in the form of exponential concentration inequalities for the error of the proposed estimator. On the technical side, our proofs rely on the generic chaining methods, and illustrate the power of this approach for statistical applications. Theory is supported by numerical experiments demonstrating that our estimator outperforms existing alternatives when data are heavy-tailed.
Clusters of primes with square-free translates

Let R be a finite set of integers satisfying appropriate local conditions. We show the existence of long clusters of primes p in bounded length intervals with p−b squarefree for all b∈R. Moreover, we can enforce that the primes p in our cluster satisfy any one of the following conditions: (1) p lies in a short interval [N,N+N7/12+ϵ], (2) p belongs to a given inhomogeneous Beatty sequence, (3) with c∈(8/9,1) fixed, pc lies in a prescribed interval mod 1 of length p−1+c+ϵ.
On the Structure of Quintic Polynomials

We study the structure of bounded degree polynomials over finite fields. Haramaty and Shpilka [STOC 2010] showed that biased degree three or four polynomials admit a strong structural property. We confirm that this is the case for degree five polynomials also. Let F=F_q be a prime field. Suppose f:F^n to F is a degree five polynomial with bias(f)=delta. We prove the following two structural properties for such f. 1. We have f= sum_{i=1}^{c} G_i H_i + Q, where G_i and H_is are nonconstant polynomials satisfying deg(G_i)+deg(H_i)<= 5 and Q is a degree <5 polynomial. Moreover, c does not depend on n. 2. There exists an Omega_{delta,q}(n) dimensional affine subspace V subseteq F^n such that f|_V is a constant. Cohen and Tal [Random 2015] proved that biased polynomials of degree at most four are constant on a subspace of dimension Omega(n). Item 2.]extends this to degree five polynomials. A corollary to Item 2. is that any degree five affine disperser for dimension k is also an affine extractor for dimension O(k). We note that Item 2. cannot hold for degrees six or higher. We obtain our results for degree five polynomials as a special case of structure theorems that we prove for biased degree d polynomials when d<|\F|+4. While the d<|F|+4 assumption seems very restrictive, we note that prior to our work such structure theorems were only known for d<|\F| by Green and Tao [Contrib. Discrete Math. 2009] and Bhowmick and Lovett [arXiv:1506.02047]. Using algorithmic regularity lemmas for polynomials developed by Bhattacharyya, et al. [SODA 2015], we show that whenever such a strong structure exists, it can be found algorithmically in time polynomial in n.
Distributed Model Predictive Control of Spatially Interconnected Systems Using Switched Cost Functions

This note proposes a distributed model predictive control (DMPC) scheme with switched cost functions for a class of spatially interconnected systems with communication constraints. A noniterative and parallel communication strategy is considered to ensure that all distributed controllers complete input updates at each single information exchange step. The proposed DMPC scheme switches the optimization index on a switching surface generated by control invariant sets. With the index-switching strategy, stability of the origin is ensured by a terminal control law. Convergence conditions of the optimal cost to zero is established taking into account the causal link between the presumed trajectory and the optimized trajectory of the previous step. The compatibility constraints preserve the quadratic program property that is desired in practical applications. It is also observed that the proposed DMPC scheme has benefits dealing with systems that need to take into account safety-related spatial constraints.
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs

We describe the first sub-quadratic sampling algorithm for the Multiplicative Attribute Graph Model (MAGM) of Kim and Leskovec (2010). We exploit the close connection between MAGM and the Kronecker Product Graph Model (KPGM) of Leskovec et al. (2010), and show that to sample a graph from a MAGM it suffices to sample small number of KPGM graphs and quilt them together. Under a restricted set of technical conditions our algorithm runs in O ￿ (log2(n)) 3 |E| ￿ time, where n is the number of nodes and |E| is the number of edges in the sampled graph. We demonstrate the scalability of our algorithm via extensive empirical evaluation; we can sample a MAGM graph with 8 million nodes and 20 billion edges in under 6 hours.
Local-global Galois theory of arithmetic function fields

We study the relationship between the local and global Galois theory of function fields over a complete discretely valued field. We give necessary and sufficient conditions for local separable extensions to descend to global extensions, and for the local absolute Galois group to inject into the global absolute Galois group. As an application we obtain a local-global principle for the index of a variety over such a function field. In this context we also study algebraic versions of van Kampen′s theorem, describing the global absolute Galois group as a direct limit of local absolute Galois groups.
Evidence on the Joint Determination of Audit and Non‐Audit Fees

In this study we investigate whether the characteristics of clients, auditors, and the auditor-client relationship simultaneously determine audit and non-audit fees. As done in prior studies, we maintain that fees proxy for the level of service provided and follow the physical flow of knowledge. Estimating single-equation models of audit and non-audit fee models, we confirm prior findings of an association between audit and non-audit fees. Studies conclude that such evidence is consistent with knowledge spillovers between the two services. However, we document empirically that audit and non-audit fees are simultaneously determined. Because the data indicate audit and non-audit fees are jointly determined, we then investigate whether previously documented associations between audit and non-audit fees are the result of biased estimation induced by using endogenous variables in single-equation models. In contrast to results from single-equation estimations, we find no association between audit and non-audit fees using a simultaneous specification of the fee system, suggesting that single-equation estimations suffer from simultaneous-equations bias. In sum, the findings are not consistent with the existence of economies of scope from the joint performance of audit and non-audit services after controlling for the joint behavior of audit and non-audit fees. Given the ongoing debate over the level of allowed non-audit services by auditors, the argument for the joint provision of audit and non-audit services is less justified than if joint-supply benefits had been documented.
Deep Keyphrase Generation

© 2017 Association for Computational Linguistics. Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seqkeyphrase.
Looijenga’s conjecture via integral-affine geometry

A cusp singularity is a surface singularity whose minimal resolution is a cycle of smooth rational curves meeting transversely. Cusp singularities come in naturally dual pairs. In 1981, Looijenga proved that whenever a cusp singularity is smoothable, the minimal resolution of the dual cusp is an anticanonical divisor of some smooth rational surface. He conjectured the converse. Recent work of Gross, Hacking, and Keel has proven Looijenga’s conjecture using methods from mirror symmetry. This paper provides an alternative proof of Looijenga’s conjecture based on a combinatorial criterion for smoothability given by Friedman and Miranda in 1983.
Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace

Human-in-the-loop manipulation is useful in when autonomous grasping is not able to deal sufficiently well with corner cases or cannot operate fast enough. Using the teleoperator's hand as an input device can provide an intuitive control method but requires mapping between pose spaces which may not be similar. We propose a low-dimensional and continuous teleoperation subspace which can be used as an intermediary for mapping between different hand pose spaces. We present an algorithm to project between pose space and teleoperation subspace. We use a non-anthropomorphic robot to experimentally prove that it is possible for teleoperation subspaces to effectively and intuitively enable teleoperation. In experiments, novice users completed pick and place tasks significantly faster using teleoperation subspace mapping than they did using state of the art teleoperation methods.
On Distribution of Poles of Eisenstein Series and the Length Spectrum of Hyperbolic Manifolds

We extend results of Bhagwat and Rajan on a strong multiplicity one property for length spectrum to hyperbolic manifolds with cusps, showing that for two even dimensional hyperbolic manifolds of finite volume, if all but finitely many closed geodesics have the same length, then all closed geodesics have the same length. When the set of exceptional lengths is infinite, but sufficiently sparse, we can show that the two manifolds must have the same volume, and in low dimensions also the same number of cusps. A main ingredient in our proof is a generalization of a result of Selberg on the distribution of poles of Eisenstein series to hyperbolic manifolds.
Improved convection cooling in steady channel flows

Improved heat transfer technologies are essential for improving overall energy efficiency. Fluid flows optimal for heat transfer in a 2D channel are calculated, and heat transfer proportional to the energy budget to the 1/5 power is obtained---an improvement over the classical 1/6 scaling law.
Modulation Spaces, Wiener Amalgam Spaces, and Brownian Motions

Abstract We study the local-in-time regularity of the Brownian motion with respect to localized variants of modulation spaces M s p , q and Wiener amalgam spaces W s p , q . We show that the periodic Brownian motion belongs locally in time to M s p , q ( T ) and W s p , q ( T ) for ( s − 1 ) q − 1 , and the condition on the indices is optimal. Moreover, with the Wiener measure μ on T , we show that ( M s p , q ( T ) , μ ) and ( W s p , q ( T ) , μ ) form abstract Wiener spaces for the same range of indices, yielding large deviation estimates. We also establish the endpoint regularity of the periodic Brownian motion with respect to a Besov-type space b ˆ p , ∞ s ( T ) . Specifically, we prove that the Brownian motion belongs to b ˆ p , ∞ s ( T ) for ( s − 1 ) p = − 1 , and it obeys a large deviation estimate. Finally, we revisit the regularity of Brownian motion on usual local Besov spaces B p , q s , and indicate the endpoint large deviation estimates.
On the Computation of Worst Attacks: a LP Framework

We consider the problem of false data injection attacks modeled as additive disturbances in various parts of a general LTI feedback system and derive necessary and sufficient conditions for the existence of stealthy unbounded attacks. We also consider the problem of characterizing the worst, bounded and stealthy attacks. This problem involves a maximization of a convex function subject to convex constraints, and hence, in principle, it is not easy to solve. However, by employing a $\ell_{\infty}$ framework, we show how tractable Linear Programming (LP) methods can be used to obtain the worst attack design. Moreover, we provide a controller synthesis iterative method to minimize the worst impact of such attacks and test its efficacy in a power system component.
A nonlinear dimensionality reduction framework using smooth geodesics

Abstract Existing dimensionality reduction methods are adept at revealing hidden underlying manifolds arising from high-dimensional data and thereby producing a low-dimensional representation. However, the smoothness of the manifolds produced by classic techniques over sparse and noisy data is not guaranteed. In fact, the embedding generated using such data may distort the geometry of the manifold and thereby produce an unfaithful embedding. Herein, we propose a framework for nonlinear dimensionality reduction that generates a manifold in terms of smooth geodesics that is designed to treat problems in which manifold measurements are either sparse or corrupted by noise. Our method generates a network structure for given high-dimensional data using a nearest neighbors search and then produces piecewise linear shortest paths that are defined as geodesics. Then, we fit points in each geodesic by a smoothing spline to emphasize the smoothness. The robustness of this approach for sparse and noisy datasets is demonstrated by the implementation of the method on synthetic and real-world datasets.
Improving Human Action Recognition by Non-action Classification

In this paper we consider the task of recognizing human actions in realistic video where human actions are dominated by irrelevant factors. We first study the benefits of removing non-action video segments, which are the ones that do not portray any human action. We then learn a nonaction classifier and use it to down-weight irrelevant video segments. The non-action classifier is trained using Action-Thread, a dataset with shot-level annotation for the occurrence or absence of a human action. The non-action classifier can be used to identify non-action shots with high precision and subsequently used to improve the performance of action recognition systems.
Model Reconstruction from Model Explanations

We show through theory and experiment that gradient-based explanations of a model quickly reveal the model itself. Our results speak to a tension between the desire to keep a proprietary model secret and the ability to offer model explanations. On the theoretical side, we give an algorithm that provably learns a two-layer ReLU network in a setting where the algorithm may query the gradient of the model with respect to chosen inputs. The number of queries is independent of the dimension and nearly optimal in its dependence on the model size. Of interest not only from a learning-theoretic perspective, this result highlights the power of gradients rather than labels as a learning primitive. Complementing our theory, we give effective heuristics for reconstructing models from gradient explanations that are orders of magnitude more query-efficient than reconstruction attacks relying on prediction interfaces.
Fast Exact Max-Kernel Search

The wide applicability of kernels makes the problem of max-kernel search ubiquitous and more general than the usual similarity search in metric spaces. We focus on solving this problem efficiently. We begin by characterizing the inherent hardness of the max-kernel search problem with a novel notion of directional concentration. Following that, we present a method to use an O(n log n) algorithm to index any set of objects (points in R or abstract objects) directly in the Hilbert space without any explicit feature representations of the objects in this space. We present the first provably O(log n) algorithm for exact max-kernel search using this index. Empirical results for a variety of data sets as well as abstract objects demonstrate up to 4 orders of magnitude speedup in some cases. Extensions for approximate max-kernel search are also presented.
On Robustness of Kernel Clustering

Clustering is an important unsupervised learning problem in machine learning and statistics. Among many existing algorithms, kernel \km has drawn much research attention due to its ability to find non-linear cluster boundaries and its inherent simplicity. There are two main approaches for kernel k-means: SVD of the kernel matrix and convex relaxations. Despite the attention kernel clustering has received both from theoretical and applied quarters, not much is known about robustness of the methods. In this paper we first introduce a semidefinite programming relaxation for the kernel clustering problem, then prove that under a suitable model specification, both K-SVD and SDP approaches are consistent in the limit, albeit SDP is strongly consistent, i.e. achieves exact recovery, whereas K-SVD is weakly consistent, i.e. the fraction of misclassified nodes vanish. Also the error bounds suggest that SDP is more resilient towards outliers, which we also demonstrate with experiments.
Video Pixel Networks

We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a four-dimensional dependency chain. The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth. The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects.
Decentralized Stochastic Control of Distributed Energy Resources

We consider the decentralized control of radial distribution systems with controllable photovoltaic inverters and energy storage resources. For such systems, we investigate the problem of designing fully decentralized controllers that minimize the expected cost of balancing demand, while guaranteeing the satisfaction of individual resource and distribution system voltage constraints. Employing a linear approximation of the branch flow model, we formulate this problem as the design of a decentralized disturbance-feedback controller that minimizes the expected value of a convex quadratic cost function, subject to robust convex quadratic constraints on the system state and input. As such problems are, in general, computationally intractable, we derive a tractable inner approximation to this decentralized control problem, which enables the efficient computation of an affine control policy via the solution of a finite-dimensional conic program. As affine policies are, in general, suboptimal for the family of systems considered, we provide an efficient method to bound their suboptimality via the optimal solution of another finite-dimensional conic program. A case study of a 12 kV radial distribution system demonstrates that decentralized affine controllers can perform close to optimal.
Benefits of V2V Communication for Autonomous and Connected Vehicles

In this paper, we investigate the benefits of vehicle-to-vehicle (V2V) communication for autonomous vehicles and provide results on how V2V information helps reduce employable time headway in the presence of parasitic lags. For a string of vehicles adopting a constant time headway policy and availing the on-board information of predecessor’s vehicle position and velocity, the minimum employable time headway ( $h_{\min }$ ) must be lower bounded by $2\tau _{0}$ for string stability, where $\tau _{0}$ is the maximum parasitic actuation lag. In this paper, we quantify the benefits of using V2V communication in terms of a reduction in the employable time headway: 1) If the position and velocity information of $r$ immediately preceding vehicles is used, then $h_{\min }$ can be reduced to ${4\tau _{0}}/{(1+r)}$ ; 2) furthermore, if the acceleration of ‘ $r$ ’ immediately preceding vehicles is used, then $h_{\min }$ can be reduced to ${2\tau _{0}}/{(1+r)}$ ; and 3) if the position, velocity, and acceleration of the immediate and the $r$ -th predecessors are used, then $h_{\min } \ge {2\tau _{0}}/{(1+r)}$ . Note that cases (2) and (3) provide the same lower bound on the minimum employable time headway; however, case (3) requires much less communicated information.
On Markov Chain Gradient Descent

Stochastic gradient methods are the workhorse (algorithms) of large-scale optimization problems in machine learning, signal processing, and other computational sciences and engineering. This paper studies Markov chain gradient descent, a variant of stochastic gradient descent where the random samples are taken on the trajectory of a Markov chain. Existing results of this method assume convex objectives and a reversible Markov chain and thus have their limitations. We establish new non-ergodic convergence under wider step sizes, for nonconvex problems, and for non-reversible finite-state Markov chains. Nonconvexity makes our method applicable to broader problem classes. Non-reversible finite-state Markov chains, on the other hand, can mix substatially faster. To obtain these results, we introduce a new technique that varies the mixing levels of the Markov chains. The reported numerical results validate our contributions.
Random road networks: the quadtree model

What does a typical road network look like? Existing generative models tend to focus on one aspect to the exclusion of others. We introduce the general-purpose quadtree model and analyze its shortest paths and maximum flow.
Hyaline: Fast and Transparent Lock-Free Memory Reclamation

We present a new lock-free safe memory reclamation algorithm, Hyaline, which is fast, scalable, and transparent to the underlying data structures. Hyaline easily handles virtually unbounded number of threads that can be created and deleted dynamically, while retaining O(1) reclamation cost. We also extend Hyaline to avoid situations where stalled threads prevent others from reclaiming newly allocated objects, a common problem with epoch-based reclamation. Our evaluation reveals that Hyaline's throughput is high -- it steadily outperformed other reclamation schemes by >10% in one test and yielded even higher gains in oversubscribed scenarios.
Existence and qualitative theory for stratified solitary water waves

Abstract This paper considers two-dimensional gravity solitary waves moving through a body of density stratified water lying below vacuum. The fluid domain is assumed to lie above an impenetrable flat ocean bed, while the interface between the water and vacuum is a free boundary where the pressure is constant. We prove that, for any smooth choice of upstream velocity field and density function, there exists a continuous curve of such solutions that includes large-amplitude surface waves. Furthermore, following this solution curve, one encounters waves that come arbitrarily close to possessing points of horizontal stagnation. We also provide a number of results characterizing the qualitative features of solitary stratified waves. In part, these include bounds on the wave speed from above and below, some of which are new even for constant density flow; an a priori bound on the velocity field and lower bound on the pressure; a proof of the nonexistence of monotone bores in this physical regime; and a theorem ensuring that all supercritical solitary waves of elevation have an axis of even symmetry.
On the ranks of elliptic curves with isogenies

In recent years, the question of whether the ranks of elliptic curves defined over ℚ are unbounded has garnered much attention. One can create refined versions of this question by restricting one’s attention to elliptic curves over ℚ with a certain algebraic structure, e.g., with a rational point of a given order. In an attempt to gather data about such questions, we look for examples of elliptic curves over ℚ with an n-isogeny and rank as large as possible. To do this, we use existing techniques due to Rogers, Rubin, Silverberg, and Nagao and develop a new technique (based on an observation made by Mazur) that is more computationally feasible when the naive heights of the elliptic curves are large.
On the number of cliques in graphs with a forbidden minor

Abstract Reed and Wood and independently Norine, Seymour, Thomas, and Wollan proved that for each positive integer t there is a constant c ( t ) such that every graph on n vertices with no K t -minor has at most c ( t ) n cliques. Wood asked in 2007 if we can take c ( t ) = c t for some absolute constant c. This question was recently answered affirmatively by Lee and Oum. In this paper, we determine the exponential constant. We prove that every graph on n vertices with no K t -minor has at most 3 2 t / 3 + o ( t ) n cliques. This bound is tight for n ≥ 4 t / 3 . More generally, let H be a connected graph on t vertices, and x denote the size (i.e., the number edges) of the largest matching in the complement of H. We prove that every graph on n vertices with no H-minor has at most max ⁡ ( 3 2 t / 3 − x / 3 + o ( t ) n , 2 t + o ( t ) n ) cliques, and this bound is tight for n ≥ max ⁡ ( 4 t / 3 − 2 x / 3 , t ) by a simple construction. Even more generally, we determine explicitly the exponential constant for the maximum number of cliques an n-vertex graph can have in a minor-closed family of graphs which is closed under disjoint union.
Convergent Power Series for Fields in Positive or Negative High-Contrast Periodic Media

We obtain convergent power series representations for Bloch waves in periodic high-contrast media. The material coefficient in the inclusions can be positive or negative. The small expansion parameter is the ratio of period cell width to wavelength, and the coefficient functions are solutions of the cell problems arising from formal asymptotic expansion. In the case of positive coefficient, the dispersion relation has an infinite sequence of branches, each represented by a convergent even power series whose leading term is a branch of the dispersion relation for the homogenized medium. In the negative case, there is a single branch.
Beyond polyhedral homotopies

Abstract We present a new algorithmic framework which utilizes tropical geometry and homotopy continuation for solving systems of polynomial equations where some of the polynomials are generic elements in linear subspaces of the polynomial ring. This approach generalizes the polyhedral homotopies by Huber and Sturmfels.
Vector-valued modular forms and the mock theta conjectures

The mock theta conjectures are ten identities involving Ramanujan’s fifth-order mock theta functions. The conjectures were proven by Hickerson in 1988 using q-series methods. Using methods from the theory of harmonic Maass forms, specifically work of Zwegers and Bringmann–Ono, Folsom reduced the proof of the mock theta conjectures to a finite computation. Both of these approaches involve proving the identities individually, relying on work of Andrews–Garvan. Here we give a unified proof of the mock theta conjectures by realizing them as an equality between two nonholomorphic vector-valued modular forms which transform according to the Weil representation. We then show that the difference of these vectors lies in a zero-dimensional vector space.
Verifiable computation with massively parallel interactive proofs

As the cloud computing paradigm has gained prominence, the need for verifiable computation has grown increasingly urgent. Protocols for verifiable computation enable a weak client to outsource difficult computations to a powerful, but untrusted server, in a way that provides the client with a guarantee that the server performed the requested computations correctly. By design, these protocols impose a minimal computational burden on the client, but they require the server to perform a very large amount of extra bookkeeping to enable a client to easily verify the results. Verifiable computation has thus remained a theoretical curiosity, and protocols for it have not been implemented in real cloud computing systems. In this paper, we assess the potential of parallel processing to help make practical verification a reality, identifying abundant data parallelism in a state-of-the-art general-purpose protocol for verifiable computation. We implement this protocol on the GPU, obtaining 40-120× server-side speedups relative to a state-of-the-art sequential implementation. For benchmark problems, our implementation thereby reduces the slowdown of the server to within factors of 100-500× relative to the original computations requested by the client. Furthermore, we reduce the already small runtime of the client by 100×. Our results demonstrate the immediate practicality of using GPUs for verifiable computation, and more generally, that protocols for verifiable computation have become sufficiently mature to deploy in real cloud computing systems.
Electromagnetic probes of heavy ion collisions: recent developments

Abstract The current status of photon and dilepton emission in ultra-relativistic heavy ion collisions is reviewed, and recent developments are highlighted. The importance of emissions at early, intermediate and late times is emphasized.