cssi/SciSciGPT-SciSciNet huggingface dataset:
SYNTHESES OF POLYMERIZABLE MONOACYLGLYCEROLS AND 1,2-DIACYL-SN-GLYCEROLS

The first chemical syntheses of polymerizable monoacylglycerol and 1,2-diacyl-sn-glycerol are reported. The monodienoylglycerol is obtained in 80% yield from 1,2-O-isopropylidene-sn-glycerol and dienoyl fatty acid. The dienoic acid is accessible in 60% yield from the base-catalyzed hydrolysis of dienoyl ester, which is synthesized from the Wittig−Horner reaction of aldehyde and trimethyl 4-phosphonocrotonate. The acylation is carried out in the presence of 4-(dimethylamino)pyridine and dicyclohexylcarbodiimide. The use of excess protected glycerol relative to fatty acid affords the acylated product in high yield. The final step is the deprotection of isopropylidene group using dilute HCl solution. The 1,2-diacyl-sn-glycerol is synthesized by acylation of 3-(4-methoxybenzyl)-sn-glycerol with dienoyl fatty acid in the presence of 4-(dimethylamino)pyridine and dicyclohexylcarbodiimide. The removal of the 4-methoxybenzyl group by dimethylboron bromide catalyzed hydrolysis is especially useful in the synthesis...
A simple and efficient one-step protocol for the preparation of alkyl-substituted ammonium tetrafluoroborate and hexafluorophosphate salts

Abstract A fast and high yielding one-step protocol using commercially available and inexpensive bulk chemicals is described for the preparation of a series of mono-, di-, and trialkyl-substituted ammonium tetrafluoroborate and hexafluorophosphate salts under nonaqueous condition.
Concerning the Solvent Effect in the Aldol Condensation

Except for the catalyst and the temperature, the nature of the solvent also affects the aldol condensation, favouring α,β-unsaturated carbonyl compounds in alcoholic solvents or β-hydroxyl carbonyl compounds in tetrahydrofuran.
Formal coupling of two alkylidyne ligands on a metal center

Reaction of the alkylidyne complexes [W(CR)Br(CO)3L] [2b (L = CO; R = C6H4-OMe-4), 2c (L = CO, R = C6H4-NMe2-4), 3a (L = PPh3; R = Ph), and 3c (L = PPh3; R = C6H4-NMe2-4)] with methyllithium in THF at -78°C affords the alkylidyne acyl tungsten complexes Li[W(CR)(COMe)Br(CO)3L] [4b (L = CO; R = C6H4-OMe-4), 4c (L = CO, R = C6H4-NMe2-4), 5a (L = PPh3; R = Ph), and 5c (L = PPh3; R = C6H4-NMe2-4)]. Reaction of complexes 4 and 5 with C2O2Br2 at -78°C-0°C in CH2Cl2 followed by addition of PPh3 gives the alkyne complexes [WBr2(CO)(CH3C = CR)(PPh3)2] (6). The formation of the alkyne ligands in complexes 6 may be considered as the result of formal coupling of two alkylidyne ligands.
Actinide Materials Research Supported by the Office of Basic Energy Sciences, U.S. Department of Energy

The fundamental research topic in actinide chemistry and materials sciences is the role that the 5f electrons play in bond formation, which provides the central focus for DOE BES-supported actinide science. Structural systematics of the actinide metals, oxides, and other compounds as a function of atomic number are well established. Magnetic measurements have shown that the light actinide metals have delocalized 5f orbitals (i.e., the 5f electrons form bands), whereas the f electrons become localized at americium. Thus, the magnetic behavior of the first part of the actinide series resembles that of the d transition metals whereas the heavier actinides exhibit behavior similar to the rare earth metals. Spectroscopic results have established electronic energy levels, crystal field splitting, and near-neighbor coordination. The 5f orbitals participate in the band structure of materials that contain the light actinide metals and some of their intermetallic compounds, and perhaps in molecular compounds. Molecular-level information on the geometry and bonding in solids, at surfaces, and in clusters can now be obtained at BES-supported facilities.
Preparation of 5,5′-pyrilidene and 5,5′-quinolidene bis-barbituric acid derivatives

NMR reaction following experiments were used to find optimal conditions for the barbituric acid double addition to aromatic and heteroaromatic carboxaldehydes. It was established that aromatic aldehydes with electron-donating substituents such as hydroxy, methoxy, and dimethylamino produce only the single addition barbituric acid adduct (barbituric acid benzylidenes). If these electron-donating substituents are transformed into electron-withdrawing substituents by virtue of protonation (NMe2 to NHMe2+) then the double barbituric acid adduct becomes the sole product of the reaction. This is also true regardless of the reaction media if strong electron-withdrawing substituents (such as a nitro group) are present. Considering that the reactive species for nitrogen containing aromatic heterocycles are actually the conjugated acids (electron deficient molecule) only the double barbituric acid adducts are isolated. All synthetic procedures presented are applicable to multi-gram scale preparations of double barbituric acid adducts.
Synthesis and Thermolysis of Enediynyl Ethyl Ethers as Precursors of Enyne−Ketenes

Enediynyl ethyl ethers 14/17 were synthesized by using the Pd(PPh3)4-catalyzed cross-coupling reactions between enynyl iodides 13/16 and (2-ethoxyethynyl)zinc chloride. Thermolysis of these enediynyl ethyl ethers in refluxing chlorobenzene (132 °C) promoted a retro-ene reaction to produce enyne−ketenes, which underwent the Moore cyclization reactions to form the biradicals having a phenyl radical center and a phenoxy radical center. The presence of two radical centers in the same molecule simultaneously provided many opportunities for intramolecular decay through disproportionation, radical−radical combination, and the formation of o-quinone methide.
Nature and Location of Organic Species in As-Synthesized Ferrierite Probed by Near-Infrared Fourier Transform Raman Spectroscopy and Multinuclear NMR

Siliceous and aluminosilicate ferrierite samples (Si/Al ratios = 18, 50, and 80) have been prepared in the presence of propylamine and pyridine and characterized by near-infrared Fourier transform Raman spectroscopy and 29Si, 27Al, and 13C NMR. Incorporation of aluminum into the framework has two main consequences. Firstly, propylammonium cations partially charge compensate for the aluminum substitution in the framework. As a consequence of this, as the Si/Al ratio is lowered and the number of propylammonium cations required for charge neutrality increases, the concentration of pyridine in the main channel decreases because the two molecules compete for space in the zeolite channels. Secondly, two types of pyridine are observed from the splitting of the ν1 breathing mode. This is proposed to arise from pyridine physically adsorbed in the 8-ring side channel and the main 10-ring channel (ν1= 991 cm-1) and pyridine located in the main channels interacting via hydrogen bonding with propylammonium cations (ν1...
Effect of Sensor Error on the Assessment of Seismic Building Damage

Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an Internet of things network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to the United States federal agencies standards. With this information, the building inventory can be classified into immediate occupancy, life safety, or collapse prevention categories. In this paper, we propose a zero velocity update technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy.
Dielectric properties of the lamellar niobates and titanoniobates AM2Nb3O10 and ATiNbO5 (A = H, K, M = Ca, Pb), and their condensation products Ca4Nb6O19 and Ti2Nb2O9

The dielectric properties of layer perovskites in the Dion−Jacobson (KCa2Nb3O10, HCa2Nb3O10, CsPb2Nb3O10, HPb2Nb3O10, KCa2NaNb4O13, KLa2NbTi2O10) and Ruddlesden−Poppper (K2La2Ti3O10) series, and of...
Novel dideoxynucleoside isosteres

Abstract Novel 5-aza-7-deaza isosteres of the anti-HIV compounds, dideoxyinosine (ddI) and dideoxyguanosine (ddG), have been prepared by glycosylation of the corresponding isosteric bases with appropriately tailored dideoxyribose components. The glycosylation reactions were regiospecific and the position of glycosylation was established by UV and high-field NMR data. However, attempted preparation of the 3- deaza-5-aza isostere of dideoxyinosine led to a different regioisomer, a ring-extended dideoxycytidine analogue, whose structure was established by X-ray crystallographic data. The 5-aza-7-deaza isosteres of ddI and ddG were more stable with respect to hydrolytic cleavage than ddI and ddG.
Central Banking in Central and Eastern Europe : Lessons From the Interwar Years' Experience

This paper examines the philosophies which inspired the institution of central banking in Central and Eastern Europe in the interwar years. Influenced by the Financial Section of the League of Nations, the new central banks adopted laws which prohibited or severely restricted the financing of government fiscal debt. They were encouraged to centralize their payments systems and manage exchange rates to keep control of the money supply and achieve monetary stability. Before long they were forced to adopt further provisions in the area of banking supervision to regulate commercial banks. This paper considers the particular cases of Czechoslovakia, Hungary and Poland.
Collective intercellular communication through ultra-fast hydrodynamic trigger waves

The biophysical relationships between sensors and actuators1-5 have been fundamental to the development of complex life forms. Swimming organisms generate abundant flows that persist in aquatic environments6-13, and responding promptly to external stimuli is key to survival14-19. Here we present the discovery of 'hydrodynamic trigger waves' in cellular communities of the protist Spirostomum ambiguum that propagate-in a manner similar to a chain reaction20-22-hundreds of times faster than their swimming speed. By coiling its cytoskeleton, Spirostomum can contract its long body by 60% within milliseconds23, experiencing accelerations that can reach forces of 14g. We show that a single cellular contraction (the transmitter) generates long-ranged vortex flows at intermediate Reynolds numbers that can, in turn, trigger neighbouring cells (the receivers). To measure the sensitivity to hydrodynamic signals in these receiver cells, we present a high-throughput suction-flow device for probing mechanosensitive ion channels24 by back-calculating the microscopic forces on the cell membrane. We analyse and quantitatively model the ultra-fast hydrodynamic trigger waves in a universal framework of antenna and percolation theory25,26, and reveal a phase transition that requires a critical colony density to sustain collective communication. Our results suggest that this signalling could help to organize cohabiting communities over large distances and influence long-term behaviour through gene expression (comparable to quorum sensing16). In more immediate terms, because contractions release toxins27, synchronized discharges could facilitate the repulsion of large predators or immobilize large prey. We postulate that numerous aquatic organisms other than protists could coordinate their behaviour using variations of hydrodynamic trigger waves.
Calculation of the autocorrelation function of the stochastic single machine infinite bus system

Critical slowing down (CSD) is the phenomenon in which a system recovers more slowly from small perturbations. CSD, as evidenced by increasing signal variance and autocorrelation, has been observed in many dynamical systems approaching a critical transition, and thus can be a useful signal of proximity to transition. In this paper, we derive autocorrelation functions for the state variables of a stochastic single machine infinite bus system (SMIB). The results show that both autocorrelation and variance increase as this system approaches a saddle-node bifurcation. The autocorrelation functions help to explain why CSD can be used as an indicator of proximity to criticality in power systems revealing, for example, how nonlinearity in the SMIB system causes these signs to appear.
Sobolev bounds and convergence of Riemannian manifolds

Abstract We consider sequences of compact Riemannian manifolds with uniform Sobolev bounds on their metric tensors, and prove that their distance functions are uniformly bounded in the Holder sense. This is done by establishing a general trace inequality on Riemannian manifolds which is an interesting result on its own. We provide examples demonstrating how each of our hypotheses are necessary. In the Appendix by the first author with Christina Sormani, we prove that sequences of compact integral current spaces without boundary (including Riemannian manifolds) that have uniform Holder bounds on their distance functions have subsequences converging in the Gromov–Hausdorff (GH) sense. If in addition they have a uniform upper bound on mass (volume) then they converge in the Sormani–Wenger Intrinsic Flat (SWIF) sense to a limit whose metric completion is the GH limit. We provide an example of a sequence developing a cusp demonstrating why the SWIF and GH limits may not agree.
The Multiple Quantile Graphical Model

We introduce the Multiple Quantile Graphical Model (MQGM), which extends the neighborhood selection approach of Meinshausen and Buhlmann for learning sparse graphical models. The latter is defined by the basic subproblem of modeling the conditional mean of one variable as a sparse function of all others. Our approach models a set of conditional quantiles of one variable as a sparse function of all others, and hence offers a much richer, more expressive class of conditional distribution estimates. We establish that, under suitable regularity conditions, the MQGM identifies the exact conditional independencies with probability tending to one as the problem size grows, even outside of the usual homoskedastic Gaussian data model. We develop an efficient algorithm for fitting the MQGM using the alternating direction method of multipliers. We also describe a strategy for sampling from the joint distribution that underlies the MQGM estimate. Lastly, we present detailed experiments that demonstrate the flexibility and effectiveness of the MQGM in modeling hetereoskedastic non-Gaussian data.
Variations on a theme of Kasteleyn, with Application to the Totally Nonnegative Grassmannian

We provide a short proof of a classical result of Kasteleyn, and prove several variants thereof. One of these results has become key in the parametrization of positroid varieties, and thus deserves the short direct proof which we provide.
Workplace accidents and self-organized criticality

Abstract The occurrence of workplace accidents is described within the context of self-organized criticality, a theory from statistical physics that governs a wide range of phenomena across physics, biology, geosciences, economics, and the social sciences. Workplace accident data from the U.S. Bureau of Labor Statistics reveal a power-law relationship between the number of accidents and their severity as measured by the number of days lost from work. This power-law scaling is indicative of workplace accidents being governed by self-organized criticality, suggesting that nearly all workplace accidents have a common underlying cause, independent of their severity. Such power-law scaling is found for all labor categories documented by the U.S. Bureau of Labor Statistics. Our results provide scientific support for the Heinrich accident triangle, with the practical implication that suppressing the rate of severe accidents requires changing the attitude toward workplace safety in general. By creating a culture that values safety, empowers individuals, and strives to continuously improve, accident rates can be suppressed across the full range of severities.
Universally measurable sets in generic extensions

A subset of a topological space is said to be \emph{universally measurable} if it is measured by the completion of each countably additive $\sigma$-finite Borel measure on the space, and \emph{universally null} if it has measure zero for each such atomless measure. In 1908, Hausdorff proved that there exist universally null sets of real numbers of cardinality $\aleph_{1}$, and thus that there exist at least $2^{\aleph_{1}}$ such sets. Laver showed in the 1970's that consistently there are just continuum many universally null sets of reals. The question of whether there exist more than continuum many universally measurable sets of reals was asked by Mauldin in 1978. We show that consistently there exist only continuum many universally measurable sets. This result also follows from work of Ciesielski and Pawlikowski on the iterated Sacks model. In the models we consider (forcing extensions by suitably-sized random algebras) every set of reals is universally measurable if and only if it and its complement are unions of ground model continuum many Borel sets.
Securing Social Media User Data: An Adversarial Approach

Social media users generate tremendous amounts of data. To better serve users, it is required to share the user-related data among researchers, advertisers and application developers. Publishing such data would raise more concerns on user privacy. To encourage data sharing and mitigate user privacy concerns, a number of anonymization and de-anonymization algorithms have been developed to help protect privacy of social media users. In this work, we propose a new adversarial attack specialized for social media data.We further provide a principled way to assess effectiveness of anonymizing different aspects of social media data. Our work sheds light on new privacy risks in social media data due to innate heterogeneity of user-generated data which require striking balance between sharing user data and protecting user privacy.
Eigenvalue bounds for matrix polynomials in generalized bases

We derive inclusion regions for the eigenvalues of matrix polynomials expressed in a general polynomial basis, which can lead to significantly better results than traditional bounds. We present several applications to engineering problems.
A SPECTROSCOPIC SURVEY OF THE FIELDS OF 28 STRONG GRAVITATIONAL LENSES: THE GROUP CATALOG

NASA [ADP-NNX10AD476, ADP-NNX10AE88G, NAS5-26555]; NSF [AST-0908280, AST-1211385]; Technology and Research Initiative Fund (TRIF) Imaging Fellowship program; NASA from the Space Telescope Science Institute [HST-HF-51250.01-A]; U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
The cup product on Hochschild cohomology via twisting cochains and applications to Koszul rings

Abstract Given an acyclic twisting cochain π : C → A , from a curved dg coalgebra C to a dg algebra A, we show that the associated twisted hom complex Hom k π ( C , A ) has cohomology equal to the Hochschild cohomology of A, as a graded ring. As a corollary we find that the Hochschild cohomology of a Koszul algebra A, along with its cup product, is a subquotient of the tensor product algebra A ! ⊗ A of A with its Koszul dual.
ON THE LIMIT AS THE SURFACE TENSION AND DENSITY RATIO TEND TO ZERO FOR THE TWO-PHASE EULER EQUATIONS

We consider the free boundary motion of two perfect incompressible fluids with different densities ρ+ and ρ-, separated by a surface of discontinuity along which the pressure experiences a jump proportional to the mean curvature by a factor e2. Assuming the Rayleigh–Taylor sign condition, and ρ- ≤ e3/2, we prove energy estimates uniform in ρ- and e. As a consequence, we obtain convergence of solutions of the interface problem to solutions of the free boundary Euler equations in vacuum without surface tension as e, ρ- → 0.
Concentration of the mixed discriminant of well-conditioned matrices

Abstract We call an n-tuple Q 1 , … , Q n of positive definite n × n real matrices α-conditioned for some α ≥ 1 if for the corresponding quadratic forms q i : R n ⟶ R we have q i ( x ) ≤ α q i ( y ) for any two vectors x , y ∈ R n of Euclidean unit length and q i ( x ) ≤ α q j ( x ) for all 1 ≤ i , j ≤ n and all x ∈ R n . An n-tuple is called doubly stochastic if the sum of Q i is the identity matrix and the trace of each Q i is 1. We prove that for any fixed α ≥ 1 the mixed discriminant of an α-conditioned doubly stochastic n-tuple is n O ( 1 ) e − n . As a corollary, for any α ≥ 1 fixed in advance, we obtain a polynomial time algorithm approximating the mixed discriminant of an α-conditioned n-tuple within a polynomial in n factor.
Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data

Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the sense that the missingness is not dependent on the values of the data. Based on incomplete data, estimators for bandable and sparse covariance matrices are proposed and their theoretical and numerical properties are investigated.Minimax rates of convergence are established under the spectral norm loss and the proposed estimators are shown to be rate-optimal under mild regularity conditions. Simulation studies demonstrate that the estimators perform well numerically. The methods are also illustrated through an application to data from four ovarian cancer studies. The key technical tools developed in this paper are of independent interest and potentially useful for a range of related problems in high-dimensional statistical inference with missing data.
Global Relation Embedding for Relation Extraction

We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9% to 89.3%.
Smart Surveillance as an Edge Network Service: From Harr-Cascade, SVM to a Lightweight CNN

Edge computing efficiently extends the realm of information technology beyond the boundary defined by cloud computing paradigm. Performing computation near the source and destination, edge computing is promising to address the challenges in many delay-sensitive applications, like real-time human surveillance. Leveraging the ubiquitously connected cameras and smart mobile devices, it enables video analytics at the edge. In recent years, many smart video surveillance approaches are proposed for object detection and tracking by using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This work explores the feasibility of two popular human-objects detection schemes, Harr-Cascade and HOG feature extraction and SVM classifier, at the edge and introduces a lightweight Convolutional Neural Network (L-CNN) leveraging the depthwise separable convolution for less computation, for human detection. Single Board computers (SBC) are used as edge devices for tests and algorithms are validated using real-world campus surveillance video streams and open data sets. The experimental results are promising that the final algorithm is able to track humans with a decent accuracy at a resource consumption affordable by edge devices in real-time manner.
Learning Hard Alignments with Variational Inference

There has recently been significant interest in hard attention models for tasks such as object recognition, visual captioning and speech recognition. Hard attention offers benefits over soft attention such as decreased computational cost, but training hard attention models can be difficult because of the discrete latent variables they introduce. Previous work used REINFORCE to approach these issues, however, it suffers from high-variance gradient estimates, resulting in slow convergence. In this paper, we tackle the problem of learning hard attention for a sequential task using variational inference methods, specifically the recently introduced Variational Inference for Monte Carlo Objectives (VIMCO) and Neural Variational Inference (NVIL). Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We demonstrate our method on a phoneme recognition task in clean and noisy environments and show that our method outperforms REINFORCE, with the difference being greater for a more complicated task.
ANNIHILATORS OF PERMUTATION MODULES

Permutation modules are fundamental in the representation theory of symmetric groups $\Sym_n$ and their corresponding Iwahori--Hecke algebras $\He = \He(\Sym_n)$. We find an explicit combinatorial basis for the annihilator of a permutation module in the "integral" case -- showing that it is a cell ideal in G.E. Murphy's cell structure of $\He$. The same result holds whenever $\He$ is semisimple, but may fail in the non-semisimple case.
Significance analysis of high-dimensional, low-sample size partially labeled data

Abstract Classification and clustering are both important topics in statistical learning. A natural question herein is whether predefined classes are really different from one another, or whether clusters are really there. Specifically, we may be interested in knowing whether the two classes defined by some class labels (when they are provided), or the two clusters tagged by a clustering algorithm (where class labels are not provided), are from the same underlying distribution. Although both are challenging questions for the high-dimensional, low-sample size data, there has been some recent development for both. However, when it is costly to manually place labels on observations, it is often that only a small portion of the class labels is available. In this article, we propose a significance analysis method for such type of data, namely partially labeled data. Our method makes use of the whole data and tries to test the class difference as if all the labels were observed. Compared to a testing method that ignores the label information, our method provides a greater power, meanwhile, maintaining the size, illustrated by a comprehensive simulation study. Theoretical properties of the proposed method are studied with emphasis on the high-dimensional, low-sample size setting. Our simulated examples help to understand when and how the information extracted from the labeled data can be effective. A real data example further illustrates the usefulness of the proposed method.
Homomorphisms from an arbitrary Specht module to one corresponding to a hook

Abstract We use the presentation of the Specht modules given by Kleshchev–Mathas–Ram to derive results about Specht modules. In particular, we determine all homomorphisms from an arbitrary Specht module to a fixed Specht module corresponding to any hook partition. Along the way, we give a complete description of the action of the standard KLR generators on the hook Specht module. This work generalizes a result of James.
Limits of sequences of Markov chains

We study the limiting object of a sequence of Markov chains analogous to the limits of graphs, hypergraphs, and other objects which have been studied. Following a suggestion of Aldous, we assign to a convergent sequence of finite Markov chains with bounded mixing times a unique limit object: an infinite Markov chain with a measurable state space. The limits of the Markov chains we consider have discrete spectra, which makes the limit theory simpler than the general graph case, and illustrates how the discrete spectrum setting (sometimes called "random-free" or "product measurable") is simpler than the general case.
AN ASYMPTOTIC FOR THE NUMBER OF SOLUTIONS TO LINEAR EQUATIONS IN PRIME NUMBERS FROM SPECIFIED CHEBOTAREV CLASSES

We extend results relating to Vinogradov's three primes theorem to provide asymptotic estimates for the number of solutions to a given linear equation in three or more prime numbers under the additional constraint that each of the primes involved satisfies specialized Chebotarev conditions. In particular, we show that such solutions can be expected to exist unless a solution would violate some local constraint.
Diffusion adaptation over networks

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed optimization, estimation, and inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to streaming data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative agents. This article provides an overview of diffusion strategies for adaptation and learning over networks, with emphasis on mean-square-error designs. Stability and performance analyses are provided and the benefits of cooperation are highlighted. Several supporting appendices are included in an effort to make the presentation self-contained for most readers.
Traffic Optimization For a Mixture of Self-interested and Compliant Agents

This paper focuses on two commonly used path assignment policies for agents traversing a congested network: self-interested routing, and system-optimum routing. In the self-interested routing policy each agent selects a path that optimizes its own utility, while in the system-optimum routing, agents are assigned paths with the goal of maximizing system performance. This paper considers a scenario where a centralized network manager wishes to optimize utilities over all agents, i.e., implement a system-optimum routing policy. In many real-life scenarios, however, the system manager is unable to influence the route assignment of all agents due to limited influence on route choice decisions. Motivated by such scenarios, a computationally tractable method is presented that computes the minimal amount of agents that the system manager needs to influence (compliant agents) in order to achieve system optimal performance. Moreover, this methodology can also determine whether a given set of compliant agents is sufficient to achieve system optimum and compute the optimal route assignment for the compliant agents to do so. Experimental results are presented showing that in several large-scale, realistic traffic networks optimal flow can be achieved with as low as 13% of the agent being compliant and up to 54%.
Ricci flow under local almost non-negative curvature conditions

Abstract We find a local solution to the Ricci flow equation under a negative lower bound for many known curvature conditions. Under a non-collapsing assumption, the flow exists for a uniform amount of time, during which the curvature stays bounded below by a controllable negative number. The curvature conditions we consider include 2-non-negative and weakly PIC 1 cases, of which the results are new. We complete the discussion of the almost preservation problem by Bamler–Cabezas-Rivas–Wilking, and the 2-non-negative case generalizes a result in 3D by Simon–Topping to higher dimensions. As an application, we use the local Ricci flow to smooth a metric space which is the limit of a sequence of manifolds with the almost non-negative curvature conditions, and show that this limit space is bi-Holder homeomorphic to a smooth manifold.
Specializations of nonsymmetric Macdonald–Koornwinder polynomials

The purpose of this article is to work out the details of the Ram–Yip formula for nonsymmetric Macdonald–Koornwinder polynomials for the double affine Hecke algebras of not-necessarily reduced affine root systems. It is shown that the $$t\rightarrow 0$$ equal-parameter specialization of nonsymmetric Macdonald polynomials admits an explicit combinatorial formula in terms of quantum alcove paths, generalizing the formula of Lenart in the untwisted case. In particular, our formula yields a definition of quantum Bruhat graph for all affine root systems. For mixed type, the proof requires the Ram–Yip formula for the nonsymmetric Koornwinder polynomials. A quantum alcove path formula is also given at $$t\rightarrow \infty $$ . As a consequence, we establish the positivity of the coefficients of nonsymmetric Macdonald polynomials under this limit, as conjectured by Cherednik and the first author. Finally, an explicit formula is given at $$q\rightarrow \infty $$ , which yields the p-adic Iwahori–Whittaker functions of Brubaker, Bump, and Licata.
Random walks on the BMW monoid: an algebraic approach

We consider Metropolis-based systematic scan algorithms for generating Birman–Murakami–Wenzl (BMW) monoid basis elements of the BMW algebra. As the BMW monoid consists of tangle diagrams, these scanning strategies can be rephrased as random walks on links and tangles. We also consider the Brauer algebra and use Metropolis-based scans to generate Brauer diagrams, giving rise to random walks on perfect matchings. Taking an algebraic perspective, we translate these walks into left multiplication operators in the BMW algebra and so give an algebraic interpretation of the Metropolis algorithm in this setting.
Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory

We consider linear regression in the high-dimensional regime where the number of observations n is smaller than the number of parameters p. A very successful approach in this setting uses 11-penalized least squares (also known as the Lasso) to search for a subset of s0 <; n parameters that best explain the data, while setting the other parameters to zero. Considerable amount of work has been devoted to characterizing the estimation and model selection problems within this approach. In this paper, we consider instead the fundamental, but far less understood, question of statistical significance. More precisely, we address the problem of computing p-values for single regression coefficients. On one hand, we develop a general upper bound on the minimax power of tests with a given significance level. We show that rigorous guarantees for earlier methods do not allow to achieve this bound, except in special cases. On the other, we prove that this upper bound is (nearly) achievable through a practical procedure in the case of random design matrices with independent entries. Our approach is based on a debiasing of the Lasso estimator. The analysis builds on a rigorous characterization of the asymptotic distribution of the Lasso estimator and its debiased version. Our result holds for optimal sample size, i.e., when n is at least on the order of s 0 log(p/s 0 ). We generalize our approach to random design matrices with independent identically distributed Gaussian rows xi ~ N(0, Σ). In this case, we prove that a similar distributional characterization (termed standard distributional limit) holds for n much larger than s0(log p) 2 .Our analysis assumes Σ is known. To cope with unknown Σ, we suggest a plug-in estimator for sparse covariances Σ and validate the method through numerical simulations. Finally, we show that for optimal sample size, n being at least of order s 0 log(p/s 0 ), the standard distributional limit for general Gaussian designs can be derived from the replica heuristics in statistical physics. This derivation suggests a stronger conjecture than the result we prove, and near-optimality of the statistical power for a large class of Gaussian designs.
The Closed Extensions of a Closed Operator

Given a densely defined and closed (but not necessarily symmetric) operator A acting on a complex Hilbert space $${\mathcal {H}}$$ , we establish a one-to-one correspondence between its closed extensions and subspaces $${\mathfrak {M}}\subset {\mathcal {D}}(A^*)$$ , that are closed with respect to the graph norm of $$A^*$$ and satisfy certain conditions. In particular, this will allow us to characterize all densely defined and closed restrictions of $$A^*$$ . After this, we will express our results using the language of Gel’fand triples generalizing the well-known results for the selfadjoint case.
FLiER: Practical Topology Update Detection Using Sparse PMUs

In this paper, we present a Fingerprint Linear Estimation Routine to identify topology changes in power networks using readings from sparsely deployed phasor measurement units (PMUs). When a power line, load, or generator trips in a network, or when a substation is reconfigured, the event leaves a unique “voltage fingerprint” of bus voltage changes that we can identify using only the portion of the network directly observed by the PMUs. The naive brute-force approach to identify a failed line from such voltage fingerprints, though simple and accurate, is slow. We derive an approximate algorithm based on a local linearization and a novel filtering approach that is faster and only slightly less accurate. We present experimental results by using the IEEE 57-bus, IEEE 118-bus, and Polish 1999-2000 winter peak networks.
Interplay of quantum size effect, anisotropy and surface stress shapes the instability of thin metal films

Morphological instability of a planar surface ([111], [011], or [001]) of an ultra-thin metal film is studied in a parameter space formed by three major effects (the quantum size effect, the surface energy anisotropy and the surface stress) that influence a film dewetting. The analysis is based on the extended Mullins equation, where the effects are cast as functions of the film thickness. The formulation of the quantum size effect (Zhang et al., Phys Rev Lett 80:5381, 1998) includes the oscillation of the surface energy with thickness caused by electron confinement. By systematically comparing the effects, their contributions into the overall stability (or instability) is highlighted.
Embeddings of Riemannian manifolds with finite eigenvector fields of connection Laplacian

We study the problem asking if one can embed manifolds into finite dimensional Euclidean spaces by taking finite number of eigenvector fields of the connection Laplacian. This problem is essential for the dimension reduction problem in manifold learning. In this paper, we provide a positive answer to the problem. Specifically, we use eigenvector fields to construct local coordinate charts with low distortion, and show that the distortion constants depend only on geometric properties of manifolds with metrics in the little Holder space $$c^{2,\alpha }$$ . Next, we use the coordinate charts to embed the entire manifold into a finite dimensional Euclidean space. The proof of the results relies on solving the elliptic system and providing estimates for eigenvector fields and the heat kernel and their gradients. We also provide approximation results for eigenvector field under the $$c^{2,\alpha }$$ perturbation.
Estimation of near surface wind speeds in strongly rotating flows

Abstract Modeling studies consistently demonstrate that the most violent winds in tornadic vortices occur in the lowest tens of meters above the surface. These velocities are unobservable by radar platforms due to line of sight considerations. In this work, a methodology is developed which utilizes parametric tangential velocity models derived from Doppler radar measurements, together with a tangential momentum and mass continuity constraint, to estimate the radial and vertical velocities in a steady axisymmetric frame. The main result is that information from observations aloft can be extrapolated into the surface layer of the vortex. The impact of the amount of information available to the retrieval is demonstrated through some numerical tests with pseudo-data.
Obfuscation resilient search through executable classification

Android applications are usually obfuscated before release, making it difficult to analyze them for malware presence or intellectual property violations. Obfuscators might hide the true intent of code by renaming variables and/or modifying program structures. It is challenging to search for executables relevant to an obfuscated application for developers to analyze efficiently. Prior approaches toward obfuscation resilient search have relied on certain structural parts of apps remaining as landmarks, un-touched by obfuscation. For instance, some prior approaches have assumed that the structural relationships between identifiers are not broken by obfuscators; others have assumed that control flow graphs maintain their structures. Both approaches can be easily defeated by a motivated obfuscator. We present a new approach, MACNETO, to search for programs relevant to obfuscated executables leveraging deep learning and principal components on instructions. MACNETO makes few assumptions about the kinds of modifications that an obfuscator might perform. We show that it has high search precision for executables obfuscated by a state-of-the-art obfuscator that changes control flow. Further, we also demonstrate the potential of MACNETO to help developers understand executables, where MACNETO infers keywords (which are from relevant un-obfuscated programs) for obfuscated executables.
Banach Spaces from Barriers in High Dimensional Ellentuck Spaces

A new hierarchy of Banach spaces $T_k(d,\theta)$, $k$ any positive integer, is constructed using barriers in high dimensional Ellentuck spaces \cite{DobrinenJSL15} following the classical framework under which a Tsirelson type norm is defined from a barrier in the Ellentuck space \cite{Argyros/TodorcevicBK}. The following structural properties of these spaces are proved. Each of these spaces contains arbitrarily large copies of $\ell_\infty^n$, with the bound constant for all $n$. For each fixed pair $d$ and $\theta$, the spaces $T_k(d,\theta)$, $k\ge 1$, are $\ell_p$-saturated, forming natural extensions of the $\ell_p$ space, where $p$ satisfies $d\theta=d^{1/p}$. Moreover, they form a strict hierarchy over the $\ell_p$ space: For any $j<k$, the space $T_j(d,\theta)$ embeds isometrically into $T_k(d,\theta)$ as a subspace which is non-isomorphic to $T_k(d,\theta)$.
Learning Confidence Sets using Support Vector Machines

The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class. An observation outside the overlap of the two sets is deemed to be from one of the two classes, while the overlap is an ambiguity region which could belong to either class. Instead of plug-in approaches, we propose a support vector classifier to construct confidence sets in a flexible manner. Theoretically, we show that the proposed learner can control the non-coverage rates and minimize the ambiguity with high probability. Efficient algorithms are developed and numerical studies illustrate the effectiveness of the proposed method.
Connected power domination in graphs

The study of power domination in graphs arises from the problem of placing a minimum number of measurement devices in an electrical network while monitoring the entire network. A power dominating set of a graph is a set of vertices from which every vertex in the graph can be observed, following a set of rules for power system monitoring. In this paper, we study the problem of finding a minimum power dominating set which is connected; the cardinality of such a set is called the connected power domination number of the graph. We show that the connected power domination number of a graph is NP-hard to compute in general, but can be computed in linear time in cactus graphs and block graphs. We also give various structural results about connected power domination, including a cut vertex decomposition and a characterization of the effects of various vertex and edge operations on the connected power domination number. Finally, we present novel integer programming formulations for power domination, connected power domination, and power propagation time, and give computational results.
Functional Calculus and Joint Torsion of Pairs of Almost Commuting Operators

This paper investigates the transformation of determinants of pairs of Fredholm operators with trace class commutators. We study the extent to which the functional calculus commutes, modulo operator ideals, with projections in a finitely summable Fredholm module. As an application, we extend some results of Carey and Pincus on determinants of Toeplitz operators and Tate tame symbols. Additionally, we obtain variational formulas for joint torsion.
Polyhyperbolic Cardinal Splines

In this note, we discuss solutions of differential equation \((D^2-\alpha ^2)^{k}u=0\) on \(\mathbb {R}\setminus \mathbb {Z}\), which we call polyhyperbolic splines. We develop the fundamental function of interpolation and prove various properties related to these splines.
Hahn analytification and connectivity of higher rank tropical varieties

We show that the tropicalization of a connected variety over a higher rank valued field is a path connected topological space. This establishes an affirmative answer to a question posed by Banerjee (J Reine Angew Math (Crelle’s J) 2015:71–87, 2013). Higher rank tropical varieties are studied as the images of “Hahn analytifications”, introduced in this paper. A Hahn analytification is a space of valuations on a scheme over a higher rank valued field. We prove that the Hahn analytification is related to higher rank tropicalization by means of an inverse limit theorem, extending well-known results in the non-Archimedean case. We also establish comparison results between the Hahn analytification and the Huber and Berkovich analytifications, as well as the Hrushovski–Loeser stable completion.
Topology Identification of Directed Dynamical Networks via Power Spectral Analysis

We address the problem of identifying the topology of an unknown weighted, directed network of LTI systems stimulated by wide-sense stationary noises of unknown power spectral densities. We propose several reconstruction algorithms by measuring the cross-power spectral densities of the network response to the input noises. The measurements are based on a series of node-knockout experiments where at each round the knocked out node broadcasts zero state without being eliminated from the network. Our first algorithm reconstructs the Boolean structure (i.e., existence and directions of links) of a directed network from a series of dynamical responses. Moreover, we propose a second algorithm to recover the exact structure of the network (including edge weights), as well as the power spectral density of the input noises, when an eigenvalue-eigenvector pair of the connectivity matrix is known (for example, Laplacian connectivity matrices). Finally, for the particular cases of nonreciprocal networks (i.e., networks with no directed edges pointing in opposite directions) and undirected networks, we propose specialized algorithms that result in a lower computational cost.