cssi/SciSciGPT-SciSciNet huggingface dataset:
Chelation-controlled asymmetric aminohalogenation reaction

The chelation-controlled asymmetric aminohalogenation of α,β-unsaturated 3-aryl- N -acyl- N -4-phenyl-2-oxazolidinones have been established by using palladium(II) acetate as the catalyst and as the chelation metal. The reaction is very convenient to perform by simply mixing the three reactants, cinnamates, N , N -dichloro- p -toluenesulfonamide and catalyst together with 4 A molecular sieves at rt in any convenient vial of appropriate size without special protection from inert gases. Unlike the previous asymmetric aminohalogenation, the ionic liquid, [BMIM][NTf 2 ], was found to be superior to [BMIM][BF 4 ] as the reaction media. It was also found that palladium(II) acetate has to be used together with 1 equiv of MeCN to achieve the opposite chelation control. The resulting absolute stereochemistry of the product was unambiguously determined by X-ray structural analysis.
Where Are the Protons in α-[HxW12O40](8-x)- (x = 2−4)?

The equilibria and speciation of the proton cryptate polyoxometalate alpha-(H2)W(12)O(40)]6- (1) were examined by NMR following the phase-transfer cation metathesis of aqueous Na(6)1 with Q+Br-/CH(2)Cl(2), leading to the isolation of the (n-Bu)4N+ (Q+) salts Q(6)1 and alpha-Q5[(H3)W(12)O(40)](Q(5)2). Several groups report salts of the protonated anions H(x)1 (x = 1 and 2) with no consensus on proton numbers or locations. Reported herein, a combination of 1H and 183W NMR evidence, elemental analysis, acid titration measurements, and H/D isotopomer assignments establishes that in nonaqueous media the internal cryptand cavity of 1 reversibly accommodates only one more proton to form 2. Because an external proton must transfer across the close-packed tungsten oxide surface of 1, which should constitute a substantial activation barrier, it is significant that the transformation is instantaneous by 1H NMR (1 equiv of HBr in CH(3)CN), whereas the reverse process is slow (t1/2 approximately 17.4 h; 1 equiv of Q+OH-).
Why pacing frequency affects the production of early afterdepolarizations in cardiomyocytes: An explanation revealed by slow-fast analysis of a minimal model

Early afterdepolarizations (EADs) are pathological voltage oscillations in cardiomyocytes that have been observed in response to a number of pharmacological agents and disease conditions. Phase-2 EADs consist of small voltage fluctuations during the plateau of an action potential, typically under conditions in which the action potential is elongated. Although a single-cell behavior, EADs can lead to tissue-level arrhythmias. Much is currently known about the biophysical mechanisms (i.e., the roles of ion channels and intracellular Ca^{2+} stores) for the various forms of EADs, due partially to the development and analysis of mathematical models. This includes the application of slow-fast analysis, which takes advantage of timescale separation inherent in the system to simplify its analysis. We take this further, using a minimal three-dimensional model to demonstrate that phase-2 EADs are canards formed in the neighborhood of a folded node singularity. This allows us to predict the number of EADs that can be produced for a given parameter set, and provides guidance on parameter changes that facilitate or inhibit EAD production. With this approach, we demonstrate why periodic stimulation, as occurs in intact heart, preferentially facilitates EAD production when applied at low frequencies. We also explain the origin of complex alternan dynamics that can occur with intermediate-frequency stimulation, in which varying numbers of EADs are produced with each pulse. These revelations fall out naturally from an understanding of folded node singularities, but are difficult to glean from knowledge of the biophysical mechanism for EADs alone. Therefore, understanding the canard mechanism is a useful complement to understanding of the biophysical mechanism that has been developed over years of experimental and computational investigations.
Genetic engineering of hybrid antibiotics : a progress report

Abstract Interspecies cloning of antibiotic biosynthesis genes in streptomycetes can be used to generate new hybrid natural product structures; the application of this approach to polyketide and its extension to thiopeptide antibiotics are summarized.
A Modular Enantioselective Approach to Construction of the Macrolactone Core of Polycavernoside A

A program directed toward a total synthesis of polycavernoside A is described. The synthesis of five building blocks is detailed. The first of two electrophilic units, the lactone 3, was prepared in four steps from the known enantiomerically pure oxirane 15. Pyranyl aldehyde 5 was elaborated in turn from l-malic acid via 10. While the route to 30 involved 3 as a starting material, dithiane 2 was obtained in a straightforward manner from 10 as well. The merging of the chiral sectors could not be accomplished by way of the lithiated dithianyl anions, presumably as a consequence of their heightened basicity. The strategic incorporation of the trienyl sector was accomplished, although no attempt was made to control the diastereoselectivity of the process.
Manipulation of Individual Carbon Nanotubes and Their Interaction with Surfaces

We demonstrate that the tip of an atomic force microscope (AFM) can be used to control the shape and position of individual multiwalled carbon nanotubes dispersed on a surface. Specifically we can bend, straighten, translate, rotate, andunder certain conditionscut nanotubes. Such manipulations are feasible due to the interaction between nanotubes and the substrate, which can stabilize highly strained nanotube configurations. Direct evidence for this interaction is provided by the study of elastic distortions of tubes interacting with other tubes and the substrate. From the observed deformations of nanotubes with 100 A diameter, for example, we obtain a binding energy of 0.8 ± 0.3 eV/A. This interaction forces nanotubes to conform to the structure of the substrate, and the resulting distortions should induce corresponding changes in their electronic structure and electrical transport properties.
SYNTHESIS OF FUSED BICYCLIC RINGS BY TANDEM RADICAL RING EXPANSION/CYCLIZATION : EVALUATING COMPETING INTRAMOLECULAR REACTIONS

Abstract One- and three-carbon “Dowd-Beckwith” ring expansions of cyclopentanones and cyclohexanones are generally successful, but tandem expansion/cyclization reactions can be compromised by competing processes. An evaluation of the competition between ring expansion, 1,5-hydrogen transfer, and 6- exo cyclization provides information on how to design successful tandem expansion cyclization sequences.
Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model.

Mathematical modeling provides the predictive ability to understand the metabolic reprogramming and complex pathways that mediate cancer cells’ proliferation. We present a mathematical model using a multiscale, multicellular approach to simulate avascular tumor growth, applied to pancreatic cancer. The model spans three distinct spatial and temporal scales. At the extracellular level, reaction diffusion equations describe nutrient concentrations over a span of seconds. At the cellular level, a lattice-based energy driven stochastic approach describes cellular phenomena including adhesion, proliferation, viability and cell state transitions, occurring on the timescale of hours. At the sub-cellular level, we incorporate a detailed kinetic model of intracellular metabolite dynamics on the timescale of minutes, which enables the cells to uptake and excrete metabolites and use the metabolites to generate energy and building blocks for cell growth. This is a particularly novel aspect of the model. Certain defined criteria for the concentrations of intracellular metabolites lead to cancer cell growth, proliferation or death. Overall, we model the evolution of the tumor in both time and space. Starting with a cluster of tumor cells, the model produces an avascular tumor that quantitatively and qualitatively mimics experimental measurements of multicellular tumor spheroids. Through our model simulations, we can investigate the response of individual intracellular species under a metabolic perturbation and investigate how that response contributes to the response of the tumor as a whole. The predicted response of intracellular metabolites under various targeted strategies are difficult to resolve with experimental techniques. Thus, the model can give novel predictions as to the response of the tumor as a whole, identifies potential therapies to impede tumor growth, and predicts the effects of those therapeutic strategies. In particular, the model provides quantitative insight into the dynamic reprogramming of tumor cells at the intracellular level in response to specific metabolic perturbations. Overall, the model is a useful framework to study targeted metabolic strategies for inhibiting tumor growth.
Single and Double Diastereoselection in Azomethine Ylide Cycloaddition Reactions with Unsaturated Chiral Bicyclic Lactams

Double diastereoselectivity data were analyzed to provide insight into the structural features that influence π-facial selectivity in 1,3-dipolar cycloadditions of chiral and achiral azomethine ylides to chiral, unsaturated bicyclic lactams. Three major steric contributions to the differences in stability (ΔΔG⧧) between competing cycloaddition transition states were identified. The first major set of steric interactions involve that between the dipoles and the substituents on the left hemisphere (R2) and concave faces of the bicyclic lactams. This effectively hindered both α- and β-approaches in the nonextended transition states shown in Figure 1. The second major steric interaction was provided by the nonbonded interactions (i) between the R1 angular substituent on the bicyclic lactam and the π-system of the dipole as shown in Figures 3 and 4. This interaction was shown to be very significant, causing reversal in π-facial attack of chiral and achiral dipoles when the angular substituent is changed from p...
Role of phosphodiesterase isoenzymes in the control of renin secretion : Effects of selective enzyme inhibitors

In most cells, the steady-state level of cAMP ultimately depends on the rate of cAMP synthesis by adenylyl cyclase and the rate of cAMP hydrolysis by cyclic nucleotide phosphodiesterases (PDEs). PDEs exist in multiple forms that have been grouped into seven families based on their substrate specificity, mode of regulation and kinetic properties. Selective inhibitors of many PDE families are now available. Examples are milrinone and trequinsin (PDE3); rolipram and Ro 20-1724 (PDE4); and zaprinast, sildenafil and didyridamole (PDE5). These inhibitors have proven to be valuable tools to investigate the role of PDEs in cell function. Representatives of most PDE families are present in the kidneys, and recent studies in this and other laboratories have provided evidence that some of them participate in the regulation of renin secretion. In particular, administration of selective PDE inhibitors has marked effects on renin secretion. For example, the PDE3 inhibitors milrinone and trequinsin increase resting renin in conscious rabbits and enhance the renin secretory response to beta-adrenergic stimulation. Milrinone also increases renin secretion in human subjects. The PDE4 inhibitors rolipram and Ro 20-1724 both increase renin secretion in rabbits and also enhance the renin response to beta-adrenergic stimulation. Studies in other laboratories have implicated other PDE families in the control of renin secretion. The aim of this review is to present current concepts concerning the PDEs and to discuss their role in the control of renin secretion by the kidneys.
Synthesis and structure of dinitroazofurazan

The synthesis of dinitroazofurazan (3) was achieved from readily available diaminofurazan (6) in two steps and 20% overall yield. The structure of the energetic material 3 was unequivocally confirmed by X-ray crystallography and found to have a crystal density of 1.742 g/cm3.
Dirty hands on troubled waters: Sanitation, access to water and child health in Ethiopia

In this paper, we investigate the impact of access to drinking water sources and sanitation facilities on the incidence of diarrheal diseases among children below 5 years of age in Ethiopia using the propensity score matching technique with a polychotomous treatment variable. We find that among the water sources traditionally considered as improved, only water piped into dwelling, yard or plot leads to a large percentage point reduction in diarrhea incidence. The other water sources, generally believed as clean, are not effective in reducing diarrhea even compared with some of the unimproved water sources. We also find that some unimproved water sources and sanitation facilities are less inferior than they are believed to be. These results suggest that the traditional way of categorizing different types of improved and unimproved water sources and sanitation facilities into a dichotomous variable, “improved” or “unimproved”, could be misleading as it masks the heterogeneous effects of the water sources and the sanitation facilities.
Effective Certification of Approximate Solutions to Systems of Equations Involving Analytic Functions

We develop algorithms for certifying an approximation to a nonsingular solution of a square system of equations built from univariate analytic functions. These algorithms are based on the existence of oracles for evaluating basic data about the input analytic functions. One approach for certification is based on α-theory while the other is based on the Krawczyk generalization of Newton's iteration. We show that the necessary oracles exist for \Dfinite\ functions and compare the two algorithmic approaches for this case using our software implementation in \sage.
Augmenting supervised neural networks with unsupervised objectives for large-scale image classification

Unsupervised learning and supervised learning are key research topics in deep learning. However, as high-capacity supervised neural networks trained with a large amount of labels have achieved remarkable success in many computer vision tasks, the availability of large-scale labeled images reduced the significance of unsupervised learning. Inspired by the recent trend toward revisiting the importance of unsupervised learning, we investigate joint supervised and unsupervised learning in a large-scale setting by augmenting existing neural networks with decoding pathways for reconstruction. First, we demonstrate that the intermediate activations of pretrained large-scale classification networks preserve almost all the information of input images except a portion of local spatial details. Then, by end-to-end training of the entire augmented architecture with the reconstructive objective, we show improvement of the network performance for supervised tasks. We evaluate several variants of autoencoders, including the recently proposed "what-where" autoencoder that uses the encoder pooling switches, to study the importance of the architecture design. Taking the 16-layer VGGNet trained under the ImageNet ILSVRC 2012 protocol as a strong baseline for image classification, our methods improve the validation-set accuracy by a noticeable margin.
A scalable null model for directed graphs matching all degree distributions: In, out, and reciprocal

Degree distributions are arguably the most important property of real world networks. The classic edge configuration model or Chung-Lu model can generate an undirected graph with any desired degree distribution. This serves as a good null model to compare algorithms or perform experimental studies. Furthermore, there are scalable algorithms that implement these models and they are invaluable in the study of graphs. However, networks in the real-world are often directed, and have a significant proportion of reciprocal edges. A stronger relation exists between two nodes when they each point to one another (reciprocal edge) as compared to when only one points to the other (one-way edge). Despite their importance, reciprocal edges have been disregarded by most directed graph models. We propose a null model for directed graphs inspired by the Chung-Lu model that matches the in-, out-, and reciprocal-degree distributions of the real graphs. Our algorithm is scalable and requires O(m) random numbers to generate a graph with m edges. We perform a series of experiments on real datasets and compare with existing graph models.
On the Andreadakis–Johnson filtration of the automorphism group of a free group

Abstract Families of non-trivial cohomology classes are given for the discrete groups belonging to the Johnson filtration of the automorphism group of a free group generated by n letters. The methods are (1) to analyze analogous classes for filtrations of a subgroup of the pure symmetric automorphism group of a free group and (2) to analyze features of these classes which are preserved by the Johnson homomorphism with values in the Lie algebra of derivations of a free Lie algebra. One consequence is that the ranks of the cohomology groups in any fixed degree i for 1 ⩽ i ⩽ n − 2 for the Johnson filtrations of IA n increase without bound. The actual classes constructed are fragile in the sense that they vanish after passage to successive filtrations; furthermore, these classes are all naturally in the image of the Johnson homomorphism on the level of cohomology. The methods are similar to those occurring within the theory of arrangements. The authors congratulate Corrado De Concini on the occasion of his 60-th birthday. One of the authors would like to thank Corrado for a wonderful time arising from a discussion about cohomology of groups with friends in the basement of Il Palazzone!
Anomalous Transient Amplification of Waves in Non-normal Photonic Media

For wave propagation in a lossy optical medium, the total optical power is commonly expected to decay with propagation distance. Using methods of non-normal operator theory, researchers challenge this notion and show that overall lossy optical materials with a small amount of distributed gain can amplify certain input signals by orders of magnitude.
Quantitative model validation techniques: New insights

Abstract This paper develops new insights into quantitative methods for the validation of computational model prediction. Four types of methods are investigated, namely classical and Bayesian hypothesis testing, a reliability-based method, and an area metric-based method. Traditional Bayesian hypothesis testing is extended based on interval hypotheses on distribution parameters and equality hypotheses on probability distributions, in order to validate models with deterministic/stochastic output for given inputs. Formulations and implementation details are outlined for both equality and interval hypotheses. Two types of validation experiments are considered—fully characterized (all the model/experimental inputs are measured and reported as point values) and partially characterized (some of the model/experimental inputs are not measured or are reported as intervals). Bayesian hypothesis testing can minimize the risk in model selection by properly choosing the model acceptance threshold, and its results can be used in model averaging to avoid Type I /II errors. It is shown that Bayesian interval hypothesis testing, the reliability-based method, and the area metric-based method can account for the existence of directional bias, where the mean predictions of a numerical model may be consistently below or above the corresponding experimental observations. It is also found that under some specific conditions, the Bayes factor metric in Bayesian equality hypothesis testing and the reliability-based metric can both be mathematically related to the p-value metric in classical hypothesis testing. Numerical studies are conducted to apply the above validation methods to gas damping prediction for radio frequency (RF) micro-electro-mechanical-system (MEMS) switches. The model of interest is a general polynomial chaos (gPC) surrogate model constructed based on expensive runs of a physics-based simulation model, and validation data are collected from fully characterized experiments.
Properness for scaled gauged maps

We prove properness of moduli stacks of gauged maps satisfying a stability conditition introduced by Mundet [42], Schmitt [48] and Ziltener [59]. The proof combines a git construction of Schmitt [48], properness for twisted stable maps by Abramovich-Vistoli [1], a variation of a boundedness argument due to Ciocan–Fontanine–Kim–Maulik [13], and a removal of singularities for bundles on surfaces in Colliot–Thelene–Sansuc [14].
Rational Functions with a Unique Critical Point

Over an algebraically closed field of positive characteristic, there exist rational functions with only one critical point. We give an elementary characterization of these functions in terms of their continued fraction expansions. Then we use this tool to discern some of the basic geometry of the space of unicritical rational functions, as well as its quotients by the SL(2)-actions of conjugation and postcomposition. We also give an application to dynamical systems with restricted ramification defined over non-Archimedean fields of positive residue characteristic.
Adjusted empirical likelihood method for the tail index of a heavy-tailed distribution

Abstract Empirical likelihood is a well-known nonparametric method in statistics and has been widely applied in statistical inference. The method has been employed by Lu and Peng (2002) to constructing confidence intervals for the tail index of a heavy-tailed distribution. It is demonstrated in Lu and Peng (2002) that the empirical likelihood-based confidence intervals perform better than confidence intervals based on normal approximation in terms of the coverage probability. In general, the empirical likelihood method can be hindered by its imprecision in the coverage probability when the sample size is small. This may cause a serious undercoverage issue when we apply the empirical likelihood to the tail index as only a very small portion of observations can be used in the estimation of the tail index. In this paper, we employ an adjusted empirical likelihood method, developed by Chen et al. (2008) and Liu and Chen (2010), to constructing confidence intervals of the tail index so as to achieve a better accuracy. We conduct a simulation study to compare the performance of the adjusted empirical likelihood method and the normal approximation method. Our simulation results indicate that the adjusted empirical likelihood method outperforms other methods in terms of the coverage probability and length of confidence intervals. We also apply the adjusted empirical likelihood method to a real data set.
Markov spectra for modular billiards

The connection between Markov’s theory of minima of indefinite binary quadratic forms and hyperbolic geodesics is well-known. We introduce some new analogues of the Markov spectrum defined in terms of modular billiards and consider the problem of characterizing that part of the spectrum below the lowest limit point.
The sigma meson from lattice QCD with two-pion interpolating operators

In this paper, we describe our studies of the sigma meson, f0(500), using two-pion correlation functions. We use lattice quantum chromodynamics in the quenched approximation with so-called clover fermions. By working at unphysical pion masses we are able to identify a would-be resonance with mass less than 2mπ, and then extrapolate to the physical point. We include the most important annihilation diagram, which is “partially disconnected” or “single annihilation”. Because this diagram is quite expensive to compute, we introduce a somewhat novel technique for the computation of all-to-all diagrams, based on momentum sources and a truncation in momentum space. In practice, we use only p = 0 modes, so the method reduces to wall sources. At the point where the mass of the pion takes its physical value, we find a resonance in the 0++ two-pion channel with a mass of approximately 609±80 MeV, consistent with the expected properties of the sigma meson, given the approximations we are making.
Wavelet-based genetic association analysis of functional phenotypes arising from high-throughput sequencing assays

Understanding how genetic variants influence cellular-level processes is an important step toward understanding how they influence important organismal-level traits, or "phenotypes," including human disease susceptibility. To this end, scientists are undertaking large-scale genetic association studies that aim to identify genetic variants associated with molecular and cellular phenotypes, such as gene expression, transcription factor binding, or chromatin accessibility. These studies use high-throughput sequencing assays (e.g., RNA-seq, ChIP-seq, DNase-seq) to obtain high-resolution data on how the traits vary along the genome in each sample. However, typical association analyses fail to exploit these high-resolution measurements, instead aggregating the data at coarser resolutions, such as genes, or windows of fixed length. Here we develop and apply statistical methods that better exploit the high-resolution data. The key idea is to treat the sequence data as measuring an underlying "function" that varies along the genome, and then, building on wavelet-based methods for functional data analysis, test for association between genetic variants and the underlying function. Applying these methods to identify genetic variants associated with chromatin accessibility (dsQTLs), we find that they identify substantially more associations than a simpler window-based analysis, and in total we identify 772 novel dsQTLs not identified by the original analysis.
Direct Feedback Alignment With Sparse Connections for Local Learning.

Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on von Neumann architectures, becomes inefficient when scaling to large networks. Commonly referred to as the weight transport problem, each neuron's dependence on the weights and errors located deeper in the network require exhaustive data movement which presents a key problem in enhancing the performance and energy-efficiency of machine-learning hardware. In this work, we propose a bio-plausible alternative to backpropagation drawing from advances in feedback alignment algorithms in which the error computation at a single synapse reduces to the product of three scalar values, satisfying the three factor rule. Using a sparse feedback matrix, we show that a neuron needs only a fraction of the information previously used by the feedback alignment algorithms to yield results which are competitive with backpropagation. Consequently, memory and compute can be partitioned and distributed whichever way produces the most efficient forward pass so long as a single error can be delivered to each neuron. We evaluate our algorithm using standard data sets, including ImageNet, to address the concern of scaling to challenging problems. Our results show orders of magnitude improvement in data movement and $2\times$ improvement in multiply-and-accumulate operations over backpropagation. All the code and results are available under https://github.com/bcrafton/ssdfa.
A Decomposition of Forecast Error in Prediction Markets

We analyze sources of error in prediction market forecasts in order to bound the difference between a security's price and the ground truth it estimates. We consider cost-function-based prediction markets in which an automated market maker adjusts security prices according to the history of trade. We decompose the forecasting error into three components: sampling error, arising because traders only possess noisy estimates of ground truth; market-maker bias, resulting from the use of a particular market maker (i.e., cost function) to facilitate trade; and convergence error, arising because, at any point in time, market prices may still be in flux. Our goal is to make explicit the tradeoffs between these error components, influenced by design decisions such as the functional form of the cost function and the amount of liquidity in the market. We consider a specific model in which traders have exponential utility and exponential-family beliefs representing noisy estimates of ground truth. In this setting, sampling error vanishes as the number of traders grows, but there is a tradeoff between the other two components. We provide both upper and lower bounds on market-maker bias and convergence error, and demonstrate via numerical simulations that these bounds are tight. Our results yield new insights into the question of how to set the market's liquidity parameter and into the forecasting benefits of enforcing coherent prices across securities.
Link before you share: Managing privacy policies through blockchain

With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. We have developed a novel framework to automatically track details about how a user's PII is stored, used and shared by the provider. We have integrated our data privacy ontology with the properties of blockchain, to develop an automated access-control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by big data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks

Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks. This paper proposes to decompose the convolutional filters over joint steerable bases across the space and the group geometry simultaneously, namely a rotation-equivariant CNN with decomposed convolutional filters (RotDCF). This decomposition facilitates computing the joint convolution, which is proved to be necessary for the group equivariance. It significantly reduces the model size and computational complexity while preserving performance, and truncation of the bases expansion serves implicitly to regularize the filters. On datasets involving in-plane and out-of-plane object rotations, RotDCF deep features demonstrate greater robustness and interpretability than regular CNNs. The stability of the equivariant representation to input variations is also proved theoretically under generic assumptions on the filters in the decomposed form. The RotDCF framework can be extended to groups other than rotations, providing a general approach which achieves both group equivariance and representation stability at a reduced model size.
Grammatical Error Correction with Neural Reinforcement Learning

We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.
Discrete gaussian sampling reduces to CVP and SVP

The discrete Gaussian De-t,s is the distribution that assigns to each vector x in a shifted lattice e --- t probability proportional to [EQUATION]. It has long been an important tool in the study of lattices. More recently, algorithms for discrete Gaussian sampling (DGS) have found many applications in computer science. In particular, polynomial-time algorithms for DGS with very high parameters s have found many uses in cryptography and in reductions between lattice problems. And, in the past year, Aggarwal, Dadush, Regev, and Stephens-Davidowitz showed 2n+o(n)-time algorithms for DGS with a much wider range of parameters and used them to obtain the current fastest known algorithms for the two most important lattice problems, the Shortest Vector Problem (SVP) and the Closest Vector Problem (CVP).Motivated by its increasing importance, we investigate the complexity of DGS itself and its relationship to CVP and SVP. Our first result is a polynomial-time dimension-preserving reduction from DGS to CVP. There is a simple reduction from CVP to DGS, so this shows that DGS is equivalent to CVP. Our second result, which we find to be more surprising, is a polynomial-time dimension-preserving reduction from centered DGS (the important special case when t = 0) to SVP. In the other direction, there is a simple reduction from γ-approximate SVP for any γ = Ω([EQUATION]), and we present some (relatively weak) evidence to suggest that this might be the best achievable approximation factor.
Interval k-Graphs and Orders

An interval k-graph is the intersection graph of a family of intervals of the real line partitioned into k classes with vertices adjacent if and only if their corresponding intervals intersect and belong to different classes. In this paper we study the cocomparability interval k-graphs; that is, the interval k-graphs whose complements have a transitive orientation and are therefore the incomparability graphs of strict partial orders. For brevity we call these orders interval k-orders. We characterize the kind of interval representations a cocomparability interval k-graph must have, and identify the structure that guarantees an order is an interval k-order. The case k = 2 is peculiar: cocomparability interval 2-graphs (equivalently proper- or unit-interval bigraphs, bipartite permutation graphs, and complements of proper circular-arc graphs to name a few) have been characterized in many ways, but we show that analogous characterizations do not hold if k > 2. We characterize the cocomparability interval 3-graphs via one forbidden subgraph and hence interval 3-orders via one forbidden suborder.
Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention’s context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014). 1
A minimally-dissipative low-Mach number solver for complex reacting flows in OpenFOAM

Abstract Large eddy simulation (LES) has become the de-facto computational tool for modeling complex reacting flows, especially in gas turbine applications. However, readily usable general-purpose LES codes for complex geometries are typically academic or proprietary/commercial in nature. The objective of this work is to develop and disseminate an open source LES tool for low-Mach number turbulent combustion using the OpenFOAM framework. In particular, a collocated-mesh approach suited for unstructured grid formulation is provided. Unlike other fluid dynamics models, LES accuracy is intricately linked to so-called primary and secondary conservation properties of the numerical discretization schemes. This implies that although the solver only evolves equations for mass, momentum, and energy, the implied discrete equation for kinetic energy (square of velocity) should be minimally-dissipative. Here, a specific spatial and temporal discretization is imposed such that this kinetic energy dissipation is minimized. The method is demonstrated using manufactured solutions approach on regular and skewed meshes for a canonical flow and a lab-scale turbulent flow problem.
Edge-Based Wedge Sampling to Estimate Triangle Counts in Very Large Graphs

The number of triangles in a graph is useful to deduce a plethora of important features of the network that the graph is modeling. However, finding the exact value of this number is computationally expensive. Hence, a number of approximation algorithms based on random sampling of edges, or wedges (adjacent edge pairs) have been proposed for estimating this value. We argue that for large sparse graphs with power-law degree distribution, random edge sampling requires sampling large number of edges before providing enough information for accurate estimation, and existing wedge sampling methods lead to biased samplings, which in turn lead to less accurate estimations. In this paper, we propose a hybrid algorithm between edge and wedge sampling that addresses the deficiencies of both approaches. We start with uniform edge sampling and then extend each selected edge to form a wedge that is more informative for estimating the overall triangle count. The core estimate we make is the number of triangles each sampled edge in the first phase participates in. This approach provides accurate approximations with very small sampling ratios, outperforming the state-of-the-art up to 8 times in sample size while providing estimations with 95% confidence.
Functions of perturbed n-tuples of commuting self-adjoint operators☆

Let (A1,…,An) and (B1,…,Bn) be n-tuples of commuting self-adjoint operators on Hilbert space. For functions f on Rn satisfying certain conditions, we obtain sharp estimates of the operator norms (or norms in operator ideals) of f(A1,…,An)−f(B1,…,Bn) in terms of the corresponding norms of Aj−Bj, 1⩽j⩽n. We obtain analogs of earlier results on estimates for functions of perturbed self-adjoint and normal operators. It turns out that for n⩾3, the methods that were used for self-adjoint and normal operators do not work. We propose a new method that works for arbitrary n. We also get sharp estimates for quasicommutators f(A1,…,An)R−Rf(B1,…,Bn) in terms of norms of AjR−RBj, 1⩽j⩽n, for a bounded linear operator R.
On the role of sparsity in Compressed Sensing and random matrix theory

We discuss applications of some concepts of Compressed Sensing in the recent work on invertibility of random matrices due to Rudelson and the author. We sketch an argument leading to the optimal bound Ω(N{su−) on the median of the smallest singular value of an N × N matrix with random independent entries. We highlight the parts of the argument where sparsity ideas played a key role.
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Motivation State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type. Results We propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora. Availability and implementation Our source code is available at https://github.com/yuzhimanhua/lm-lstm-crf. Supplementary information Supplementary data are available at Bioinformatics online.
Semi-extraspecial Groups

We survey the results regarding semi-extraspecial p-groups. Semi-extraspecial groups can be viewed as generalizations of extraspecial groups. We present the connections between semi-extraspecial groups, Camina groups, and VZ-groups, and give upper bounds on the order of the center and the orders of abelian normal subgroups. We define ultraspecial groups to be semi-extraspecial groups where the center is as large as possible, and demonstrate a connection between ultraspecial groups that have at least two abelian subgroups whose order is the maximum and semifields.
Functional central limit theorem for Brownian particles in domains with Robin boundary condition

Abstract We rigorously derive non-equilibrium space–time fluctuation for the particle density of a system of reflected diffusions in bounded Lipschitz domains in R d . The particles are independent and are killed by a time-dependent potential which is asymptotically proportional to the boundary local time. We generalize the functional analytic framework introduced by Kotelenez [20] , [21] to deal with time-dependent perturbations. Our proof relies on Dirichlet form method rather than the machineries derived from Kotelenez's sub-martingale inequality. Our result holds for any symmetric reflected diffusion, for any bounded Lipschitz domain and for any dimension d ≥ 1 .
Maximal function inequalities and a theorem of Birch

In this paper we prove an analogue of the discrete spherical maximal theorem of Magyar, Stein and Wainger, an analogue which concerns maximal functions associated to homogenous algebraic hypersurfaces. Let p be a homogenous polynomial in n variables with integer coefficients of degree d > 1. The maximal functions we consider are defined by $${A_*}f(y) = \begin{array}{*{20}{c}} {\sup } \\ {N \geq 1} \end{array}|\frac{1}{{r(N)}}\sum\limits_{p(x) = 0;x \in {{[N]}^n}} {f(y - x)|} $$ for functions f : ℤn → ℂ, where [N] = {−N,−N + 1, …, N} and r(N) represents the number of integral points on the surface defined by p(x) = 0 inside the n-cube [N]n. It is shown here that the operators A* are bounded on lp in the optimal range p > 1 under certain regularity assumptions on the polynomial p.
Learning Sampling Distributions for Robot Motion Planning

A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally, these samples are drawn either probabilistically or deterministically to uniformly cover the state space. Yet, the motion of many robotic systems is often restricted to “small” regions of the state space, due to e.g. differential constraints or collision-avoidance constraints. To accelerate the planning process, it is thus desirable to devise non-uniform sampling strategies that favor sampling in those regions where an optimal solution might lie. This paper proposes a methodology for nonuniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling. The sampling distribution is computed through a conditional variational autoencoder, allowing sample generation from the latent space conditioned on the specific planning problem. This methodology is general, can be used in combination with any sampling-based planner, and can effectively exploit the underlying structure of a planning problem while maintaining the theoretical guarantees of sampling-based approaches. Specifically, on several planning problems, the proposed methodology is shown to effectively learn representations for the relevant regions of the state space, resulting in an order of magnitude improvement in terms of success rate and convergence to the optimal cost.
Ricci flow neckpinches without rotational symmetry

We study “warped Berger” solutions of Ricci flow: generalized warped products with the metric induced on each fiber {s}×SU(2) a left-invariant Berger metric. We prove that this structure is preserv...
Classification of isoparametric hypersurfaces in spheres with $(g,m)=(6,1)$

We classify isoparametric hypersurfaces in spheres with $(g,m)=(6,1)$ and thereby reprove a result of Dorfmeister and Neher.
A brief review of ferroelectric control of magnetoresistance in organic spin valves

Abstract Magnetoelectric coupling has been a trending research topic in both organic and inorganic materials and hybrids. The concept of controlling magnetism using an electric field is particularly appealing in energy efficient applications. In this spirit, ferroelectricity has been introduced to organic spin valves to manipulate the magneto transport, where the spin transport through the ferromagnet/organic spacer interfaces (spinterface) are under intensive study. The ferroelectric materials in the organic spin valves provide a knob to vary the interfacial energy alignment and the interfacial crystal structures, both are critical for the spin transport. In this review, we introduce the recent efforts of controlling magnetoresistance of organic spin valves using ferroelectricity, where the ferroelectric material is either inserted as an interfacial layer or used as a spacer material. The realization of the ferroelectric control of magneto transport in organic spin valve, advances our understanding in the spin transport through the ferromagnet/organic interface, and suggests more functionality of organic spintronic devices.
High fidelity universal set of quantum gates using non-adiabatic rapid passage

Numerical simulation results are presented which suggest that a class of non-adiabaticrapid passage sweeps first realized experimentally in 1991 should be capable of implementinga universal set of quantum gates Gu that operate with high fidelity. The gatesconstituting Gu are the Hadamard and NOT gates, together with variants of the phase,O/8, and controlled-phase gates. The universality of Gu is established by showing that itcan construct the universal set consisting of Hadamard, phase, O/8, and controlled-NOTgates. Sweep parameter values are provided which simulations indicate will produce thedifferent gates in Gu, and for which the gate error probability Pe satisfies: (i) Pe > 10-4for the one-qubit gates; and (ii) Pe > 1.27×10-3 for the modified controlled-phase gate.The sweeps in this class are non-composite and generate controllable quantum interferenceeffects that allow the gates in Gu to operate non-adiabatically while maintaininghigh fidelity. These interference effects have been observed using NMR, and it has previouslybeen shown how these rapid passage sweeps can be applied to atomic systemsusing electric fields. Here we show how these sweeps can be applied to both superconductingcharge and flux qubit systems. The simulations suggest that the universal setof gates Gu produced by these rapid passage sweeps shows promise as possible elementsof a fault-tolerant scheme for quantum computing.
Stokes waves with constant vorticity: I. Numerical computation

Periodic traveling waves are numerically computed in a constant vorticity flow subject to the force of gravity. The Stokes wave problem is formulated via a conformal mapping as a nonlinear pseudo-differential equation, involving a periodic Hilbert transform for a strip, and solved by the Newton-GMRES method. It works well with a fast Fourier transform and is more effective than a boundary integral method. The result is in excellent agreement, qualitatively and quantitatively, with earlier ones. For strong positive vorticity, in the finite or infinite depth, overhanging profiles are found as the steepness increases and tend to a touching wave, whose profile self-intersects somewhere along the trough line, trapping an air bubble; the numerical solutions become unphysical as the steepness increases further and make a gap in the wave speed versus steepness plane; a touching wave then takes over and the physical solutions follow in the wave speed versus steepness plane until they ultimately tend to an extreme wave, which exhibits a sharp corner at the crest. Overhanging waves of nearly maximum heights are found to approach rigid body rotation of a fluid disk as the strength of positive vorticity increases.