cssi/SciSciGPT-SciSciNet huggingface dataset:
The first genetic linkage map for Fraxinus pennsylvanica and syntenic relationships with four related species

The genetic linkage map for green ash (Fraxinus pennsylvanica) contains 1201 DNA markers in 23 linkage groups spanning 2008.87cM. The green ash map shows stronger synteny with coffee than tomato. Green ash (Fraxinus pennsylvanica) is an outcrossing, diploid (2n = 46) hardwood tree species, native to North America. Native ash species in North America are being threatened by the rapid spread of the emerald ash borer (EAB, Agrilus planipennis), an invasive pest from Asia. Green ash, the most widely distributed ash species, is severely affected by EAB infestation, yet few genomic resources for genetic studies and improvement of green ash are available. In this study, a total of 5712 high quality single nucleotide polymorphisms (SNPs) were discovered using a minimum allele frequency of 1% across the entire genome through genotyping-by-sequencing. We also screened hundreds of genomic- and EST-based microsatellite markers (SSRs) from previous de novo assemblies (Staton et al., PLoS ONE 10:e0145031, 2015; Lane et al., BMC Genom 17:702, 2016). A first genetic linkage map of green ash was constructed from 90 individuals in a full-sib family, combining 2719 SNP and 84 SSR segregating markers among the parental maps. The consensus SNP and SSR map contains a total of 1201 markers in 23 linkage groups spanning 2008.87 cM, at an average inter-marker distance of 1.67 cM with a minimum logarithm of odds of 6 and maximum recombination fraction of 0.40. Comparisons of the organization the green ash map with the genomes of asterid species coffee and tomato, and genomes of the rosid species poplar and peach, showed areas of conserved gene order, with overall synteny strongest with coffee.
Design and Synthesis of C-2 Substituted Chiral Imidazolium Ionic Liquids from Amino Acid Derivatives

A series of novel chiral ionic liquids (CILs) has been synthesized and fully characterized. The reaction of 1-methyl-2-imidazolecarboxaldehyde and chiral amino alcohols followed by reduction is key...
Accoutrements of a molecular computer: switches, memory components and alligator clips

Abstract Several second generation memory components consisting of oligo(phenylene ethynylene)s containing easily reducible functionalities consisting of either nitro or quinone cores have been synthesized for incorporation into molecular electronic devices. Additionally, two new types of contacts between organic compounds and a metal surface based on diazonium salts or pyridine have been synthesized and integrated into molecules for use in molecular electronic devices.
Ca2+-activation kinetics modulate successive puff/spark amplitude, duration and inter-event-interval correlations in a Langevin model of stochastic Ca2+ release

Abstract Through theoretical analysis of the statistics of stochastic calcium (Ca 2+ ) release (i.e., the amplitude, duration and inter-event interval of simulated Ca 2+ puffs and sparks), we show that a Langevin description of the collective gating of Ca 2+ channels may be a good approximation to the corresponding Markov chain model when the number of Ca 2+ channels per Ca 2+ release unit (CaRU) is in the physiological range. The Langevin description of stochastic Ca 2+ release facilitates our investigation of correlations between successive puff/spark amplitudes, durations and inter-spark intervals, and how such puff/spark statistics depend on the number of channels per release site and the kinetics of Ca 2+ -mediated inactivation of open channels. When Ca 2+ inactivation/de-inactivation rates are intermediate—i.e., the termination of Ca 2+ puff/sparks is caused by an increase in the number of inactivated channels—the correlation between successive puff/spark amplitudes is negative, while the correlations between puff/spark amplitudes and the duration of the preceding or subsequent inter-spark interval are positive. These correlations are significantly reduced or change signs when inactivation/de-inactivation rates are extreme (slow or fast) and puff/sparks terminate via stochastic attrition.
Spin and Reaction Dynamics in Flexible Polymethylene Biradicals As Studied by EPR, NMR, and Optical Spectroscopy and Magnetic Field Effects. Measurements and Mechanisms of Scalar Electron Spin-Spin Coupling

A series of spectroscopic measurements on flexible polymethylene biradicals in liquid solution is reported. Time resolved electron paramagnetic resonance, chemically induced nuclear spin polarization, transient optical absorption, and external magnetic field effects on lifetimes are used to unravel the kinetics of two types of biradical, acyl-alkyl and bisalkyl, obtained from the photoinduced Norrish type I α-cleavage reaction of permethylated cycloalkanones
The pixyl (Px) group: A novel photocleavable protecting group for primary alcohols

Abstract The 9-phenylxanthyl (pixyl or Px) moiety has been investigated as a potential photocleavable protecting group for primary alcohols. Alcohols 2a-e were protected in good yield by treatment with 9-chloro-9-phenylxanthene in dry pyridine at room temperature. Irradiation of the alcohol derivatives in neutral aqueous acetonitrile regenerates 2a-e and the 9-phenylxanthyl alcohol.
How multilingual is Multilingual BERT

In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs.
Single step quantum search using problem structure

The structure of satisfiability problems is used to improve search algorithms for quantum computers and to reduce their required coherence times by using only a single coherent evaluation of problem properties. The structure of random k-SAT allows the determination of the asymptotic average behavior of these algorithms, showing they improve on the quantum algorithms, such as amplitude amplification, that ignore detailed problem structure but remain exponential for hard problem instances. Compared to good classical methods, the algorithm performs better, on average, for weakly and highly constrained problems, but worse for hard cases. The analytic techniques introduced here apply also to other quantum algorithms, supplementing the limited evaluation possible with classical simulations, and showing how quantum computing can use the ensemble properties of NP search problems.
Synthesis of α-alkyl-α-aminosilanes by rhodium-catalyzed hydrosilylation of Boc-protected vinyl amines

Abstract α-Aminosilanes can be readily prepared by rhodium-catalyzed hydrosilylation of Boc-substituted enamines, followed by acidic removal of the Boc group. This convergent approach allows for the rapid synthesis of new aminosilane structures, useful for their bioactivity and unique reactivity.
Macromolecular ligands for homogeneous catalysts

Strategies to use macromolecular-metal complexes in homogeneous catalysis are discussed and summarized. General advantages, problems and future opportunities in this broad research area are discussed.
Broadening the Aldolase Catalytic Antibody Repertoire by Combining Reactive Immunization and Transition State Theory: New Enantio‐ and Diastereoselectivities

Nine efficient aldolase antibodies were generated by using hapten 1. This hapten unites reactive immunization and the transition state analogue approach in a single molecule. Characterization of two of these antibodies reveals that they are highly proficient (up to 1000-fold better than any other antibody catalyst) and enantioselective catalysts for aldol and retro-aldol reactions and exhibit enantio- and diastereoselectivities opposite to that of antibody 38C2.
Factorization of Asplund operators

Abstract We give necessary and sufficient conditions for an operator A : X → Y on a Banach space having a shrinking FDD to factor through a Banach space Z such that the Szlenk index of Z is equal to the Szlenk index of A. We also prove that for every ordinal ξ ∈ ( 0 , ω 1 ) ∖ { ω η : η ω 1 a limit ordinal } , there exists a Banach space G ξ having a shrinking basis and Szlenk index ω ξ such that for any separable Banach space X and any operator A : X → Y having Szlenk index less than ω ξ , A factors through a subspace and through a quotient of G ξ , and if X has a shrinking FDD, A factors through G ξ .
WASSERSTEIN METRIC-DRIVEN BAYESIAN INVERSION WITH APPLICATIONS TO SIGNAL PROCESSING

We present a Bayesian framework based on a new exponential likelihood function driven by the quadratic Wasserstien metric. Compared to conventional Bayesian models based on Gaussian likelihood functions driven by the least-squares norm ($L_2$ norm), the new framework features several advantages. First, the new framework does not rely on the likelihood of the measurement noise and hence can treat complicated noise structures such as combined additive and multiplicative noise. Secondly, unlike the normal likelihood function, the Wasserstein-based exponential likelihood function does not usually generate multiple local extrema. As a result, the new framework features better convergence to correct posteriors when a Markov Chain Monte Carlo sampling algorithm is employed. Thirdly, in the particular case of signal processing problems, while a normal likelihood function measures only the amplitude differences between the observed and simulated signals, the new likelihood function can capture both the amplitude and the phase differences. We apply the new framework to a class of signal processing problems, that is, the inverse uncertainty quantification of waveforms, and demonstrate its advantages compared to Bayesian models with normal likelihood functions.
Towards Synthesizing Complex Programs From Input-Output Examples

In recent years, deep learning techniques have been developed to improve the performance of program synthesis from input-output examples. Albeit its significant progress, the programs that can be synthesized by state-of-the-art approaches are still simple in terms of their complexity. In this work, we move a significant step forward along this direction by proposing a new class of challenging tasks in the domain of program synthesis from input-output examples: learning a context-free parser from pairs of input programs and their parse trees. We show that this class of tasks are much more challenging than previously studied tasks, and the test accuracy of existing approaches is almost 0%. We tackle the challenges by developing three novel techniques inspired by three novel observations, which reveal the key ingredients of using deep learning to synthesize a complex program. First, the use of a non-differentiable machine is the key to effectively restrict the search space. Thus our proposed approach learns a neural program operating a domain-specific non-differentiable machine. Second, recursion is the key to achieve generalizability. Thus, we bake-in the notion of recursion in the design of our non-differentiable machine. Third, reinforcement learning is the key to learn how to operate the non-differentiable machine, but it is also hard to train the model effectively with existing reinforcement learning algorithms from a cold boot. We develop a novel two-phase reinforcement learning-based search algorithm to overcome this issue. In our evaluation, we show that using our novel approach, neural parsing programs can be learned to achieve 100% test accuracy on test inputs that are 500x longer than the training samples.
Peak-to-average power ratio of good codes for Gaussian channel

Consider a problem of forward error-correction for the additive white Gaussian noise (AWGN) channel. For finite blocklength codes, the backoff from the channel capacity is inversely proportional to the square root of the blocklength. In this paper, it is shown that the codes achieving this tradeoff must necessarily have peak-to-average power ratio (PAPR) proportional to logarithm of the blocklength. This is extended to codes approaching capacity slower, and to PAPR measured at the output of an orthogonal frequency division multiplex- ing modulator. As a by-product, the convergence of (Smith's) amplitude-constrained AWGN capacity to Shannon's classical formula is characterized in the regime of large amplitudes. This converse-type result builds upon recent contributions in the study of empirical output distributions of good channel codes.
Non-unitary set-theoretical solutions to the Quantum Yang-Baxter Equation

We develop a theory of non-unitary set-theoretical solutions to the Quantum Yang-Baxter equation. Our results generalize those obtained by Etingof, Schedler and the author. We remark that some of our constructions are similar to constructions obtained by Lu, Yan and Zhu.
Layer Potentials and Boundary-Value Problems for Second Order Elliptic Operators With Data in Besov Spaces

This monograph presents a comprehensive treatment of second order divergence form elliptic operators with bounded measurable t-independent coefficients in spaces of fractional smoothness, in Besov and weighted L classes. We establish: (1) Mapping properties for the double and single layer potentials, as well as the Newton potential; (2) Extrapolation-type solvability results: the fact that solvability of the Dirichlet or Neumann boundary value problem at any given L space automatically assures their solvability in an extended range of Besov spaces; (3) Well-posedness for the non-homogeneous boundary value problems. In particular, we prove well-posedness of the non-homogeneous Dirichlet problem with data in Besov spaces for operators with real, not necessarily symmetric coefficients. 2010 Mathematics Subject Classification. Primary 35J25, Secondary 31B20, 35C15, 46E35.
Admissibility in Partial Conjunction Testing

Meta-analysis combines results from multiple studies aiming to increase power in finding their common effect. It would typically reject the null hypothesis of no effect if any one of the studies sh...
Top-down and Bottom-up Evaluation Procedurally Integrated

This paper describes how the Logic Programming System XSB combines top-down and bottom-up computation through the mechanisms of variant tabling and subsumptive tabling with abstraction, respectively.It is well known that top-down evaluation of logical rules in Prolog has a procedural interpretation as recursive procedure invocation (Kowalski 1986). Tabling adds the intuition of short-circuiting redundant computations (Warren 1992). This paper shows how to introduce into tabled logic program evaluation a bottom-up component, whose procedural intuition is the initialization of a data structure, in which a relation is initially computed and filled, on first demand, and then used throughout the remainder of a larger computation for efficient lookup. This allows many Prolog programs to be expressed fully declaratively, programs which formerly required procedural features, such as assert, to be made efficient.
Clustering with Noisy Queries

In this paper, we provide a rigorous theoretical study of clustering with noisy queries. Given a set of $n$ elements, our goal is to recover the true clustering by asking minimum number of pairwise queries to an oracle. Oracle can answer queries of the form ``do elements $u$ and $v$ belong to the same cluster?''-the queries can be asked interactively (adaptive queries), or non-adaptively up-front, but its answer can be erroneous with probability $p$. In this paper, we provide the first information theoretic lower bound on the number of queries for clustering with noisy oracle in both situations. We design novel algorithms that closely match this query complexity lower bound, even when the number of clusters is unknown. Moreover, we design computationally efficient algorithms both for the adaptive and non-adaptive settings. The problem captures/generalizes multiple application scenarios. It is directly motivated by the growing body of work that use crowdsourcing for {\em entity resolution}, a fundamental and challenging data mining task aimed to identify all records in a database referring to the same entity. Here crowd represents the noisy oracle, and the number of queries directly relates to the cost of crowdsourcing. Another application comes from the problem of sign edge prediction in social network, where social interactions can be both positive and negative, and one must identify the sign of all pair-wise interactions by querying a few pairs. Furthermore, clustering with noisy oracle is intimately connected to correlation clustering, leading to improvement therein. Finally, it introduces a new direction of study in the popular stochastic block model where one has an incomplete stochastic block model matrix to recover the clusters.
Symmetric Pairs and Self-Adjoint Extensions of Operators, with Applications to Energy Networks

We provide a streamlined construction of the Friedrichs extension of a densely-defined self-adjoint and semibounded operator A on a Hilbert space $$\mathcal {H}$$ , by means of a symmetric pair of operators. A symmetric pair is comprised of densely defined operators $$J: \mathcal {H}_1 \rightarrow \mathcal {H}_2$$ and $$K: \mathcal {H}_2 \rightarrow \mathcal {H}_1$$ which are compatible in a certain sense. With the appropriate definitions of $$\mathcal {H}_1$$ and J in terms of A and $$\mathcal {H}$$ , we show that $$(\textit{JJ}^\star )^{-1}$$ is the Friedrichs extension of A. Furthermore, we use related ideas (including the notion of unbounded containment) to construct a generalization of the construction of the Krein extension of A as laid out in a previous paper of the authors. These results are applied to the study of the graph Laplacian on infinite networks, in relation to the Hilbert spaces $$\ell ^2(G)$$ and $$\mathcal {H}_{\mathcal {E}}$$ (the energy space).
Event Representations for Automated Story Generation with Deep Neural Nets

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.
Topological recursion for open intersection numbers

We present a topological recursion formula for calculating the intersection numbers defined on the moduli space of open Riemann surfaces. The spectral curve is $x = \frac{1}{2}y^2$, the same as spectral curve used to calculate intersection numbers for closed Riemann surfaces, but the formula itself is a variation of the usual Eynard-Orantin recursion. It looks like the recursion formula used for spectral curves of degree 3, and also includes features present in $\beta$-deformed models. The recursion formula suggests a conjectural refinement to the generating function that allows for distinguishing intersection numbers on moduli spaces with different numbers of boundary components.
Optimal Quantum Circuits for Nearest-Neighbor Architectures

We show that the depth of quantum circuits in the realistic architecture where a classical controller determines which local interactions to apply on the kD grid Z^k where k >= 2 is the same (up to a constant factor) as in the standard model where arbitrary interactions are allowed. This allows minimum-depth circuits (up to a constant factor) for the nearest-neighbor architecture to be obtained from minimum-depth circuits in the standard abstract model. Our work therefore justifies the standard assumption that interactions can be performed between arbitrary pairs of qubits. In particular, our results imply that Shor's algorithm, controlled operations and fanouts can be implemented in constant depth, polynomial size and polynomial width in this architecture. We also present optimal non-adaptive quantum circuits for controlled operations and fanouts on a kD grid. These circuits have depth Theta(sqrt[k](n)), size Theta(n) and width Theta(n). Our lower bound also applies to a more general class of operations.
WHERE ARE THE BINARY SOURCE GALACTIC MICROLENSING EVENTS

Though there have been some galactic microlensing events which show a clear signature of a binary lens, no event has yet been claimed as due to lensing of a binary source. Here I argue that this may be due to the fact that most of the binary source events show light curves which can be fitted with the simpler model of a blended single source.
Vehicle Routing Problem with Time Windows: A Deterministic Annealing approach

The Vehicle Routing Problem with Time-Windows (VRPTW) is an important problem in allocating resources on networks in time and space. We present in this paper a Deterministic Annealing (DA)-based approach to solving the VRPTW with its aspects of routing and scheduling, as well as to model additional constraints of heterogeneous vehicles and shipments. This is the first time, to our knowledge, that a DA approach has been used for problems in the class of the VRPTW. We describe how the DA approach can be adapted to generate an effective heuristic approach to the VRPTW. Our DA approach is also designed to not get trapped in local minima, and demonstrates less sensitivity to initial solutions. The algorithm trades off routing and scheduling in an n-dimensional space using a tunable parameter that allows us to generate qualitatively good solutions. These solutions differ in the degree of intersection of the routes, making the case for transfer points where shipments can be exchanged. Simulation results on randomly generated instances show that the constraints are respected and demonstrate near optimal results (when verifiable) in terms of schedules and tour length of individual tours in each solution.
The Zarankiewicz problem in 3-partite graphs

Let $F$ be a graph, $k \geq 2$ be an integer, and write $\mathrm{ex}_{ \chi \leq k } (n , F)$ for the maximum number of edges in an $n$-vertex graph that is $k$-partite and has no subgraph isomorphic to $F$. The function $\mathrm{ex}_{ \chi \leq 2} ( n , F)$ has been studied by many researchers. Finding $\mathrm{ex}_{ \chi \leq 2} (n , K_{s,t})$ is a special case of the Zarankiewicz problem. We prove an analogue of the K\"{o}v\'{a}ri-S\'{o}s-Tur\'{a}n Theorem by showing \[ \mathrm{ex}_{ \chi \leq 3} (n , K_{s,t} ) \leq \left( \frac{1}{3} \right)^{1 - 1/s} \left( \frac{ t - 1}{2} + o(1) \right)^{1/s} n^{2 - 1/s} \] for $2 \leq s \leq t$. Using Sidon sets constructed by Bose and Chowla, we prove that this upper bound in asymptotically best possible in the case that $s = 2$ and $t \geq 3$ is odd, i.e., $\mathrm{ex}_{ \chi \leq 3} ( n , K_{2,2t+1} ) = \sqrt{ \frac{t}{3}} n^{3/2} + o(n^{3/2})$ for $t \geq 1$.
Multireference Alignment Is Easier With an Aperiodic Translation Distribution

In the multireference alignment model, a signal is observed by the action of a random circular translation and the addition of Gaussian noise. The goal is to recover the signal’s orbit by accessing multiple independent observations. Of particular interest is the sample complexity, i.e., the number of observations/samples needed in terms of the signal-to-noise ratio (SNR) (the signal energy divided by the noise variance) in order to drive the mean-square error to zero. Previous work showed that if the translations are drawn from the uniform distribution, then, in the low SNR regime, the sample complexity of the problem scales as $\omega (1/ \mathrm {SNR}^{3})$ . In this paper, using a generalization of the Chapman–Robbins bound for orbits and expansions of the $\chi ^{2}$ divergence at low SNR, we show that in the same regime the sample complexity for any aperiodic translation distribution scales as $\omega (1/ \mathrm {SNR}^{2})$ . This rate is achieved by a simple spectral algorithm. We propose two additional algorithms based on non-convex optimization and expectation–maximization. We also draw a connection between the multireference alignment problem and the spiked covariance model.
Rate optimal binary linear locally repairable codes with small availability

A locally repairable code with availability has the property that every code symbol can be recovered from multiple, disjoint subsets of other symbols of small size. In particular, a code symbol is said to have (r, t)-availability if it can be recovered from t disjoint subsets, each of size at most r. A code with availability is said to be rate optimal, if its rate is maximum among the class of codes with given locality, availability, and alphabet size. This paper focuses on rate-optimal binary, linear codes with small availability, and makes three contributions. First, it establishes tight upper bounds on the rate of binary linear codes with (r, 2) and (2, 3) availability. Second, it establishes a uniqueness result for binary rate-optimal codes, showing that for certain classes of binary linear codes with (r, 2) and (2, 3)-availability, any rate-optimal code must be a direct sum of shorter rate-optimal codes. Finally, it presents a class of locally repairable codes associated with convex polyhedra, especially, focusing on the codes associated with the Platonic solids. It demonstrates that these codes are locally repairable with t = 2, and that the codes associated with (geometric) dual polyhedra are (coding theoretic) duals of each other.
Cross-View Image Synthesis Using Conditional GANs

Learning to generate natural scenes has always been a challenging task in computer vision. It is even more painstaking when the generation is conditioned on images with drastically different views. This is mainly because understanding, corresponding, and transforming appearance and semantic information across the views is not trivial. In this paper, we attempt to solve the novel problem of cross-view image synthesis, aerial to street-view and vice versa, using conditional generative adversarial networks (cGAN). Two new architectures called Crossview Fork (X-Fork) and Crossview Sequential (X-Seq) are proposed to generate scenes with resolutions of 64A—64 and 256A—256 pixels. X-Fork architecture has a single discriminator and a single generator. The generator hallucinates both the image and its semantic segmentation in the target view. X-Seq architecture utilizes two cGANs. The first one generates the target image which is subsequently fed to the second cGAN for generating its corresponding semantic segmentation map. The feedback from the second cGAN helps the first cGAN generate sharper images. Both of our proposed architectures learn to generate natural images as well as their semantic segmentation maps. The proposed methods show that they are able to capture and maintain the true semantics of objects in source and target views better than the traditional image-to-image translation method which considers only the visual appearance of the scene. Extensive qualitative and quantitative evaluations support the effectiveness of our frameworks, compared to two state of the art methods, for natural scene generation across drastically different views.
Random forest ensemble of support vector regression models for solar power forecasting

To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of meteorological data from several sources. In this study though, the present and the past solar power forecasts from several models, as well as the associated meteorological data, are incorporated into the random forest to combine and improve the accuracy of the day-ahead solar power forecasts. The performance of the combined model is evaluated over the entire year and compared with other combining techniques.
Collapsing with a lower bound on the curvature operator

Cheeger and Gromov showed that F-structures are related to collapse with a double-sided curvature bound. We define fibered F-structures and extend some of the Cheeger–Gromov results to the setting of collapse with a lower bound on the curvature operator.
Can we gain more from orthogonality regularizations in training deep CNNs

This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enforce it in more effective and easy-to-use ways? We develop novel orthogonality regularizations on training deep CNNs, utilizing various advanced analytical tools such as mutual coherence and restricted isometry property. These plug-and-play regularizations can be conveniently incorporated into training almost any CNN without extra hassle. We then benchmark their effects on state-of-the-art models: ResNet, WideResNet, and ResNeXt, on several most popular computer vision datasets: CIFAR-10, CIFAR-100, SVHN and ImageNet. We observe consistent performance gains after applying those proposed regularizations, in terms of both the final accuracies achieved, and faster and more stable convergences. We have made our codes and pre-trained models publicly available.
Slow wave phenomena in photonic crystals

Slow light in photonic crystals and other periodic structures is associated with stationary points of the photonic dispersion relation, where the group velocity of light vanishes. It is shown that in certain cases, the vanishing group velocity is accompanied by the so-called frozen mode regime, when the incident light can be completely converted into the slow mode with huge diverging amplitude. The frozen mode regime is a qualitatively new wave phenomenon – it does not reduce to any known electromagnetic resonance. Formally, the frozen mode regime is not a resonance, in a sense that it is not particularly sensitive to the size and shape of the photonic crystal. The frozen mode regime is more robust and powerful, compared to any known slow-wave resonance. It has much higher tolerance to absorption and structural imperfections.
Opinion Dynamics in Networks: Convergence, Stability and Lack of Explosion

Inspired by the work of Kempe et al. [Kempe, Kleinberg, Oren, Slivkins, EC 2013], we introduce and analyze a model on opinion formation; the update rule of our dynamics is a simplified version of that of [Kempe, Kleinberg, Oren, Slivkins, EC 2013]. We assume that the population is partitioned into types whose interaction pattern is specified by a graph. Interaction leads to population mass moving from types of smaller mass to those of bigger mass. We show that starting uniformly at random over all population vectors on the simplex, our dynamics converges point-wise with probability one to an independent set. This settles an open problem of [Kempe, Kleinberg, Oren, Slivkins, EC 2013], as applicable to our dynamics. We believe that our techniques can be used to settle the open problem for the Kempe et al. dynamics as well. Next, we extend the model of Kempe et al. by introducing the notion of birth and death of types, with the interaction graph evolving appropriately. Birth of types is determined by a Bernoulli process and types die when their population mass is less than epsilon (a parameter). We show that if the births are infrequent, then there are long periods of "stability" in which there is no population mass that moves. Finally we show that even if births are frequent and "stability" is not attained, the total number of types does not explode: it remains logarithmic in 1/epsilon.
On some applications of unstable Adams operations to the topology of Kac-Moody groups

Localized at almost all primes, we describe the structure of differentials in several important spectral sequences that compute the cohomology of classifying spaces of topological Kac-Moody groups. In particular, we show that for all but a finite set of primes, these spectral sequences collapse and that there are no additive extension problems. We also describe some appealing consequences of these results. The main tool is the use of the naturality properties of unstable Adams operations on these classifying spaces.
An integral quadratic constraint framework for real-time steady-state optimization of linear time-invariant systems

Achieving optimal steady-state performance in real-time is an increasingly necessary requirement of many critical infrastructure systems. In pursuit of this goal, this paper builds a systematic design framework of feedback controllers for Linear Time-Invariant (LTI) systems that continuously track the optimal solution of some predefined optimization problem. We logically divide the proposed solution into three components. The first component estimates the system state from the output measurements. The second component uses the estimated state and computes a drift direction based on an optimization algorithm. The third component calculates an input to the LTI system that aims to drive the system toward the optimal steady-state. We analyze the equilibrium characteristics of the closed-loop system and provide conditions for optimality and stability. Our analysis shows that the proposed solution guarantees optimal steady-state performance, even in the presence of constant disturbances. Furthermore, by leveraging recent results on the analysis of optimization algorithms using Integral Quadratic Constraints (IQCs), the proposed framework can translate input-output properties of our optimization component into sufficient conditions, based on linear matrix inequalities (LMIs), for global exponential asymptotic stability of the closed-loop system. We illustrate several resulting controller designs using a numerical example.
Blind Deconvolutional Phase Retrieval via Convex Programming

We consider the task of recovering two real or complex $m$-vectors from phaseless Fourier measurements of their circular convolution. Our method is a novel convex relaxation that is based on a lifted matrix recovery formulation that allows a nontrivial convex relaxation of the bilinear measurements from convolution. We prove that if the two signals belong to known random subspaces of dimensions $k$ and $n$, then they can be recovered up to the inherent scaling ambiguity with $m >> (k+n) \log^2 m$ phaseless measurements. Our method provides the first theoretical recovery guarantee for this problem by a computationally efficient algorithm and does not require a solution estimate to be computed for initialization. Our proof is based Rademacher complexity estimates. Additionally, we provide an ADMM implementation of the method and provide numerical experiments that verify the theory.
On reduced stable pair invariants

Let $$X = S \times E$$ be the product of a K3 surface S and an elliptic curve E. Reduced stable pair invariants of X can be defined via (1) cutting down the reduced virtual class with incidence conditions or (2) the Behrend function weighted Euler characteristic of the quotient of the moduli space by the translation action of E. We show that (2) arises naturally as the degree of a virtual class, and that the invariants (1) and (2) agree. This has applications to deformation invariance, rationality and a DT/PT correspondence for reduced invariants of $$S \times E$$ .
Motivic integral of K3 surfaces over a non-archimedean field

Abstract We prove a formula expressing the motivic integral (Loeser and Sebag, 2003) [34] of a K3 surface over C ( ( t ) ) with semi-stable reduction in terms of the associated limit mixed Hodge structure. Secondly, for every smooth variety over a complete discrete valuation field we define an analogue of the monodromy pairing, constructed by Grothendieck in the case of abelian varieties, and prove that our monodromy pairing is a birational invariant of the variety. Finally, we propose a conjectural formula for the motivic integral of maximally degenerate K3 surfaces over an arbitrary complete discrete valuation field and prove this conjecture for Kummer K3 surfaces.
Inference of Fine-Grained Event Causality from Blogs and Films.

Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80% of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls

We propose a rank-k variant of the classical Frank-Wolfe algorithm to solve convex optimization over a trace-norm ball. Our algorithm replaces the top singular-vector computation (1-SVD) in Frank-Wolfe with a top-k singular-vector computation (k-SVD), which can be done by repeatedly applying 1-SVD k times. Alternatively, our algorithm can be viewed as a rank-k restricted version of projected gradient descent. We show that our algorithm has a linear convergence rate when the objective function is smooth and strongly convex, and the optimal solution has rank at most k. This improves the convergence rate and the total time complexity of the Frank-Wolfe method and its variants.
Some extensions of Hilbert–Kunz multiplicity

Let R be an excellent Noetherian ring of prime characteristic. Consider an arbitrary nested pair of ideals (or more generally, a nested pair of submodules of a fixed finite module). We do not assume that their quotient has finite length. In this paper, we develop various sufficient numerical criteria for when the tight closures of these ideals (or submodules) match. For some of the criteria we only prove sufficiency, while some are shown to be equivalent to the tight closures matching. We compare the various numerical measures (in some cases demonstrating that the different measures give truly different numerical results) and explore special cases where equivalence with matching tight closure can be shown. All of our measures derive ultimately from Hilbert–Kunz multiplicity.
A new perspective on boosting in linear regression via subgradient optimization and relatives

We analyze boosting algorithms [Ann. Statist. 29 (2001) 1189–1232; Ann. Statist. 28 (2000) 337–407; Ann. Statist. 32 (2004) 407–499] in linear regression from a new perspective: that of modern first-order methods in convex optimization. We show that classic boosting algorithms in linear regression, namely the incremental forward stagewise algorithm ($\text{FS}_{\varepsilon}$) and least squares boosting [LS-BOOST$(\varepsilon)$], can be viewed as subgradient descent to minimize the loss function defined as the maximum absolute correlation between the features and residuals. We also propose a minor modification of $\text{FS}_{\varepsilon}$ that yields an algorithm for the LASSO, and that may be easily extended to an algorithm that computes the LASSO path for different values of the regularization parameter. Furthermore, we show that these new algorithms for the LASSO may also be interpreted as the same master algorithm (subgradient descent), applied to a regularized version of the maximum absolute correlation loss function. We derive novel, comprehensive computational guarantees for several boosting algorithms in linear regression (including LS-BOOST$(\varepsilon)$ and $\text{FS}_{\varepsilon}$) by using techniques of first-order methods in convex optimization. Our computational guarantees inform us about the statistical properties of boosting algorithms. In particular, they provide, for the first time, a precise theoretical description of the amount of data-fidelity and regularization imparted by running a boosting algorithm with a prespecified learning rate for a fixed but arbitrary number of iterations, for any dataset.
When Effective Field Theories Fail

In this talk, I describe and defend four non-standard claims about four effective field theories, and try to extract some lessons about the limits of effective field theory. The four theses (and a capsule diagnosis given in parentheses) are: 1) Kaon loops are not a reliable part of chiral perturbation theory (dimensional regularization does not know about the chiral scale), 2) Regge physics is inappropriately missing from SCET (an infinite set of scales are needed) 3) There is likely a barrier in the use of EFT in general relativity in the extreme infrared (curvature effects build up) and 4) Gauge non-invariant operators should be included in describing physics beyond the Standard Model (as they could probe the idea of emergent gauge symmetry and falsify string theory).
NONCOMMUTATIVE MIXMASTER COSMOLOGIES

In this paper we investigate a variant of the classical mixmaster universe model of anisotropic cosmology, where the spatial sections are noncommutative 3-tori. We consider ways in which the discrete dynamical system describing the mixmaster dynamics can be extended to act on the noncommutative torus moduli, and how the resulting dynamics differs from the classical one, for example, in the appearance of exotic smooth structures. We discuss properties of the spectral action, focussing on how the slow-roll inflation potential determined by the spectral action affects the mixmaster dynamics. We relate the model to other recent results on spectral action computation and we identify other physical contexts in which this model may be relevant.
Randomized Sketches of Convex Programs With Sharp Guarantees

Random projection (RP) is a classical technique for reducing storage and computational costs. We analyze RP-based approximations of convex programs, in which the original optimization problem is approximated by solving a lower dimensional problem. Such dimensionality reduction is essential in computation-limited settings, since the complexity of general convex programming can be quite high (e.g., cubic for quadratic programs, and substantially higher for semidefinite programs). In addition to computational savings, RP is also useful for reducing memory usage, and has useful properties for privacy-preserving optimization. We prove that the approximation ratio of this procedure can be bounded in terms of the geometry of the constraint set. For a broad class of RPs, including those based on various sub-Gaussian distributions as well as randomized Hadamard and Fourier transforms, the data matrix defining the cost function can be projected to a dimension proportional to the squared Gaussian width of the tangent cone of the constraint set at the original solution. This effective dimension of the convex program is often substantially smaller than the original dimension. We illustrate consequences of our theory for various cases, including unconstrained and $\ell _{1}$ -constrained least squares, support vector machines, low-rank matrix estimation, and discuss implications for privacy-preserving optimization, as well as connections with denoising and compressed sensing.
A Web-scale system for scientific knowledge exploration

To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure. In this work, we present a large-scale system to (1) identify hundreds of thousands of scientific concepts, (2) tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and (3) build a six-level concept hierarchy with a subsumption-based model. The system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships.
On the Cauchy problem for integro-differential operators in Sobolev classes and the martingale problem

Abstract The existence and uniqueness in Sobolev spaces of solutions of the Cauchy problem to parabolic integro-differential equation with variable coefficients of the order α ∈ ( 0 , 2 ) is investigated. The principal part of the operator has kernel m ( t , x , y ) / | y | d + α with a bounded nondegenerate m, Holder in x and measurable in y. The lower order part has bounded and measurable coefficients. The result is applied to prove the existence and uniqueness of the corresponding martingale problem.
On the finite generation of relative cohomology for Lie superalgebras

The author establishes finite-generation of the cohomology ring of a classical Lie superalgebra relative to an even subsuperalgebra. A spectral sequence is constructed to provide conditions for when this relative cohomology ring is Cohen-Macaulay. With finite generation established, support varieties for modules are defined via the relative cohomology, which generalize those of [BKN-1]