cssi/SciSciGPT-SciSciNet huggingface dataset:
De novo origins of multicellularity in response to predation

The transition from unicellular to multicellular life was one of a few major events in the history of life that created new opportunities for more complex biological systems to evolve. Predation is hypothesized as one selective pressure that may have driven the evolution of multicellularity. Here we show that de novo origins of simple multicellularity can evolve in response to predation. We subjected outcrossed populations of the unicellular green alga Chlamydomonas reinhardtii to selection by the filter-feeding predator Paramecium tetraurelia. Two of five experimental populations evolved multicellular structures not observed in unselected control populations within ~750 asexual generations. Considerable variation exists in the evolved multicellular life cycles, with both cell number and propagule size varying among isolates. Survival assays show that evolved multicellular traits provide effective protection against predation. These results support the hypothesis that selection imposed by predators may have played a role in some origins of multicellularity.
Synthesis of a new class of conformationally rigid phosphino-oxazolines: highly enantioselective ligands for Ir-catalyzed asymmetric hydrogenation.

A new class of conformationally rigid phosphino-oxazoline ligands were synthesized from readily available enantiopure phenyl glycinol. Ir complexes with these ligands are air-stable and highly enan...
The first transition metal–ligand complex-catalyzed regioselective and stereoselective aminohalogenation of cinnamic esters

Abstract Transition metal–ligand complex-catalyzed regio- and stereoselective aminohalogenation of cinnamic esters has been developed using p-TsNCl2 as the nitrogen and chlorine sources. Dichloro-(1,10-phenanthroline)-palladium (II) as the catalyst can be handled very conveniently due to its property of less hygroscopic as compared with other aminohalogenation catalysts. The reaction is suggested to proceed through the mechanism involving N-tosyl N-chloro aziridinium intermediate. Eight examples are presented with modest to good yields (56–82%) and excellent stereoselectivity (>10:1).
Novel heterocyclic systems. Synthesis of 2,7-dimethyl-10-oxa-1,8-diaza-anthracen-9-one and derivatives

Synthesis of novel heterocycles, which contain the unique 10-oxa-1,8-diazaanthracen-9-one tricyclic core, is reported. The core structure was assembled via a dehydrative-cyclization strategy.
Synthesis of 2-guanyl-4-(3-substituted-phenyl)thiazoles with potent histamine H2-antagonism activity

The syntheses of novel 2-guanyl-4-(3-substituted-phenyl)thiazoles showing potent histamine H 2 -antagonism activity is reported. The most potent compound is 17 where the substitution at the 3-position of the phenyl ring is an N-methylamidino function
A Gene for Genetic Background in Zea mays: Fine-Mapping enhancer of teosinte branched1.2 to a YABBY Class Transcription Factor

The effects of an allelic substitution at a gene often depend critically on genetic background, i.e., the genotypes at other genes in the genome. During the domestication of maize from its wild ancestor (teosinte), an allelic substitution at teosinte branched (tb1) caused changes in both plant and ear architecture. The effects of tb1 on phenotype were shown to depend on multiple background loci, including one called enhancer of tb1.2 (etb1.2). We mapped etb1.2 to a YABBY class transcription factor (ZmYAB2.1) and showed that the maize alleles of ZmYAB2.1 are either expressed at a lower level than teosinte alleles or disrupted by insertions in the sequences. tb1 and etb1.2 interact epistatically to control the length of internodes within the maize ear, which affects how densely the kernels are packed on the ear. The interaction effect is also observed at the level of gene expression, with tb1 acting as a repressor of ZmYAB2.1 expression. Curiously, ZmYAB2.1 was previously identified as a candidate gene for another domestication trait in maize, nonshattering ears. Consistent with this proposed role, ZmYAB2.1 is expressed in a narrow band of cells in immature ears that appears to represent a vestigial abscission (shattering) zone. Expression in this band of cells may also underlie the effect on internode elongation. The identification of ZmYAB2.1 as a background factor interacting with tb1 is a first step toward a gene-level understanding of how tb1 and the background within which it works evolved in concert during maize domestication.
Alkaline Earth Metal Oxyhalides Revisited – Syntheses and Crystal Structures of Sr4OBr6, Ba4OBr6 and Ba2OI2

Single crystals of the compounds Ca4OCl6, Sr4OBr6, Ba4OBr6, and Ba2OI2 were obtained by solid-state reactions. The crystals of Ba2OI2 are transparent and colorless and isopointal to K2ZnO2 adopting the orthorhombic space group Ibam (no. 72, Z = 4) with the cell parameters a = 747.20(9), b = 1392.02(18), and c = 678.12(9) pm. Sr4OBr6 and Ba4OBr6 are isotypic to Ba4OCl6 (or isopointal to K6ZnO4) and crystallize in the hexagonal space group P63mc (no. 186, Z = 2) exhibiting the cell parameters a = 982.20(4) and c = 750.41(7) pm for Sr4OBr6 and a = 1030.10(2) and c = 785.92(4) pm for Ba4OBr6. In the ternary systems Ca-O-X (X = Cl, Br or I) the only compound found other than the starting materials was the already known Ca4OCl6 which is also isotypic to Ba4OCl6 crystallizing in the hexagonal space group P63mc (no. 186, Z = 2) with the cell parameters a = 903.30(6) and c = 683.27(8) pm.
Lead as its own luminescent sensor.

Lead (II) in polar organic solvents such as acetone, acetonitrile, and propylenecarbonate with excess bromide generates the highly luminescent lead−halide cluster Pb4Br113-. This in situ sensor does not rely on a host−guest relationship and, thus, is intrinsically selective and sensitive, allowing for the detection of lead at nanomolar concentrations. The cluster's emission maximum and relaxation kinetics are temperature dependent and indicate a close spacing of intralead and intracluster electronic energy levels.
Esterification of carboxylic acids with boron trichloride

Abstract Treatment of carboxylic acids with boron trichloride followed by addition of alcohol provides the carboxylic ester. This esterification, following BCl 3 ether or ester OC cleavage reaction conditions, proceeds cleanly in good yields with most substrates. Cleavage of benzyl esters with boron trichloride then treating with methanol affords the methyl ester.
Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss.

Metabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear. To address these questions, we developed a novel framework, coupling a reductive evolution model with a multi-species genome-scale metabolic model to simulate the evolution of two-species microbial communities. Simulating thousands of independent evolutionary trajectories, we surprisingly found that under certain environmental and evolutionary settings metabolic dependencies emerged frequently even though our model does not include explicit selection for cooperation. Evolved dependencies involved cross-feeding of a diverse set of metabolites, reflecting constraints imposed by metabolic network architecture. We additionally found metabolic ‘missed opportunities’, wherein species failed to capitalize on metabolites made available by their partners. Examining the genes deleted in each evolutionary trajectory and the deletion timing further revealed both genome-wide properties and specific metabolic mechanisms associated with species interaction. Our findings provide insight into the evolution of cooperative interaction among microbial species and a unique view into the way such relationships emerge.
Substituent effects in the conversion of azido- and diazidotrinitrobenzenes to benzofuroxans and difuroxans

The preparation of a number of substituted dinitrobenxofurazan oxides (dinitrobenxofuroxans) and nitrobenzobis (oxadiozoles) (nitrobenzodifuroxans), in which the substituents were amino, fluoro, formamido, hydroxy, and ureido groups, by thermolysis of azido precursors is described. The ease of thermolysis appears to be an inverse function of the angle of twist of the nitro groups ortho to the azido group from the plane of the benzene ring. Amino- and hydroxy-substituted dinitrobenzofuroxans and nitrobenzodifuroxans were found to be much more stable than their non-nitro-substituted counterparts
Novel Heterocycloaddition Reaction of Glycals

Diacyl thione species 1 has been generated and reacted in situ with both pyranoid and furanoid glycals to form novel [4 + 2] cycloadducts. Factors such as protecting groups and configuration of substituents in the glycals along with medium effects were varied to discover influences on face selectivity and reactivity. A qualitative correlation of reactivity with the HOMO−LUMO gap between the glycal (HOMO) and the heterodienic species (LUMO) is observed. In one example, the isolation of byproducts suggests that the cycloaddition may in fact be stepwise.
Parity Reversion in Real Exchange Rates: Fast, Slow, or Not at All?

This paper tests for purchasing power parity (PPP) using real effective exchange rate data for 90 developed and developing countries in the post-Bretton Woods period. Support for PPP is found, since the majority of countries experience finite deviations of real exchange rates from parity. The speed of parity reversion is found to be typically much faster for developed countries than for developing countries and to be considerably faster for countries with flexible nominal exchange rate regimes compared with countries having fixed nominal exchange rate regimes. [JEL C22, F31, F41]
Measuring the Importance of User-Generated Content to Search Engines

Search engines are some of the most popular and profitable intelligent technologies in existence. Recent research, however, has suggested that search engines may be surprisingly dependent on user-created content like Wikipedia articles to address user information needs. In this paper, we perform a rigorous audit of the extent to which Google leverages Wikipedia and other user-generated content to respond to queries. Analyzing results for six types of important queries (e.g. most popular, trending, expensive advertising), we observe that Wikipedia appears in over 80% of results pages for some query types and is by far the most prevalent individual content source across all query types. More generally, our results provide empirical information to inform a nascent but rapidly-growing debate surrounding a highlyconsequential question: Do users provide enough value to intelligent technologies that they should receive more of the economic benefits from intelligent technologies?
De-Anonymization of Dynamic Online Social Networks via Persistent Structures

Service providers of Online Social Networks (OSNs) periodically publish anonymized OSN data, which creates an opportunity for adversaries to de-anonymize the data and identify target users. Most commonly, these adversaries use de-anonymization mechanisms that focus on static graphs. Some mechanisms separate dynamic OSN data into slices of static graphs, in order to apply a traditional de-anonymization attack. However, these mechanisms do not account for the evolution of OSNs, which limits their attack performance. In this paper, we provide a novel angle, persistent homology, to capture the evolution of OSNs. Persistent homology barcodes show the birth time and death time of holes, i.e., polygons, in OSN graphs. After extracting the evolution of holes, we apply a two-phase de-anonymization attack. First, holes are mapped together according to the similarity of birth&#x002F;death time. Second, already mapped holes are converted into super nodes and we view them as seed nodes. We then grow the mapping based on these seed nodes. Our de-anonymization mechanism is extremely compatible to the adversaries who suffer latency in relationship collection, which is very similar to real-world cases.
An Index Formula in Connection with Meromorphic Approximation

Let \(\Phi \) be a continuous \(n\times n\) matrix-valued function on the unit circle \(\mathbb T \) such that the \((k-1)\)st singular value of the Hankel operator with symbol \(\Phi \) is greater than the \(k\)th singular value. In this case, it is well-known that \(\Phi \) has a unique superoptimal meromorphic approximant \(Q\) in \(H^{\infty }_{(k)}\); that is, \(Q\) has at most \(k\) poles in the unit disc \(\mathbb D \) (in the sense that the McMillan degree of \(Q\) in \(\mathbb D \) is at most \(k\)) and \(Q\) minimizes the essential suprema of singular values \(s_{j}\left((\Phi -Q)(\zeta )\right)\!, j\ge 0\), with respect to the lexicographic ordering. For each \(j\ge 0\), the essential supremum of \(s_{j}\left((\Phi -Q)(\zeta )\right)\) is called the \(j\)th superoptimal singular value of degree \(k\) of \(\Phi \). We prove that if \(\Phi \) has \(n\) non-zero superoptimal singular values of degree \(k\), then the Toeplitz operator \(T_{\Phi -Q}\) with symbol \(\Phi -Q\) is Fredholm and has index $$ \mathrm{ind}T_{\Phi -Q}=\dim \ker T_{\Phi -Q}=2k+\dim \mathcal E , $$ where \(\mathcal E =\{ \xi \in \ker H_{Q}: \Vert H_{\Phi }\xi \Vert _{2}=\Vert (\Phi -Q)\xi \Vert _{2}\}\) and \(H_{\Phi }\) denotes the Hankel operator with symbol \(\Phi \). This result can in fact be extended from continuous matrix-valued functions to the wider class of \(k\)-admissible matrix-valued functions, i.e. essentially bounded \(n\times n\) matrix-valued functions \(\Phi \) on \(\mathbb T \) for which the essential norm of the Hankel operator \(H_{\Phi }\) is strictly less than the smallest non-zero superoptimal singular value of degree \(k\) of \(\Phi \).
Finding the Force – Consistent Particle Seeding for Satellite Aerodynamics

When calculating satellite trajectories in low-earth orbit, engineers need to adequately estimate aerodynamic forces. But to this day, obtaining the drag acting on the complicated shapes of modern spacecraft suffers from many sources of error. While part of the problem is the uncertain density in the upper atmosphere, this works focuses on improving the modeling of interacting rarified gases and satellite surfaces. The only numerical approach that currently captures effects in this flow regime—like self-shadowing and multiple molecular reflections—is known as test-particle Monte Carlo. This method executes a ray-tracing algorithm to follow particles that pass through a control volume containing the spacecraft and accumulates the momentum transfer to the body surfaces. Statistical fluctuations inherent in the approach demand particle numbers in the order of millions, often making this scheme too costly to be practical. This work presents a parallel test-particle Monte Carlo method that takes advantage of both GPUs and multi-core CPUs. The speed at which this model can run with millions of particles allowed exploring a regime where a flaw in the models initial particle seeding was revealed. Our new model introduces an analytical fix based on seeding the calculation with an initial distribution of particles at the boundary of a spherical control volume and computing the integral for the correct number flux. This work includes verification of the proposed model using analytical solutions for several simple geometries and demonstrates uses for studying aero-stabilization of the Phobos-Grunt Martian probe and pose-estimation for the ICESat mission.
Linguistically-Informed Self-Attention for Semantic Role Labeling

Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.
Real-time scheduling via reinforcement learning

Cyber-physical systems, such as mobile robots, must respond adaptively to dynamic operating conditions. Effective operation of these systems requires that sensing and actuation tasks are performed in a timely manner. Additionally, execution of mission specific tasks such as imaging a room must be balanced against the need to perform more general tasks such as obstacle avoidance. This problem has been addressed by maintaining relative utilization of shared resources among tasks near a user-specified target level. Producing optimal scheduling strategies requires complete prior knowledge of task behavior, which is unlikely to be available in practice. Instead, suitable scheduling strategies must be learned online through interaction with the system. We consider the sample complexity of reinforcement learning in this domain, and demonstrate that while the problem state space is countably infinite, we may leverage the problem's structure to guarantee efficient learning.
An Extremal Effective Survey About Extremal Effective Cycles in Moduli Spaces of Curves

We survey recent developments and open problems about extremal effective divisors and higher codimension cycles in moduli spaces of curves.
An equivalence between high dimensional Bayes optimal inference and M-estimation

Due to the computational difficulty of performing MMSE (minimum mean squared error) inference, maximum a posteriori (MAP) is often used as a surrogate. However, the accuracy of MAP is suboptimal for high dimensional inference, where the number of model parameters is of the same order as the number of samples. In this work we demonstrate how MMSE performance is asymptotically achievable via optimization with an appropriately selected convex penalty and regularization function which are a smoothed version of the widely applied MAP algorithm. Our findings provide a new derivation and interpretation for recent optimal M-estimators discovered by El Karoui, et. al. PNAS 2013 as well as extending to non-additive noise models. We demonstrate the performance of these optimal M-estimators with numerical simulations. Overall, at the heart of our work is the revelation of a remarkable equivalence between two seemingly very different computational problems: namely that of high dimensional Bayesian integration, and high dimensional convex optimization. In essence we show that the former computationally difficult integral may be computed by solving the latter, simpler optimization problem.
Explicit Non-malleable Extractors, Multi-source Extractors, and Almost Optimal Privacy Amplification Protocols

We make progress in the following three problems: 1. Constructing optimal seeded non-malleable extractors, 2. Constructing optimal privacy amplification protocols with an active adversary, for any possible security parameter, 3. Constructing extractors for independent weak random sources, when the min-entropy is extremely small (i.e., near logarithmic). For the first two problems, the best known non-malleable extractors by Chattopadhyay, Goyal and Li, and by Cohen all require seed length and min-entropy with quadratic loss in parameters. As a result, the best known explicit privacy amplification protocols with an active adversary, which achieve two rounds of communication and optimal entropy loss was sub-optimal in the min-entropy of the source. In this paper we give an explicit non-malleable extractor that works for nearly optimal seed length and min-entropy, and yields a two-round privacy amplification protocol with optimal entropy loss for almost all ranges of the security parameter. For the third problem, we improve upon a very recent result by Cohen and Schulman and give an explicit extractor that uses an absolute constant number of sources, each with almost logarithmic min-entropy. The key ingredient in all our constructions is a generalized, and much more efficient version of the independence preserving merger introduced by Cohen, which we call non-malleable independence preserving merger. Our construction of the merger also simplifies that of Cohen and Schulman, and may be of independent interest.
Lower estimates on microstates free entropy dimension

By proving that certain free stochastic differential equations have stationary solutions, we give a lower estimate on the microstates free entropy dimension of certain $n$-tuples $X_{1},...,X_{n}$: we show that Abstract. By proving that certain free stochastic differential equations with analytic coefficients have stationary solutions, we give a lower estimate on the microstates free entropy dimension of certain n-tuples X_{1},...,X_{n}. In particular, we show that \delta_{0}(X_{1},...,X_{n})\geq\dim_{M\bar{\otimes}M^{o}}V where M=W^{*}(X_{1},...,X_{n}) and V=\{(\partial(X_{1}),...,\partial(X_{n})):\partial\in\mathcal{C}\} is the set of values of derivations A=\mathbb{C}[X_{1},... X_{n}]\to A\otimes A with the property that \partial^{*}\partial(A)\subset A. We show that for q sufficiently small (depending on n) and X_{1},...,X_{n} a q-semicircular family, \delta_{0}(X_{1},...,X_{n})>1. In particular, for small q, q-deformed free group factors have no Cartan subalgebras. An essential tool in our analysis is a free analog of an inequality between Wasserstein distance and Fisher information introduced by Otto and Villani (and also studied in the free case by Biane and Voiculescu).
Predictive Cyber-security Analytics Framework: A non-homogenous Markov model for Security Quantification

Numerous security metrics have been proposed in the past for protecting computer networks. However we still lack effective techniques to accurately measure the predictive security risk of an enterprise taking into account the dynamic attributes associated with vulnerabilities that can change over time. In this paper we present a stochastic security framework for obtaining quantitative measures of security using attack graphs. Our model is novel as existing research in attack graph analysis do not consider the temporal aspects associated with the vulnerabilities, such as the availability of exploits and patches which can affect the overall network security based on how the vulnerabilities are interconnected and leveraged to compromise the system. Gaining a better understanding of the relationship between vulnerabilities and their lifecycle events can provide security practitioners a better understanding of their state of security. In order to have a more realistic representation of how the security state of the network would vary over time, a nonhomogeneous model is developed which incorporates a time dependent covariate, namely the vulnerability age. The daily transition-probability matrices are estimated using Frei's Vulnerability Lifecycle model. We also leverage the trusted CVSS metric domain to analyze how the total exploitability and impact measures evolve over a time period for a given network.
Ab initio thermodynamics of intrinsic oxygen vacancies in ceria

Nonstoichiometric ceria(CeO_(2−δ)) is a candidate reaction medium to facilitate two-step water splitting cycles and generate hydrogen. Improving upon its thermodynamic suitability through doping requires an understanding of its vacancy thermodynamics. Using density functional theory (DFT) calculations and cluster expansion-based Monte Carlo simulations, we have studied the high-temperature thermodynamics of intrinsic oxygen vacancies in ceria. The DFT+U approach was used to get the ground state energies of various vacancy configurations in ceria, which were subsequently fit to a cluster expansion Hamiltonian to efficiently model the configurational dependence of energy. The effect of lattice vibrations was incorporated through a temperature-dependent cluster expansion. Lattice Monte Carlo simulations using the cluster expansion Hamiltonian were able to detect the miscibility gap in the phase diagram of ceria. The inclusion of vibrational and electronic entropy effects made the agreement with experiments quantitative. The deviation from an ideal solution model was quantified by calculating as a function of nonstoichiometry, (a) the solid state entropy from Monte Carlo simulations, and (b) Warren-Cowley short range order parameters of various pair clusters.
Indirect Mechanism Design for Efficient and Stable Renewable Energy Aggregation

Mechanism design is studied for aggregating renewable power producers (RPPs) in a two-settlement power market. Employing an indirect mechanism design framework, a payoff allocation mechanism is derived from the competitive equilibrium of an especially formulated market with transferrable payoff. Given the designed mechanism, the strategic behaviors of the participating RPPs entail a non-cooperative game: It is proven that a unique pure Nash equilibrium exists among the RPPs, for which a closed-form expression is found. Moreover, it is proven that the designed mechanism achieves a number of key desirable properties at the NE: these include efficiency (i.e., an ideal “Price of Anarchy” of one), stability (i.e., “in the core” from a coalitional game theoretic perspective), and no collusion. In addition, it is shown that a set of desirable “ex-post” properties are also achieved by the designed mechanism. Extensive simulations are conducted and corroborate the theoretical results.
A high-order radial basis function (RBF) Leray projection method for the solution of the incompressible unsteady Stokes equations

Abstract A new projection method based on radial basis functions (RBFs) is presented for discretizing the incompressible unsteady Stokes equations in irregular geometries. The novelty of the method comes from the application of a new technique for computing the Leray–Helmholtz projection of a vector field using generalized interpolation with divergence-free and curl-free RBFs. Unlike traditional projection methods, this new method enables matching both tangential and normal components of divergence-free vector fields on the domain boundary. This allows incompressibility of the velocity field to be enforced without any time-splitting or pressure boundary conditions. Spatial derivatives are approximated using collocation with global RBFs so that the method only requires samples of the field at (possibly scattered) nodes over the domain. Numerical results are presented demonstrating high-order convergence in both space (between 5th and 6th orders) and time (up to 4th order) for some model problems in two dimensional irregular geometries.
Informational and Causal Architecture of Discrete-Time Renewal Processes

Renewal processes are broadly used to model stochastic behavior consisting of isolated events separated by periods of quiescence, whose durations are specified by a given probability law. Here, we identify the minimal sufficient statistic for their prediction (the set of causal states), calculate the historical memory capacity required to store those states (statistical complexity), delineate what information is predictable (excess entropy), and decompose the entropy of a single measurement into that shared with the past, future, or both. The causal state equivalence relation defines a new subclass of renewal processes with a finite number of causal states despite having an unbounded interevent count distribution. We use the resulting formulae to analyze the output of the parametrized Simple Nonunifilar Source, generated by a simple two-state hidden Markov model, but with an infinite-state machine presentation. All in all, the results lay the groundwork for analyzing more complex processes with infinite statistical complexity and infinite excess entropy.
Stability of the lanczos method for matrix function approximation

Theoretically elegant and ubiquitous in practice, the Lanczos method can approximate f(A)x for any symmetric matrix A ∈ Rn×n, vector x ∈ Rn, and function f. In exact arithmetic, the method's error after k iterations is bounded by the error of the best degree-k polynomial uniformly approximating the scalar function f(x) on the range [λmin(A), λmax(A)]. However, despite decades of work, it has been unclear if this powerful guarantee holds in finite precision. We resolve this problem, proving that when maxx∈[λmin,λmax]|f(x)| ≤ C, Lanczos essentially matches the exact arithmetic guarantee if computations use roughly log(nC|A|) bits of precision. Our proof extends work of Druskin and Knizhnerman [11], leveraging the stability of the classic Chebyshev recurrence to bound the stability of any polynomial approximating f(x). We also study the special case of f(A) = A−1 for positive definite A, where stronger guarantees hold for Lanczos. In exact arithmetic the algorithm performs as well as the best polynomial approximating 1/x at each of A's eigenvalues, rather than on the full range [λmin(A), λmax(A)]. In seminal work, Greenbaum gives a natural approach to extending this bound to finite precision: she proves that finite precision Lanczos and the related conjugate gradient method match any polynomial approximating 1/x in a tiny range around each eigenvalue [17]. For A−1, Greenbaum's bound appears stronger than our result. However, we exhibit matrices with condition number k where exact arithmetic Lanczos converges in polylog(k) iterations, but Greenbaum's bound predicts at best Ω(k1/5) iterations in finite precision. It thus cannot offer more than a polynomial improvement over the 0(k1/2) bound achievable via our result for general f(A). Our analysis bounds the power of stable approximating polynomials and raises the question of if they fully characterize the behavior of finite precision Lanczos in solving linear systems. If they do, convergence in less than poly(K) iterations cannot be expected, even for matrices with clustered, skewed, or otherwise favorable eigenvalue distributions.
A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (Kim, 2014; Kalchbrenner et al., 2014; Johnson and Zhang, 2014; Zhang et al., 2016). However, these models require practitioners to specify an exact model architecture and set accompanying hyperparameters, including the filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance, which makes it a modern standard baseline method akin to Support Vector Machine (SVMs) and logistic regression. We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification in real world settings.
On the stratification of noncommutative prime spectra

We study rational actions of an algebraic torus G by automorphisms on an associative algebra R. The G-action on R induces a stratification of the prime spectrum Spec R which was introduced by Goodearl and Letzter. For a noetherian algebra R, Goodearl and Letzter showed that the strata of Spec R are isomorphic to the spectra of certain commutative Laurent polynomial algebras. The purpose of this note is to give a new proof of this result which works for arbitrary algebras R.
Revisiting Decomposable Submodular Function Minimization with Incidence Relations

We introduce a new approach to decomposable submodular function minimization (DSFM) that exploits incidence relations. Incidence relations describe which variables effectively influence the component functions, and when properly utilized, they allow for improving the convergence rates of DSFM solvers. Our main results include the precise parametrization of the DSFM problem based on incidence relations, the development of new scalable alternative projections and parallel coordinate descent methods and an accompanying rigorous analysis of their convergence rates.
An empirical approach to demographic inference with genomic data

Inference with population genetic data usually treats the population pedigree as a nuisance parameter, the unobserved product of a past history of random mating. However, the history of genetic relationships in a given population is a fixed, unobserved object, and so an alternative approach is to treat this network of relationships as a complex object we wish to learn about, by observing how genomes have been noisily passed down through it. This paper explores this point of view, showing how to translate questions about population genetic data into calculations with a Poisson process of mutations on all ancestral genomes. This method is applied to give a robust interpretation to the f4 statistic used to identify admixture, and to design a new statistic that measures covariances in mean times to most recent common ancestor between two pairs of sequences. The method more generally interprets population genetic statistics in terms of sums of specific functions over ancestral genomes, thereby providing concrete, broadly interpretable interpretations for these statistics. This provides a method for describing demographic history without simplified demographic models. More generally, it brings into focus the population pedigree, which is averaged over in model-based demographic inference.
Generic separable metric structures

Abstract We compare three notions of genericity of separable metric structures. Our analysis provides a general model theoretic technique of showing that structures are generic in descriptive set theoretic (topological) sense and in measure theoretic sense. In particular, it gives a new perspective on Vershik's theorems on genericity and randomness of Urysohn's space among separable metric spaces.
Quantitative shrinking target properties for rotations and interval exchanges

This paper presents quantitative shrinking target results for rotations and interval exchange transformations. To do this a quantitative version of a unique ergodicity criterion of Boshernitzan is established.
Learning with Latent Language

The named concepts and compositional operators present in natural language provide a rich source of information about the abstractions humans use to navigate the world. Can this linguistic background knowledge improve the generality and efficiency of learned classifiers and control policies? This paper aims to show that using the space of natural language strings as a parameter space is an effective way to capture natural task structure. In a pretraining phase, we learn a language interpretation model that transforms inputs (e.g. images) into outputs (e.g. labels) given natural language descriptions. To learn a new concept (e.g. a classifier), we search directly in the space of descriptions to minimize the interpreter’s loss on training examples. Crucially, our models do not require language data to learn these concepts: language is used only in pretraining to impose structure on subsequent learning. Results on image classification, text editing, and reinforcement learning show that, in all settings, models with a linguistic parameterization outperform those without.
Low rank matrix recovery for joint array self-calibration and sparse model DoA estimation

In this work, combined calibration and DoA estimation is approached as an extension of the formulation for the Single Measurement Vector (SMV) model of self-calibration to the Multiple Measurement Model (MMV) case. By taking advantage of multiple snapshots, a modified nuclear norm minimization problem is proposed to recover a low-rank larger dimension matrix. We also give the definition of a linear operator for the MMV model, and give its corresponding matrix representation to generate a variant of a convex optimization problem. In order to mitigate the computational complexity of the approach, singular value decomposition (SVD) is applied to reduce the problem size. The performance of the proposed methods are demonstrated by numerical simulations.
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm

Deep neural networks (DNNs) have begun to have a pervasive impact on various applications of machine learning. However, the problem of finding an optimal DNN architecture for large applications is challenging. Common approaches go for deeper and larger DNN architectures but may incur substantial redundancy. To address these problems, we introduce a network growth algorithm that complements network pruning to learn both weights and compact DNN architectures during training. We propose a DNN synthesis tool (NeST) that combines both methods to automate the generation of compact and accurate DNNs. NeST starts with a randomly initialized sparse network called the seed architecture. It iteratively tunes the architecture with gradient-based growth and magnitude-based pruning of neurons and connections. Our experimental results show that NeST yields accurate, yet very compact DNNs, with a wide range of seed architecture selection. For the LeNet-300-100 (LeNet-5) architecture, we reduce network parameters by $70.2\times$70.2× ($74.3\times$74.3×) and floating-point operations (FLOPs) by $79.4\times$79.4× ($43.7\times$43.7×). For the AlexNet, VGG-16, and ResNet-50 architectures, we reduce network parameters (FLOPs) by $15.7\times$15.7× ($4.6\times$4.6×), $33.2\times$33.2× ($8.9\times$8.9×), and $4.1\times$4.1× ($2.1\times$2.1×) respectively. NeST's grow-and-prune paradigm delivers significant additional parameter and FLOPs reduction relative to pruning-only methods.
On the number of prime factors of values of the sum-of-proper-divisors function

Abstract Let ω ( n ) (resp. Ω ( n ) ) denote the number of prime divisors (resp. with multiplicity) of a natural number n . In 1917, Hardy and Ramanujan proved that the normal order of ω ( n ) is log ⁡ log ⁡ n , and the same is true of Ω ( n ) ; roughly speaking, a typical natural number n has about log ⁡ log ⁡ n prime factors. We prove a similar result for ω ( s ( n ) ) , where s ( n ) denotes the sum of the proper divisors of n : For all n ≤ x not belonging to a set of size o ( x ) , | ω ( s ( n ) ) − log ⁡ log ⁡ s ( n ) | ϵ log ⁡ log ⁡ s ( n ) , and the same is true for Ω ( s ( n ) ) .
Physical ageing of spreading droplets in a viscous ambient phase

In this work, we study the spontaneous spreading of water droplets immersed in oil and report an unexpectedly slow kinetic regime not described by previous spreading models. We can quantitatively describe the observed regime crossover and spreading rate in the late kinetic regime with an analytical model considering the presence of periodic metastable states induced by nanoscale topographic features (characteristic area ~4 nm2, height ~1 nm) observed via atomic force microscopy. The analytical model proposed in this work reveals that certain combinations of droplet volume and nanoscale topographic parameters can significantly hinder or promote wetting processes such as spreading, wicking, and imbibition.
Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access

A stochastic multi-user multi-armed bandit framework is used to develop algorithms for uncoordinated spectrum access. In contrast to prior work, the number of users is assumed to be unknown to each user and can possibly exceed the number of channels. Also, in contrast to prior work, it is assumed that rewards can be non-zero even under collisions. The proposed algorithm consists of an estimation phase and an allocation phase. It is shown that if every user adopts the algorithm, the system wide regret is constant with time with high probability. The regret guarantees hold for any number of users and channels, i.e., even when the number of users is less than the number of channels. The algorithm is extended to the dynamic case where the number of users in the system evolves over time and our algorithm leads to sub-linear regret.
Electrochemical cells with intermediate capacitor elements

Abstract Our goal is to electronically regulate electrochemical cells. For this, we introduced a third element, called the gate, which was placed between the cathode and the anode electrodes of the cell. Voltage applied to this element controlled the electronic current in the external circuit. The change in the cell's current was attributed to local change in the electrolyte potential, which impacted the flow of ions within the cell. We provide simulations and experimental data as a proof of concept. This is but the first step toward a demonstration of a two-dimensional, bi-carrier ion transistors.
Monotone-light factorizations in coarse geometry

Abstract We introduce large scale analogues of topological monotone and light maps, which we call coarsely monotone and coarsely light maps respectively. We show that these two classes of maps constitute a factorization system on the coarse category. We also show how coarsely monotone maps arise from a reflection in a similar way to classically monotone maps, and prove that coarsely monotone maps are stable under those pullbacks which exist in the coarse category. For the case of maps between proper metric spaces, we exhibit some connections between the coarse and classical notions of monotone and light using the Higson corona. Finally, we look at some coarse properties which are preserved by coarsely light maps such as finite asymptotic dimension and exactness, and make some remarks on the situation for groups and group homomorphisms.
Hand2Face: Automatic synthesis and recognition of hand over face occlusions

A person's face discloses important information about their affective state. Although there has been extensive research on recognition of facial expressions, the performance of existing approaches is challenged by facial occlusions. Facial occlusions are often treated as noise and discarded in recognition of affective states. However, hand over face occlusions can provide additional information for recognition of some affective states such as curiosity, frustration and boredom. One of the reasons that this problem has not gained attention is the lack of naturalistic occluded faces that contain hand over face occlusions as well as other types of occlusions. Traditional approaches for obtaining affective data are time demanding and expensive, which limits researchers in affective computing to work on small datasets. This limitation affects the generalizability of models and deprives researchers from taking advantage of recent advances in deep learning that have shown great success in many fields but require large volumes of data. In this paper, we first introduce a novel framework for synthesizing naturalistic facial occlusions from an initial dataset of non-occluded faces and separate images of hands, reducing the costly process of data collection and annotation. We then propose a model for facial occlusion type recognition to differentiate between hand over face occlusions and other types of occlusions such as scarves, hair, glasses and objects. Finally, we present a model to localize hand over face occlusions and identify the occluded regions of the face.
Efficient estimation of cardiac conductivities via POD-DEIM model order reduction

Abstract Clinical oriented applications of computational electrocardiology require efficient and reliable identification of patient-specific parameters of mathematical models based on available measures. In particular, the estimation of cardiac conductivities in models of potential propagation is crucial, since they have major quantitative impact on the solution. Available estimates of cardiac conductivities are significantly diverse in the literature and the definition of experimental/mathematical estimation techniques is an open problem with important practical implications in clinics. We have recently proposed a methodology based on a variational procedure, where the reliability is confirmed by numerical experiments. In this paper we explore model-order-reduction techniques to fit the estimation procedure into timelines of clinical interest. Specifically we consider the Monodomain model and resort to Proper Orthogonal Decomposition (POD) techniques to take advantage of an offline step when solving iteratively the electrocardiological forward model online. In addition, we perform the Discrete Empirical Interpolation Method (DEIM) to tackle the nonlinearity of the model. While standard POD techniques usually fail in this kind of problems, due to the wave-front propagation dynamics, an educated novel sampling of the parameter space based on the concept of Domain of Effectiveness introduced here dramatically reduces the computational cost of the inverse solver by at least 95%.
Amenable groups and smooth topology of 4-manifolds

A smooth five-dimensional s-cobordism becomes a smooth product if stabilized by a finite number n of (S2 × S2 × [0, 1])’s. We show that for amenable fundamental groups, the minimal n is subextensive in covers, i.e. n(cover) index cover → 0. We focus on the notion of sweepout width, which is a bridge between 4-dimensional topology and coarse geometry.
On sharp bounds for marginal densities of product measures

We discuss optimal constants in a recent result of Rudelson and Vershynin on marginal densities. We show that if f is a probability density on R n of the form f(x) = П =1 f i (x i ), where each f i is a density on R, say bounded by one, then the density of any marginal π E (f) is bounded by 2 k/2, where k is the dimension of E. The proof relies on an adaptation of Ball’s approach to cube slicing, carried out for functions. Motivated by inequalities for dual affine quermassintegrals, we also prove an isoperimetric inequality for certain averages of the marginals of such f for which the cube is the extremal case.
An ancient solution of the Ricci flow in dimension 4 converging to the Euclidean Schwarzschild metric

In this paper, we prove the existence of an ancient solution to the Ricci flow whose limit at $t=-\infty$ is the Euclidean Schwarzschild metric.