cssi/SciSciGPT-SciSciNet huggingface dataset:
Keck II Observations of Hemispherical Differences in H2O2 on Europa

We present results from Keck II observations of Europa over four consecutive nights using the near-infrared spectrograph. Spectra were collected in the 3.14-4.0 μm range, enabling detection and monitoring of the 3.5 μm feature due to hydrogen peroxide. Galileo Near-Infrared Mapping Spectrometer results first revealed hydrogen peroxide on Europa in the anti-Jovian region of the leading hemisphere at a percent by number abundance of 0.13% ± 0.07% relative to water. We find comparable results for the two nights over which we observed the leading hemisphere. Significantly, we observed a small amount of hydrogen peroxide (~0.04%) during observations of Europa's anti-Jovian and sub-Jovian hemispheres. Almost no hydrogen peroxide was detected during observations of just the trailing hemisphere. We conclude that the Galileo observations likely represent the maximum hydrogen peroxide concentration, the exception potentially being the cold water ice regions of the poles, which are not readily observable from the ground. Our mapping of the peroxide abundance across Europa requires revisions to previous estimates for Europa's global surface abundance of oxidants and leads to a reduction in the total oxidant delivery expected for the subsurface ocean if an exchange of surface material with the ocean occurs.
Epigenomic plasticity of Arabidopsis msh1 mutants under prolonged cold stress.

Dynamic transcriptional and epigenetic changes enable rapid adaptive benefit to environmental fluctuations. However, the underlying mechanisms and the extent to which this occurs are not well known. MutS Homolog 1 (MSH1) mutants cause heritable developmental phenotypes accompanied by modulation of defense, phytohormone, stress-response, and circadian rhythm genes, as well as heritable changes in DNA methylation patterns. Consistent with gene expression changes, msh1 mutants display enhanced tolerance for abiotic stress including drought and salt stress, while showing increased susceptibility to freezing temperatures. Despite changes in defense and biotic stress-response genes, msh1 mutants showed increasing susceptibility to the bacterial pathogen Pseudomonas syringae. Our results suggest that chronic cold and low light stress (10°C, 150 μmol m-2 s-1) influences non-CG methylation to a greater degree in msh1 mutants compared to wild-type Col-0. Furthermore, CHG changes are more closely pericentromeric, whereas CHH changes are generally more dispersed. This increased variation in non-CG methylation pattern does not significantly affect the msh1-derived enhanced growth behavior after mutants are crossed with isogenic wild type, reiterating the importance of CG methylation changes in msh1-derived enhanced vigor. These results indicate that msh1methylome is hyper-responsive to environmental stress in a manner distinct from the wild-type response, but CG methylation changes are potentially responsible for growth vigor changes in the crossed progeny.
Kwanzoquinones A–G and other constituents of Hemerocallis fulva ‘Kwanzo’ roots and their activity against the human pathogenic trematode Schistosoma mansoni

Abstract Schistosomiasis is a debilitating disease caused by parasitic trematodes of the genus Schistosoma that afflicts 200 million people worldwide. Daylilies (Hemerocallis spp.) have been used in Asia for the treatment of schistosomiasis; however, the active principles have not been fully characterized. In our studies of Hemerocallis fulva ‘Kwanzo’ Kaempfer roots, we have isolated seven new anthraquinones, kwanzoquinones A ( 1 ), B ( 2 ), C ( 4 ), D ( 5 ), E ( 6 ), F ( 7 ), and G ( 9 ), two known anthraquinones, 2-hydroxychrysophanol ( 3 ) and rhein ( 8 ), one new naphthalene glycoside, 5-hydroxydianellin ( 11 ), one known naphthalene glycoside, dianellin ( 10 ), one known flavone, 6-methylluteolin ( 12 ), and α-tocopherol. The structures of the compounds were elucidated by spectroscopic and chemical methods. Compounds 1–11 and the monoacetates of kwanzoquinones A and B, 1a and 2a , respectively, were tested for their activity against multiple life-stages of Schistosoma mansoni. Compound 3 immobilized all cercariae within 15 s at 3.1 μg/mL. However, upon removal of the compound, 20% of the immobilized cercariae recovered after 24 h. In contrast, compound 6 immobilized cercariae within 12–14 min at 25 μg/mL. Following removal of the compound, all cercariae died within 24 h. The adult worms were also immobilized within 16 h by compounds 3 and 6 at 50 μg/mL. None of the compounds had an effect on the schistosomula stage.
Oligonucleotide synthesis as a tool in drug discovery research

Facile methods for oligonucleotide synthesis have been responsible for the development of several enabling technologies, thereby creating new opportunities in pharmaceutical R&D. It has therefore become necessary for successful drug discovery enterprises to be well versed in the rapid synthesis and distribution of oligonucleotides. To achieve this goal, automated DNA synthesizers have been coupled with downstream automated workstations and a relational database to keep track of all aspects of the oligonucleotide production process. The use of the workstation approach allows for the rapid synthesis, purification, analysis and distribution of oligonucleotides and their analogs.
Facile preparation and reactivity of polystyrene-supported (dichloroiodo)benzene: a convenient recyclable reagent for chlorination and oxidation

Abstract A facile one-pot preparation of polystyrene-supported (dichloroiodo)benzene (loading of -ICl2 up to 1.35 mmol/g) from polystyrene, iodine, and bleach has been developed. This recyclable reagent is useful for efficient chlorination of organic substrates and selective oxidation of various alcohols to the corresponding carbonyl compounds in high yields under mild conditions. The final products are conveniently separated from the polymeric byproduct by simple filtration and isolated in good purity after evaporation of solvent.
Chemoenzymic Production of Lactams from Aliphatic α,ω-Dinitriles

Five- and six-membered ring lactams have been prepared by first converting an aliphatic α,ω-dinitrile to an ω-cyanocarboxylic acid ammonium salt, using a microbial cell catalyst having an aliphatic nitrilase activity (Acidovorax facilis 72W, ATCC 55746) or a combination of nitrile hydratase and amidase activities (Comamonas testosteroni 5-MGAM-4D, ATCC 55744). The ω-cyanocarboxylic acid ammonium salt was then directly converted to the corresponding lactam by hydrogenation in aqueous solution, without isolation of the intermediate ω-cyanocarboxylic acid or ω-aminocarboxylic acid. Only one of two possible lactam products was produced from α-alkyl-substituted α,ω-dinitriles, where the nitrilase of A. facilis 72W regioselectively hydrolyzed only the ω-cyano group to produce a single cyanocarboxylic acid ammonium salt in greater than 98% yield.
Singularities in inflationary cosmology: A Review

We review here some recent results that show that inflationary cosmological models must contain initial singularities. We also present a new singularity theorem. The question of the initial singularity re-emerges in inflationary cosmology because inflation is known to be generically futureeternal, It is natural to ask, therefore, if inflationary models can be continued into the infinite past in a non-singular way. The results that we discuss show that the answer to the question is “no.” This means that we cannot use inflation as a way of avoiding the question of the birth of the Universe. We also argue that our new theorem suggests — in a sense that we explain in the paper — that the Universe cannot be infinitely old.
Hierarchical patient-centric caregiver network method for clinical outcomes study

In clinical outcome studies, analysis has traditionally been performed using patient-level factors, with minor attention given to provider-level features. However, the nature of care coordination and collaboration between caregivers (providers) may also be important in determining patient outcomes. Using data from patients admitted to intensive care units at a large tertiary care hospital, we modeled the caregivers that provided medical service to a specific patient as patient-centric subnetwork embedded within larger caregiver networks of the institute. The caregiver networks were composed of caregivers who treated either a cohort of patients with particular disease or any patient regardless of disease. Our model can generate patient-specific caregiver network features at multiple levels, and we demonstrate that these multilevel network features, in addition to patient-level features, are significant predictors of length of hospital stay and in-hospital mortality.
A New Synthesis of γ-Lactams Based on the Reaction of Vinyl Sulfilimines with Dichloroketene

The reactions of several aryl-, furanyl-, and vinyl substituted sulfilimines with dichloroketene proceeded at 25 °C to yield thioalkyl substituted γ-lactams which, in turn, were converted to a variety of nitrogen-containing substrates.
Active inductive logic programming for code search

Modern search techniques either cannot efficiently incorporate human feedback to refine search results or cannot express structural or semantic properties of desired code. The key insight of our interactive code search technique Alice is that user feedback can be actively incorporated to allow users to easily express and refine search queries. We design a query language to model the structure and semantics of code as logic facts. Given a code example with user annotations, Alice automatically extracts a logic query from code features that are tagged as important. Users can refine the search query by labeling one or more examples as desired (positive) or irrelevant (negative). Alice then infers a new logic query that separates positive examples from negative examples via active inductive logic programming. Our comprehensive simulation experiment shows that Alice removes a large number of false positives quickly by actively incorporating user feedback. Its search algorithm is also robust to user labeling mistakes. Our choice of leveraging both positive and negative examples and using nested program structure as an inductive bias is effective in refining search queries. Compared with an existing interactive code search technique, Alice does not require a user to manually construct a search pattern and yet achieves comparable precision and recall with much fewer search iterations. A case study with real developers shows that Alice is easy to use and helps express complex code patterns.
Online High Rank Matrix Completion

Recent advances in matrix completion enable data imputation in full-rank matrices by exploiting low dimensional (nonlinear) latent structure. In this paper, we develop a new model for high rank matrix completion (HRMC), together with batch and online methods to fit the model and out-of-sample extension to complete new data. The method works by (implicitly) mapping the data into a high dimensional polynomial feature space using the kernel trick; importantly, the data occupies a low dimensional subspace in this feature space, even when the original data matrix is of full-rank. The online method can handle streaming or sequential data and adapt to non-stationary latent structure, and enjoys much lower space and time complexity than previous methods for HRMC. For example, the time complexity is reduced from $O(n^3)$ to $O(r^3)$, where $n$ is the number of data points, $r$ is the matrix rank in the feature space, and $r\ll n$. We also provide guidance on sampling rate required for these methods to succeed. Experimental results on synthetic data and motion data validate the performance of the proposed methods.
Ancestry-specific polygenic scores and SNP heritability of 25(OH)D in African- and European-ancestry populations

Vitamin D inadequacy, assessed by 25-hydroxyvitamin D [25(OH)D], affects around 50% of adults in the United States and is associated with numerous adverse health outcomes. Blood 25(OH)D concentrations are influenced by genetic factors that may determine how much vitamin D intake is required to reach optimal 25(OH)D. Despite large genome-wide association studies (GWASs), only a small portion of the genetic factors contributing to differences in 25(OH)D has been discovered. Therefore, knowledge of a fuller set of genetic factors could be useful for risk prediction of 25(OH)D inadequacy, personalized vitamin D supplementation, and prevention of downstream morbidity and mortality. Using PRSice and weights from published African- and European-ancestry GWAS summary statistics, ancestry-specific polygenic scores (PGSs) were created to capture a more complete set of genetic factors in those of European (n = 9569) or African ancestry (n = 2761) from three cohort studies. The PGS for African ancestry was derived using all input SNPs (a p value cutoff of 1.0) and had an R2 of 0.3%; for European ancestry, the optimal PGS used a p value cutoff of 3.5 × 10−4 in the target/tuning dataset and had an R2 of 1.0% in the validation cohort. Those with highest genetic risk had 25(OH)D that was 2.8–3.0 ng/mL lower than those with lowest genetic risk (p = 0.0463–3.2 × 10−13), requiring an additional 467–500 IU of vitamin D intake to maintain equivalent 25(OH)D. PGSs are a powerful predictive tool that could be leveraged for personalized vitamin D supplementation to prevent the negative downstream effects of 25(OH)D inadequacy.
Regularity defect stabilization of powers of an ideal

When I is an ideal of a standard graded algebra S with homogeneous maximal ideal \mm, it is known by the work of several authors that the Castelnuovo-Mumford regularity of I^m ultimately becomes a linear function dm + e for m \gg 0. We give several constraints on the behavior of what may be termed the \emph{regularity defect} (the sequence e_m = \reg I^m - dm). When I is \mm-primary we give a family of bounds on the first differences of the e_m, including an upper bound on the increasing part of the sequence; for example, we show that the e_i cannot increase for i \geq \dim(S). When I is a monomial ideal, we show that the e_i become constant for i \geq n(n-1)(d-1), where n = \dim(S).
Adaptive Randomized Dimension Reduction on Massive Data

The scalability of statistical estimators is of increasing importance in modern applications. One approach to implementing scalable algorithms is to compress data into a low dimensional latent space using dimension reduction methods. In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages. We adapt recent randomized low-rank approximation algorithms to provide an efficient solution to principal component analysis (PCA), and we use this efficient solver to improve parameter estimation in large-scale linear mixed models (LMM) for association mapping in statistical and quantitative genomics. A key observation in this paper is that randomization serves a dual role, improving both computational and statistical performance by implicitly regularizing the covariance matrix estimate of the random effect in a LMM. These statistical and computational advantages are highlighted in our experiments on simulated data and large-scale genomic studies.
Secure coordination with a two-sided helper

We investigate the problem of secure source coding with a two-sided helper in a game-theoretic framework. Alice (A) and Helen (H) view iid correlated information sequences $X^n$ and $Y^n$ respectively. Alice communicates to Bob (B) at rate $R$, while H broadcasts a message to both A and B at rate $R_H$. Additionally, A and B share secret key $K$ at rate $R_0$ that is independent of $(X^n,Y^n)$. An active adversary, Eve (E) sees all communication links while having access to a (possibly degraded) version of the past information. We characterize the rate-payoff region for this problem. We also solve the problem when the link from A to B is private. Our work recovers previous results of Schieler-Cuff and Kittichokechai et al.
Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors

Abstract Let π denote the intractable posterior density that results when the likelihood from a multivariate linear regression model with errors from a scale mixture of normals is combined with the standard non-informative prior. There is a simple data augmentation algorithm (based on latent data from the mixing density) that can be used to explore π . Let h and d denote the mixing density and the dimension of the regression model, respectively. Hobert et al. (2018) have recently shown that, if h converges to 0 at the origin at an appropriate rate, and ∫ 0 ∞ u d ∕ 2 h ( u ) d u ∞ , then the Markov chains underlying the data augmentation (DA) algorithm and an alternative Haar parameter expanded DA (PX-DA) algorithm are both geometrically ergodic. Their results are established using probabilistic techniques based on drift and minorization conditions. In this paper, spectral analytic techniques are used to establish that something much stronger than geometric ergodicity often holds. In particular, it is shown that, under simple conditions on h , the Markov operators defined by the DA and Haar PX-DA Markov chains are trace-class, i.e., compact with summable eigenvalues. Many standard mixing densities satisfy the conditions developed in this paper. Indeed, the new results imply that the DA and Haar PX-DA Markov operators are trace-class whenever the mixing density is generalized inverse Gaussian, log-normal, Frechet (with shape parameter larger than d ∕ 2 ), or inverted Gamma (with shape parameter larger than d ∕ 2 ).
CHARACTERISTIC STRUCTURE OF STAR-FORMING CLOUDS

This paper presents a new method to diagnose the star-forming potential of a molecular cloud region from the probability density function of its column density (N-pdf). This method provides expressions for the column density and mass profiles of a symmetric filament having the same N-pdf as a filamentary region. The central concentration of this characteristic filament can distinguish regions and can quantify their fertility for star formation. Profiles are calculated for N-pdfs which are pure lognormal, pure power law, or a combination. In relation to models of singular polytropic cylinders, characteristic filaments can be unbound, bound, or collapsing depending on their central concentration. Such filamentary models of the dynamical state of N-pdf gas are more relevant to star-forming regions than are spherical collapse models. The star formation fertility of a bound or collapsing filament is quantified by its mean mass accretion rate when in radial free fall. For a given mass per length, the fertility increases with the filament mean column density and with its initial concentration. In selected regions the fertility of their characteristic filaments increases with the level of star formation.
Measuring and regularizing networks in function space

To optimize a neural network one often thinks of optimizing its parameters, but it is ultimately a matter of optimizing the function that maps inputs to outputs. Since a change in the parameters might serve as a poor proxy for the change in the function, it is of some concern that primacy is given to parameters but that the correspondence has not been tested. Here, we show that it is simple and computationally feasible to calculate distances between functions in a $L^2$ Hilbert space. We examine how typical networks behave in this space, and compare how parameter $\ell^2$ distances compare to function $L^2$ distances between various points of an optimization trajectory. We find that the two distances are nontrivially related. In particular, the $L^2/\ell^2$ ratio decreases throughout optimization, reaching a steady value around when test error plateaus. We then investigate how the $L^2$ distance could be applied directly to optimization. We first propose that in multitask learning, one can avoid catastrophic forgetting by directly limiting how much the input/output function changes between tasks. Secondly, we propose a new learning rule that constrains the distance a network can travel through $L^2$-space in any one update. This allows new examples to be learned in a way that minimally interferes with what has previously been learned. These applications demonstrate how one can measure and regularize function distances directly, without relying on parameters or local approximations like loss curvature.
Irreversible Brownian Heat Engine

We model a Brownian heat engine as a Brownian particle that hops in a periodic ratchet potential where the ratchet potential is coupled with a linearly decreasing background temperature. We show that the efficiency of such Brownian heat engine approaches the efficiency of endoreversible engine $$\eta =1-\sqrt{{T_{c}/T_{h}}}$$ [23]. On the other hand, the maximum power efficiency of the engine approaches $$\eta ^{MAX}=1-({T_{c}/T_{h}})^{1\over 4}$$ . It is shown that the optimized efficiency always lies between the efficiency at quasistatic limit and the efficiency at maximum power while the efficiency at maximum power is always less than the optimized efficiency since the fast motion of the particle comes at the expense of the energy cost. If the heat exchange at the boundary of the heat baths is included, we show that such a Brownian heat engine has a higher performance when acting as a refrigerator than when operating as a device subjected to a piecewise constant temperature. The role of time on the performance of the motor is also explored via numerical simulations. Our numerical results depict that the time t and the external load dictate the direction of the particle velocity. Moreover, the performance of the heat engine improves with time. At large t (steady state), the velocity, the efficiency and the coefficient of performance of the refrigerator attain their maximum value. Furthermore, we study the effect of temperature by considering a viscous friction that decreases exponentially as the background temperature increases. Our result depicts that the Brownian particle exhibits a fast unidirectional motion when the viscous friction is temperature dependent than that of constant viscous friction. Moreover, the efficiency of this motor is considerably enhanced when the viscous friction is temperature dependent. On the hand, the motor exhibits a higher performance of the refrigerator when the viscous friction is taken to be constant.
Differentiable Consistency Constraints for Improved Deep Speech Enhancement

In recent years, deep networks have led to dramatic improvements in speech enhancement by framing it as a data-driven pattern recognition problem. In many modern enhancement systems, large amounts of data are used to train a deep network to estimate masks for complex-valued short-time Fourier transforms (STFTs) to suppress noise and preserve speech. However, current masking approaches often neglect two important constraints: STFT consistency and mixture consistency. Without STFT consistency, the system’s output is not necessarily the STFT of a time-domain signal, and without mixture consistency, the sum of the estimated sources does not necessarily equal the input mixture. Furthermore, the only previous approaches that apply mixture consistency use real-valued masks; mixture consistency has been ignored for complex-valued masks.
Modulational instability in nonlinear nonlocal equations of regularized long wave type

Abstract We study the stability and instability of periodic traveling waves in the vicinity of the origin in the spectral plane, for equations of Benjamin–Bona–Mahony (BBM) and regularized Boussinesq types permitting nonlocal dispersion. We extend recent results for equations of Korteweg–de Vries type and derive modulational instability indices as functions of the wave number of the underlying wave. We show that a sufficiently small, periodic traveling wave of the BBM equation is spectrally unstable to long wavelength perturbations if the wave number is greater than a critical value and a sufficiently small, periodic traveling wave of the regularized Boussinesq equation is stable to square integrable perturbations.
Curvilinear mode-I/mode-II interface fracture with a curvature-dependent surface tension on the boundary

A new model of fracture mechanics considered previously by Sendova and Walton \cite{SendovaWalton2010}, Zemlyanova \cite{Zemlyanova2013}, and Zemlyanova and Walton \cite{Zemlyanova2012} is further developed on the example of a mixed mode curvilinear interface fracture located on the boundary of a partially debonded thin elastic inclusion embedded in an infinite thin elastic matrix. The effect of the nano-structure of the material is incorporated into the model in the form of a curvature-depended surface tension acting on the boundary of the fracture. It is shown that the introduction of the surface tension allows to eliminate the classical oscillating and power singularities of the order $1/2$ present in the linear elastic fracture mechanics. The mathematical methods used to solve the problem are based on the Muskhelishvili's complex potentials and the Savruk's integral representations. The mechanical problem is reduced to the system of singular integro-differential equations which is further reduced to a system of weakly-singular integral equations. The numerical computations and comparison with known results are presented.
Sparsity-based Defense Against Adversarial Attacks on Linear Classifiers

Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing. However, recent work raises important questions about the robustness of such architectures, by showing that it is possible to induce classification errors through tiny, almost imperceptible, perturbations. Vulnerability to such “adversarial attacks”, or “adversarial examples”, has been conjectured to be due to the excessive linearity of deep networks. In this paper, we study this phenomenon in the setting of a linear classifier, and show that it is possible to exploit sparsity in natural data to combat $\ell_{\infty}$ -bounded adversarial perturbations. Specifically, we demonstrate the efficacy of a sparsifying front end via an ensemble averaged analysis, and experimental results for the MNIST handwritten digit database. To the best of our knowledge, this is the first work to show that sparsity provides a theoretically rigorous framework for defense against adversarial attacks.
New Results on Finite-Time Stability: Geometric Conditions and Finite-Time Controllers

This paper presents novel controllers that yield finite-time stability for linear systems. We first present a necessary and sufficient condition for the origin of a scalar system to be finite-time stable. Then we present novel finite-time controllers based on vector fields and barrier functions to demonstrate the utility of this geometric condition. We also consider the general class of linear controllable systems, and present a continuous feedback control law to stabilize the system in finite time. Finally, we present simulation results for each of these cases, showing the efficacy of the designed control laws.
New studies of randomized augmentation and additive preprocessing

Abstract • A standard Gaussian random matrix (hereafter referred to just as Gaussian matrix) has full rank with probability 1 and is well-conditioned with a probability quite close to 1 and converging to 1 fast as the matrix deviates from the square shape and becomes more rectangular. • If we append sufficiently many Gaussian rows or columns to any normalized and possibly rank deficient or ill-conditioned matrix, then the augmented matrix has full rank with probability 1 and is well-conditioned with a probability close to 1. • We specify and prove these properties of augmentation and extend them to additive preprocessing, that is, to adding a product of two rectangular Gaussian matrices. • By applying our randomization techniques to a matrix that has numerical rank r, we accelerate the known algorithms for the approximation of its trailing singular spaces, associated with all its positive singular values, except for the r largest values. • Our algorithms use much fewer random parameters and run much faster when various random sparse and structured preprocessors replace Gaussian. Empirically the outputs of the resulting algorithms are as accurate as the outputs under Gaussian preprocessing. • Our novel duality techniques provides formal support, so far missing, for these empirical observations and opens door to de-randomization of our preprocessing and to further acceleration and simplification of our algorithms by using more efficient sparse and structured preprocessors. • Our techniques and our progress can be applied to various other fundamental matrix computations such as the celebrated low-rank approximation of a matrix by means of random sampling.
Data-driven perception of neuron point process with unknown unknowns

Identification of patterns from discrete data time-series for statistical inference, threat detection, social opinion dynamics, brain activity prediction has received recent momentum. In addition to the huge data size, the associated challenges are, for example, (i) missing data to construct a closed time-varying complex network, and (ii) contribution of unknown sources which are not probed. Towards this end, the current work focuses on statistical neuron system model with multi-covariates and unknown inputs. Previous research of neuron activity analysis is mainly concerned with effects from spiking history of the target neuron and the interaction with other neurons in the system while ignoring the influence of unknown stimuli. We propose to use unknown unknowns, which describes the effect of unknown stimuli, undetected neuron activities and all other hidden sources of error. The generalized linear model links neuron spiking behavior with past activities in the ensemble neuron system, as well as the unknown influence. We develop a maximum likelihood estimation method based on fixed-point iteration. The fixed-point iterations converge fast, and besides, the proposed methods can be efficiently parallelized to offer computational advantage especially when the input spiking trains are over long time-horizon. The developed framework provides an intuition into the meaning of having extra degrees-of-freedom in the data to support the need for unknowns. The proposed algorithm is applied to simulated spike trains and on real-world experimental data of mouse somatosensory, mouse retina and cat retina. The implementation shows a successful increase of the model likelihood with respect to the conditional intensity function, and it also reveals the convergence with iterations. Results suggest that the neural connection model with unknown unknowns can efficiently estimate the statistical properties of the process by increasing the network likelihood.
On a viable first order formulation of relativistic viscous fluids and its applications to cosmology

We consider a first-order formulation of relativistic fluids with bulk viscosity based on a stress-energy tensor introduced by Lichnerowicz. Choosing a barotropic equation-of-state, we show that this theory satisfies basic physical requirements and, under the further assumption of vanishing vorticity, that the equations of motion are causal, both in the case of a fixed background and when the equations are coupled to Einstein’s equations. Furthermore, Lichnerowicz’s proposal does not fit into the general framework of first-order theories studied by Hiscock and Lindblom, and hence their instability results do not apply. These conclusions apply to the full-fledged nonlinear theory, without any equilibrium or near equilibrium assumptions. Similarities and differences between the approach explored here and other theories of relativistic viscosity, including the Mueller–Israel–Stewart formulation, are addressed. Cosmological models based on the Lichnerowicz stress-energy tensor are studied. As the topic of (relativistic) viscous fluids is also of interest outside the general relativity and cosmology communities, such as, for instance, in applications involving heavy-ion collisions, we make our presentation largely self-contained.
Implicit Renewal Theory and Power Tails on Trees

We extend Goldie’s (1991) implicit renewal theorem to enable the analysis of recursions on weighted branching trees. We illustrate the developed method by deriving the powertail asymptotics of the distributions of the solutions R to R d = N=1 Ci Ri + Q, R d
Exact and inexact subsampled Newton methods for optimization

The paper studies the solution of stochastic optimization problems in which approximations to the gradient and Hessian are obtained through subsampling. We first consider Newton-like methods that employ these approximations and discuss how to coordinate the accuracy in the gradient and Hessian to yield a superlinear rate of convergence in expectation. The second part of the paper analyzes an inexact Newton method that solves linear systems approximately using the conjugate gradient (CG) method, and that samples the Hessian and not the gradient (the gradient is assumed to be exact). We provide a complexity analysis for this method based on the properties of the CG iteration and the quality of the Hessian approximation, and compare it with a method that employs a stochastic gradient iteration instead of the CG method. We report preliminary numerical results that illustrate the performance of inexact subsampled Newton methods on machine learning applications based on logistic regression.
A Bayesian evidence synthesis approach to estimate disease prevalence in hard-to-reach populations: hepatitis C in New York City.

Abstract Existing methods to estimate the prevalence of chronic hepatitis C (HCV) in New York City (NYC) are limited in scope and fail to assess hard-to-reach subpopulations with highest risk such as injecting drug users (IDUs). To address these limitations, we employ a Bayesian multi-parameter evidence synthesis model to systematically combine multiple sources of data, account for bias in certain data sources, and provide unbiased HCV prevalence estimates with associated uncertainty. Our approach improves on previous estimates by explicitly accounting for injecting drug use and including data from high-risk subpopulations such as the incarcerated, and is more inclusive, utilizing ten NYC data sources. In addition, we derive two new equations to allow age at first injecting drug use data for former and current IDUs to be incorporated into the Bayesian evidence synthesis, a first for this type of model. Our estimated overall HCV prevalence as of 2012 among NYC adults aged 20–59 years is 2.78% (95% CI 2.61–2.94%), which represents between 124,900 and 140,000 chronic HCV cases. These estimates suggest that HCV prevalence in NYC is higher than previously indicated from household surveys (2.2%) and the surveillance system (2.37%), and that HCV transmission is increasing among young injecting adults in NYC. An ancillary benefit from our results is an estimate of current IDUs aged 20–59 in NYC: 0.58% or 27,600 individuals.
The FRT-construction via quantum affine algebras and smash products

Abstract For every element w in the Weyl group of a simple Lie algebra g , De Concini, Kac, and Procesi defined a subalgebra U q w of the quantized universal enveloping algebra U q ( g ) . The algebra U q w is a deformation of the universal enveloping algebra U ( n + ∩ w . n − ) . We construct smash products of certain finite-type De Concini–Kac–Procesi algebras to obtain ones of affine type; we have analogous constructions in types A n and D n . We show that the multiplication in the affine type De Concini–Kac–Procesi algebras arising from this smash product construction can be twisted by a cocycle to produce certain subalgebras related to the corresponding Faddeev–Reshetikhin–Takhtajan bialgebras.
L^p regularity of homogeneous elliptic differential operators with constant coefficients on R^N

Let A be a homogeneous elliptic differential operator of order m on RN with constant complex coefficients. A special case of the main result is as follows:
Sample-Optimal Identity Testing with High Probability.

We study the problem of testing identity against a given distribution (a.k.a. goodness-of-fit) with a focus on the high confidence regime. More precisely, given samples from an unknown distribution $p$ over $n$ elements, an explicitly given distribution $q$, and parameters $0< \epsilon, \delta < 1$, we wish to distinguish, with probability at least $1-\delta$, whether the distributions are identical versus $\epsilon$-far in total variation (or statistical) distance. Existing work has focused on the constant confidence regime, i.e., the case that $\delta = \Omega(1)$, for which the sample complexity of identity testing is known to be $\Theta(\sqrt{n}/\epsilon^2)$. Typical applications of distribution property testing require small values of the confidence parameter $\delta$ (which correspond to small "$p$-values" in the statistical hypothesis testing terminology). Prior work achieved arbitrarily small values of $\delta$ via black-box amplification, which multiplies the required number of samples by $\Theta(\log(1/\delta))$. We show that this upper bound is suboptimal for any $\delta = o(1)$, and give a new identity tester that achieves the optimal sample complexity. Our new upper and lower bounds show that the optimal sample complexity of identity testing is \[ \Theta\left( \frac{1}{\epsilon^2}\left(\sqrt{n \log(1/\delta)} + \log(1/\delta) \right)\right) \] for any $n, \epsilon$, and $\delta$. For the special case of uniformity testing, where the given distribution is the uniform distribution $U_n$ over the domain, our new tester is surprisingly simple: to test whether $p = U_n$ versus $\mathrm{d}_{TV}(p, U_n) \geq \epsilon$, we simply threshold $\mathrm{d}_{TV}(\hat{p}, U_n)$, where $\hat{p}$ is the empirical probability distribution. We believe that our novel analysis techniques may be useful for other distribution testing problems as well.
Structural Analysis and Optimal Design of Distributed System Throttlers

In this paper, we investigate the performance analysis and synthesis of distributed system throttlers (DST). A throttler is a mechanism that limits the flow rate of incoming metrics, e.g., byte per second, network bandwidth usage, capacity, traffic, etc. This can be used to protect a service's backend/clients from getting overloaded, or to reduce the effects of uncertainties in demand for shared services. We study performance deterioration of DSTs subject to demand uncertainty. We then consider network synthesis problems that aim to improve the performance of noisy DSTs via communication link modifications as well as server update cycle modifications.
CRITERIA FOR VANISHING OF TOR OVER COMPLETE INTERSECTIONS

In this paper we exploit properties of Dao's eta-pairing as well as techniques of Huneke, Jorgensen, and Wiegand to study the vanishing of Tor_i(M,N) of finitely generated modules M, N over complete intersections. We prove vanishing of Tor_i(M, N) for all positive integers i under depth conditions on M, N, and their tensor product. Our arguments improve a result of Dao and establish a new connection between the vanishing of Tor and the depth of tensor products.
Cover Song Synthesis by Analogy

In this work, we pose and address the following "cover song analogies" problem: given a song A by artist 1 and a cover song A' of this song by artist 2, and given a different song B by artist 1, synthesize a song B' which is a cover of B in the style of artist 2. Normally, such a polyphonic style transfer problem would be quite challenging, but we show how the cover songs example constrains the problem, making it easier to solve. First, we extract the longest common beat-synchronous subsequence between A and A', and we time stretch the corresponding beat intervals in A' so that they align with A. We then derive a version of joint 2D convolutional NMF, which we apply to the constant-Q spectrograms of the synchronized segments to learn a translation dictionary of sound templates from A to A'. Finally, we apply the learned templates as filters to the song B, and we mash up the translated filtered components into the synthesized song B' using audio mosaicing. We showcase our algorithm on several examples, including a synthesized cover version of Michael Jackson's "Bad" by Alien Ant Farm, learned from the latter's "Smooth Criminal" cover.'
Confounder adjustment in multiple hypothesis testing

We consider large-scale studies in which thousands of significance tests are performed simultaneously. In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable(s) of interest (e.g., treatment variable, phenotype) and the outcome. Over the past decade, many statistical methods have been proposed to adjust for the confounders in hypothesis testing. We unify these methods in the same framework, generalize them to include multiple primary variables and multiple nuisance variables, and analyze their statistical properties. In particular, we provide theoretical guarantees for RUV-4 [Gagnon-Bartsch, Jacob and Speed (2013)] and LEAPP [Ann. Appl. Stat. 6 (2012) 1664–1688], which correspond to two different identification conditions in the framework: the first requires a set of “negative controls” that are known a priori to follow the null distribution; the second requires the true nonnulls to be sparse. Two different estimators which are based on RUV-4 and LEAPP are then applied to these two scenarios. We show that if the confounding factors are strong, the resulting estimators can be asymptotically as powerful as the oracle estimator which observes the latent confounding factors. For hypothesis testing, we show the asymptotic $z$-tests based on the estimators can control the type I error. Numerical experiments show that the false discovery rate is also controlled by the Benjamini–Hochberg procedure when the sample size is reasonably large.
Quickest search over Brownian channels

In this paper we resolve an open problem proposed by Lai, Vincent Poor, Xin, and Georgiadis [Quickest search over multiple sequences. IEEE Trans. Inf. Theory 57(8) (2011), pp. 5375–5386]. Consider a sequence of Brownian motions with unknown drift equal to one or zero, which may be observed one at a time. We give a procedure for finding, as quickly as possible, a process which is a Brownian motion with non-zero drift. This original quickest search problem, in which the filtration itself is dependent on the observation strategy, is reduced to a single filtration impulse control and optimal stopping problem, which is in turn reduced to an optimal stopping problem for a reflected diffusion, which can be explicitly solved.
Acoustic Data-Driven Lexicon Learning Based on a Greedy Pronunciation Selection Framework.

Speech recognition systems for irregularly-spelled languages like English normally require hand-written pronunciations. In this paper, we describe a system for automatically obtaining pronunciations of words for which pronunciations are not available, but for which transcribed data exists. Our method integrates information from the letter sequence and from the acoustic evidence. The novel aspect of the problem that we address is the problem of how to prune entries from such a lexicon (since, empirically, lexicons with too many entries do not tend to be good for ASR performance). Experiments on various ASR tasks show that, with the proposed framework, starting with an initial lexicon of several thousand words, we are able to learn a lexicon which performs close to a full expert lexicon in terms of WER performance on test data, and is better than lexicons built using G2P alone or with a pruning criterion based on pronunciation probability.
Prior convictions: Black-box adversarial attacks with bandits and priors

We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and we demonstrate that the current state-of-the-art methods are optimal in a natural sense. Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors. We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples. The resulting methods use two to four times fewer queries and fail two to five times less often than the current state-of-the-art.
An abelian category of motivic sheaves

Abstract A category of motivic “sheaves” is constructed over a variety in characteristic 0 using Nori’s method. Although the relationship with many alternative constructions remains to be clarified, it does have many of the properties one expects. For example, it is abelian and has Betti, Hodge and l -adic realizations, and it has a Tannakian subcategory of motivic local systems.
Relaxations of GF(4)-Representable Matroids

We consider the $\mathrm{GF}(4)$-representable matroids with a circuit-hyperplane such that the matroid obtained by relaxing the circuit-hyperplane is also $\mathrm{GF}(4)$-representable. We characterize the structure of these matroids as an application of structure theorems for the classes of $U_{2,4}$-fragile and $\{U_{2,5},U_{3,5}\}$-fragile matroids. In addition, we characterize the forbidden submatrices in $\mathrm{GF}(4)$-representations of these matroids.
Stringy Maxwell charge and the magnetic dipole moment

It is shown that stringy charge leads naturally to an observable magnetic moment of the electron.
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough. We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model. We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world.
ON MODIFIED REEDY AND MODIFIED PROJECTIVE MODEL STRUCTURES

Variations on the notions of Reedy model structures and projective model structures on categories of diagrams in a model category are introduced. These allow one to choose only a subset of the entries when dening weak equivalences, or to use dierent model categories at dierent entries of the diagrams. As a result, a bisimplicial model category that can be used to recover the algebraic K-theory for any Waldhausen subcategory of a model category is produced.