cssi/SciSciGPT-SciSciNet huggingface dataset:
Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks

Blockage of some ion channels and in particular, the hERG (human Ether-a’-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.
Fourier transforms of irregular mixed homogeneous hypersurface measures

With the help of Van der Corput lemmas, decay estimates are proven for Fourier transforms of mixed homogeneous hypersurface measures with densities that can be quite irregular. The primary results are local in nature, but can be extended to global theorems in an appropriate sense. The estimates are sharp for a certain range of indices in the theorems.
Decentralized Bayesian learning in dynamic games

We study the problem of Bayesian learning in a dynamical system involving strategic agents with asymmetric information. In a series of seminal papers in the literature, this problem has been studied under a simplifying model where selfish players appear sequentially and act once in the game, based on private noisy observations of the system state and public observation of past players' actions. It is shown that there exist information cascades where users discard their private information and mimic the action of their predecessor. In this paper, we provide a framework for studying Bayesian learning dynamics in a more general setting than the one described above. In particular, our model incorporates cases where players participate for the whole duration of the game, and cases where an endogenous process selects which subset of players will act at each time instance. The proposed methodology hinges on a sequential decomposition for finding perfect Bayesian equilibria (PBE) of a general class of dynamic games with asymmetric information, where user-specific states evolve as conditionally independent Markov processes and users make independent noisy observations of their states. Using our methodology, we study a specific dynamic learning model where players make decisions about investing in the team, based on their estimates of everyone's types. We characterize a set of informational cascades for this problem where learning stops for the team as a whole.
A Tate duality theorem for local Galois symbols II; The semi-abelian case

Abstract This paper is a continuation to [Gaz17] . For every integer n ≥ 1 , we consider the generalized Galois symbol K ( k ; G 1 , G 2 ) / n → s n H 2 ( k , G 1 [ n ] ⊗ G 2 [ n ] ) , where k is a finite extension of Q p , G 1 , G 2 are semi-abelian varieties over k and K ( k ; G 1 , G 2 ) is the Somekawa K-group attached to G 1 , G 2 . Under some mild assumptions, we describe the exact annihilator of the image of s n under the Tate duality perfect pairing, H 2 ( k , G 1 [ n ] ⊗ G 2 [ n ] ) × H 0 ( k , Hom ( G 1 [ n ] ⊗ G 2 [ n ] , μ n ) ) → Z / n . An important special case is when both G 1 , G 2 are abelian varieties with split semistable reduction. In this case we prove a finiteness result, which gives an application to zero-cycles on abelian varieties and products of curves.
ON THE SIMPLICITY OF LIE ALGEBRAS ASSOCIATED TO LEAVITT ALGEBRAS

For any field &#x1d542; and integer n ≥ 2, we consider the Leavitt algebra L &#x1d542;(n); for any integer d ≥ 1, we form the matrix ring S = M d (L &#x1d542;(n)). S is an associative algebra, but we view S as a Lie algebra using the bracket [a, b] = ab − ba for a, b ∈ S. We denote this Lie algebra as S −, and consider its Lie subalgebra [S −, S −]. In our main result, we show that [S −, S −] is a simple Lie algebra if and only if char(&#x1d542;) divides n − 1 and char(&#x1d542;) does not divide d. In particular, when d = 1, we get that [L &#x1d542;(n)−, L &#x1d542;(n)−] is a simple Lie algebra if and only if char(&#x1d542;) divides n − 1.
A Percolation-Based Thresholding Method with Applications in Functional Connectivity Analysis

Despite the recent advances in developing more effective thresholding methods to convert weighted networks to unweighted counterparts, there are still several limitations that need to be addressed. One such limitation is the inability of the most existing thresholding methods to take into account the topological properties of the original weighted networks during the binarization process, which could ultimately result in unweighted networks that have drastically different topological properties than the original weighted networks. In this study, we propose a new thresholding method based on the percolation theory to address this limitation. The performance of the proposed method was validated and compared to the existing thresholding methods using simulated and real-world functional connectivity networks in the brain. Comparison of macroscopic and microscopic properties of the resulted unweighted networks to the original weighted networks suggests that the proposed thresholding method can successfully maintain the topological properties of the original weighted networks.
Shuffle-Compatible Permutation Statistics II: The Exterior Peak Set

This paper is a continuation of the work "Shuffle-compatible permutation statistics" by Gessel and Zhuang (but can be read independently from the latter). We study the shuffle-compatibility of permutation statistics — a concept introduced by Gessel and Zhuang, although various instances of it have appeared throughout the literature before. We prove that (as Gessel and Zhuang have conjectured) the exterior peak set statistic (Epk) is shuffle-compatible. We furthermore introduce the concept of an "LR-shuffle-compatible" statistic, which is stronger than shuffle-compatibility. We prove that Epk and a few other statistics are LR-shuffle-compatible. Furthermore, we connect these concepts with the quasisymmetric functions, in particular the dendriform structure on them.
A class of loops categorically isomorphic to Bruck loops of odd order

We define a new variety of loops, Γ-loops. After showing Γ-loops are power-associative, our main goal is showing a categorical isomorphism between Bruck loops of odd order and Γ-loops of odd order. Once this has been established, we can use the well known structure of Bruck loops of odd order to derive the Odd Order, Lagrange and Cauchy Theorems for Γ-loops of odd order, as well as the nontriviality of the center of finite Γ-p-loops (p odd). Finally, we answer a question posed by Jedlicka, Kinyon and Vojtěchovský about the existence of Hall π-subloops and Sylow p-subloops in commutative automorphic loops.
Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input

In this paper, we explore neural network models that learn to associate segments of spoken audio captions with the semantically relevant portions of natural images that they refer to. We demonstrate that these audio-visual associative localizations emerge from network-internal representations learned as a by-product of training to perform an image-audio retrieval task. Our models operate directly on the image pixels and speech waveform, and do not rely on any conventional supervision in the form of labels, segmentations, or alignments between the modalities during training. We perform analysis using the Places 205 and ADE20k datasets demonstrating that our models implicitly learn semantically-coupled object and word detectors.
Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering

Hierarchical clustering is a popular method for analyzing data which associates a tree to a dataset. Hartigan consistency has been used extensively as a framework to analyze such clustering algorithms from a statistical point of view. Still, as we show in the paper, a tree which is Hartigan consistent with a given density can look very different than the correct limit tree. Specifically, Hartigan consistency permits two types of undesirable configurations which we term over-segmentation and improper nesting. Moreover, Hartigan consistency is a limit property and does not directly quantify difference between trees. In this paper we identify two limit properties, separation and minimality, which address both over-segmentation and improper nesting and together imply (but are not implied by) Hartigan consistency. We proceed to introduce a merge distortion metric between hierarchical clusterings and show that convergence in our distance implies both separation and minimality. We also prove that uniform separation and minimality imply convergence in the merge distortion metric. Furthermore, we show that our merge distortion metric is stable under perturbations of the density. Finally, we demonstrate applicability of these concepts by proving convergence results for two clustering algorithms. First, we show convergence (and hence separation and minimality) of the recent robust single linkage algorithm of Chaudhuri and Dasgupta (2010). Second, we provide convergence results on manifolds for topological split tree clustering.
General Presentations of Algebras.

Abstract For any finite dimensional basic associative algebra, we study the presentation spaces and their relation with the representation spaces. We prove two theorems about a general presentation, one on its subrepresentations and the other on its canonical decomposition. As a special case, we consider rigid presentations. We show how to complete a rigid presentation and study the number of nonisomorphic direct summands and different complements. Based on that, we construct a simplicial complex governing the canonical decompositions of rigid presentations and provide some examples.
Differential Harnack estimates for backward heat equations with potentials under the Ricci flow

Abstract In this paper, we derive a general evolution formula for possible Harnack quantities. As a consequence, we prove several differential Harnack inequalities for positive solutions of backward heat-type equations with potentials (including the conjugate heat equation) under the Ricci flow. We shall also derive Perelman's Harnack inequality for the fundamental solution of the conjugate heat equation under the Ricci flow.
Slicing inequalities for measures of convex bodies

Abstract We consider the following problem. Does there exist an absolute constant C so that for every n ∈ N , every integer 1 ≤ k n , every origin-symmetric convex body L in R n , and every measure μ with non-negative even continuous density in R n , (1) μ ( L ) ≤ C k max H ∈ Gr n − k ⁡ μ ( L ∩ H ) | L | k / n , where Gr n − k is the Grassmanian of ( n − k ) -dimensional subspaces of R n , and | L | stands for volume? This question is an extension to arbitrary measures (in place of volume) and to sections of arbitrary codimension k of the slicing problem, a major open problem in convex geometry. It was proved in [18] , [19] that (1) holds for arbitrary origin-symmetric convex bodies, all k and all μ with C ≤ O ( n ) . In this article, we prove inequality (1) with an absolute constant C for unconditional convex bodies and for duals of bodies with bounded volume ratio. We also prove that for every λ ∈ ( 0 , 1 ) there exists a constant C = C ( λ ) so that inequality (1) holds for every n ∈ N , every origin-symmetric convex body L in R n , every measure μ with continuous density and the codimension of sections k ≥ λ n . The proofs are based on a stability result for generalized intersection bodies and on estimates of the outer volume ratio distance from an arbitrary convex body to the classes of generalized intersection bodies. In the last section, we show that for some measures the behavior of minimal sections may be very different from the case of volume.
A Convex Duality Framework for GANs

Generative adversarial network (GAN) is a minimax game between a generator mimicking the true model and a discriminator distinguishing the samples produced by the generator from the real training samples. Given an unconstrained discriminator able to approximate any function, this game reduces to finding the generative model minimizing a divergence measure, e.g. the Jensen-Shannon (JS) divergence, to the data distribution. However, in practice the discriminator is constrained to be in a smaller class F such as neural nets. Then, a natural question is how the divergence minimization interpretation changes as we constrain F. In this work, we address this question by developing a convex duality framework for analyzing GANs. For a convex set F, this duality framework interprets the original GAN formulation as finding the generative model with minimum JS-divergence to the distributions penalized to match the moments of the data distribution, with the moments specified by the discriminators in F. We show that this interpretation more generally holds for f-GAN and Wasserstein GAN. As a byproduct, we apply the duality framework to a hybrid of f-divergence and Wasserstein distance. Unlike the f-divergence, we prove that the proposed hybrid divergence changes continuously with the generative model, which suggests regularizing the discriminator's Lipschitz constant in f-GAN and vanilla GAN. We numerically evaluate the power of the suggested regularization schemes for improving GAN's training performance.
Flexibility of Schubert classes

Abstract In this note, we discuss the flexibility of Schubert classes in homogeneous varieties. We give several constructions for representing multiples of a Schubert class by irreducible subvarieties. We sharpen [22, Theorem 3.1] by proving that every positive multiple of an obstructed class in a cominuscule homogeneous variety can be represented by an irreducible subvariety.
Learning Vector Autoregressive Models With Latent Processes.

We study the problem of learning the support of transition matrix between random processes in a Vector Autoregressive (VAR) model from samples when a subset of the processes are latent. It is well known that ignoring the effect of the latent processes may lead to very different estimates of the influences among observed processes, and we are concerned with identifying the influences among the observed processes, those between the latent ones, and those from the latent to the observed ones. We show that the support of transition matrix among the observed processes and lengths of all latent paths between any two observed processes can be identified successfully under some conditions on the VAR model. From the lengths of latent paths, we reconstruct the latent subgraph (representing the influences among the latent processes) with a minimum number of variables uniquely if its topology is a directed tree. Furthermore, we propose an algorithm that finds all possible minimal latent graphs under some conditions on the lengths of latent paths. Our results apply to both non-Gaussian and Gaussian cases, and experimental results on various synthetic and real-world datasets validate our theoretical results.
A novel Galerkin method for solving PDES on the sphere using highly localized kernel bases

We present a novel Galerkin method for solving partial differential equations on the sphere. The problem is discretized by a highly localized basis which is easily constructed. The stiffness matrix entries are computed by a recently developed quadrature formula unique to the localized bases we consider. We present error estimates and investigate the stability of the discrete stiffness matrix. Implementation and numerical experiments are discussed.
A quotient stack related to the Weyl algebra

Abstract Let A denote the ring of differential operators on the affine line with its two usual generators t and d d t given degrees +1 and −1 respectively. Let X be the stack having coarse moduli space the affine line Spec k [ z ] and isotropy groups Z / 2 at each integer point. Then the category of graded A-modules is equivalent to the category of quasi-coherent sheaves on X .
Augmented robust PCA for foreground-background separation on noisy, moving camera video

This work presents a novel approach for robust PCA with total variation regularization for foreground-background separation and denoising on noisy, moving camera video. Our proposed algorithm registers the raw (possibly corrupted) frames of a video and then jointly processes the registered frames to produce a decomposition of the scene into a low-rank background component that captures the static components of the scene, a smooth foreground component that captures the dynamic components of the scene, and a sparse component that isolates corruptions. Unlike existing methods, our proposed algorithm produces a panoramic low-rank component that spans the entire field of view, automatically stitching together corrupted data from partially overlapping scenes. The low-rank portion of our robust PCA model is based on a recently discovered optimal low-rank matrix estimator (OptShrink) that requires no parameter tuning. We demonstrate the performance of our algorithm on both static and moving camera videos corrupted by noise and outliers.
Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories

Probabilistic Inference Modulo Theories (PIMT) is a recent framework that expands exact inference on graphical models to use richer languages that include arithmetic, equalities, and inequalities on both integers and real numbers. In this paper, we expand PIMT to a lifted version that also processes random functions and relations. This enhancement is achieved by adapting Inversion, a method from Lifted First-Order Probabilistic Inference literature, to also be modulo theories. This results in the first algorithm for exact probabilistic inference that efficiently and simultaneously exploits random relations and functions, arithmetic, equalities and inequalities.
Black-box importance sampling

Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated. We address this problem by studying black-box importance sampling methods that calculate importance weights for samples generated from any unknown proposal or black-box mechanism. Our method allows us to use better and richer proposals to solve difficult problems, and (somewhat counter-intuitively) also has the additional benefit of improving the estimation accuracy beyond typical importance sampling. Both theoretical and empirical analyses are provided.
Generalized recurrence and the nonwandering set for products

Abstract For continuous maps of compact metric spaces f : X → X and g : Y → Y and for various notions of topological recurrence, we study the relationship between recurrence for f and g and recurrence for the product map f × g : X × Y → X × Y . For the generalized recurrent set GR, we see that GR ( f × g ) = GR ( f ) × GR ( g ) . For the nonwandering set NW, we see that NW ( f × g ) ⊂ NW ( f ) × NW ( g ) and give necessary and sufficient conditions on f for equality for every g. We also consider product recurrence for the chain recurrent set, the strong chain recurrent set, and the Mane set.
KP solitons and total positivity for the Grassmannian

Soliton solutions of the KP equation have been studied since 1970, when Kadomtsev and Petviashvili proposed a two-dimensional nonlinear dispersive wave equation now known as the KP equation. It is well-known that one can use the Wronskian method to construct a soliton solution to the KP equation from each point of the real Grassmannian $$Gr_{k,n}$$ . More recently, several authors (Biondini and Chakravarty, J Math Phys 47:033514, 2006; Biondini and Kodama, J. Phys A Math Gen 36:10519–10536, 2003; Chakravarty and Kodama, J Phys A Math Theor 41:275209, 2008; Chakravarty and Kodama, Stud Appl Math 123:83–151, 2009; Kodama, J Phys A Math Gen 37:11169–11190, 2004) have studied the regular solutions that one obtains in this way: these come from points of the totally non-negative part of the Grassmannian $$(Gr_{k,n})_{\ge 0}$$ . In this paper we exhibit a surprising connection between the theory of total positivity for the Grassmannian, and the structure of regular soliton solutions to the KP equation. By exploiting this connection, we obtain new insights into the structure of KP solitons, as well as new interpretations of the combinatorial objects indexing cells of $$(Gr_{k,n})_{\ge 0}$$ (Postnikov, http://front.math.ucdavis.edu/math.CO/0609764 ). In particular, we completely classify the spatial patterns of the soliton solutions coming from $$(Gr_{k,n})_{\ge 0}$$ when the absolute value of the time parameter is sufficiently large. We demonstrate an intriguing connection between soliton graphs for $$(Gr_{k,n})_{>0}$$ and the cluster algebras of Fomin and Zelevinsky (J Am Math Soc 15:497–529, 2002), and we use this connection to solve the inverse problem for generic KP solitons coming from $$(Gr_{k,n})_{>0}$$ . Finally we construct all the soliton graphs for $$(Gr_{2,n})_{>0}$$ using the triangulations of an $$n$$ -gon.
Spatial models of vector-host epidemics with directed movement of vectors over long distances.

Abstract We investigate a time-dependent spatial vector-host epidemic model with non-coincident domains for the vector and host populations. The host population resides in small non-overlapping sub-regions, while the vector population resides throughout a much larger region. The dynamics of the populations are modeled by a reaction-diffusion-advection compartmental system of partial differential equations. The disease is transmitted through vector and host populations in criss-cross fashion. We establish global well-posedness and uniform a prior bounds as well as the long-term behavior. The model is applied to simulate the outbreak of bluetongue disease in sheep transmitted by midges infected with bluetongue virus. We show that the long-range directed movement of the midge population, due to wind-aided movement, enhances the transmission of the disease to sheep in distant sites.
The maximum size of a partial spread in a finite projective space

Let n and t be positive integers with t (qr−1)/(q−1), then the maximum size, i.e., cardinality, of a partial (t−1)-spread of PG(n−1,q) is (qn−qt+r)/(qt−1)+1. This essentially settles a main open problem in this area. Prior to this result, this maximum size was only known for r∈{0,1} and for r=q=2.
On the difficulty of deciding asymptotic stability of cubic homogeneous vector fields

It is well-known that asymptotic stability (AS) of homogeneous polynomial vector fields of degree one (i.e., linear systems) can be decided in polynomial time e.g. by searching for a quadratic Lyapunov function. Since homogeneous vector fields of even degree can never be AS, the next interesting degree to consider is equal to three. In this paper, we prove that deciding AS of homogeneous cubic vector fields is strongly NP-hard and pose the question of determining whether it is even decidable. As a byproduct of the reduction that establishes our NP-hardness result, we obtain a Lyapunov-inspired technique for proving positivity of forms. We also show that for asymptotically stable homogeneous cubic vector fields in as few as two variables, the minimum degree of a polynomial Lyapunov function can be arbitrarily large. Finally, we show that there is no monotonicity in the degree of polynomial Lyapunov functions that prove AS; i.e., a homogeneous cubic vector field with no homogeneous polynomial Lyapunov function of some degree d can very well have a homogeneous polynomial Lyapunov function of degree less than d.
Mathieu subspaces of associative algebras

Abstract Motivated by the Mathieu conjecture (Mathieu, 1997 [M] ), the image conjecture (Zhao, 2010 [Z3] ) and the well-known Jacobian conjecture (Keller, 1939 [K] ; see also Bass et al., 1982 [BCW] and van den Essen, 2000 [E1] ), the notion of Mathieu subspaces as a natural generalization of the notion of ideals has been introduced recently in Zhao (2010) [Z4] for associative algebras. In this paper, we first study algebraic elements in the radicals of Mathieu subspaces of associative algebras over fields and prove some properties and characterizations of Mathieu subspaces with algebraic radicals. We then give some characterizations or classifications for strongly simple algebras (the algebras with no non-trivial Mathieu subspaces) over arbitrary commutative rings, and for quasi-stable algebras (the algebras all of whose subspaces that do not contain the identity element of the algebra are Mathieu subspaces) over arbitrary fields. Furthermore, co-dimension one Mathieu subspaces and the minimal non-trivial Mathieu subspaces of the matrix algebras over fields are also completely determined.
Typically-correct derandomization for small time and space

Suppose a language L can be decided by a bounded-error randomized algorithm that runs in space S and time n · poly(S). We give a randomized algorithm for L that still runs in space O(S) and time n · poly(S) that uses only O(S) random bits; our algorithm has a low failure probability on all but a negligible fraction of inputs of each length. As an immediate corollary, there is a deterministic algorithm for L that runs in space O(S) and succeeds on all but a negligible fraction of inputs of each length. We also give several other complexity-theoretic applications of our technique.
Dynamic ecological system measures

Abstract The system decomposition theory has recently been developed for the dynamic analysis of nonlinear compartmental systems. The application of this theory to the ecosystem analysis has also been introduced in a separate article. Based on this methodology, multiple new dynamic ecological system measures and indices of matrix, vector, and scalar types are systematically introduced in the present paper. These mathematical system analysis tools are quantitative ecological indicators that monitor the flow distribution and storage organization, quantify the direct, indirect, acyclic, cycling, and transfer ( diact ) effects and utilities of one compartment on another, identify the system efficiencies and stress, measure the compartmental exposures to system flows, determine the residence times and compartmental activity levels, and ascertain the system resilience and resistance in the case of disturbances. The proposed dynamic system measures and indices, thus, extract detailed information about ecosystems’ characteristics, as well as their functions, properties, behaviors, and various other system attributes that are potentially hidden in and even obscured by data. A dynamic technique for the quantitative characterization and classification of main interspecific interactions and the determination of their strength within food webs is also developed based on the diact effect and utility indices. Moreover, major concepts and quantities in the current static network analyses are also extended to nonlinear dynamic settings and integrated with the proposed dynamic measures and indices in this unifying mathematical framework. Therefore, the proposed methodology enables a holistic view and analysis of ecological systems. We consider that the proposed methodology brings a novel complex system theory to the service of urgent and challenging environmental problems of the day and has the potential to lead the way to a more formalistic ecological science.
Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients’ breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes.
TWA -- Ticket Locks Augmented with a Waiting Array

The classic ticket lock is simple and compact, consisting of ticket and grant fields. Arriving threads atomically fetch-and-increment ticket to obtain an assigned ticket value, and then wait for grant to become equal to that value, at which point the thread holds the lock. The corresponding unlock operation simply increments grant. This simple design has short code paths and fast handover (transfer of ownership) under light contention, but may suffer degraded scalability under high contention when multiple threads busy wait on the grant field – so-called global spinning.
Towards realistic practices in low-resource natural language processing: The development set

Development sets are impractical to obtain for real low-resource languages, since using all available data for training is often more effective. However, development sets are widely used in research papers that purport to deal with low-resource natural language processing (NLP). Here, we aim to answer the following questions: Does using a development set for early stopping in the low-resource setting influence results as compared to a more realistic alternative, where the number of training epochs is tuned on development languages? And does it lead to overestimation or underestimation of performance? We repeat multiple experiments from recent work on neural models for low-resource NLP and compare results for models obtained by training with and without development sets. On average over languages, absolute accuracy differs by up to 1.4%. However, for some languages and tasks, differences are as big as 18.0% accuracy. Our results highlight the importance of realistic experimental setups in the publication of low-resource NLP research results.
DppNet: Approximating Determinantal Point Processes with Deep Networks

Determinantal point processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items. For this reason, they have gained prominence in many machine learning applications that rely on subset selection. However, sampling from a DPP over a ground set of size N is a costly operation, requiring in general an O(N^3) preprocessing cost and an O(Nk^3) sampling cost for subsets of size k. We approach this problem by introducing DppNets: generative deep models that produce DPP-like samples for arbitrary ground sets. We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors. We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPPs so powerful and unique. Empirically, we show across multiple datasets that DPPNET is orders of magnitude faster than competing approaches for DPP sampling, while generating high-likelihood samples and performing as well as DPPs on downstream tasks.
The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza

The prediction and characterization of outbreaks of infectious diseases such as influenza remains an open and important problem. This paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten outbreaks collected from two locations over five years. We model outbreaks with compartment models and expliccitly model non-influenza influenza-like illnesses.
Long-run inflation expectations in the shrinking upper tail

Abstract Consumer inflation expectations are highly disperse, with some households reporting very high inflation forecasts. In recent years, disagreement in longer-run inflation expectations has fallen, reflecting compression in the upper part of the distribution. The 75th percentile of the distribution of longer-run inflation forecast has fallen 0.21 percentage points per year since 2012 and is at an all-time low. I show that the decline in long-run inflation expectations at the upper end of the distribution seems to reflect improvement in consumers’ general economic sentiment, rather than stronger anchoring of inflation expectations.
Model reconstruction from temporal data for coupled oscillator networks

In a complex system, the interactions between individual agents often lead to emergent collective behavior such as spontaneous synchronization, swarming, and pattern formation. Beyond the intrinsic properties of the agents, the topology of the network of interactions can have a dramatic influence over the dynamics. In many studies, researchers start with a specific model for both the intrinsic dynamics of each agent and the interaction network and attempt to learn about the dynamics of the model. Here, we consider the inverse problem: given data from a system, can one learn about the model and the underlying network? We investigate arbitrary networks of coupled phase oscillators that can exhibit both synchronous and asynchronous dynamics. We demonstrate that, given sufficient observational data on the transient evolution of each oscillator, machine learning can reconstruct the interaction network and identify the intrinsic dynamics.
Characterizing and Avoiding Negative Transfer

When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task. However, when transferring knowledge from a less related source, it may inversely hurt the target performance, a phenomenon known as negative transfer. Despite its pervasiveness, negative transfer is usually described in an informal manner, lacking rigorous definition, careful analysis, or systematic treatment. This paper proposes a formal definition of negative transfer and analyzes three important aspects thereof. Stemming from this analysis, a novel technique is proposed to circumvent negative transfer by filtering out unrelated source data. Based on adversarial networks, the technique is highly generic and can be applied to a wide range of transfer learning algorithms. The proposed approach is evaluated on six state-of-the-art deep transfer methods via experiments on four benchmark datasets with varying levels of difficulty. Empirically, the proposed method consistently improves the performance of all baseline methods and largely avoids negative transfer, even when the source data is degenerate.
Distributed state estimation by a network of observers under communication and measurement delays

Abstract In this paper we study the distributed state estimation of autonomous dynamic systems, under arbitrarily large communication and measurement delays. In the distributed estimation problem, we consider a network of observer agents that cannot independently determine all the states of the observed plant from their own collected measurements, but they must collaborate with neighboring agents to agree on the correct state estimation. Our current work considers the presence of arbitrarily large time delays in the communication and measurements of the observer agents, and we propose a framework for distributed observers following the low gain methodology. Sufficient conditions for the stability of the corresponding observation error dynamics are derived, including an upper bound for the low gain parameter of the observer equations. In the presence of bounded noisy measurements from the plant, we derive the upper-bound on the local estimation errors. The results of the distributed state estimation is then applied to propose a solution to the leader–follower synchronization problem for multi-agent systems under leader-agent and inter-agent communication delays. An illustrative example is also presented to verify the effectiveness of the theoretical analysis.
Gene expression of endangered coral (Orbicella spp.) in Flower Garden Banks National Marine Sanctuary after Hurricane Harvey

About 190 km south of the Texas–Louisiana border, the East and West Flower Garden Banks (FGB) have maintained >50% coral cover with infrequent and minor incidents of disease or bleaching since monitoring began in the 1970s. However, a mortality event, affecting 5.6 ha (2.6% of the area) of the East FGB, occurred in late July 2016 and coincided with storm-generated freshwater runoff extending offshore and over the reef system. To capture the immediate effects of storm-driven freshwater runoff on coral and symbiont physiology, we leveraged the heavy rainfall associated with Hurricane Harvey in late August 2017 by sampling FGB corals at two time points: September 2017, when surface water salinity was reduced (~34 ppt); and one month later when salinity had returned to typical levels (~36 ppt in October 2017). Tissue samples (N = 47) collected midday were immediately preserved for gene expression profiling from two congeneric coral species (Orbicella faveolata and Orbicella franksi) from the East and West FGB to determine the physiological consequences of storm-derived runoff. In the coral, differences between host species and sampling time points accounted for the majority of differentially expressed genes. Gene ontology enrichment for genes differentially expressed immediately after Hurricane Harvey indicated increases in cellular oxidative stress responses. Although tissue loss was not observed on FGB reefs following Hurricane Harvey, our results suggest that poor water quality following this storm caused FGB corals to experience sub-lethal stress. We also found dramatic expression differences across sampling time points in the coral’s algal symbiont, Breviolum minutum. Some of these differentially expressed genes may be involved in the symbionts’ response to changing environments, including a group of differentially expressed post-transcriptional RNA modification genes. In this study, we cannot disentangle the effects of reduced salinity from the collection time point, so these expression patterns could also be related to seasonality. These findings highlight the urgent need for continued monitoring of these reef systems to establish a baseline for gene expression of healthy corals in the FGB system across seasons, as well as the need for integrated solutions to manage stormwater runoff in the Gulf of Mexico.
DeepXplore: automated whitebox testing of deep learning systems

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains such as self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs. We design, implement, and evaluate DeepXplore, the first white-box framework for systematically testing real-world DL systems. First, we introduce neuron coverage for measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques. DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets such as ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model's accuracy by up to 3%.
A Conceptually Well-Founded Characterization of Iterated Admissibility Using an "All I Know" Operator.

Brandenburger, Friedenberg, and Keisler provide an epistemic characterization of iterated admissibility (IA), also known as iterated deletion of weakly dominated strategies, where uncertainty is represented using LPSs (lexicographic probability sequences). Their characterization holds in a rich structure called a complete structure, where all types are possible. In earlier work, we gave a characterization of iterated admissibility using an "all I know" operator, that captures the intuition that "all the agent knows" is that agents satisfy the appropriate rationality assumptions. That characterization did not need complete structures and used probability structures, not LPSs. However, that characterization did not deal with Samuelson's conceptual concern regarding IA, namely, that at higher levels, players do not consider possible strategies that were used to justify their choice of strategy at lower levels. In this paper, we give a characterization of IA using the all I know operator that does deal with Samuelson's concern. However, it uses LPSs. We then show how to modify the characterization using notions of "approximate belief" and "approximately all I know" so as to deal with Samuelson's concern while still working with probability structures.
Spectral theory of H/sup /spl infin// filters and smoothers

This paper probes the spectral theory of the L/sup /spl infin// filtering/smoothing problem in the 2 block format. The solution for the filter/smoother which satisfies a L/sup /spl infin// performance bound, is presented. Using spectral properties of the problem, it is proved that extending the scope of acceptable compensators from filters to smoothers can not improve the L/sup /spl infin// performance.
Early evidence of digital labor in accounting: Innovation with Robotic Process Automation

Abstract Robotic Process Automation (RPA) is an emerging technology that enables the automation of rules-based business processes and tasks through the use of software bots. Drawing upon the theory of Task-Technology Fit (TTF) and Technology-to-Performance Chain (TPC) (Goodhue and Thompson 1995) and research on expert systems (Messier and Hansen 1987; Sutton 1990), this study explores emerging themes surrounding bot implementation for accounting and finance tasks. We collect and analyze interview data from adopters of RPA and document task suitability, task-technology fit, implementation issues, and resulting performance outcomes. We find that securing technical capability is only a part of RPA implementation process. Organizations engage in standardization and optimization of processes, develop scorecard-like tools to rank tasks, adjust governance structures to include digital employees, and redefine internal controls. Organizations benefit from automating only certain processes, those that are structured, repeated, rules-based, and with digital inputs. Along with cost savings, organizations experience improved process documentation, lower error rates, more accurate measurement of process performance, and better report quality.
Fourier Analysis From Networked Measurements Using Time Synchronization

Can frequency-domain analysis be made from time-domain measurements taken over networks with poor temporal characteristics, that is, poor or no guarantees about when commands or data will be delivered, such as the public Internet? Our answer to this question is ldquoyes.rdquo Provided that the times that samples are taken are measured accurately, Fourier analysis can be performed, even when the samples are taken at nonuniform intervals. Due to the tendency of packet delays in networks to have long-tailed probability distributions, it is also desirable not to make assumptions concerning the probability distribution of the time intervals between samples. We show that the combination of network time synchronization and stabilization of measurement apparatus using the IEEE 1588 Protocol and the methods for Fourier analysis of nonuniformly spaced data from the literature are sufficient to provide spectra from measurements taken with networked sensors. The key steps of our method are an accurate timestamping of the measurements followed by a Fourier transform. The Fourier transform may be accomplished by either resampling the measurements into a constant rate so that fast Fourier transforms may be used or performing a least squares fit of a sine/cosine basis to the data. Both approaches were tested using measurements taken over the public Internet using measurement apparatus synchronized using IEEE 1588.
On transformations of Markov chains and Poisson boundary

A discrete-time Markov chain can be transformed into a new Markov chain by looking at its states along iterations of an almost surely finite stopping time. By the optional stopping theorem, any bounded harmonic function with respect to the transition function of the original chain is harmonic with respect to the transition function of the transformed chain. The reverse inclusion is in general not true. Our main result provides a sufficient condition on the stopping time which guarantees that the space of bounded harmonic functions for the transformed chain embeds in the space of bounded harmonic sequences for the original chain. We also obtain a similar result on positive unbounded harmonic functions, under some additional conditions. Our work was motivated by and is analogous to Forghani-Kaimanovich, the well-studied case when the Markov chain is a random walk on a discrete group.