cssi/SciSciGPT-SciSciNet huggingface dataset:
Robust Adaptive Routing Under Uncertainty

We consider the problem of finding an optimal history-dependent routing strategy on a directed graph weighted by stochastic arc costs when the objective is to minimize the risk of spending more than a prescribed budget. To help mitigate the impact of the lack of information on the arc cost probability distributions, we introduce a robust counterpart where the distributions are only known through confidence intervals on some statistics such as the mean, the mean absolute deviation, and any quantile. Leveraging recent results in distributionally robust optimization, we develop a general-purpose algorithm to compute an approximate optimal strategy. To illustrate the benefits of the robust approach, we run numerical experiments with field data from the Singapore road network. The e-companion is available at https://doi.org/10.1287/opre.2017.1662.
Numerical Inverse Scattering for the Toda Lattice

We present a method to compute the inverse scattering transform (IST) for the famed Toda lattice by solving the associated Riemann–Hilbert (RH) problem numerically. Deformations for the RH problem are incorporated so that the IST can be evaluated in $${\mathcal O(1)}$$ operations for arbitrary points in the (n, t)-domain, including short- and long-time regimes. No time-stepping is required to compute the solution because (n, t) appear as parameters in the associated RH problem. The solution of the Toda lattice is computed in long-time asymptotic regions where the asymptotics are not known rigorously.
Life versus dark energy: How an advanced civilization could resist the accelerating expansion of the universe

Abstract The presence of dark energy in our universe is causing space to expand at an accelerating rate. As a result, over the next approximately 100 billion years, all stars residing beyond the Local Group will fall beyond the cosmic horizon and become not only unobservable, but entirely inaccessible, thus limiting how much energy could one day be extracted from them. Here, we consider the likely response of a highly advanced civilization to this situation. In particular, we argue that in order to maximize its access to usable energy, a sufficiently advanced civilization would chose to expand rapidly outward, build Dyson Spheres or similar structures around encountered stars, and use the energy that is harnessed to accelerate those stars away from the approaching horizon and toward the center of the civilization. We find that such efforts will be most effective for stars with masses in the range of M ∼ ( 0 . 2 – 1 ) M ⊙ , and could lead to the harvesting of stars within a region extending out to several tens of Mpc in radius, potentially increasing the total amount of energy that is available to a future civilization by a factor of several thousand. We also discuss the observable signatures of a civilization elsewhere in the universe that is currently in this state of stellar harvesting.
Stein Variational Gradient Descent Without Gradient

Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions. However, the standard SVGD requires calculating the gradient of the target density and cannot be applied when the gradient is unavailable. In this work, we develop a gradient-free variant of SVGD (GF-SVGD), which replaces the true gradient with a surrogate gradient, and corrects the induced bias by re-weighting the gradients in a proper form. We show that our GF-SVGD can be viewed as the standard SVGD with a special choice of kernel, and hence directly inherits the theoretical properties of SVGD. We shed insights on the empirical choice of the surrogate gradient and propose an annealed GF-SVGD that leverages the idea of simulated annealing to improve the performance on high dimensional complex distributions. Empirical studies show that our method consistently outperforms a number of recent advanced gradient-free MCMC methods.
Independence of iterated Whitehead doubles

A theorem of Furuta and Fintushel-Stern provides a criterion for a collection of Seifert fibred homology spheres to be independent in the homology cobordism group of oriented homology 3-spheres. In this article we use these results and some 4-dimensional constructions to produce infinite families of positive torus knots whose iterated Whitehead doubles are independent in the smooth concordance group.
Syndrome decoding of reed-muller codes and tensor decomposition over finite fields

Reed-Muller codes are some of the oldest and most widely studied error-correcting codes, of interest for both their algebraic structure as well as their many algorithmic properties. A recent beautiful result of Saptharishi, Shpilka and Volk [SSV17] showed that for binary Reed-Muller codes of length n and distance d = O(1), one can correct polylog(n) random errors in poly(n) time (which is well beyond the worst-case error tolerance of O(1)). In this paper, we consider the problem of syndrome decoding Reed-Muller codes from random errors. More specifically, given the polylog(n)-bit long syndrome vector of a codeword corrupted in polylog(n) random coordinates, we would like to compute the locations of the codeword corruptions. This problem turns out to be equivalent to a basic question about computing tensor decomposition of random low-rank tensors over finite fields. Our main result is that syndrome decoding of Reed-Muller codes (and the equivalent tensor decomposition problem) can be solved efficiently, i.e., in polylog(n) time. We give two algorithms for this problem: 1. The first algorithm is a finite field variant of a classical algorithm for tensor decomposition over real numbers due to Jennrich. This also gives an alternate proof for the main result of [SSV17]. 2. The second algorithm is obtained by implementing the steps of [SSV17]'s Berlekamp-Welch-style decoding algorithm in sublinear-time. The main new ingredient is an algorithm for solving certain kinds of systems of polynomial equations.
Quantum entanglement in the initial and final state of relativistic heavy ion collisions

The possibility of quantum entanglement leading to a seemingly thermal distribution of the initial partonic state that maps to the final hadronic state in the evolution of the deconfined phase generated in relativistic heavy ion collisions is discussed in the context of early thermalization and final state particle distributions obtained in experiments at RHIC and the LHC.
Power Flow Solvers for Direct Current Networks

With increasing smart grid direct current (DC) deployments in distribution feeders, microgrids, buildings, and high-voltage transmission, there is a need for better understanding the landscape of power flow (PF) solutions as well as for efficient PF solvers with performance guarantees. This paper puts forth three approaches with complementary strengths towards coping with the PF task in DC power systems. We consider a possibly meshed network hosting ZIP loads and constant-voltage/power generators. The first approach relies on a monotone mapping. In the absence of constant-power generation, the related iterates converge to the high-voltage solution, if one exists. To handle generators operating in constant-power mode at any time, an alternative Z-bus method is studied. For bounded constant-power generation and demand, the analysis establishes the existence and uniqueness of a PF solution within a predefined ball. Moreover, the Z-bus updates converge to this solution. Third, an energy function approach shows that under limited constant-power demand, all PF solutions are local minima of a function. The derived conditions can be checked without knowing the system state. The applicability of the conditions and the performance of the algorithms are numerically validated on a radial distribution feeder and two meshed transmission systems under varying loading conditions.
Excited-state contamination in nucleon correlators from chiral perturbation theory

Techniques to compute hadron properties from lattice QCD rely upon the limit of long time separation. For baryons, the signal-to-noise problem often restricts one to time separations that are not ideally long, and for which couplings to excited states can obstruct the isolation of ground-state baryon properties. We consider excited-state contamination in nucleon two- and three-point functions. Using chiral perturbation theory, we determine couplings to pion-nucleon and pion-delta excited states. In two-point functions, these contributions are small, in accordance with general properties of the spectral weights on a torus. For the axial-current correlation function in the nucleon, the sign of excited-state contributions suggests overestimation of the nucleon axial charge. Thus contamination from pion-nucleon excited states will not likely explain the trend in lattice QCD data.
The moduli space of maps with crosscaps: the relative signs of the natural automorphisms

Just as a symmetric surface with separating fixed locus halves into two oriented bordered surfaces, an arbitrary symmetric surface halves into two oriented symmetric half-surfaces, i.e. surfaces with crosscaps. Motivated in part by the string theory view of real Gromov-Witten invariants, we previously introduced moduli spaces of maps from surfaces with crosscaps, developed the relevant Fredholm theory, and resolved the orientability problem in this setting. In this paper, we determine the relative signs of the automorphisms of these moduli spaces induced by interchanges of boundary components of the domain and by the anti-symplectic involution on the target manifold, without any global assumptions on the latter. As immediate applications, we describe sufficient conditions for the moduli spaces of real genus 1 maps and for real maps with separating fixed locus to be orientable; we treat the general genus 2+ case in a separate paper. Our sign computations also lead to an extension of recent Floer-theoretic applications of anti-symplectic involutions and to a related reformulation of these results in a more natural way.
Strong Data Processing Inequalities and $\Phi $ -Sobolev Inequalities for Discrete Channels

The noisiness of a channel can be measured by comparing suitable functionals of the input and output distributions. For instance, the worst case ratio of output relative entropy to input relative entropy for all possible pairs of input distributions is bounded from above by unity, by the data processing theorem. However, for a fixed reference input distribution, this quantity may be strictly smaller than one, giving the so-called strong data processing inequalities (SDPIs). The same considerations apply to an arbitrary $\Phi $ -divergence. This paper presents a systematic study of optimal constants in the SDPIs for discrete channels, including their variational characterizations, upper and lower bounds, structural results for channels on product probability spaces, and the relationship between the SDPIs and the so-called $\Phi $ -Sobolev inequalities (another class of inequalities that can be used to quantify the noisiness of a channel by controlling entropy-like functionals of the input distribution by suitable measures of input–output correlation). Several applications to information theory, discrete probability, and statistical physics are discussed.
On low dimensional KC-spaces

Abstract The KC property, a separation axiom between weakly Hausdorff and Hausdorff, requires compact subsets to be closed. Various assumptions involving local conditions, dimension, connectivity, and homotopy show certain KC-spaces are in fact Hausdorff. Several low dimensional examples of compact, connected, non-Hausdorff KC-spaces are exhibited in which the nested intersection of compact connected subsets fails to be connected.
Singularity of the generator subalgebra in $q$-Gaussian algebras

Given $-1 < q < 1$ and a separable real Hilbert space with dimension no less than 2, we prove that the generator subalgebra in the $q$-Gaussian algebra is singular.
Randomized polynomial-time root counting in prime power rings

Suppose $k,p\!\in\!\mathbb{N}$ with $p$ prime and $f\!\in\!\mathbb{Z}[x]$ is a univariate polynomial with degree $d$ and all coefficients having absolute value less than $p^k$. We give a Las Vegas randomized algorithm that computes the number of roots of $f$ in $\mathbb{Z}/\!\left(p^k\right)$ within time $d^3(k\log p)^{2+o(1)}$. (We in fact prove a more intricate complexity bound that is slightly better.) The best previous general algorithm had (deterministic) complexity exponential in $k$. We also present some experimental data evincing the potential practicality of our algorithm.
Rapid Adaptation with Conditionally Shifted Neurons.

We describe a mechanism by which artificial neural networks can learn rapid adaptation – the ability to adapt on the fly, with little data, to new tasks – that we call conditionally shifted neurons. We apply this mechanism in the framework of metalearning, where the aim is to replicate some of the flexibility of human learning in machines. Conditionally shifted neurons modify their activation values with task-specific shifts retrieved from a memory module, which is populated rapidly based on limited task experience. On meta-learning benchmarks from the vision and language domains, models augmented with conditionally shifted neurons achieve state-of-the-art results.
Near-separable Non-negative Matrix Factorization with $\ell_1$- and Bregman Loss Functions

Recently, a family of tractable NMF algorithms have been proposed under the assumption that the data matrix satisfies a separability condition Donoho & Stodden (2003); Arora et al. (2012). Geometrically, this condition reformulates the NMF problem as that of finding the extreme rays of the conical hull of a finite set of vectors. In this paper, we develop several extensions of the conical hull procedures of Kumar et al. (2013) for robust (l1) approximations and Bregman divergences. Our methods inherit all the advantages of Kumar et al. (2013) including scalability and noise-tolerance. We show that on foreground-background separation problems in computer vision, robust near-separable NMFs match the performance of Robust PCA, considered state of the art on these problems, with an order of magnitude faster training time. We also demonstrate applications in exemplar selection settings.
Fast and robust tensor decomposition with applications to dictionary learning.

We develop fast spectral algorithms for tensor decomposition that match the robustness guarantees of the best known polynomial-time algorithms for this problem based on the sum-of-squares (SOS) semidefinite programming hierarchy. Our algorithms can decompose a 4-tensor with $n$-dimensional orthonormal components in the presence of error with constant spectral norm (when viewed as an $n^2$-by-$n^2$ matrix). The running time is $n^5$ which is close to linear in the input size $n^4$. We also obtain algorithms with similar running time to learn sparsely-used orthogonal dictionaries even when feature representations have constant relative sparsity and non-independent coordinates. The only previous polynomial-time algorithms to solve these problem are based on solving large semidefinite programs. In contrast, our algorithms are easy to implement directly and are based on spectral projections and tensor-mode rearrangements. Or work is inspired by recent of Hopkins, Schramm, Shi, and Steurer (STOC'16) that shows how fast spectral algorithms can achieve the guarantees of SOS for average-case problems. In this work, we introduce general techniques to capture the guarantees of SOS for worst-case problems.
Scalable Bayesian Nonparametric Clustering and Classification.

We develop a scalable multistep Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is “embarrassingly parallel” an...
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts

We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential short-text classification (i.e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.
Thermoelectric properties of nanocomposite heavy fermion CeCu6

Abstract We report on the thermoelectric performance of the heavy fermion compound CeCu 6 nanocomposite samples. Measurements of Seebeck coefficient, electrical resistivity and thermal conductivity are presented over the temperature range 5 T ZT ) was optimized by varying the sample hot-pressing temperature. Thermal conductivity measurements show that the lowest hot pressing temperature (450 °C) produces the lowest thermal conductivity. Electrical resistivity is strongly influenced by hot pressing temperature and drops by a factor of ∼3.4 as the hot pressing temperature is lowered from 800 to 450 °C. Seebeck coefficient shows a slight increase over other samples when hot pressed at 800 °C. Our ZT calculations show a broad peak with a maximum value of 0.024 at ∼60 K for the sample hot pressed at 800 °C.
Excited meson radiative transitions from lattice QCD using variationally optimized operators

We explore the use of ``optimized'' operators, designed to interpolate only a single meson eigenstate, in three-point correlation functions with a vector-current insertion. These operators are constructed as linear combinations in a large basis of meson interpolating fields using a variational analysis of matrices of two-point correlation functions. After performing such a determination at both zero and nonzero momentum, we compute three-point functions and are able to study radiative transition matrix elements featuring excited-state mesons. The required two- and three-point correlation functions are efficiently computed using the distillation framework in which there is a factorization between quark propagation and operator construction, allowing for a large number of meson operators of definite momentum to be considered. We illustrate the method with a calculation using anisotopic lattices having three flavors of dynamical quark all tuned to the physical strange quark mass, considering form factors and transitions of pseudoscalar and vector meson excitations. The dependence on photon virtuality for a number of form factors and transitions is extracted, and some discussion of excited-state phenomenology is presented.
Atomic Layer Deposition and Superconducting Properties of NbSi Films

Atomic layer deposition was used to synthesize niobium silicide (NbSi) films with a 1:1 stoichiometry, using NbF5 and Si2H6 as precursors. The growth mechanism at 200 °C was examined by in-situ qua...
Minimality of symplectic fiber sums along spheres

In this note we complete the discussion of minimality of symplectic fiber sums. We find, that for fiber sums along spheres the minimality of the sum is determined by the cases discussed by M. Usher and one additional case: If the sum is the result of the rational blow-down of a symplectic -4-sphere in X, then it is non-minimal if X contains a certain configuration of exceptional spheres in relation to this -4-sphere.
Sparse depth sensing for resource-constrained robots:

We consider the case in which a robot has to navigate in an unknown environment, but does not have enough on-board power or payload to carry a traditional depth sensor (e.g., a 3D lidar) and thus c...
Invariant measure and large time dynamics of the cubic Klein–Gordon equation in 3D

In this paper we construct an invariant probability measure concentrated on $$H^2(K)\times H^1(K)$$ for a general cubic Klein–Gordon equation (including the case of the wave equation). Here K represents both the 3-dimensional torus or a bounded domain with smooth boundary in $${\mathbb {R}}^3$$ . That allows to deduce some corollaries on the long time behaviour of the flow of the equation in a probabilistic sense. We also establish qualitative properties of the constructed measure. This work extends the fluctuation–dissipation-limit approach to PDEs having only one (coercive) conservation law.
Global well-posedness and scattering for the defocusing energy-supercritical cubic nonlinear wave equation

Abstract In this paper, we consider the defocusing cubic nonlinear wave equation u t t − Δ u + | u | 2 u = 0 in the energy-supercritical regime, in dimensions d ⩾ 6 , with no radial assumption on the initial data. We prove that if a solution satisfies an a priori bound in the critical homogeneous Sobolev space throughout its maximal interval of existence, that is, u ∈ L t ∞ ( H ˙ x s c × H ˙ x s c − 1 ) , then the solution is global and it scatters. Our analysis is based on the methods of the recent works of Kenig and Merle (2008) [21] and Killip and Visan (2010) [26] , [27] treating the energy-supercritical nonlinear Schrodinger and wave equations.
Dissimilarity-Based Sparse Subset Selection

Finding an informative subset of a large collection of data points or models is at the center of many problems in computer vision, recommender systems, bio/health informatics as well as image and natural language processing. Given pairwise dissimilarities between the elements of a ‘source set’ and a ‘target set,’ we consider the problem of finding a subset of the source set, called representatives or exemplars , that can efficiently describe the target set. We formulate the problem as a row-sparsity regularized trace minimization problem. Since the proposed formulation is, in general, NP-hard, we consider a convex relaxation. The solution of our optimization finds representatives and the assignment of each element of the target set to each representative, hence, obtaining a clustering. We analyze the solution of our proposed optimization as a function of the regularization parameter. We show that when the two sets jointly partition into multiple groups, our algorithm finds representatives from all groups and reveals clustering of the sets. In addition, we show that the proposed framework can effectively deal with outliers. Our algorithm works with arbitrary dissimilarities, which can be asymmetric or violate the triangle inequality. To efficiently implement our algorithm, we consider an Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. We show that the ADMM implementation allows to parallelize the algorithm, hence further reducing the computational time. Finally, by experiments on real-world datasets, we show that our proposed algorithm improves the state of the art on the two problems of scene categorization using representative images and time-series modeling and segmentation using representative models.
Measurement-Wise Occlusion in Multi-Object Tracking

Handling object interaction is a fundamental challenge in practical multi-object tracking, even for simple interactive effects such as one object temporarily occluding another. We formalize the problem of occlusion in tracking with two different abstractions. In object-wise occlusion, objects that are occluded by other objects do not generate measurements. In measurement-wise occlusion, a previously unstudied approach, all objects may generate measurements but some measurements may be occluded by others. While the relative validity of each abstraction depends on the situation and sensor, measurement-wise occlusion fits into probabilistic multi-object tracking algorithms with much looser assumptions on object interaction. Its value is demonstrated by showing that it naturally derives a popular approximation for lidar tracking, and by an example of visual tracking in image space.
A Complex Analogue of Toda’s Theorem

Toda (SIAM J. Comput. 20(5):865–877, 1991) proved in 1989 that the (discrete) polynomial time hierarchy, PH, is contained in the class P #P , namely the class of languages that can be decided by a Turing machine in polynomial time given access to an oracle with the power to compute a function in the counting complexity class #P. This result, which illustrates the power of counting, is considered to be a seminal result in computational complexity theory. An analogous result (with a compactness hypothesis) in the complexity theory over the reals (in the sense of Blum–Shub–Smale real machines (Blum et al. in Bull. Am. Math. Soc. 21(1):1–46, 1989) was proved in Basu and Zell (Found. Comput. Math. 10(4):429–454, 2010). Unlike Toda’s proof in the discrete case, which relied on sophisticated combinatorial arguments, the proof in Basu and Zell (Found. Comput. Math. 10(4):429–454, 2010) is topological in nature; the properties of the topological join are used in a fundamental way. However, the constructions used in Basu and Zell (Found. Comput. Math. 10(4):429–454, 2010) were semi-algebraic—they used real inequalities in an essential way and as such do not extend to the complex case. In this paper, we extend the techniques developed in Basu and Zell (Found. Comput. Math. 10(4):429–454, 2010) to the complex projective case. A key role is played by the complex join of quasi-projective complex varieties. As a consequence, we obtain a complex analogue of Toda’s theorem. The results of this paper, combined with those in Basu and Zell (Found. Comput. Math. 10(4):429–454, 2010), illustrate the central role of the Poincare polynomial in algorithmic algebraic geometry, as well as in computational complexity theory over the complex and real numbers: the ability to compute it efficiently enables one to decide in polynomial time all languages in the (compact) polynomial hierarchy over the appropriate field.
Bipyramids and bounds on volumes of hyperbolic links

Abstract We present two methods to decompose a link complement into bipyramids, which have a variety of applications. Here, we apply them to obtain new upper bounds on volume for hyperbolic link complements in terms of the combinatorics of their projections.
Beyond Binomial and Negative Binomial: Adaptation in Bernoulli Parameter Estimation

Estimating the parameter of a Bernoulli process arises in many applications, including photon-efficient active imaging where each illumination period is regarded as a single Bernoulli trial. Motivated by acquisition efficiency when multiple Bernoulli processes (e.g., multiple pixels) are of interest, we formulate the allocation of trials under a constraint on the mean as an optimal resource allocation problem. An oracle-aided trial allocation demonstrates that there can be a significant advantage from varying the allocation for different processes and inspires the introduction of a simple trial allocation gain quantity. Motivated by achieving this gain without an oracle, in this paper, we present a trellis-based framework for representing and optimizing stopping rules. Considering the convenient case of Beta priors, three implementable stopping rules with similar performances are explored, and the simplest of these is shown to asymptotically achieve the oracle-aided trial allocation. These approaches are further extended to estimating functions of a Bernoulli parameter. In simulations inspired by realistic active imaging scenarios, we demonstrate significant mean-squared error improvements up to 4.36 dB for the estimation of $p$ and up to 1.86 dB for the estimation of $\log p$ .
Deep Variational Information Bottleneck

We present a variational approximation to the information bottleneck of Tishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method "Deep Variational Information Bottleneck", or Deep VIB. We show that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack.
Fusing Censored Dependent Data for Distributed Detection

In this paper, we consider a distributed detection problem for a censoring sensor network where each sensor's communication rate is significantly reduced by transmitting only “informative” observations to the Fusion Center (FC), and censoring those deemed “uninformative”. While the independence of data from censoring sensors is often assumed in previous research, we explore spatial dependence among observations. Our focus is on designing the fusion rule under the Neyman-Pearson (NP) framework that takes into account the spatial dependence among observations. Two transmission scenarios are considered: one where uncensored observations are transmitted directly to the FC, and second where they are first quantized and then transmitted to further improve transmission efficiency. Copula-based Generalized Likelihood Ratio Test (GLRT) for censored data is proposed with both continuous and discrete messages received at the FC corresponding to different transmission strategies. We address the computational issues of the copula-based GLRTs involving multidimensional integrals by presenting more efficient fusion rules, based on the key idea of injecting controlled noise at the FC before fusion. Although, the signal-to-noise ratio (SNR) is reduced by introducing controlled noise at the receiver, simulation results demonstrate that the resulting noise-aided fusion approach based on adding artificial noise performs very closely to the exact copula-based GLRTs. Copula-based GLRTs and their noise-aided counterparts by exploiting the spatial dependence greatly improve detection performance compared with the fusion rule under independence assumption.
A Diophantine Frobenius problem related to Riemann surfaces

We obtain sharp upper and lower bounds on a certain four-dimensional Frobenius number determined by a prime pair (p, q), 2 < p < q, including exact formulae for two infinite subclasses of such pairs. Our work is motivated by the study of compact Riemann surfaces which can be realized as a semi-regular pq-fold coverings of surfaces of lower genus. In this context, the Frobenius number is (up to an additive translation) the largest genus in which no surface is such a covering. In many cases it is also the largest genus in which no surface admits an automorphism of order pq. The general t-dimensional Frobenius problem (t� 3) is NP-hard, and it may be that our restricted problem retains this property. 1
Secure state estimation: Optimal guarantees against sensor attacks in the presence of noise

Motivated by the need to secure cyber-physical systems against attacks, we consider the problem of estimating the state of a noisy linear dynamical system when a subset of sensors is arbitrarily corrupted by an adversary. We propose a secure state estimation algorithm and derive (optimal) bounds on the achievable state estimation error. In addition, as a result of independent interest, we give a coding theoretic interpretation for prior work on secure state estimation against sensor attacks in a noiseless dynamical system.
PARSEVAL FRAMES FOR ICC GROUPS

Abstract We analyze Parseval frames generated by the action of an ICC group on a Hilbert space. We parametrize the set of all such Parseval frames by operators in the commutant of the corresponding representation. We characterize when two such frames are strongly disjoint. We prove an undersampling result showing that if the representation has a Parseval frame of equal norm vectors of norm 1 N , the Hilbert space is spanned by an orthonormal basis generated by a subgroup. As applications we obtain some sufficient conditions under which a unitary representation admits a Parseval frame which is spanned by a Riesz sequences generated by a subgroup. In particular, every subrepresentation of the left-regular representation of a free group has this property.
Im2Flow: Motion Hallucination from Static Images for Action Recognition

Existing methods to recognize actions in static images take the images at their face value, learning the appearances-objects, scenes, and body poses-that distinguish each action class. However, such models are deprived of the rich dynamic structure and motions that also define human activity. We propose an approach that hallucinates the unobserved future motion implied by a single snapshot to help static-image action recognition. The key idea is to learn a prior over short-term dynamics from thousands of unlabeled videos, infer the anticipated optical flow on novel static images, and then train discriminative models that exploit both streams of information. Our main contributions are twofold. First, we devise an encoder-decoder convolutional neural network and a novel optical flow encoding that can translate a static image into an accurate flow map. Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition. On seven datasets, we demonstrate the power of the approach. It not only achieves state-of-the-art accuracy for dense optical flow prediction, but also consistently enhances recognition of actions and dynamic scenes.
Twisted modules and co-invariants for commutative vertex algebras of jet schemes

Abstract Let Z ⊂ A k be an affine scheme over C and J Z its jet scheme. It is well-known that C [ J Z ] , the coordinate ring of J Z , has the structure of a commutative vertex algebra. This paper develops the orbifold theory for C [ J Z ] . A finite-order linear automorphism g of Z acts by vertex algebra automorphisms on C [ J Z ] . We show that C [ J g Z ] , where J g Z is the scheme of g-twisted jets has the structure of a g-twisted C [ J Z ] module. We consider spaces of orbifold coinvariants valued in the modules C [ J g Z ] on orbicurves [ Y / G ] , with Y a smooth projective curve and G a finite group, and show that these are isomorphic to C [ Z G ] .
A Rapid and Automated Sample-to-Result Candida auris Real-Time PCR Assay for High-Throughput Testing of Surveillance Samples with the BD Max Open System.

ABSTRACT The multidrug-resistant yeast pathogen Candida auris continues to cause outbreaks and clusters of clinical cases worldwide. Previously, we developed a real-time PCR assay for the detection of C. auris from surveillance samples (L. Leach, Y. Zhu, and S. Chaturvedi, J Clin Microbiol 56:e01223-17, 2018, https://doi.org/10.1128/JCM.01223-17). The assay played a crucial role in the ongoing investigations of the C. auris outbreak in New York City. To ease the implementation of the assay in other laboratories, we developed an automated sample-to-result real-time C. auris PCR assay using the BD Max open system. We optimized sample extraction at three different temperatures and four incubation periods. Sensitivity was determined using eight pools of patient samples, and specificity was calculated using four clades of C. auris and closely and distantly related yeasts. Three independent extractions and testing of two patient sample pools in quadruplicate yielded assay precision. BD Max optimum assay conditions were as follows: DNA extraction at 75°C for 20 min and the use of PerfeCTa multiplex quantitative PCR (qPCR) ToughMix. The limit of detection (LOD) of the assay was one C. auris CFU/PCR. We detected all four clades of C. auris without cross-reactivity to other yeasts. Of the 110 patient surveillance samples tested, 50 were positive for C. auris using the BD Max system with 96% clinical sensitivity and 94% accuracy compared to the results of the manual assay. The BD Max assay allows high-throughput C. auris screening of 180 surveillance samples in a 12-h workday.
Addict Free - A Smart and Connected Relapse Intervention Mobile App

It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods. Current studies suggest that those factors can be utilized for better relapse interventions, however, there is no relapse prevention application that makes use of those factors. In this paper, we introduce a mobile app called "Addict Free", which records user profiles, tracks relapse history and summarizes recovering statistics to help users better understand their recovering situations. Also, this app builds a relapse recovering community, which allows users to ask for advice and encouragement, and share relapse prevention experience. Moreover, machine learning algorithms that ingest spatial and temporal factors are utilized to predict relapse, based on which helpful addiction diversion activities are recommended by a recovering recommendation algorithm. By interacting with users, this app targets at providing smart suggestions that aim to stop relapse, especially for alcohol and tobacco addiction users.
Oral Administration of Clinically Relevant Antimalarial Drugs Does Not Modify the Murine Gut Microbiota

Malaria is an infectious disease responsible for the death of around 450,000 people annually. As an effective vaccine against the parasite that causes malaria is not available, antimalarial drug treatments are critical in fighting the disease. Previous data has shown that the gut microbiota is important in modulating the severity of malaria. Although it is well appreciated that antibiotics substantially alter the gut microbiota, it is largely unknown how antimalarial drugs impact the gut microbiota. We show here that the two commonly used artemisinin combination therapies of artesunate plus amodiaquine and artemether plus lumefantrine do not change the gut microbiota. The overall relative species abundance and alpha diversity remained stable after treatment, while beta diversity analysis showed minimal changes due to drug treatment, which were transient and quickly returned to baseline. Additionally, treatment with antimalarial drugs did not change the kinetics of later Plasmodium infection. Taken together, antimalarial drug administration does not affect the gut microbiota.
A Commercially Available Ruthenium Compound for Catalytic Hydrophosphination

Hydrophosphination with a commercially available ruthenium compound, bis(cyclopentadienylruthenium dicarbonyl) dimer ([CpRu(CO)2]2), was explored. Styrene derivatives or Michael acceptors react readily with either primary or secondary phosphines in the presence of 0.1 mol% of [CpRu(CO)2]2 under photolysis with an inexpensive and commercially available UV-A 9W lamp. In comparison to related photoactivated hydrophosphination reactions with [CpFe(CO)2]2 as a catalyst, these ruthenium-catalyzed reactions proceed at greater relative rates with lower catalyst loadings.
The complexity of the classification problems of finite-dimensional continua

Abstract We consider the homeomorphic classification of finite-dimensional continua as well as several related equivalence relations. We show that, when n ≥ 2 , the classification problem of n-dimensional continua is strictly more complex than the isomorphism problem of countable graphs. We also obtain results that compare the relative complexity of various equivalence relations.
Big data analytics for seismic fracture identification using amplitude-based statistics

Present-day innovations in seismic acquisition tools and techniques have enabled the acquisition of detailed seismic datasets, which in many cases are extremely large (on the order of terabytes to petabytes). However, data analysis tools for extracting information on critical subsurface features such as fractures are still evolving. Traditional methods rely on time-consuming iterative workflows, which involve computing seismic attributes, de-noising, and expert interpretation. Additionally, with the increasingly widespread acquisition of time-lapse seismic surveys (4D), there is a heightened demand for reliable automated workflows to assist feature interpretation from seismic data. We present a novel data-driven tool for fast fracture identification in BIG post-stack seismic datasets, motivated by techniques developed for real-time face detection. The proposed algorithm computes spatio-temporal amplitude statistics using Haar-like bases, in order to characterize the seismic amplitude properties that correspond to fracture occurrence in a unit window or voxel. Under this approach, the amplitude data is decomposed into a collection of simple-to-calculate “mini-attributes,” which carry information on the amplitude gradient and curvature characteristics at varying locations and scales. These features then serve as inputs to a cascade of boosted classification tree models, which select and combine the most discriminative features to develop a probabilistic binary classification model. This overall approach helps to eliminate the computationally intensive and subjective use of ad hoc seismic attributes in existing approaches. We first demonstrate the viability of the proposed methodology for identifying discrete macro-fractures in a 2D synthetic seismic dataset. Next, we validate the approach using 3D post-stack seismic data from the Niobrara Shale interval within the Teapot Dome field. We show the applicability of the proposed framework for identifying sub-seismic fractures, by considering the amplitude profile adjacent to interpreted formation microimager (FMI) well log data. The upscaled spatial distribution of the predicted fractures shows agreement with existing geological studies and aligns with interpreted large-scale faults within the interval of interest.
Determining r-Robustness of Digraphs Using Mixed Integer Linear Programming

Convergence guarantees of many resilient consensus algorithms are based on the graph theoretic properties of rand (r, s)-robustness. These algorithms guarantee consensus of normally behaving agents in the presence of a bounded number of arbitrarily misbehaving agents if the values of the integers $r$ and $s$ are sufficiently high. However, determining the largest integer $r$ for which an arbitrary digraph is r-robust is highly nontrivial. This paper introduces a novel method for calculating this value using mixed integer linear programming. The method only requires knowledge of the graph Laplacian matrix, and can be formulated with affine objective and constraints, except for the integer constraint. Integer programming methods such as branch-and-bound can allow both lower and upper bounds on $r$ to be iteratively tightened. Simulations suggest the proposed method demonstrates greater efficiency than prior algorithms.
Periods, cycles, and $L$-functions: a relative trace formula approach

This is a report for the author's talk in ICM-2018. Motivated by the formulas of Gross--Zagier and Waldspurger, we review conjectures and theorems on automorphic period integrals, special cycles on Shimura varieties, and their connection to central values of L-functions and their derivatives. We focus on the global Gan--Gross--Prasad conjectures, their arithmetic versions and some variants in the author's joint work with Rapoport and Smithling. We discuss the approach of relative trace formulas and the arithmetic fundamental lemma conjecture. In the function field setting, Z. Yun and the author obtain a formula for higher order derivatives of L-functions in terms of special cycles on the moduli space of Drinfeld Shtukas.
Motif and Hypergraph Correlation Clustering

Motivated by applications in social and biological network analysis we introduce a new form of agnostic clustering termed motif correlation clustering , which aims to minimize the cost of clustering errors associated with both edges and higher-order network structures. The problem may be succinctly described as follows: Given a complete graph $G$ , partition the vertices of the graph so that certain predetermined “important” subgraphs mostly lie within the same cluster, while “less relevant” subgraphs are allowed to lie across clusters. Our contributions are as follows: We first introduce several variants of motif correlation clustering and then show that these clustering problems are NP-hard. We then proceed to describe polynomial-time clustering algorithms that provide constant approximation guarantees for the problems at hand. Despite following the frequently used LP relaxation and rounding procedure, the algorithms involve a sophisticated and carefully designed neighborhood growing step that combines information about both edges and motifs. We conclude with several examples illustrating the performance of the developed algorithms on synthetic and real networks.
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures

Scalable communication is of utmost importance for reliable dissemination of time-sensitive information in cooperative vehicular ad-hoc networks (VANETs), which is, in turn, an essential prerequisite for the proper operation of the critical cooperative safety applications. The model-based communication (MBC) is a recently-explored scalability solution proposed in the literature, which has shown a promising potential to reduce the channel congestion to a great extent. In this work, based on the MBC notion, a technology-agnostic hybrid model selection policy for Vehicle-to-Everything (V2X) communication is proposed which benefits from the characteristics of the non-parametric Bayesian inference techniques, specifically Gaussian Processes. The results show the effectiveness of the proposed communication architecture on both reducing the required message exchange rate and increasing the remote agent tracking precision.
Computed chaos or numerical errors

Discrete numerical methods with finite time-steps represen t a practical technique to solve initial-value problems involving nonli near differential equations. These methods seem particularly useful to the study of chaos since no analytical chaotic solution is currently available. Using the well-known Lorenz equations as an example, it is demonstrated that numerically computed results and their associated statistical properties are time-step dependent. There are two reasons for this behavior. First, chaotic differential equations are unstable so that any small error is amplified exponentially near an unstable manifold. The more serious and lesser-known reason is that stable and unstable manifolds of singular points associated with differential equations can form virtual separatrices. The existence of a virtual separ atrix presents the possibility of a computed trajectory actually "jumping" through it due to the finite time-steps of discrete numerical methods. Such behavior violates the uniqueness theory of differential equations and amplifies the numerical errors explosively. These reasons imply that, even if computed results are bounded, their independence on time-step should be established before accepting them as useful numerical approximations to the true solution of the differential equations. However, due to these exponential and explosive amplifications of numerical errors, no computed chaotic solutions of differential equations independent of integration-time step have been found. Thus, reports of computed non-periodic solutions of chaotic differential equations are simply consequencesof unstably amplified truncation errors, and are not approximate solutions of the associateddifferential equations.
Gamma-ray Bursts Induced by Turbulent Reconnection

We consider a simple model for gamma-ray bursts induced by magnetic reconnection in turbulent media. The magnetic field in a jet is subject to kink instabilities, which distort the regular structure of the spiral magnetic field, drive turbulence, and trigger reconnection. The resulting reconnection takes place in a high Reynolds number medium, where turbulence is further enhanced and in turn accelerates the reconnection process. This boot-strap reconnection gives rise to bursts of reconnection events, through which the free energy of magnetic field is transformed into a gamma-ray burst. The efficiency of magnetic reconnection and magnetic energy dissipation is not constrained by microphysical properties of plasmas.