cssi/SciSciGPT-SciSciNet huggingface dataset:
Global cross-calibration of Landsat spectral mixture models

Abstract Data continuity for the Landsat program relies on accurate cross-calibration among sensors. The Landsat 8 Operational Land Imager (OLI) has been shown to exhibit superior performance to the sensors on Landsats 4–7 with respect to radiometric calibration, signal to noise, and geolocation. However, improvements to the positioning of the spectral response functions on the OLI have resulted in known biases for commonly used spectral indices because the new band responses integrate absorption features differently from previous Landsat sensors. The objective of this analysis is to quantify the impact of these changes on linear spectral mixture models that use imagery collected by different Landsat sensors. The 2013 underflight of Landsat 7 and Landsat 8 provides an opportunity to cross calibrate the spectral mixing spaces of the ETM+ and OLI sensors using near-simultaneous acquisitions of radiance measurements from a wide variety of land cover types worldwide. We use 80,910,343 pairs of OLI and ETM+ spectra to characterize the Landsat 8 OLI spectral mixing space and perform a cross-calibration with Landsat 7 ETM+. This new global collection of Landsat spectra spans a greater spectral diversity than those used in prior studies and the resulting Substrate, Vegetation, and Dark (SVD) spectral endmembers (EMs) supplant prior global Landsat EMs. We find only minor (− 0.01
Isometry group of Sasaki–Einstein metric

Abstract In this short paper we prove a conjecture of Martelli–Sparks–Yau regarding the isometry group of a Sasaki–Einstein metric.
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back

Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a single task is available. The method is formalized as pseudo-task augmentation, in which models are trained with multiple decoders for each task. Pseudo-tasks simulate the effect of training towards closely-related tasks drawn from the same universe. In a suite of experiments, pseudo-task augmentation is shown to improve performance on single-task learning problems. When combined with multitask learning, further improvements are achieved, including state-of-the-art performance on the CelebA dataset, showing that pseudo-task augmentation and multitask learning have complementary value. All in all, pseudo-task augmentation is a broadly applicable and efficient way to boost performance in deep learning systems.
Phase-Matched Extreme-Ultraviolet Frequency-Comb Generation

We achieve phase-matched high-harmonic generation at 77 MHz repetition rate, by using high-temperature gas mixtures to increase gas-jet velocity and reduce plasma accumulation. We generate record power of ~2mW at 97nm and ~0.9mW at 67nm.
The topological complexity of the free product

We prove the formula $$\begin{aligned} {\mathrm {TC}\,}(G*H)=\max \{{\mathrm {TC}\,}(G), {\mathrm {TC}\,}(H), cd(G\times H)\} \end{aligned}$$ for the topological complexity of the free product of groups.
Bayesian projection of life expectancy accounting for the HIV/AIDS epidemic.

Background While probabilistic projection methods for projecting life expectancy exist, few account for covariates related to life expectancy. Generalized HIV/AIDS epidemics have a large, immediate negative impact on the life expectancy in a country, but this impact can be mitigated by widespread use of antiretroviral therapy (ART). Thus, projection methods for countries with generalized HIV/AIDS epidemics could be improved by accounting for HIV prevalence, the future course of the epidemic, and ART coverage. Methods We extend the current Bayesian probabilistic life expectancy projection methods of Raftery et al. (2013) to account for HIV prevalence and adult ART coverage in countries with generalized HIV/AIDS epidemics. Results We evaluate our method using out-of-sample validation. We find that the proposed method performs better than the method that does not account for HIV prevalence or ART coverage for projections of life expectancy in countries with a generalized epidemic, while projections for countries without an epidemic remain essentially unchanged. Conclusions In general, our projections show rapid recovery to pre-epidemic life expectancy levels in the presence of widespread ART coverage. After the initial life expectancy recovery, we project a steady rise in life expectancy until the end of the century. Contribution We develop a simple Bayesian hierarchical model for long-term projections of life expectancy while accounting for HIV/AIDS prevalence and coverage of ART. The method produces well-calibrated projections for countries with generalized HIV/AIDS epidemics up to 2100 while having limited data demands.
Colorings, determinants and Alexander polynomials for spatial graphs

A balanced spatial graph has an integer weight on each edge, so that the directed sum of the weights at each vertex is zero. We describe the Alexander module and polynomial for balanced spatial graphs (originally due to S. Kinoshita, Alexander polynomials as isotopy invariants I, Osaka Math. J. 10 (1958) 263–271.), and examine their behavior under some common operations on the graph. We use the Alexander module to define the determinant and p-colorings of a balanced spatial graph, and provide examples. We show that the determinant of a spatial graph determines for which p the graph is p-colorable, and that a p-coloring of a graph corresponds to a representation of the fundamental group of its complement into a metacyclic group Γ(p,m,k). We finish by proving some properties of the Alexander polynomial.
Counting Motifs with Graph Sampling.

Applied researchers often construct a network from a random sample of nodes in order to infer properties of the parent network. Two of the most widely used sampling schemes are subgraph sampling, where we sample each vertex independently with probability $p$ and observe the subgraph induced by the sampled vertices, and neighborhood sampling, where we additionally observe the edges between the sampled vertices and their neighbors. In this paper, we study the problem of estimating the number of motifs as induced subgraphs under both models from a statistical perspective. We show that: for any connected $h$ on $k$ vertices, to estimate $s=\mathsf{s}(h,G)$, the number of copies of $h$ in the parent graph $G$ of maximum degree $d$, with a multiplicative error of $\epsilon$, (a) For subgraph sampling, the optimal sampling ratio $p$ is $\Theta_{k}(\max\{ (s\epsilon^2)^{-\frac{1}{k}}, \; \frac{d^{k-1}}{s\epsilon^{2}} \})$, achieved by Horvitz-Thompson type of estimators. (b) For neighborhood sampling, we propose a family of estimators, encompassing and outperforming the Horvitz-Thompson estimator and achieving the sampling ratio $O_{k}(\min\{ (\frac{d}{s\epsilon^2})^{\frac{1}{k-1}}, \; \sqrt{\frac{d^{k-2}}{s\epsilon^2}}\})$. This is shown to be optimal for all motifs with at most $4$ vertices and cliques of all sizes. The matching minimax lower bounds are established using certain algebraic properties of subgraph counts. These results quantify how much more informative neighborhood sampling is than subgraph sampling, as empirically verified by experiments on both synthetic and real-world data. We also address the issue of adaptation to the unknown maximum degree, and study specific problems for parent graphs with additional structures, e.g., trees or planar graphs.
Neural Architecture Search with Reinforcement Learning

Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214.
On multiplicity in restriction of tempered representations of p -adic groups

We establish an equality between two multiplicities: one in the restriction of tempered representations of a p-adic group to its closed subgroup with the same derived group; and one occurring in their corresponding component groups in Langlands dual sides, so-called $$\mathcal {S}$$ -groups, under working hypotheses about the tempered local Langlands conjecture and the internal structure of tempered L-packets. This provides a formula of the multiplicity for p-adic groups by means of dimensions of irreducible representations of their $$\mathcal {S}$$ -groups.
A locally hyperbolic 3-manifold that is not hyperbolic

We construct a locally hyperbolic 3-manifold $M_\infty$ such that $\pi_ 1(M_\infty)$ has no divisible subgroup. We then show that $M_\infty$ is not homeomorphic to any complete hyperbolic manifold. This answers a question of Agol [DHM06,Mar07].
Uniformization by square domains

Let \(\Omega \) be a finitely connected subdomain of the Riemann sphere \(\widehat{\mathbb C}\) with \(\infty \in \Omega \). We find an extremal problem for conformal maps on \(\Omega \) with suitable normalization at \(\infty \) whose unique solution is a map onto a square domain, that is, a domain in \(\widehat{\mathbb C}\) whose complementary components are (possibly degenerate) squares with sides parallel to the real or the imaginary axis.
Hierarchical Overlapping Clustering of Network Data Using Cut Metrics

A novel method to obtain hierarchical and overlapping clusters from network data—i.e., a set of nodes endowed with pairwise dissimilarities—is presented. The introduced method is hierarchical in the sense that it outputs a nested collection of groupings of the node set depending on the resolution or degree of similarity desired, and it is overlapping since it allows nodes to belong to more than one group. Our construction is rooted on the facts that a hierarchical (non-overlapping) clustering of a network can be equivalently represented by a finite ultrametric space and that a convex combination of ultrametrics results in a cut metric. By applying a hierarchical (non-overlapping) clustering method to multiple dithered versions of a given network, and then, convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric. Furthermore, the so-called overlapping function is presented as a tool for gaining insights about the data by identifying meaningful resolutions of the obtained hierarchical structure. Additionally, we explore hierarchical overlapping quasi-clustering methods that preserve the asymmetry of the data contained in directed networks. Finally, the presented method is illustrated via synthetic and real-world classification problems including handwritten digit classification and authorship attribution of famous plays.
Adaptive multi-step prediction based EKF to power system dynamic state estimation

Power system dynamic state estimation is essential to monitoring and controlling power system stability. Kalman filtering approaches are predominant in estimation of synchronous machine dynamic states (i.e. rotor angle and rotor speed). This paper proposes an adaptive multi-step prediction (AMSP) approach to improve the extended Kalman filter's (EKF) performance in estimating the dynamic states of a synchronous machine. The proposed approach consists of three major steps. First, two indexes are defined to quantify the nonlinearity levels of the state transition function and measurement function, respectively. Second, based on the nonlinearity indexes, a multi prediction factor (Mp) is defined to determine the number of prediction steps. And finally, to mitigate the nonlinearity impact on dynamic state estimation (DSE) accuracy, the prediction step repeats a few time based on Mp before performing the correction step. The two-area four-machine system is used to evaluate the effectiveness of the proposed AMSP approach. It is shown through the Monte-Carlo method that a good trade-off between estimation accuracy and computational time can be achieved effectively through the proposed AMSP approach.
Graph Laplacian Spectrum and Primary Frequency Regulation

We present a framework based on spectral graph theory that captures the interplay among network topology, system inertia, and generator and load damping in determining the overall grid behavior and performance. Specifically, we show that the impact of network topology on a power system can be quantified through the network Laplacian eigenvalues, and such eigenvalues determine the grid robustness against low frequency disturbances. Moreover, we can explicitly decompose the frequency signal along scaled Laplacian eigenvectors when damping-inertia ratios are uniform across buses. The insight revealed by this framework partially explains why load-side participation in frequency regulation not only makes the system respond faster, but also helps lower the system nadir after a disturbance. Finally, by presenting a new controller specifically tailored to suppress high frequency disturbances, we demonstrate that our results can provide useful guidelines in the controller design for load-side primary frequency regulation. This improved controller is simulated on the IEEE 39-bus New England interconnection system to illustrate its robustness against high frequency oscillations compared to both the conventional droop control and a recent controller design.
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets

We develop a novel computationally efficient and general framework for robust hypothesis testing. The new framework features a new way to construct uncertainty sets under the null and the alternative distributions, which are sets centered around the empirical distribution defined via Wasserstein metric, thus our approach is data-driven and free of distributional assumptions. We develop a convex safe approximation of the minimax formulation and show that such approximation renders a nearly-optimal detector among the family of all possible tests. By exploiting the structure of the least favorable distribution, we also develop a tractable reformulation of such approximation, with complexity independent of the dimension of observation space and can be nearly sample-size-independent in general. Real-data example using human activity data demonstrated the excellent performance of the new robust detector.
Loan guarantees and credit supply

Abstract The efficiency of federal lending guarantees depends on whether guarantees increase lending supply or simply act as a subsidy to lenders. We use notches in the guarantee rate schedule for Small Business Administration (SBA) loans to estimate the elasticity of bank lending volume to loan guarantees. We show significant bunching in the loan distribution on the side of the size threshold that carries a more generous loan guarantee. The excess mass implies that increasing guarantee generosity by one percentage point of loan principal would increase per-loan lending volume by $19,000. Excess mass increases in periods with guarantee generosity, and placebo results indicate that the effect disappears when the guarantee notch is eliminated.
Successful pressing sequences for a bicolored graph and binary matrices

Abstract We apply matrix theory over F 2 to understand the nature of so-called “successful pressing sequences” of black-and-white vertex-colored graphs. These sequences arise in computational phylogenetics, where, by a celebrated result of Hannenhalli and Pevzner, the space of sortings-by-reversal of a signed permutation can be described by pressing sequences. In particular, we offer several alternative linear-algebraic and graph-theoretic characterizations of successful pressing sequences, describe the relation between such sequences, and provide bounds on the number of them. We also offer several open problems that arose as a result of the present work.
Defining a Metric Space of Host Logs and Operational Use Cases

Host logs, in particular, Windows Event Logs, are a valuable source of information often collected by security operation centers (SOCs). The semi-structured nature of host logs inhibits automated analytics, and while manual analysis is common, the sheer volume makes manual inspection of all logs impossible. Although many powerful algorithms for analyzing time-series and sequential data exist, utilization of such algorithms for most cyber security applications is either infeasible or requires tailored, research-intensive preparations. In particular, basic mathematic and algorithmic developments for providing a generalized, meaningful similarity metric on system logs is needed to bridge the gap between many existing sequential data mining methods and this currently available but under-utilized data source. In this paper, we provide a rigorous definition of a metric product space on Windows Event Logs, providing an embedding that allows for the application of established machine learning and time-series analysis methods. We then demonstrate the utility and flexibility of this embedding with multiple use-cases on real data: (1) comparing known infected to new host log streams for attack detection and forensics, (2) collapsing similar streams of logs into semantically-meaningful groups (by user, by role), thereby reducing the quantity of data but not the content, (3) clustering logs as well as short sequences of logs to identify and visualize user behaviors and background processes over time. Overall, we provide a metric space framework for general host logs and log sequences that respects semantic similarity and facilitates a wide variety of data science analytics to these logs without data-specific preparations for each.
Unanimous Prediction for 100\% Precision with Application to Learning Semantic Mappings

Can we train a system that, on any new input, either says "don't know" or makes a prediction that is guaranteed to be correct? We answer the question in the affirmative provided our model family is well-specified. Specifically, we introduce the unanimity principle: only predict when all models consistent with the training data predict the same output. We operationalize this principle for semantic parsing, the task of mapping utterances to logical forms. We develop a simple, efficient method that reasons over the infinite set of all consistent models by only checking two of the models. We prove that our method obtains 100% precision even with a modest amount of training data from a possibly adversarial distribution. Empirically, we demonstrate the effectiveness of our approach on the standard GeoQuery dataset.
Resilient Leader-Follower Consensus to Arbitrary Reference Values

The problem of consensus in the presence of misbehaving agents has increasingly attracted attention in the literature. Prior results have established algorithms and graph structures for multi-agent networks which guarantee the consensus of normally behaving agents in the presence of a bounded number of misbehaving agents. The final consensus value is guaranteed to fall within the convex hull of initial agent states. However, the problem of consensus tracking considers consensus to arbitrary reference values which may not lie within such bounds. Conditions for consensus tracking in the presence of misbehaving agents has not been fully studied. This paper presents conditions for a network of agents using the W-MSR algorithm to achieve this objective.
Limitations on detecting row covariance in the presence of column covariance

Abstract Many inference techniques for multivariate data analysis assume that the rows of the data matrix are realizations of independent and identically distributed random vectors. Such an assumption will be met, for example, if the rows of the data matrix are multivariate measurements on a set of independently sampled units. In the absence of an independent random sample, a relevant question is whether or not a statistical model that assumes such row exchangeability is plausible. One method for assessing this plausibility is a statistical test of row covariation. Maintenance of a constant type I error rate regardless of the column covariance or matrix mean can be accomplished with a test that is invariant under an appropriate group of transformations. In the context of a class of elliptically contoured matrix-variate regression models (such as matrix normal models), it is shown that there are no non-trivial invariant tests if the number of rows is not sufficiently larger than the number of columns. Furthermore, even if the number of rows is large, there are no non-trivial invariant tests that have power to detect arbitrary row covariance in the presence of arbitrary column covariance. However, biased tests can be constructed that have power to detect certain types of row covariance that may be encountered in practice.
Blind Recovery of Sparse Signals From Subsampled Convolution

Subsampled blind deconvolution is the recovery of two unknown signals from samples of their convolution. To overcome the ill-posedness of this problem, solutions based on priors tailored to specific practical application have been developed. In particular, sparsity models have provided promising priors. However, in spite of the empirical success of these methods in many applications, existing analyses are rather limited in two main ways: by disparity between the theoretical assumptions on the signal and/or measurement model versus practical setups; or by failure to provide a performance guarantee for parameter values within the optimal regime defined by the information theoretic limits. In particular, it has been shown that a naive sparsity model is not a strong enough prior for identifiability in the blind deconvolution problem. Instead, in addition to sparsity, we adopt a conic constraint, which enforces spectral flatness of the signals. Under this prior together with random dictionary models, we show that the unknown sparse signals can be recovered from samples of their convolution at a rate scaling near optimally with the problem parameters. We also propose an iterative algorithm that is guaranteed to provide robust recovery at the same near optimal sample complexity provided that certain projection steps in the algorithm are successful. In our analysis, we have not verified the success of these projection steps, but these steps are inactive with high probability. Numerical results show the empirical performance of the iterative algorithm agrees with the performance guarantee.
Optimal deterministic auctions with correlated priors

We revisit the problem of designing the profit-maximizing single-item auction, solved by Myerson in his seminal paper for the case in which bidder valuations are independently distributed. We focus on general joint distributions, either discrete or Lipschitz-continuous, seeking the optimal deterministic incentive compatible auction. We give a geometric characterization of the optimal auction, resulting in a duality theorem and an efficient algorithm for finding the optimal deterministic auction in the two-bidder case and an NP-completeness result for three or more bidders.
High-frequency resolvent estimates on asymptotically Euclidean warped products

We consider the resolvent on asymptotically Euclidean warped product manifolds in an appropriate 0-Gevrey class, with trapped sets consisting of only finitely many components. We prove a new dichot...
Identifying Inconsistencies in Fission Product Yield Evaluations with Prompt Neutron Emission

We present a self-consistency analysis of fission product yield evaluations. Anomalous yields are determined using a series of simple conservation checks and comparing charge distributions with com...
Diophantine exponents of affine subspaces: The simultaneous approximation case

Abstract We apply nondivergence estimates for flows on homogeneous spaces to compute Diophantine exponents of affine subspaces of R n and their nondegenerate submanifolds.
Location Privacy in Mobile Edge Clouds: A Chaff-Based Approach

In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with a focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood detection, we consider both heuristic strategies that mimic the user’s mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy or its online variation can drive the eavesdropper’s tracking accuracy to zero when the user’s mobility is sufficiently random. We further propose extended strategies that utilize randomization to defend against an advanced eavesdropper aware of the strategy. The efficacy of our solutions is verified through both synthetic and trace-driven simulations.
Performance Gains of Optimal Antenna Deployment for Massive MIMO Systems

We consider the uplink of a single-cell multi-user multiple-input multiple-output (MIMO) system with several single antenna transmitters/users and one base station with &#x1D4A9; antennas in the $N\rightarrow\infty$ regime. The base station antennas are evenly distributed to $n$ admissable locations throughout the cell. First, we show that a reliable (per-user) rate of $O(\log n)$ is achievable through optimal locational optimization of base station antennas. We also prove that an $O(\log n)$ rate is the best possible. Therefore, in contrast to a centralized or circular deployment, where the achievable rate is at most a constant, the rate with a general deployment can grow logarithmically with $n$, resulting in a certain form of ``macromultiplexing gain.'' Second, using tools from high-resolution quantization theory, we derive an accurate formula for the best achievable rate given any $n$ and any user density function. According to our formula, the dependence of the optimal rate on the user density function $f$ is curiously only through the differential entropy of $f$. In fact, the optimal rate decreases linearly with the differential entropy, and the worst-case scenario is a uniform user density. Numerical simulations confirm our analytical findings.
USP9X Deubiquitylates DVL2 to Regulate WNT Pathway Specification

The WNT signaling network is comprised of multiple receptors that relay various input signals via distinct transduction pathways to execute multiple complex and context-specific output processes. Integrity of the WNT signaling network relies on proper specification between canonical and noncanonical pathways, which presents a regulatory challenge given that several signal transducing elements are shared between pathways. Here, we report that USP9X, a deubiquitylase, and WWP1, an E3 ubiquitin ligase, regulate a ubiquitin rheostat on DVL2, a WNT signaling protein. Our findings indicate that USP9X-mediated deubiquitylation of DVL2 is required for canonical WNT activation, while increased DVL2 ubiquitylation is associated with localization to actin-rich projections and activation of the planar cell polarity (PCP) pathway. We propose that a WWP1-USP9X axis regulates a ubiquitin rheostat on DVL2 that specifies its participation in either canonical WNT or WNT-PCP pathways. These findings have important implications for therapeutic targeting of USP9X in human cancer.
Double Aztec rectangles

Abstract We investigate the connection between lozenge tilings and domino tilings by introducing a new family of regions obtained by attaching two different Aztec rectangles. We prove a simple product formula for the generating functions of the tilings of the new regions, which involves the statistics as in the Aztec diamond theorem (Elkies et al. (1992) [2] , [3] ). Moreover, we consider the connection between the generating function and MacMahon's q-enumeration of plane partitions fitting in a given box
Projector quantum Monte Carlo with averaged vs explicit spin-orbit effects: Applications to tungsten molecular systems

Abstract We present a recently developed projector quantum Monte Carlo method for calculations of electronic structure in systems with spin-orbit interactions. The method solves for many-body eigenstates in the presence of spin-orbit using the fixed-phase approximation. The trial wave function is built from two-component spinors and explicit Jastrow correlation factors while the core electrons are eliminated by relativistic effective core potentials with explicit spin-orbit terms. We apply this method to WO and W2 molecules that enables us to build multi-reference wave functions and analyze the impact of both electron correlations and the spin-orbit terms.
DG-GL: Differential geometry-based geometric learning of molecular datasets.

Motivation Despite its great success in various physical modeling, differential geometry (DG) has rarely been devised as a versatile tool for analyzing large, diverse, and complex molecular and biomolecular datasets because of the limited understanding of its potential power in dimensionality reduction and its ability to encode essential chemical and biological information in differentiable manifolds. Results We put forward a differential geometry-based geometric learning (DG-GL) hypothesis that the intrinsic physics of three-dimensional (3D) molecular structures lies on a family of low-dimensional manifolds embedded in a high-dimensional data space. We encode crucial chemical, physical, and biological information into 2D element interactive manifolds, extracted from a high-dimensional structural data space via a multiscale discrete-to-continuum mapping using differentiable density estimators. Differential geometry apparatuses are utilized to construct element interactive curvatures in analytical forms for certain analytically differentiable density estimators. These low-dimensional differential geometry representations are paired with a robust machine learning algorithm to showcase their descriptive and predictive powers for large, diverse, and complex molecular and biomolecular datasets. Extensive numerical experiments are carried out to demonstrate that the proposed DG-GL strategy outperforms other advanced methods in the predictions of drug discovery-related protein-ligand binding affinity, drug toxicity, and molecular solvation free energy. Availability and implementation http://weilab.math.msu.edu/DG-GL/ Contact: wei@math.msu.edu.
Perception-Informed Autonomous Environment Augmentation with Modular Robots

We present a system enabling a modular robot to autonomously build structures in order to accomplish high-level tasks. Building structures allows the robot to surmount large obstacles, expanding the set of tasks it can perform. This addresses a common weakness of modular robot systems, which often struggle to traverse large obstacles. This paper presents the hardware, perception, and planning tools that comprise our system. An environment characterization algorithm identifies features in the environment that can be augmented to create a path between two disconnected regions of the environment. Specially-designed building blocks enable the robot to create structures that can augment the environment to make obstacles traversable. A high-level planner reasons about the task, robot locomotion capabilities, and environment to decide if and where to augment the environment in order to perform the desired task. We validate our system in hardware experiments.
Certified ethical hacker v.10 online course: a case study

CEH v.10 Self-study Course is an online course preparing learners for one of the most prestige cyber security certifications in the world - the Certified Ethical Hacker (CEH) v.10 Certification. Due to a pay wall and the practical rather than theoretical nature, most researchers have limited exposure to this course. For the first time, this paper will analyze the course's instructional design based on the highest US national standards, giving fair evaluations, and identifying five general directions for improvements. The main contribution is a 30-point check list that can be used to evaluate any other online courses. Methodology involves mapping QualityMatters and iNacol evaluation items into 30 items within five categories of "Meaningful", "Engaging", "Measurable", "Accessible", and "Scalable". This instructional design evaluation strategy is more intuitive to business leaders, and allows the combination of any other standards that are relevant to an organization's evaluation needs.
A High Order Semi-Lagrangian Discontinuous Galerkin Method for the Two-Dimensional Incompressible Euler Equations and the Guiding Center Vlasov Model Without Operator Splitting

In this paper, we generalize a high order semi-Lagrangian (SL) discontinuous Galerkin (DG) method for multi-dimensional linear transport equations without operator splitting developed in Cai et al. (J Sci Comput 73(2–3):514–542, 2017) to the 2D time dependent incompressible Euler equations in the vorticity-stream function formulation and the guiding center Vlasov model. We adopt a local DG method for Poisson’s equation of these models. For tracing the characteristics, we adopt a high order characteristics tracing mechanism based on a prediction-correction technique. The SLDG with large time-stepping size might be subject to extreme distortion of upstream cells. To avoid this problem, we propose a novel adaptive time-stepping strategy by controlling the relative deviation of areas of upstream cells.
Inverting nonlinear dimensionality reduction with scale-free radial basis function interpolation

Abstract Nonlinear dimensionality reduction embeddings computed from datasets do not provide a mechanism to compute the inverse map. In this paper, we address the problem of computing a stable inverse map to such a general bi-Lipschitz map. Our approach relies on radial basis functions (RBFs) to interpolate the inverse map everywhere on the low-dimensional image of the forward map. We demonstrate that the scale-free cubic RBF kernel performs better than the Gaussian kernel: it does not suffer from ill-conditioning, and does not require the choice of a scale. The proposed construction is shown to be similar to the Nystrom extension of the eigenvectors of the symmetric normalized graph Laplacian matrix. Based on this observation, we provide a new interpretation of the Nystrom extension with suggestions for improvement.
Admissibility Conditions for Degenerate Cyclotomic BMW Algebras

We study admissibility conditions for the parameters of degenerate cyclotomic Birman–Wenzl–Murakami (BMW) algebras. We show that the u–admissibility condition of Ariki, Mathas, and Rui is equivalent to a simple module theoretic condition.
Information-theoretic Limits for Community Detection in Network Models

We analyze the information-theoretic limits for the recovery of node labels in several network models. This includes the Stochastic Block Model, the Exponential Random Graph Model, the Latent Space Model, the Directed Preferential Attachment Model, and the Directed Small-world Model. For the Stochastic Block Model, the non-recoverability condition depends on the probabilities of having edges inside a community, and between different communities. For the Latent Space Model, the non-recoverability condition depends on the dimension of the latent space, and how far and spread are the communities in the latent space. For the Directed Preferential Attachment Model and the Directed Small-world Model, the non-recoverability condition depends on the ratio between homophily and neighborhood size. We also consider dynamic versions of the Stochastic Block Model and the Latent Space Model.
Anisotropy Reversal of the Upper Critical Field at Low Temperatures and Spin-Locked Superconductivity in K2Cr3As3

We report measurements of the anisotropic upper critical field ${H}_{c2}(T)$ for ${\mathrm{K}}_{2}{\mathrm{Cr}}_{3}{\mathrm{As}}_{3}$ single crystals up to 60 T and $Tg0.6\phantom{\rule{0.28em}{0ex}}\mathrm{K}$. Our results show that the upper critical field parallel to the Cr chains, ${H}_{c2}^{\ensuremath{\parallel}}(T)$, exhibits a paramagnetically limited behavior, whereas the shape of the ${H}_{c2}^{\ensuremath{\perp}}(T)$ curve (perpendicular to the Cr chains) has no evidence of paramagnetic effects. As a result, the curves ${H}_{c2}^{\ensuremath{\perp}}(T)$ and ${H}_{c2}^{\ensuremath{\parallel}}(T)$ cross at $T\ensuremath{\approx}4\phantom{\rule{0.28em}{0ex}}\mathrm{K}$, so that the anisotropy parameter ${\ensuremath{\gamma}}_{H}(T)={H}_{c2}^{\ensuremath{\perp}}/{H}_{c2}^{\ensuremath{\parallel}}(T)$ increases from ${\ensuremath{\gamma}}_{H}({T}_{c})\ensuremath{\approx}0.35$ near ${T}_{c}$ to ${\ensuremath{\gamma}}_{H}(0)\ensuremath{\approx}1.7$ at 0.6 K. The paramagnetically limited behavior of ${H}_{c2}^{\ensuremath{\parallel}}(T)$ is inconsistent with triplet superconductivity but suggests a form of singlet superconductivity with the electron spins locked onto the direction of Cr chains.
Dynamic coloring parameters for graphs with given genus

Abstract A proper vertex coloring of a graph G is r - dynamic if for each v ∈ V ( G ) , at least min { r , d ( v ) } colors appear in N G ( v ) . In this paper we investigate r -dynamic versions of coloring, list coloring, and paintability. We prove that planar and toroidal graphs are 3-dynamically 10-colorable, and this bound is sharp for toroidal graphs. We also give bounds on the minimum number of colors needed for any r in terms of the genus of the graph: for sufficiently large r , every graph with genus g is r -dynamically ( ( r + 1 ) ( g + 5 ) + 3 ) -colorable when g ≤ 2 and r -dynamically ( ( r + 1 ) ( 2 g + 2 ) + 3 ) -colorable when g ≥ 3 . Furthermore, each of these upper bounds for r -dynamic k -colorability also holds for r -dynamic k -choosability and for r -dynamic k -paintability.
New limits for knowledge compilation and applications to exact model counting

We show new limits on the efficiency of using current techniques to make exact probabilistic inference for large classes of natural problems. In particular we show new lower bounds on knowledge compilation to SDD and DNNF forms. We give strong lower bounds on the complexity of SDD representations by relating SDD size to best-partition communication complexity. We use this relationship to prove exponential lower bounds on the SDD size for representing a large class of problems that occur naturally as queries over probabilistic databases. A consequence is that for representing unions of conjunctive queries, SDDs are not qualitatively more concise than OBDDs. We also derive simple examples for which SDDs must be exponentially less concise than FBDDs. Finally, we derive exponential lower bounds on the sizes of DNNF representations using a new quasipolynomial simulation of DNNFs by nondeterministic FBDDs.
Voevodsky's mixed motives versus Kontsevich's noncommutative mixed motives

Abstract We prove that the quotient of Voevodsky's category of geometric mixed motives DM gm by the endofunctor − ⊗ Q ( 1 ) [ 2 ] embeds fully-faithfully into Kontsevich's category of noncommutative mixed motives KMM. We show moreover that this embedding is compatible with the one between Chow motives and noncommutative Chow motives. Along the way, we relate KMM with Morel–Voevodsky's stable A 1 -homotopy category, recover the twisted algebraic K-theory of Kahn–Levine from KMM, and extend Elmendorf–Mandell's foundational work on multicategories to a broader setting.
Compatible Hamiltonian operators for the Krichever–Novikov equation

Abstract It has been proved by Sokolov that Krichever–Novikov equation's hierarchy is hamiltonian for the Hamiltonian operator H 0 = u x ∂ − 1 u x and possesses two weakly non-local recursion operators of degrees 4 and 6, L 4 and L 6 . We show here that H 0 , L 4 H 0 and L 6 H 0 are compatible Hamiltonians operators for which the Krichever–Novikov equation's hierarchy is hamiltonian.
Hematopoietic Stem Cell Intracellular Levels of Ca2+ to the Rescue! What Next?

Calcium ions (Ca2+), ubiquitous signaling and second messenger molecules, are communicators for the transmission of messages in numerous cell functions. In this issue of Cell Stem Cell, Luchsinger et al. (2019) provide evidence through the use of transplantation and mechanistic studies for the finding that, "Harnessing Hematopoietic Stem Cell Low Intracellular Calcium Improves Their Maintenance In Vitro."
Learning from Extrapolated Corrections

Our goal is to enable robots to learn cost functions from user guidance. Often it is difficult or impossible for users to provide full demonstrations, so corrections have emerged as an easier guidance channel. However, when robots learn cost functions from corrections rather than demonstrations, they have to extrapolate a small amount of information - the change of a waypoint along the way – to the rest of the trajectory. We cast this extrapolation problem as online function approximation, which exposes different ways in which the robot can interpret what trajectory the person intended, depending on the function space used for the approximation. Our simulation results and user study suggest that using function spaces with non-Euclidean norms can better capture what users intend, particularly if environments are uncluttered. This, in turn, can lead to the robot learning a more accurate cost function and improves the user’s subjective perceptions of the robot.
A Comparison of Partisan-Gerrymandering Measures

Abstract We compare and contrast 14 measures that have been proposed for the purpose of quantifying partisan gerrymandering. We consider measures that, rather than examining the shapes of districts...
Bayesian additive regression trees and the General BART model

Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers through the details of BART, from what it is to why it works. This tutorial is aimed at providing such a resource. In addition to explaining the different components of BART using simple examples, we also discuss a framework, the General BART model that unifies some of the recent BART extensions, including semiparametric models, correlated outcomes, and statistical matching problems in surveys, and models with weaker distributional assumptions. By showing how these models fit into a single framework, we hope to demonstrate a simple way of applying BART to research problems that go beyond the original independent continuous or binary outcomes framework.
The Relationship between Oral Narrative Production and Expository Text Comprehension of Fifth-Grade Students.

The purpose of the present study is to determine if there is a relationship between oral narrative production and the reading comprehension of expository text. The researcher measured both overall ...
Conflict, Household Victimization, and Welfare – Does the Perpetrator Matter?

This paper studies the relationship between conflict and household welfare by using a detailed panel data set of household victimization across the most conflict-affected regions in Nigeria between 2010 and 2017, during a time characterized by a sharp increase in conflict. The North East region has been hardest hit with the recent Boko Haram insurgency. The North Central region has seen clashes between herders and farmers over land and resources. Several militant groups operate in the oil-producing Niger Delta region, where their aim is to extract resources by disrupting oil production. By exploiting the plausibly exogenous variation in the timing, intensity, and spatial distribution of victimization, we find that becoming a victim of conflict leads to higher food insecurity and decreased consumption. Since different types of actors have different motivations for their actions, the consequences of victimization might vary depending on the perpetrator. We find that events perpetrated by insurgents are the most detrimental to consumption, whereas food insecurity increases as a consequence of both insurgent and criminal activity. This is in line with the results being strongest in the North East, which also has the highest intensity of conflict. We also find that property-related events are more detrimental to consumption and food insecurity than are violent events. Likewise, we find suggestive evidence that violent events, as well as events perpetrated by insurgents and bandits, are detrimental to mental health. Our findings highlight the importance of collecting nuanced information of victimization in conflict-affected areas.
Laminin-driven Epac/Rap1 regulation of epithelial barriers on decellularized matrix

Decellularized tissues offer a unique tool for developing regenerative biomaterials or in vitro platforms for the study of cell-extracellular matrix (ECM) interactions. One main challenge associated with decellularized lung tissue is that ECM components can be stripped away or altered by the detergents used to remove cellular debris. Without characterizing the composition of lung decellularized ECM (dECM) and the cellular response caused by the altered composition, it is difficult to utilize dECM for regeneration and specifically, engineering the complexities of the alveolar-capillary barrier. This study takes steps towards uncovering if dECM must be enhanced with lost ECM proteins to achieve proper epithelial barrier formation. To achieve this, the epithelial barrier function was assessed on dECM coatings with and without the systematic addition of several key basement membrane proteins. After comparing barrier function on collagen I, fibronectin, laminin, and dECM in varying combinations as an in vitro coating, the alveolar epithelium exhibited superior barrier function when dECM was supplemented with laminin as evidenced by trans-epithelial electrical resistance (TEER) and permeability assays. Increased barrier resistance with laminin addition was associated with upregulation of Claudin-18, E-cadherin, and junction adhesion molecule (JAM)-A, and stabilization of zonula occludens (ZO)-1 at junction complexes. The Epac/Rap1 pathway was observed to play a role in the ECM-mediated barrier function determined by protein expression and Epac inhibition. These findings revealed potential ECM coatings and molecular therapeutic targets for improved regeneration with decellularized scaffolds. STATEMENT OF SIGNIFICANCE: Efforts to produce a transplantable organ-scale biomaterial for lung regeneration has not been entirely successful to date, due to incomplete cell-cell junction formation, ultimately leading to severe edema in vivo. To fully understand the process of alveolar junction formation on ECM-derived biomaterials, this research has characterized and tailored decellularized ECM (dECM) to mitigate reductions in barrier strength or cell attachment caused by abnormal ECM compositions or detergent damage to dECM. These results indicate that laminin-driven Epac signaling plays a vital role in the stabilization of the alveolar barrier. Addition of laminin or Epac agonists during alveolar regeneration can reduce epithelial permeability within bioengineered lungs.