cssi/SciSciGPT-SciSciNet huggingface dataset:
Modified Enskog kinetic theory for strongly coupled plasmas

Concepts underlying the Enskog kinetic theory of hard-spheres are applied to include short-range correlation effects in a model for transport coefficients of strongly coupled plasmas. The approach is based on an extension of the effective potential transport theory [S. D. Baalrud and J. Daligault, Phys. Rev. Lett. 110, 235001 (2013)] to include an exclusion radius surrounding individual charged particles that is associated with Coulomb repulsion. This is obtained by analogy with the finite size of hard spheres in Enskog's theory. Predictions for the self-diffusion and shear viscosity coefficients of the one-component plasma are tested against molecular dynamics simulations. The theory is found to accurately capture the kinetic contributions to the transport coefficients, but not the potential contributions that arise at very strong coupling ($\mathrm{\ensuremath{\Gamma}}\ensuremath{\gtrsim}30$). Considerations related to a first-principles generalization of Enskog's kinetic equation to continuous potentials are also discussed.
A Weak Galerkin Finite Element Method for a Type of Fourth Order Problem Arising from Fluorescence Tomography

In this paper, an innovative and effective numerical algorithm by the use of weak Galerkin (WG) finite element methods is proposed for a type of fourth order problem arising from fluorescence tomography. Fluorescence tomography is emerging as an in vivo non-invasive 3D imaging technique reconstructing images that characterize the distribution of molecules tagged by fluorophores. Weak second order elliptic operator and its discrete version are introduced for a class of discontinuous functions defined on a finite element partition of the domain consisting of general polygons or polyhedra. An error estimate of optimal order is derived in an $$H^2_{\kappa }$$Hź2-equivalent norm for the WG finite element solutions. Error estimates of optimal order except the lowest order finite element in the usual $$L^2$$L2 norm are established for the WG finite element approximations. Numerical tests are presented to demonstrate the accuracy and efficiency of the theory established for the WG numerical algorithm.
Nilpotence in Group Cohomology

We study bounds on nilpotence in H*(BG) , the mod p cohomology of the classifying space of a compact Lie group G . Part of this is a report of our previous work on this problem, updated to reflect the consequences of Peter Symonds's recent verification of Dave Benson's Regularity Conjecture. New results are given for finite p -groups, leading to good bounds on nilpotence in H*(BP) determined by the subgroup structure of the p -group P .
Transistor Switches Using Active Piezoelectric Gate Barriers

This paper explores the consequences of introducing a piezoelectric gate barrier in a normal field-effect transistor. Because of the positive feedback of strain and piezoelectric charge, internal charge amplification occurs in such an electromechanical capacitor resulting in a negative capacitance. The first consequence of this amplification is a boost in the ON-current of the transistor. As a second consequence, employing the Lagrangian method, we find that using the negative capacitance of a highly compliant piezoelectric barrier, one can potentially reduce the subthreshold slope of a transistor below the room-temperature Boltzmann limit of 60 mV/decade. However, this may come at the cost of hysteretic behavior in the transfer characteristics.
A geometric approach to confidence regions and bands for functional parameters

Summary Functional data analysis is now a well-established discipline of statistics, with its core concepts and perspectives in place. Despite this, there are still fundamental statistical questions which have received relatively little attention. One of these is the systematic construction of confidence regions for functional parameters. This work is concerned with developing, understanding and visualizing such regions. We provide a general strategy for constructing confidence regions in a real separable Hilbert space by using hyperellipsoids and hyper-rectangles. We then propose specific implementations which work especially well in practice. They provide powerful hypothesis tests and useful visualization tools without relying on simulation. We also demonstrate the negative result that nearly all regions, including our own, have zero coverage when working with empirical covariances. To overcome this challenge we propose a new paradigm for evaluating confidence regions by showing that the distance between an estimated region and the desired region (with proper coverage) tends to 0 faster than the regions shrink to a point. We call this phenomena ghosting and refer to the empirical regions as ghost regions. We illustrate the proposed methods in a simulation study and an application to fractional anisotropy tract profile data.
Divisibility of Weil sums of binomials

Consider the Weil sum $W_{F,d}(u)=\sum_{x \in F} \psi(x^d+u x)$, where $F$ is a finite field of characteristic $p$, $\psi$ is the canonical additive character of $F$, $d$ is coprime to $|F^*|$, and $u \in F^*$. We say that $W_{F,d}(u)$ is three-valued when it assumes precisely three distinct values as $u$ runs through $F^*$: this is the minimum number of distinct values in the nondegenerate case, and three-valued $W_{F,d}$ are rare and desirable. When $W_{F,d}$ is three-valued, we give a lower bound on the $p$-adic valuation of the values. This enables us to prove the characteristic $3$ case of a 1976 conjecture of Helleseth: when $p=3$ and $[F:{\mathbb F}_3]$ is a power of $2$, we show that $W_{F,d}$ cannot be three-valued.
The abelian monoid of fusion-stable finite sets is free

We show that the abelian monoid of isomorphism classes of G-stable finite S-sets is free for a finite group G with Sylow p-subgroup S; here a finite S-set is called G-stable if it has isomorphic restrictions to G-conjugate subgroups of S. These G-stable S-sets are of interest, e.g., in homotopy theory. We prove freeness by constructing an explicit (but somewhat nonobvious) basis, whose elements are in one-to-one correspondence with the G-conjugacy classes of subgroups in S. As a central tool of independent interest, we give a detailed description of the embedding of the Burnside ring for a saturated fusion system into its associated ghost ring.
Chromatic homology, Khovanov homology, and torsion

Abstract In the first few homological gradings, there is an isomorphism between the Khovanov homology of a link and the categorification of the chromatic polynomial of a graph related to the link. In this article, we show that all torsion in the categorification of the chromatic polynomial is of order two, and hence all torsion in Khovanov homology in the gradings where the isomorphism is defined is of order two. We also prove that odd Khovanov homology is torsion-free in its first few homological gradings.
On the Brauer–Picard groups of fusion categories

We develop methods of computation of the Brauer–Picard groups of fusion categories and apply them to compute such groups for several classes of fusion categories of prime power dimension: representation categories of elementary abelian groups with twisted associativity, extra special p-groups, and the Kac–Paljutkin Hopf algebra. We conclude that many finite groups of Lie type occur as composition factors of the Brauer–Picard groups of pointed fusion categories.
Tight bounds on the round complexity of the distributed maximum coverage problem

We study the maximum k-set coverage problem in the following distributed setting. A collection of input sets S1, ..., Sm over a universe [n] is partitioned across p machines and the goal is to find k sets whose union covers the most number of elements. The computation proceeds in rounds where in each round machines communicate information to each other. Specifically, in each round, all machines simultaneously send a message to a central coordinator who then communicates back to all machines a summary to guide the computation for the next round. At the end of the last round, the coordinator outputs the answer. The main measures of efficiency in this setting are the approximation ratio of the returned solution, the communication cost of each machine, and the number of rounds of computation. Our main result is an asymptotically tight bound on the tradeoff between these three measures for the distributed maximum coverage problem. We first show that any r-round protocol for this problem either incurs a communication cost of k · mΩ(1/r) or only achieves an approximation factor of kΩ(1/r). This in particular implies that any protocol that simultaneously achieves good approximation ratio (O(1) approximation) and good communication cost (O(n) communication per machine), essentially requires logarithmic (in k) number of rounds. We complement our lower bound result by showing that there exist an r-round protocol that achieves an [EQUATION]-approximation (essentially best possible) with a communication cost of k · mO(1/r) as well as an r-round protocol that achieves a kO(1/r)-approximation with only O(n) communication per each machine (essentially best possible). We further use our results in this distributed setting to obtain new bounds for maximum coverage in two other main models of computation for massive datasets, namely, the dynamic streaming model and the MapReduce model.
Hurwitz theory and the double ramification cycle

This survey grew out of notes accompanying a cycle of lectures at the workshop Modern Trends in Gromov–Witten Theory, in Hannover. The lectures are devoted to interactions between Hurwitz theory and Gromov–Witten theory, with a particular eye to the contributions made to the understanding of the Double Ramification Cycle, a cycle in the moduli space of curves that compactifies the double Hurwitz locus. We explore the algebro-combinatorial properties of single and double Hurwitz numbers, and the connections with intersection theoretic problems on appropriate moduli spaces. We survey several results by many groups of people on the subject, but, perhaps more importantly, collect a number of conjectures and problems which are still open.
Learning Metric Graphs for Neuron Segmentation in Electron Microscopy Images

In the deep metric learning approach to image segmentation, a convolutional net densely generates feature vectors at the pixels of an image. Pairs of feature vectors are trained to be similar or different, depending on whether the corresponding pixels belong to same or different ground truth segments. To segment a new image, the feature vectors are computed and clustered. Both empirically and theoretically, it is unclear whether or when deep metric learning is superior to the more conventional approach of directly predicting an affinity graph with a convolutional net. We compare the two approaches using brain images from serial section electron microscopy images, which constitute an especially challenging example of instance segmentation. We first show that seed-based postprocessing of the feature vectors, as originally proposed, produces inferior accuracy because it is difficult for the convolutional net to predict feature vectors that remain uniform across large objects. Then we consider postprocessing by thresholding a nearest neighbor graph followed by connected components. In this case, segmentations from a “metric graph” turn out to be competitive or even superior to segmentations from a directly predicted affinity graph. To explain these findings theoretically, we invoke the property that the metric function satisfies the triangle inequality. Then we show with an example where this constraint suppresses noise, causing connected components to more robustly segment a metric graph than an unconstrained affinity graph.
Homotopical Adjoint Lifting Theorem

This paper provides a homotopical version of the adjoint lifting theorem in category theory, allowing for Quillen equivalences to be lifted from monoidal model categories to categories of algebras over colored operads. The generality of our approach allows us to simultaneously answer questions of rectification and of changing the base model category to a Quillen equivalent one. We work in the setting of colored operads, and we do not require them to be $$\Sigma $$ -cofibrant. Special cases of our main theorem recover many known results regarding rectification and change of model category, as well as numerous new results. In particular, we recover a recent result of Richter–Shipley about a zig-zag of Quillen equivalences between commutative $$H\mathbb {Q}$$ -algebra spectra and commutative differential graded $$\mathbb {Q}$$ -algebras, but our version involves only three Quillen equivalences instead of six. We also work out the theory of how to lift Quillen equivalences to categories of colored operad algebras after a left Bousfield localization.
Safe and Nested Subgame Solving for Imperfect-Information Games

In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games. Nevertheless, it is possible to first approximate a solution for the whole game and then improve it in individual subgames. This is referred to as subgame solving. We introduce subgame-solving techniques that outperform prior methods both in theory and practice. We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation. Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability. These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold'em poker.
An Electron-Ion Collider at Jefferson lab

Long term plans for the investigation of the quark and gluon structure of matter have for some time focussed on the possibility of an electron-ion collider, with the nuclear physics communities associated with JLab and BNL being particularly active. We briefly outline the current thinking on this subject at Jefferson lab.
A Hilbert bundle description of differential K-theory

Abstract We give an infinite dimensional description of the differential K-theory of a manifold M. The generators are triples [ H , A , ω ] where H is a Z 2 -graded Hilbert bundle on M, A is a superconnection on H and ω is a differential form on M. The relations involve eta forms. We show that the ensuing group is the differential K-group K ˇ 0 ( M ) . In addition, we construct the pushforward of a finite dimensional cocycle under a proper submersion with a Riemannian structure. We give the analogous description of the odd differential K-group K ˇ 1 ( M ) . Finally, we give a model for twisted differential K-theory.
Chernoff Index for Cox Test of Separate Parametric Families.

The asymptotic efficiency of a generalized likelihood ratio test proposed by Cox is studied under the large deviations framework for error probabilities developed by Chernoff. In particular, two separate parametric families of hypotheses are considered [In Proc. 4th Berkeley Sympos. Math. Statist. and Prob. (1961) 105–123; J. Roy. Statist. Soc. Ser. B 24 (1962) 406–424]. The significance level is set such that the maximal type I and type II error probabilities for the generalized likelihood ratio test decay exponentially fast with the same rate. We derive the analytic form of such a rate that is also known as the Chernoff index [Ann. Math. Stat. 23 (1952) 493–507], a relative efficiency measure when there is no preference between the null and the alternative hypotheses. We further extend the analysis to approximate error probabilities when the two families are not completely separated. Discussions are provided concerning the implications of the present result on model selection.
Community detection on euclidean random graphs

We study Community Detection (CD) on a class of sparse spatial random graphs embedded in the Euclidean space. Our random graph is the planted-partition version of the random connection model studied in Stochastic Geometry. Each node has two labels - an i.i.d. uniform {−1, +1} valued community label and a Rd valued location label which form the support of a Poisson Point Process of intensity λ on Rd. Conditional on the labels, edges are drawn independently at random depending both on the Euclidean distance between the nodes and the community labels on the nodes. The CD problem then consists in estimating the partition of nodes into communities better than at random, based on an observation of the random graph and the spatial location labels on nodes. We establish a non-trivial phase-transition for this problem in terms of λ. We show that for small λ, there exists no algorithm for CD, For large λ, our algorithm solves CD efficiently. We show that for small λ, there exists no algorithm for CD. For large λ, we propose an algorithm which solves CD efficiently. In certain special cases, we establish the exact threshold on λ which separates the existence of an algorithm which solves CD from the impossibility of any algorithm. We also establish a distinguishability result which says that one can always efficiently infer the existence of a partition given the graph and the spatial locations even when one cannot identify the partition better than at random. This is a new phenomenon not observed thus far in any non-spatial Erdős-Renyi based models.
Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models

We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predict their category, location, orientation and size with separate neural network modules. Our pipeline naturally supports automatic completion of partial scenes, as well as synthesis of complete scenes, without any modifications. Our method is significantly faster than the previous image-based method, and generates results that outperforms it and other state-of-the-art deep generative scene models in terms of faithfulness to training data and perceived visual quality.
Minimum forcing sets for Miura folding patterns

We introduce the study of forcing sets in mathematical origami. The origami material folds flat along straight line segments called creases, each of which is assigned a folding direction of mountain or valley. A subset F of creases is forcing if the global folding mountain/valley assignment can be deduced from its restriction to F. In this paper we focus on one particular class of foldable patterns called Miura-ori, which divide the plane into congruent parallelograms using horizontal lines and zigzag vertical lines. We develop efficient algorithms for constructing a minimum forcing set of a Miura-ori map, and for deciding whether a given set of creases is forcing or not. We also provide tight bounds on the size of a forcing set, establishing that the standard mountain-valley assignment for the Miura-ori is the one that requires the most creases in its forcing sets. Additionally, given a partial mountain/valley assignment to a subset of creases of a Miura-ori map, we determine whether the assignment domain can be extended to a locally flat-foldable pattern on all the creases. At the heart of our results is a novel correspondence between flat-foldable Miura-ori maps and 3-colorings of grid graphs.
Backprop as Functor: A compositional perspective on supervised learning

A supervised learning algorithm searches over a set of functions $A\rightarrow B$ parametrised by a space $P$ to find the best approximation to some ideal function $f:A\rightarrow B$ . It does this by taking examples $(a, f(a))\in A\times B$ , and updating the parameter according to some rule. We define a category where these update rules may be composed, and show that gradient descent-with respect to a fixed step size and an error function satisfying a certain property-defines a monoidal functor from a category of parametrised functions to this category of update rules. A key contribution is the notion of request function. This provides a structural perspective on backpropagation, giving a broad generalisation of neural networks and linking it with structures from bidirectional programming and open games.
On doubly robust estimation for logistic partially linear models

Abstract Consider a logistic partially linear model, in which the logit of the mean of a binary response is related to a linear function of some covariates and a nonparametric function of other covariates. We derive simple, doubly robust estimators of coefficients in the linear component. Such estimators remain consistent if either a nuisance model is correctly specified for the nonparametric component, or another nuisance model is correctly specified for the means of the covariates of interest given other covariates and the response at a fixed value.
How Do Older Workers Use Nontraditional Jobs

Working consistently through one's early 60s is key to retirement security. However, workers without access to retirement plans and health insurance will likely struggle to achieve such security. This paper uses the Health and Retirement Study to identify nontraditional jobs – which lack these benefits – and applies sequence analysis to explore how workers aged 50–62 use them. The results suggest that most nontraditional jobs are used by workers consistently, and that fewer workers use these jobs briefly or as a bridge to retirement. Workers consistently in nontraditional jobs end up with less retirement income and are more likely to be depressed.
Visualizing the PHATE of Neural Networks

Understanding why and how certain neural networks outperform others is key to guiding future development of network architectures and optimization methods. To this end, we introduce a novel visualization algorithm that reveals the internal geometry of such networks: Multislice PHATE (M-PHATE), the first method designed explicitly to visualize how a neural network's hidden representations of data evolve throughout the course of training. We demonstrate that our visualization provides intuitive, detailed summaries of the learning dynamics beyond simple global measures (i.e., validation loss and accuracy), without the need to access validation data. Furthermore, M-PHATE better captures both the dynamics and community structure of the hidden units as compared to visualization based on standard dimensionality reduction methods (e.g., ISOMAP, t-SNE). We demonstrate M-PHATE with two vignettes: continual learning and generalization. In the former, the M-PHATE visualizations display the mechanism of "catastrophic forgetting" which is a major challenge for learning in task-switching contexts. In the latter, our visualizations reveal how increased heterogeneity among hidden units correlates with improved generalization performance. An implementation of M-PHATE, along with scripts to reproduce the figures in this paper, is available at https://github.com/scottgigante/M-PHATE.
Multi-Staged Regulation of Lipid Signaling Mediators during Myogenesis by COX-1/2 Pathways

Cyclooxygenases (COXs), including COX-1 and -2, are enzymes essential for lipid mediator (LMs) syntheses from arachidonic acid (AA), such as prostaglandins (PGs). Furthermore, COXs could interplay with other enzymes such as lipoxygenases (LOXs) and cytochrome P450s (CYPs) to regulate the signaling of LMs. In this study, to comprehensively analyze the function of COX-1 and -2 in regulating the signaling of bioactive LMs in skeletal muscle, mouse primary myoblasts and C2C12 cells were transfected with specific COX-1 and -2 siRNAs, followed by targeted lipidomic analysis and customized quantitative PCR gene array analysis. Knocking down COXs, particularly COX-1, significantly reduced the release of PGs from muscle cells, especially PGE2 and PGF2α, as well as oleoylethanolamide (OEA) and arachidonoylethanolamine (AEA). Moreover, COXs could interplay with LOXs to regulate the signaling of hydroxyeicosatetraenoic acids (HETEs). The changes in LMs are associated with the expression of genes, such as Itrp1 (calcium signaling) and Myh7 (myogenic differentiation), in skeletal muscle. In conclusion, both COX-1 and -2 contribute to LMs production during myogenesis in vitro, and COXs could interact with LOXs during this process. These interactions and the fine-tuning of the levels of these LMs are most likely important for skeletal muscle myogenesis, and potentially, muscle repair and regeneration.
Bayesian Persuasive Driving

In the autonomous driving area, interaction between vehicles is still a piece of puzzle which has not been fully resolved. The ability to intelligently and safely interact with other vehicles can not only improve self driving quality but also be beneficial to the global driving environment. In this paper, a Bayesian persuasive driving algorithm based on optimization is proposed, where the ego vehicle is the persuader (information sender) and the surrounding vehicle is the persuadee (information receiver). In the persuasion process, the ego vehicle aims at changing the surrounding vehicle's posterior belief of the world state by providing certain information via signaling in order to achieve a lower cost for both players. The information received by the surrounding vehicle and its belief of the world state are described by Gaussian distributions. Simulation results in several common traffic scenarios are provided to demonstrate the proposed algorithm's capability of handling interaction situations involving surrounding vehicles with different driving characteristics.
Block Edit Errors with Transpositions: Deterministic Document Exchange Protocols and Almost Optimal Binary Codes.

Document exchange and error correcting codes are two fundamental problems regarding communications. In the first problem, Alice and Bob each holds a string, and the goal is for Alice to send a short sketch to Bob, so that Bob can recover Alice's string. In the second problem, Alice sends a message with some redundant information to Bob through a channel that can add adversarial errors, and the goal is for Bob to correctly recover the message despite the errors. In a recent work \cite{CJLW18}, the authors constructed explicit deterministic document exchange protocols and binary error correcting codes for edit errors with almost optimal parameters.\ Unfortunately, the constructions in \cite{CJLW18} do not work for other common errors such as block transpositions. In this paper, we generalize the constructions in \cite{CJLW18} to handle a much larger class of errors. These include bursts of insertions and deletions, as well as block transpositions. Specifically, we consider document exchange and error correcting codes where the total number of block insertions, block deletions, and block transpositions is at most $k \leq \alpha n/\log n$ for some constant $0<\alpha<1$. In addition, the total number of bits inserted and deleted by the first two kinds of operations is at most $t \leq \beta n$ for some constant $0<\beta<1$, where $n$ is the length of Alice's string or message. We construct explicit, deterministic document exchange protocols with sketch size $ O( (k \log n +t) \log^2 \frac{n}{k\log n + t} )$ and explicit binary error correcting code with $O(k \log n \log \log \log n+t)$ redundant bits.
Multi-cell ECM compaction is predictable via superposition of nonlinear cell dynamics linearized in augmented state space

Cells interacting through an extracellular matrix (ECM) exhibit emergent behaviors resulting from collective intercellular interaction. In wound healing and tissue development, characteristic compaction of ECM gel is induced by multiple cells that generate tensions in the ECM fibers and coordinate their actions with other cells. Computational prediction of collective cell-ECM interaction based on first principles is highly complex especially as the number of cells increase. Here, we introduce a computationally-efficient method for predicting nonlinear behaviors of multiple cells interacting mechanically through a 3-D ECM fiber network. The key enabling technique is superposition of single cell computational models to predict multicellular behaviors. While cell-ECM interactions are highly nonlinear, they can be linearized accurately with a unique method, termed Dual-Faceted Linearization. This method recasts the original nonlinear dynamics in an augmented space where the system behaves more linearly. The independent state variables are augmented by combining auxiliary variables that inform nonlinear elements involved in the system. This computational method involves a) expressing the original nonlinear state equations with two sets of linear dynamic equations b) reducing the order of the augmented linear system via principal component analysis and c) superposing individual single cell-ECM dynamics to predict collective behaviors of multiple cells. The method is computationally efficient compared to original nonlinear dynamic simulation and accurate compared to traditional Taylor expansion linearization. Furthermore, we reproduce reported experimental results of multi-cell induced ECM compaction.
Colloidal Gelation in Liquid Metals Enables Functional Nanocomposites of 2D Metal Carbides (MXenes) and Lightweight Metals.

Nanomaterials dispersed in different media, such as liquids or polymers, generate a variety of functional composites with synergistic properties. In this work, we discuss liquid metals as the nanomaterials' dispersion media. For example, 2D transition-metal carbides and nitrides (MXenes) can be efficiently dispersed in liquid Ga and lightweight alloys of Al, Mg, and Li. We show that the Lifshitz theory predicts strong van der Waals attraction between nanoscale objects interacting through liquid metals. However, a uniform distribution of MXenes in liquid metals can be achieved through colloidal gelation, where particles form self-supporting networks stable against macroscopic phase segregation. This network acts as a reinforcement boosting mechanical properties of the resulting metal-matrix composite. By choosing Mg-Li alloy as an example of ultralightweight metal matrix and Ti3C2Tx MXene as a nanoscale reinforcement, we apply a liquid metal gelation technique to fabricate functional nanocomposites with an up to 57% increase in the specific yield strength without compromising the matrix alloy's plasticity. MXenes largely retain their phase and 2D morphology after processing in liquid Mg-Li alloy at 700 °C. The 2D morphology enables formation of a strong semicoherent interface between MXene and metal matrix, manifested by biaxial strain of the MXene lattice inside the metal matrix. This work expands applications for MXenes and shows the potential for developing MXene-reinforced metal matrix composites for structural alloys and other emerging applications with metal-MXene interfaces, such as batteries and supercapacitors.
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics

A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment. Several methods have been proposed to learn dynamics models from data that inform model-based control algorithms. While such learning-based approaches can model locally observed behaviors, they fail to generalize to more complex dynamics and under long time horizons. In this work, we introduce a differentiable physics simulator for rigid body dynamics. Leveraging various techniques for differential equation integration and gradient calculation, we compare different methods for parameter estimation that allow us to infer the simulation parameters that are relevant to estimation and control of physical systems. In the context of trajectory optimization, we introduce a closed-loop model-predictive control algorithm that infers the simulation parameters through experience while achieving cost-minimizing performance.
Decentralized distribution-sampled classification models with application to brain imaging.

Abstract Background In this age of big data, certain models require very large data stores in order to be informative and accurate. In many cases however, the data are stored in separate locations requiring data transfer between local sites which can cause various practical hurdles, such as privacy concerns or heavy network load. This is especially true for medical imaging data, which can be constrained due to the health insurance portability and accountability act (HIPAA) which provides security protocols for medical data. Medical imaging datasets can also contain many thousands or millions of features, requiring heavy network load. New method Our research expands upon current decentralized classification research by implementing a new singleshot method for both neural networks and support vector machines. Our approach is to estimate the statistical distribution of the data at each local site and pass this information to the other local sites where each site resamples from the individual distributions and trains a model on both locally available data and the resampled data. The model for each local site produces its own accuracy value which are then averaged together to produce the global average accuracy. Results We show applications of our approach to handwritten digit classification as well as to multi-subject classification of brain imaging data collected from patients with schizophrenia and healthy controls. Overall, the results showed comparable classification accuracy to the centralized model with lower network load than multishot methods. Comparison with existing methods Many decentralized classifiers are multishot, requiring heavy network traffic. Our model attempts to alleviate this load while preserving prediction accuracy. Conclusions We show that our proposed approach performs comparably to a centralized approach while minimizing network traffic compared to multishot methods.
The role of atmospheric pressure on Mars surface properties and early Mars climate modeling

Abstract The thermal inertia of a planetary surface is a compound function of the regolith thermal conductivity, density and specific heat. On planetary bodies with atmospheres, the conductivity of the surface must account for the contributions of both the solid component of the surface as well as that of atmospheric gas found in the interstitial pore spaces. Today, variations in thermal inertia and thermal conductivity on Mars affect the size and timing of areas for which surface temperatures exceed the melting point temperature of water, which is a necessary-but-not-sufficient prerequisite for surface liquid water. Models of past Mars climate, when the atmosphere may have been significantly thicker than at present, have largely neglected the potential role of interstitial atmospheric gas as a thermally conducting element of the ‘surface,’ though we show here that such underestimation of surface conductivity and thermal inertia has no appreciable effect on models of past Mars climate. In more recent Mars history, changes in obliquity have a similar effect of inflating or collapsing the Mars atmosphere, though to a lesser extent. Orbital changes will also modify surface thermal properties, leading to variations in surface conductivity (and thermal inertia) on 105–107 year cycles. We show that these variations, in fact, should not be neglected. We propose an obliquity-driven cycle of surface evolution that drives variability in surface thermal inertia, and suggest that the potential for liquid water at the surface should increase with time following large, positive excursions in Mars' obliquity.
A Sturm Liouville theorem for quadratic operator pencils

Abstract We establish a Sturm–Liouville theorem for quadratic operator pencils with matrix-valued potentials counting their unstable real roots, with applications to stability of waves. Such pencils arise, for example, in reduction of eigenvalue systems to higher-order scalar problems.
Hemagglutinin Stability Regulates H1N1 Influenza Virus Replication and Pathogenicity in Mice by Modulating Type I Interferon Responses in Dendritic Cells

Hemagglutinin (HA) stability, or the pH at which HA is activated to cause membrane fusion, has been associated with the replication, pathogenicity, transmissibility, and interspecies adaptation of influenza A viruses. Here, we investigated the mechanisms by which a destabilizing HA mutation, Y17H (activation pH, 6.0), attenuates virus replication and pathogenicity in DBA/2 mice compared to wild-type (WT) virus (activation pH, 5.5). The extracellular lung pH was measured to be near neutral (pH 6.9 to 7.5). WT and Y17H viruses had similar environmental stability at pH 7.0; thus, extracellular inactivation was unlikely to attenuate the Y17H virus. The Y17H virus had accelerated replication kinetics in MDCK, A549, and RAW 264.7 cells when inoculated at a multiplicity of infection (MOI) of 3 PFU/cell. The destabilizing mutation also increased early infectivity and type I interferon (IFN) responses in mouse bone marrow-derived dendritic cells (DCs). In contrast, the HA-Y17H mutation reduced virus replication in murine airway murine nasal epithelial cell and murine tracheal epithelial cell cultures and attenuated virus replication, virus spread, the severity of infection, and cellular infiltration in the lungs of mice. Normalizing virus infection and weight loss in mice by inoculating them with Y17H virus at a dose 500-fold higher than that of WT virus revealed that the destabilized mutant virus triggered the upregulation of more host genes and increased type I IFN responses and cytokine expression in DBA/2 mouse lungs. Overall, HA destabilization decreased virulence in mice by boosting early infection in DCs, resulting in the greater activation of antiviral responses, including the type I IFN response. These studies reveal that HA stability may regulate pathogenicity by modulating IFN responses.IMPORTANCE Diverse influenza A viruses circulate in wild aquatic birds, occasionally infecting farm animals. Rarely, an avian- or swine-origin influenza virus adapts to humans and starts a pandemic. Seasonal and many universal influenza vaccines target the HA surface protein, which is a key component of pandemic influenza viruses. Understanding the HA properties needed for replication and pathogenicity in mammals may guide response efforts to control influenza. Some antiviral drugs and broadly reactive influenza vaccines that target the HA protein have suffered resistance due to destabilizing HA mutations that do not compromise replicative fitness in cell culture. Here, we show that despite not compromising fitness in standard cell cultures, a destabilizing H1N1 HA stalk mutation greatly diminishes viral replication and pathogenicity in vivo by modulating type I IFN responses. This encourages targeting the HA stalk with antiviral drugs and vaccines as well as reevaluating previous candidates that were susceptible to destabilizing resistance mutations.
Shifting baselines in coral conservation

Reef-building corals are increasingly exposed to warming ocean temperatures. Their immediate response to this rise in temperature is to expel their symbiotic algae and turn white, or bleach. It is ...
Perception of whole-body motion during balance perturbations is impaired in Parkinson's disease and is associated with balance impairment.

Abstract Background In addition to motor deficits, Parkinson's disease (PD) may cause perceptual impairments. The role of perceptual impairments in sensorimotor function is unclear, and has typically been studied in single-joint motions. Research question We hypothesized that perception of whole-body motion is impaired in PD and contributes to balance impairments. We tested (1) whether directional acuity to whole body perturbations during standing was worse in people with PD compared to neurotypical older adults (NOA), and (2) whether balance ability, as assessed by the MiniBESTest, was associated with poor directional acuity in either group. Methods Participants were exposed to pairs of support-surface translation perturbations in a two-alternative forced choice testing paradigm developed previously in a young healthy population. The first perturbation of each pair that was to be judged by participants was directly backward, and the second perturbation deviated from the left or right from the backward direction by 1°–44°. Participants reported whether the perturbations in each pair were in the “same” or “different” direction. Judgements from 24 to 67 perturbation pairs were used to calculate directional acuity thresholds corresponding to “just-noticeable differences” in perturbation direction. Linear mixed models determined associations between directional thresholds and clinical variables including MDS-UPDRS-III score, age, and MiniBESTest score. Results 20 PD (64 ± 7 y, 12 male, ≥12 h since last intake of antiparkinsonian medications) and 12 NOA (64 ± 8, 6 male) were assessed. Directional thresholds were higher (worse) among PD participants (17.6 ± 5.9° vs. 12.8 ± 3.3°, P Significance Perception of whole-body motion is impaired in PD and may contribute to impaired balance and falls.
Computing vibrational eigenstates with tree tensor network states (TTNS)

We present how to compute vibrational eigenstates with tree tensor network states (TTNSs), the underlying ansatz behind the multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) method. The eigenstates are computed with an algorithm that is based on the density matrix renormalization group (DMRG). We apply this to compute the vibrational spectrum of acetonitrile (CH3CN) to high accuracy and compare TTNSs with matrix product states (MPSs), the ansatz behind the DMRG. The presented optimization scheme converges much faster than ML-MCTDH-based optimization. For this particular system, we found no major advantage of the more general TTNS over MPS. We highlight that for both TTNS and MPS, the usage of an adaptive bond dimension significantly reduces the amount of required parameters. We furthermore propose a procedure to find good trees.
Evaluation Uncertainty in Data-Driven Self-Driving Testing

Safety evaluation of self-driving technologies has been extensively studied. One recent approach uses Monte Carlo based evaluation to estimate the occurrence probabilities of safety-critical events as safety measures. These Monte Carlo samples are generated from stochastic input models constructed based on real-world data. In this paper, we propose an approach to assess the impact on the probability estimates from the evaluation procedures due to the estimation error caused by data variability. Our proposed method merges the classical bootstrap method for estimating input uncertainty with a likelihood ratio based scheme to reuse experiment outputs. This approach is economical and efficient in terms of implementation costs in assessing input uncertainty for the evaluation of self-driving technology. We use an example in autonomous vehicle (AV) safety evaluation to demonstrate the proposed approach as a diagnostic tool for the quality of the fitted input model.
Refractive change in children with accommodative esotropia.

Objective To determine whether there is a measurable change in hyperopia in children with accommodative esotropia over time. Methods and analysis A retrospective cohort of children with fully or partially accommodative esotropia diagnosed by age 7 years, followed to age 10 or older, and with at least two cycloplegic refractions, one before age 7 years and one after age 10 years. The annual change was calculated from linear mixed-effect models, overall and during two age periods with subgroup analysis by baseline refractive error ( Results 405 subjects were studied. Mean age at first and last visit was 3.2 and 12.1 years, respectively, with mean 7.6 cycloplegic refractions. The annual change (95% CI) in refractive error was −0.071 (−0.087 to –0.055) D/yr. Between ages 3 and 7, hyperopia among children with baseline hyperopia Conclusion Hyperopia in children with accommodative esotropia is stable or increases up to age 7 years, depending on baseline hyperopia, but decreases gradually between ages 7 and 15 years regardless of baseline refractive error.
NOTES ON SOME ASPECTS OF THE TAXONOMY OF “GENERATIONS” OF RIGHTS

This brief Essay examines the relationship between so-called first, second, and third generation rights. It is often thought that second and third generation rights supplement and deepen the commitments to human flourishing that underlie first generation rights. The Essay argues that the generations of rights have rather different conceptual underpinnings and that there may be serious conflicts among particular realizations of first, second, and third generation rights. An optimization strategy of the sort suggested by Robert Alexy, suitably adapted, might provide some ad hoc solutions, but it may be that the generations of rights ultimately are irreconcilable (in the large, though of course not in many particular instances).
Neural codes and the factor complex

Abstract We introduce the factor complex of a neural code, and show how intervals and maximal codewords are captured by the combinatorics of factor complexes. We use these results to obtain algebraic and combinatorial characterizations of max-intersection-complete codes, as well as a new combinatorial characterization of intersection-complete codes.
Slow Beam Steering and NOMA for Indoor Multi-User Visible Light Communications

Visible light communication (VLC) is an emerging technology that enables broadband data rates using the visible spectrum. In this paper, considering slow beam steering where VLC beam directions are assumed to be fixed during a transmission frame, we find the steering angles that simultaneously serve multiple users within the frame duration and maximize the data rates. This is achieved by solving a non-convex optimization problem using a grid-based search and majorization-minimization (MM) procedure. Subsequently, we consider multiple steerable beams with a larger number of users in the network and propose an algorithm to cluster users and serve each cluster with a separate beam. We optimize the transmit power of each beam to maximize the data rates. Finally, we propose a non-orthogonal multiple access (NOMA) scheme for the beam steering and user clustering scenario, to further increase the data rates of the users. The simulation results show that the proposed beam steering method can efficiently serve a high number of users, and with power optimization, a sum rate gain up to thirteen times is possible. The simulation results for NOMA suggests an additional 10 Mbps sum rate gain for each NOMA user pair.