cssi/SciSciGPT-SciSciNet huggingface dataset:
An extremal inequality for long Markov chains

Let X, Y be jointly Gaussian vectors, and consider random variables U, V that satisfy the Markov constraint U — X — Y — V. We prove an extremal inequality relating the mutual informations between all (4 2 ) pairs of random variables from the set (U, X, Y, V). As a first application, we show that the rate region for the two-encoder quadratic Gaussian source coding problem follows as an immediate corollary of the the extremal inequality. In a second application, we establish the rate region for a vector-Gaussian source coding problem where Lowner-John ellipsoids are approximated based on rate-constrained descriptions of the data.
Global existence and scattering for the nonlinear Schrodinger equation on Schwarzschild manifolds

We consider the nonlinear Schrodinger equation with a pure power repulsive nonlinearity on Schwarzschild manifolds. Equations of this type arise when a nonlinear wave equation on a Schwarzschild manifold is written in Hamiltonian form, cf. (2), (10). For radial solutions with sufficiently localized initial data, we obtain global existence, L p estimates, and the existence and asymptotic completeness of the wave operators. Our approach is based on a dilation identity and global space-time estimates.
On the traceless SU(2) character variety of the 6-punctured 2-sphere

We exhibit the traceless SU(2) character variety of a 6-punctured 2-sphere as a 2-fold branched cover of ℂP3, branched over the singular Kummer surface, with the branch locus in R(S2, 6) corresponding to the binary dihedral representations. This follows from an analysis of the map induced on SU(2) character varieties by the 2-fold branched cover Fn−1 → S2 branched over 2n points, combined with the theorem of Narasimhan–Ramanan which identifies R(F2) with ℂP3. The singular points of R(S2, 6) correspond to abelian representations, and we prove that each has a neighborhood in R(S2, 6) homeomorphic to a cone on S2 × S3.
Permutations that Destroy Arithmetic Progressions in Elementary $p$-Groups

Given an abelian group $G$, it is natural to ask whether there exists a permutation $\pi$ of $G$ that "destroys" all nontrivial 3-term arithmetic progressions (APs), in the sense that $\pi(b) - \pi(a) \neq \pi(c) - \pi(b)$ for every ordered triple $(a,b,c) \in G^3$ satisfying $b-a = c-b \neq 0$. This question was resolved for infinite groups $G$ by Hegarty, who showed that there exists an AP-destroying permutation of $G$ if and only if $G/\Omega_2(G)$ has the same cardinality as $G$, where $\Omega_2(G)$ denotes the subgroup of all elements in $G$ whose order divides $2$. In the case when $G$ is finite, however, only partial results have been obtained thus far. Hegarty has conjectured that an AP-destroying permutation of $G$ exists if $G = \mathbb{Z}/n\mathbb{Z}$ for all $n \neq 2,3,5,7$, and together with Martinsson, he has proven the conjecture for all $n > 1.4 \times 10^{14}$. In this paper, we show that if $p$ is a prime and $k$ is a positive integer, then there is an AP-destroying permutation of the elementary $p$-group $(\mathbb{Z}/p\mathbb{Z})^k$ if and only if $p$ is odd and $(p,k) \not\in \{(3,1),(5,1), (7,1)\}$.
Accuracy and stability of inversion of power series

This article considers the numerical inversion of the power series $p(x)=1+b_{1}x+b_{2}x^{2}+\cdots$ to compute the inverse series $q(x)$ satisfying $p(x)q(x)=1$. Numerical inversion is a special case of triangular back-substitution, which has been known for its beguiling numerical stability since the classic work of Wilkinson (1961). We prove the numerical stability of inversion of power series and obtain bounds on numerical error. A range of examples show these bounds to be quite good. When $p(x)$ is a polynomial and $x=a$ is a root with $p(a)=0$, we show that root deflation via the simple division $p(x)/(x-a)$ can trigger instabilities relevant to polynomial root finding and computation of finite-difference weights. When $p(x)$ is a polynomial, the accuracy of the computed inverse $q(x)$ is connected to the pseudozeros of $p(x)$.
Distributed Power Allocations in Heterogeneous Networks With Dual Connectivity Using Backhaul State Information

Long Term Evolution (LTE) release 12 proposes the use of dual connectivity in heterogeneous cellular networks, where a user equipment (UE) maintains parallel connections to a macrocell base station and to a low-tier node such as a picocell base station or relay. In this paper, we propose distributed multi-objective power control where each UE independently adapts its transmit power on its dual connections, where the uplinks could be of unequal bandwidth and have non-ideal backhaul links. In the proposed optimization, the UEs dynamically switch between data rate maximization and transmit power minimization as the backhaul load varies. To address the coupling between interference and the backhaul load, we propose a low-overhead convergence mechanism which does not require explicit coordination between UEs and also derive a closed-form expression of the transmit power levels at equilibrium. Simulation results show that our scheme achieves higher aggregate end-to-end data rate and significant power saving in comparison to a scheme that employs a greedy algorithm and a scheme that employs only waterfilling.
Instrument-Armed Bandits

We extend the classic multi-armed bandit (MAB) model to the setting of noncompliance, where the arm pull is a mere instrument and the treatment applied may differ from it, which gives rise to the instrument-armed bandit (IAB) problem. The IAB setting is relevant whenever the experimental units are human since free will, ethics, and the law may prohibit unrestricted or forced application of treatment. In particular, the setting is relevant in bandit models of dynamic clinical trials and other controlled trials on human interventions. Nonetheless, the setting has not been fully investigate in the bandit literature. We show that there are various and divergent notions of regret in this setting, all of which coincide only in the classic MAB setting. We characterize the behavior of these regrets and analyze standard MAB algorithms. We argue for a particular kind of regret that captures the causal effect of treatments but show that standard MAB algorithms cannot achieve sublinear control on this regret. Instead, we develop new algorithms for the IAB problem, prove new regret bounds for them, and compare them to standard MAB algorithms in numerical examples.
Continued fractions on the Heisenberg group

We provide a generalization of continued fractions to the Heisenberg group. We prove an explicit estimate on the rate of convergence of the infinite continued fraction and several surprising analogs of classical formulas about continued fractions. We then discuss dynamical properties of the associated Gauss map, comparing them with base-$b$ expansions on the Heisenberg group and continued fractions on the complex plane.
Magnetically Driven Warping and Precession of Accretion Disks: Implications for Exotic Stellar Variabilities

The inner region of the accretion disk around a magnetized star is subjected to magnetic torques that induce warping and precession of the disk. These torques arise from interactions between the stellar field and the induced electric currents in the disk. These novel magnetic effects give rise to some ``exotic'' stellar variabilities, and may play an important role in explaining a number of puzzling behaviors related to disk accretion onto magnetic stars, such as mHz QPOs in X-ray pulsars, long-term periodicities of X-ray binaries (including precession of jets), low-Frequency (10-50 Hz) QPO's in low-mass X-ray binaries, and photometric variabilities of T Tauri stars.
Learning style similarity for searching infographics

Infographics are complex graphic designs integrating text, images, charts and sketches. Despite the increasing popularity of infographics and the rapid growth of online design portfolios, little research investigates how we can take advantage of these design resources. In this paper we present a method for measuring the style similarity between infographics. Based on human perception data collected from crowdsourced experiments, we use computer vision and machine learning algorithms to learn a style similarity metric for infographic designs. We evaluate different visual features and learning algorithms and find that a combination of color histograms and Histograms-of-Gradients (HoG) features is most effective in characterizing the style of infographics. We demonstrate our similarity metric on a preliminary image retrieval test.
Improved Convergence Rates for Distributed Resource Allocation

In this paper, we develop a class of decentralized algorithms for solving a convex resource allocation optimization problem over a connected network. By observing a connection between the resource allocation and the consensus optimization, we propose a novel class of algorithms for solving the resource allocation problem with improved convergence guarantees. Specifically, we introduce an algorithm for solving the resource allocation problem with an o(1/k) convergence rate when the agents' objective functions are generally convex and per agent local constraints are allowed; we then introduce a gradient-based algorithm for the case when per agent local constraints are absent and show that such scheme achieves geometric convergence with an improved scalability. We also provide a projection-gradient-based algorithm which can handle smooth objective and simple constraints more efficiently.
On $K-P$ sub-Riemannian Problems and their Cut Locus

The problem of finding minimizing geodesics for a manifold $M$ with a sub-Riemannian structure is equivalent to the time optimal control of a driftless system on $M$ with a bound on the control. We consider here a class of sub-Riemannian problems on the classical Lie groups $G$ where the dynamical equations are of the form $\dot{x}=\sum\nolimits_{j}X_{j}(x)u_{j}$ and the $X_{j}=X_{j}(x)$ are right invariant vector fields on $G$ and $u_{j}:=u_{j}(t)$ the controls. The vector fields $X_{j}$ are assumed to belong to the P part of a Cartan K-P decomposition. These types of problems admit a group of symmetries $K$ which act on $G$ by conjugation. Under the assumption that the minimal isotropy group in $K$ is discrete, we prove that we can reduce the problem to a Riemannian problem on the regular part of the associated quotient space $G/K$ . On this part we define the corresponding quotient metric. For the special cases of the K-P decomposition of $SU(n)$ of type AIII we prove that the assumption on the minimal isotropy group is verified. As an example of application of the techniques discussed we find the cut locus of a K-P optimal control problem on $SU(2)$ .
XLNet: Generalized Autoregressive Pretraining for Language Understanding

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment setting, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.
A Facial Affect Analysis System for Autism Spectrum Disorder

In this paper, we introduce an end-to-end machine learning-based system for classifying autism spectrum disorder (ASD) using facial attributes such as expressions, action units, arousal, and valence. Our system classifies ASD using representations of different facial attributes from convolutional neural networks, which are trained on images in the wild. Our experimental results show that different facial attributes used in our system are statistically significant and improve sensitivity, specificity, and F1 score of ASD classification by a large margin. In particular, the addition of different facial attributes improves the performance of ASD classification by about 7% which achieves a F1 score of 76%.
Screening for cystic fibrosis-related diabetes and prediabetes: Evaluating 1,5-anhydroglucitol, fructosamine, glycated albumin, and hemoglobin A1c.

OBJECTIVE Dysglycemia is prevalent in cystic fibrosis (CF) but screening with annual oral glucose tolerance tests (OGTT) can be burdensome. We investigated alternate glycemic markers-hemoglobin A1c (HbA1c), 1,5-anhydroglucitol (1,5AG), fructosamine (FA), and glycated albumin (GA)-as screening tests for CF-related diabetes (CFRD) and pre-diabetes (CFPD) in youth with CF as defined by the gold-standard OGTT 2-hour glucose (2hG). METHODS Youth 10 to 18 years with CF had a 1,5AG, FA, GA, HbA1c, and 2-hour OGTT collected. Correlations between all glycemic markers and 2hG were evaluated. Area under the receiver operative characteristic (ROC-AUC) curves were generated. Optimal cut points for predicting CFPD (2hG ≥ 140 mg/dL) and CFRD (2hG ≥ 200 mg/dL) were determined. RESULTS Fifty-eight youth with CF were included (2hG < 140, n = 16; CFPD, n = 33; CFRD, n = 9; 41% male, mean ± SD age 14.2 ± 3.6 years, BMI z-score 0.0 ± 0.8, % predicted forced expiratory volume in 1 second [FEV1] 89.9 ± 15.1, % predicted forced vital capacity [FVC] 103.2 ± 14.6). ROC-AUC's for all alternate markers were low for CFPD (0.52-0.67) and CFRD (0.56-0.61). At a cut point of 5.5%, HbA1c had 78% sensitivity (95% CI: 0.45-0.94) and 41% specificity (95% CI: 0.28-0.55) for identifying CFRD, correlating to a ROC-AUC of 0.61 (95% CI: 0.42-0.8). CONCLUSIONS All alternate markers tested demonstrate poor diagnostic accuracy for identifying CFRD by 2hG.
Prediction of thermal conductivity in dielectrics using fast, spectrally-resolved phonon transport simulations

Abstract We present a new method for predicting effective thermal conductivity ( κ eff ) in materials, informed by ab initio material property simulations. Using the Boltzmann transport equation in a self-adjoint angular flux formulation, we performed simulations in silicon at room temperatures over length scales varying from 10 nm to 10 μ m and report temperature distributions, spectral heat flux and thermal conductivity. Our implementation utilizes a Richardson iteration on a modified version of the phonon scattering source. In this method, a closure term is introduced to the transport equation which acts as a redistribution kernel for the total energy bath of the system. This term is an effective indicator of the degree of disorder between the spectral phonon radiance and the angular phonon intensity of the transport system. We employ polarization, density of states and full dispersion spectra to resolve thermal conductivity with numerous angular and spatial discretizations.
Detecting Social Influence in Event Cascades by Comparing Discriminative Rankers

The global dynamics of event cascades are often governed by the local dynamics of peer influence. However, detecting social influence from observational data is challenging due to confounds like homophily and practical issues like missing data. We propose a simple discriminative method to detect influence from observational data. The core of the approach is to train a ranking algorithm to predict the source of the next event in a cascade, and compare its out-of-sample accuracy against a competitive baseline which lacks access to features corresponding to social influence. We analyze synthetically generated data to show that this method correctly identifies influence in the presence of confounds, and is robust to both missing data and misspecification --- unlike well-known alternatives. We apply the method to two real-world datasets: (1) the co-sponsorship of legislation in the U.S. House of Representatives on a social network of shared campaign donors; (2) rumors about the Higgs boson discovery on a follower network of $10^5$ Twitter accounts. Our model identifies the role of social influence in these scenarios and uses it to make more accurate predictions about the future trajectory of cascades.
Simple Policy Evaluation for Data-Rich Iterative Tasks

A data-based policy for iterative control task is presented. The proposed strategy is model-free and can be applied whenever safe input and state trajectories of a system performing an iterative task are available. These trajectories, together with a user-defined cost function, are exploited to construct a piecewise affine approximation to the value function. The approximated value function is then used to evaluate the control policy by solving a linear program. We show that for linear system subject to convex cost and constraints, the proposed strategy guarantees closed-loop constraint satisfaction and performance bounds for the closed-loop trajectory. We evaluate the proposed strategy in simulations and experiments, the latter carried out on the Berkeley Autonomous Race Car (BARC) platform. We show that the proposed strategy is able to reduce the computation time by one order of magnitude while achieving the same performance as our model-based control algorithm.
The Reluctant African: The Foreign Policy Journalism of Louis Lomax, 1960–1968

ABSTRACTLouis Lomax was a popular public figure in the 1960s who is often relegated to tertiary status in historical accounts of both civil rights and its foreign policy outgrowths. He was the writ...
dsRNA-Seq: Identification of Viral Infection by Purifying and Sequencing dsRNA.

RNA viruses are a major source of emerging and re-emerging infectious diseases around the world. We developed a method to identify RNA viruses that is based on the fact that RNA viruses produce double-stranded RNA (dsRNA) while replicating. Purifying and sequencing dsRNA from the total RNA isolated from infected tissue allowed us to recover dsRNA virus sequences and replicated sequences from single-stranded RNA (ssRNA) viruses. We refer to this approach as dsRNA-Seq. By assembling dsRNA sequences into contigs we identified full length or partial RNA viral genomes of varying genome types infecting mammalian culture samples, identified a known viral disease agent in laboratory infected mice, and successfully detected naturally occurring RNA viral infections in reptiles. Here, we show that dsRNA-Seq is a preferable method for identifying viruses in organisms that don’t have sequenced genomes and/or commercially available rRNA depletion reagents. In addition, a significant advantage of this method is the ability to identify replicated viral sequences of ssRNA viruses, which is useful for distinguishing infectious viral agents from potential noninfectious viral particles or contaminants.
Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics

Abstract One of the most controversial practices in resting-state fMRI functional connectivity studies is whether or not to regress out the global average brain signal (GS) during artifact removal. Some groups have argued that it is absolutely essential to regress out the GS in order to fully remove head motion, respiration, and other global imaging artifacts. Others have argued that removing the GS distorts the resulting correlation matrices and inappropriately alters the results of group comparisons and relationships to behavior. At the core of this argument is the assessment of dimensionality in terms of the number of brain networks with uncorrelated time series. If the dimensionality is high, then the distortions due to GS removal could be effectively negligible. In the current paper, we examine the dimensionality of resting-state fMRI data using principal component analyses (PCA) and network clustering analyses. In two independent datasets (Set 1: N = 62, Set 2: N = 32), scree plots of the eigenvalues level off at or prior to 10 principal components, with prominent elbows at 3 and 7 components. While network clustering analyses have previously demonstrated that numerous networks can be distinguished with high thresholding of the voxel-wise correlation matrices, lower thresholding reveals a lower-dimensional hierarchical structure, with the first prominent branch at 2 networks (corresponding to the previously described “task-positive"/"task-negative” distinction) and further stable subdivisions at 4, 7 and 17. Since inter-correlated time series within these larger branches do not cancel to zero when averaged, the hierarchical nature of the correlation structure results in low effective dimensionality. Consistent with this, partial correlation analyses revealed that network-specific variance remains present in the GS at each level of the hierarchy, accounting for at least 14–18% of the overall GS variance in each dataset. These results demonstrate that GS regression is expected to remove substantial portions of network-specific brain signals along with artifacts, not simply whole-brain signals corresponding to arousal levels. We highlight alternative means of controlling for residual global artifacts when not removing the GS.
Optimal density lower bound on nonisentropic gas dynamics

Abstract In this paper, we prove a time dependent lower bound on density in the optimal order O ( 1 / ( 1 + t ) ) for the general smooth nonisentropic flow of compressible Euler equations.
Computing Euler Obstruction Functions Using Maximum Likelihood Degrees

We give a numerical algorithm computing Euler obstruction functions using maximum likelihood degrees. The maximum likelihood degree is a well-studied property of a variety in algebraic statistics and computational algebraic geometry. In this article we use this degree to give a new way to compute Euler obstruction functions. We define the maximum likelihood obstruction function and show how it coincides with the Euler obstruction function. With this insight, we are able to bring new tools of computational algebraic geometry to study Euler obstruction functions.
Adolescent sex offender registration policy: Perspectives on general deterrence potential from criminology and developmental psychology.

Sex offender registration policies have expanded rapidly, now extending to adolescent offenders across the nation. Policies mandating registration are based, in part, on arguments that registration is needed to prevent dangerous sex offenders from committing additional offenses and that risk of registration deters would-be offenders from offending in the first place. Research suggests that registration does not serve the former specific deterrent function for adolescents, but less is known regarding the latter goal of general deterrence. The disciplines of criminology and developmental psychology both offer important theoretical perspectives, but these frameworks have yet to be applied to this unique context. Criminological theory on perceptions of sanction risk offers clear predictions about the potential for registration to serve as a deterrent for would-be adolescent sex offenders. Yet, the literature has not meaningfully acknowledged that (a) most adolescents are probably unaware of the parameters of registration (e.g., age restrictions, range of registerable offenses), and (b) even if adolescents are aware of registration risk, they are unlikely to be effectively deterred by it because of a variety of developmental vulnerabilities (e.g., psychosocial immaturity, perceived normativeness of certain nonviolent sexual offenses that can result in registration). We argue that key elements of criminological and developmental psychological theory must be integrated to gain a better understanding of whether sex offender registration policy can have a general deterrent effect for adolescents. We conclude with a call for interdisciplinary research that addresses the general deterrence gap and its role in guiding empirically driven policy reform.
New bounds on the price of bandit feedback for mistake-bounded online multiclass learning

Abstract This paper is about two generalizations of the mistake bound model to online multiclass classification. In the standard model, the learner receives the correct classification at the end of each round, and in the bandit model, the learner only finds out whether its prediction was correct or not. For a set F of multiclass classifiers, let opt std ( F ) and opt bandit ( F ) be the optimal bounds for learning F according to these two models. We show that an opt bandit ( F ) ≤ ( 1 + o ( 1 ) ) ( | Y | ln ⁡ | Y | ) opt std ( F ) bound is the best possible up to the leading constant, closing a Θ ( log ⁡ | Y | ) factor gap.
Deep Active Subspaces: A Scalable Method for High-Dimensional Uncertainty Propagation

A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for the construction of accurate surrogate models. Such efforts are particularly important to applications constrained by high-dimensional uncertain parameter spaces. The difficulty of accurate surrogate modeling in such systems, is further compounded by data scarcity brought about by the large cost of forward model evaluations. Traditional response surface techniques, such as Gaussian process regression (or Kriging) and polynomial chaos are difficult to scale to high dimensions. To make surrogate modeling tractable in expensive high-dimensional systems, one must resort to dimensionality reduction of the stochastic parameter space. A recent dimensionality reduction technique that has shown great promise is the method of `active subspaces'. The classical formulation of active subspaces, unfortunately, requires gradient information from the forward model - often impossible to obtain. In this work, we present a simple, scalable method for recovering active subspaces in high-dimensional stochastic systems, without gradient-information that relies on a reparameterization of the orthogonal active subspace projection matrix, and couple this formulation with deep neural networks. We demonstrate our approach on synthetic and real world datasets and show favorable predictive comparison to classical active subspaces.
Prototype Measurements of the Columbia River Estuary

Development of navigational regulation of estuary since 1882 is reviewed; measurement program to define environmental conditions affecting shoaling; description of types of directional current velocity meters used; velocity-azimuth-depth assembly; salinity-temperature assembly; data tabulated and shown in diagrams.
Gaussian Fluctuation for the Number of Particles in Airy, Bessel, Sine, and Other Determinantal Random Point Fields

We prove the Central Limit Theorem (CLT) for the number of eigenvalues near the spectrum edge for certain Hermitian ensembles of random matrices. To derive our results, we use a general theorem, essentially due to Costin and Lebowitz, concerning the Gaussian fluctuation of the number of particles in random point fields with determinantal correlation functions. As another corollary of the Costin–Lebowitz Theorem we prove the CLT for the empirical distribution function of the eigenvalues of random matrices from classical compact groups.
Theoretical and experimental emittance measurements for a thin liquid sheet flow

Surface tension forces at the edges of a thin liquid (approximately 200 microns) sheet flow result in a triangularly shaped sheet. Such a geometry is ideal for an external flow radiator. Since the fluid must have very low vapor pressure, Dow Corning 705 silicone oil was used and the emittance of a flowing sheet of oil was determined by two methods. The emittance was derived as a function of the temperature drop between the top of the sheet and the coalescence point of the sheet, the sink temperature, the volumetric flow and the length of the sheet. the emittance for the oil was also calculated using an extinction coefficient determined from spectral transmittance data of the oil. The oil's emittance ranges from .67 to .87 depending on the sheet thickness and sheet temperature. The emittance derived from the temperature drop was slightly less than the emittance calculated from transmittance data. An investigation of temperature fluctuation upstream of the slit plate was also done. The fluctuations were determined to be negligible, not affecting the temperature drop which was due to radiation.
Explicit expression of anti-black biases in the full-service restaurant industry: Legal responsibilities and risks

Abstract Anecdotes detailing racial prejudices and discriminatory treatment of Black Americans while dining in full-service restaurants surface routinely in the popular press. These anecdotes are buttressed by a small number of studies documenting evidence of racist language and observed mistreatment of Black restaurant customers. In an effort to further explore the pervasiveness and correlates of such explicit expression of anti-Black biases in restaurant workplaces, this study presents findings from an online survey that was administered in two independent, large, and demographically diverse samples of current restaurant employees. We find that on average between 15 % (Qualtrics sample) and 24 % (Facebook sample) of participants agreed with statements indicating that racist language and discrimination against Black customers is common in the restaurants where they work. We explore the individual and organizational correlates of such explicit expression of anti-Black biases in restaurant workplaces and highlight the associated legal ramifications. We conclude by encouraging restaurateurs to take action against manifestations of racial prejudice in their establishments in order to fulfill their legal responsibilities to both their customers and their employees and thereby to minimize the threat of litigation.
Minimal surfaces from infinitesimal deformations of circle packings

Abstract We study circle packings with the combinatorics of a triangulated disk in the plane and parametrize deformations of circle packings in terms of vertex rotation and cross ratios. We show that there is a Weierstrass representation formula relating infinitesimal deformations of circle packings to discrete minimal surfaces of Koebe type. Furthermore, every minimal surface of Koebe type can be extended naturally to a discrete minimal surface of general type. In this way, discrete minimal surfaces via Steiner's formula are unified.
The initiation of audit committee interlocks and the contagion of accounting policy choices: evidence from special items

We document that the initiation of audit committee interlocks is associated with contagion in reported special items. We argue that this is, in part, attributable to contagion of accounting policy choices. We find that the special items of newly interlocked firms, unrelated before interlock, become positively correlated afterward, suggesting information transfer starts with interlock formation. This result holds for negative special items, key components of special items (asset impairments, restructuring costs, and gains/losses from asset sales), and is stronger for larger firms and for firms within the same industry.
QC-Automator: Deep Learning-Based Automated Quality Control for Diffusion MR Images

Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly error-prone, and infeasible, especially considering the growing number of consortium-like studies, underlining the need for automation of the process. In this paper, we have developed a deep-learning-based automated quality control (QC) tool, QC-Automator, for dMRI data, that can handle a variety of artifacts such as motion, multiband interleaving, ghosting, susceptibility, herringbone, and chemical shifts. QC-Automator uses convolutional neural networks along with transfer learning to train the automated artifact detection on a labeled dataset of ∼332,000 slices of dMRI data, from 155 unique subjects and 5 scanners with different dMRI acquisitions, achieving a 98% accuracy in detecting artifacts. The method is fast and paves the way for efficient and effective artifact detection in large datasets. It is also demonstrated to be replicable on other datasets with different acquisition parameters.
Robot Learning via Human Adversarial Games

Much work in robotics has focused on “humanin-the-loop” learning techniques that improve the efficiency of the learning process. However, these algorithms have made the strong assumption of a cooperating human supervisor that assists the robot. In reality, human observers tend to also act in an adversarial manner towards deployed robotic systems. We show that this can in fact improve the robustness of the learned models by proposing a physical framework that leverages perturbations applied by a human adversary, guiding the robot towards more robust models. In a manipulation task, we show that grasping success improves significantly when the robot trains with a human adversary as compared to training in a self-supervised manner.
Division Algebras, Galois Fields, Quadratic Residues

Intended for mathematical physicists interested in applications of the division algebras to physics, this article highlights some of their more elegant properties with connections to the theories of Galois fields and quadratic residues.
Dependence of fluorodeoxyglucose (FDG) uptake on cell cycle and dry mass: a single-cell study using a multi-modal radiography platform

High glucose uptake by cancer compared to normal tissues has long been utilized in fluorodeoxyglucose-based positron emission tomography (FDG-PET) as a contrast mechanism. The FDG uptake rate has been further related to the proliferative potential of cancer, specifically the proliferation index (PI) - the proportion of cells in S, G2 or M phases. The underlying hypothesis was that the cells preparing for cell division would consume more energy and metabolites as building blocks for biosynthesis. Despite the wide clinical use, mixed reports exist in the literature on the relationship between FDG uptake and PI. This may be due to the large variation in cancer types or methods adopted for the measurements. Of note, the existing methods can only measure the average properties of a tumor mass or cell population with highly-heterogeneous constituents. In this study, we have built a multi-modal live-cell radiography system and measured the [18F]FDG uptake by single HeLa cells together with their dry mass and cell cycle phase. The results show that HeLa cells take up twice more [18F]FDG in S, G2 or M phases than in G1 phase, which confirms the association between FDG uptake and PI at a single-cell level. Importantly, we show that [18F]FDG uptake and cell dry mass have a positive correlation in HeLa cells, which suggests that high [18F]FDG uptake in S, G2 or M phases can be largely attributed to increased dry mass, rather than the activities preparing for cell division. This interpretation is consistent with recent observations that the energy required for the preparation of cell division is much smaller than that for maintaining house-keeping proteins.
Link prediction in networks using effective transitions

Abstract We introduce a new method for predicting the formation or the detection of unobserved links in real-world networks, which we refer to as the method of effective transitions. This method relies on the theory of isospectral matrix reductions to compute the probability of eventually transitioning from one vertex to another in a (biased) random walk on the network. This link prediction technique is quite versatile and can be used to predict links in networks that are directed or undirected and are either weighted or unweighted. We apply this method to a large number of social, technological, and natural networks and show that it is competitive with other link predictors, often outperforming them. We also provide a method of approximating our effective transition method and show that aside from having much lower temporal complexity, this approximation often provides more accurate predictions than the original effective transition method. We also prove a number of results regarding our effective transition algorithm and its approximation.
Peasant Culture and Urbanization in Yugoslavia

An implication of contemporary change which has wide tacit recognition but which to date has not been studied extensively is the degree to which we are in the process of approaching a uniform world culture. The total actual ization of this assumption, particularly in the ideologica sense, does not appear likely in the foreseeable future: however, in scientific and associated technological field aspects of universality are already a reality. Forms of or ganization are increasingly evident which doubtless deriv from shared values about the instrumental utility of science and derivative technology for the maintenance o contemporary society.
Gold nanorod plasmonic upconversion microlaser

Plasmonic–photonic interactions have stimulated significant interdisciplinary interest, leading to rapid innovations in solar design and biosensors. However, the development of an optically pumped plasmonic laser has failed to keep pace due to the difficulty of integrating a plasmonic gain material with a suitable pump source. In the present work, we develop a method for coating high quality factor toroidal optical cavities with gold nanorods, forming a photonic–plasmonic laser. By leveraging the two-photon upconversion capability of the nanorods, lasing at 581 nm with a 20 μW threshold is demonstrated.
Load on the Typical Poisson Voronoi Cell With Clustered User Distribution

In this letter, we characterize the distribution of the number of users associated with the typical base station (BS), termed the typical cell load , in a cellular network where the BSs are distributed as a homogeneous Poisson point process (PPP) and the users are distributed as an independent Poisson cluster process (PCP). In this setting, we derive the exact expressions for the first two moments of the typical cell load. Given the computational complexity of evaluating the higher moments, we derive easy-to-use approximations for the probability generating function (PGF) of the typical cell load, which can be inverted to obtain the probability mass function (PMF).
Compact electron storage rings

There have been many recent developments in the area of compact storage rings. Such rings would have critical wavelengths of typically 10 A, achieved with beam energies of several hundreds of MeV and superconducting dipole fields of around 5 Tesla. Although the primary motivation for progress in this area is that of commercial x-ray lithography, such sources might be an attractive source for college campuses to operate. They would be useful for many programs in materials science, solid state, x-ray microscopy and other biological areas. We discuss the properties of such sources and review developments around the world, primarily in the USA, japan and W. Germany.
4 – Explosive Ordnance

Publisher Summary Lead azide was a common ingredient in primers and fuses from the time of the Civil War and most certainly can be encountered in older burial sites. When lead azide is encased in a copper container and water gets into the container, copper azide, a hypersensitive compound, can form. As explosive fillers deteriorate over time, some explosives may form sensitive crystals that could detonate if subjected to heat, shock, or friction. Chemical munitions contain toxics that present additional safety risks. High explosive fillers, deteriorated explosives, and chemical munitions are a few examples of military munitions where the filler itself requires special safety considerations. In addition to the detonation danger, many ordinary explosives and breakdown products are toxic. Dinitrotoluene (DNT), for example, is carcinogenic. Expray is a unique, aerosol-based explosive detection system field test kit for explosives detection. Most explosives are toxic, particularly in drinking water. The older the site, the more explosive residue will enter the groundwater. Even explosives considered insoluble will eventually be found in groundwater. Several methods are in use for treating explosive contaminated groundwater such as Fenton's reagent. Detonating the munitions underwater significantly reduces the noise. The problem with underwater detonations of multiple munitions is that it may be difficult to know if all of the munitions have been destroyed because of water turbidity or a soft bottom into which unexploded munitions may have been driven.
8 – Particle Accelerators

Publisher Summary This chapter describes the particle accelerators and reviews the nature of the forces on charges. The chapter also describes the arrangement and principle of operation of several important kinds of particle accelerators. Charged particles, such as electrons and ions of light elements are brought to high speed and energy by particle accelerators, which employ electric and magnetic fields in various ways. In high-voltage machines, a beam of ions is accelerated directly through a large potential difference, produced by special voltage multiplier circuits or by carrying charge to a positive electrode. In the linear accelerator, ions are given successive accelerations in gaps between tubes lined up in a row, and in the cyclotron ions are similarly accelerated but move in circular orbits because of the applied magnetic field. In the betatron, a changing magnetic field produces an electric field that accelerates electrons to relativistic speeds in the synchrotron; both radio frequency and time-varying magnetic field are used. High-energy nuclear physics research is carried out through the use of such accelerators. Through several spallation processes, high-energy charged particles can produce large numbers of neutrons that have a variety of applications.
8 – Leaf Diseases

Publisher Summary This chapter discusses the disorders that affect the leaves of shade trees. The importance of a leaf disease on a shade tree depends upon the type of tree infected, its previous health history, and the type and severity of the disease. The chapter focuses on the leaf diseases caused by fungi. Hardwood leaf diseases are grouped into six categories: leaf spot, leaf blotch, anthracnose, powdery mildew, leaf-blister, and shot-hole. The fungi that attack each tree species are different but have similar life cycles on hardwood leaves. Most fungal pathogens of hardwood leaves remain dormant in the dead leaves on the ground or attached to the tree during the winter. Some pathogens survive in the buds or on dead twigs and branches. In the spring, during wet weather, the pathogens become active and discharge spores that are carried by wind and rain-splash. The spores land on the young expanding leaves. The spores germinate and penetrate the leaves. During summer, more spores are produced in the infected areas that can cause new leaf infections during wet weather.
Trust and Reciprocity in Incentive Contracting

Principals can attempt to get agents to perform certain actions preferable to the principal by using ex post punishments or rewards to align incentives. Field data is mixed on whether, and to what extent, such informal incentive contracting (paradoxically) crowds out ecient solutions to the agency problem. This paper explores, via a novel set of laboratory experiments, the impact of ex post incentives on informal contracts between principals and agents in bargaining environments in which there are gains from exchange and when there is an opportunity for the principal to relay a no-cost demand of the division of those gains. Incentive contracting in these environments does not crowd-out o-equilibrium cooperation, and at high incentive levels cooperation is crowded in.
Chapter 1 – Rubber Elasticity: Basic Concepts and Behavior

The single most important property of elastomers is their ability to undergo large elastic deformations. In this chapter, the theoretical treatment is briefly outlined and shown to account quite successfully for the observed elastic behavior of rubbery materials. The special case of small elastic deformations is then discussed in some detail because of its technical importance. Finally, attention is drawn to some aspects of rubber elasticity that are still little understood.
Chapter 9 – Pharmacokinetics

This chapter discusses the concepts related to the use of drugs in vivo, specifically from the point of drug absorption, distribution throughout the body, metabolism by the hepatic (and other) systems and excretion (primarily through the renal system). The combination of these processes in vivo are considered as well as the various in vitro assays used to predict pharmacokinetics effects.
Deficits in olfactory sensitivity in a mouse model of Parkinson’s disease revealed by plethysmography of odor-evoked sniffing

Hyposmia is evident in over 90% of Parkinson’s disease (PD) patients. A characteristic of PD is intraneuronal deposits composed in part of α-synuclein fibrils. Based on the analysis of post-mortem PD patients, Braak and colleagues suggested that early in the disease α-synuclein pathology is present in the dorsal motor nucleus of the vagus, as well as the olfactory bulb and anterior olfactory nucleus, and then later affects other interconnected brain regions. Here, we bilaterally injected α-synuclein preformed fibrils into the olfactory bulbs of wild type male and female mice. Six months after injection, the anterior olfactory nucleus and piriform cortex displayed a high α-synuclein pathology load. We evaluated olfactory perceptual function by monitoring odor-evoked sniffing behavior in a plethysmograph at one-, three- and six-months after injection. No overt impairments in the ability to engage in sniffing were evident in any group, suggesting preservation of the ability to coordinate respiration. At all-time points, females injected with fibrils exhibited reduced odor detection sensitivity, which was observed with the semi-automated plethysmography apparatus, but not a buried pellet test. In future studies, this sensitive methodology for assessing olfactory detection deficits could be used to define how α-synuclein pathology affects other aspects of olfactory perception and to clarify the neuropathological underpinnings of these deficits.
Interpolating splines on graphs for data science applications

Abstract We introduce intrinsic interpolatory bases for data structured on graphs and derive properties of those bases. Polyharmonic Lagrange functions are shown to satisfy exponential decay away from their centers. The decay depends on the density of the zeros of the Lagrange function, showing that they scale with the density of the data. These results indicate that Lagrange-type bases are ideal building blocks for analyzing data on graphs, and we illustrate their use in kernel-based machine learning applications.