cssi/SciSciGPT-SciSciNet huggingface dataset:
Thermodynamics of Ion Separation by Electrosorption

We present a simple, top-down approach for the calculation of minimum energy consumption of electrosorptive ion separation using variational form of the (Gibbs) free energy. We focus and expand on the case of electrostatic capacitive deionization (CDI). The theoretical framework is independent of details of the double-layer charge distribution and is applicable to any thermodynamically consistent model, such as the Gouy–Chapman–Stern and modified Donnan models. We demonstrate that, under certain assumptions, the minimum required electric work energy is indeed equivalent to the free energy of separation. Using the theory, we define the thermodynamic efficiency of CDI. We show that the thermodynamic efficiency of current experimental CDI systems is currently very low, around 1% for most existing systems. We applied this knowledge and constructed and operated a CDI cell to show that judicious selection of the materials, geometry, and process parameters can lead to a 9% thermodynamic efficiency and 4.6 kT per...
MaskRNN: Instance Level Video Object Segmentation

Instance level video object segmentation is an important technique for video editing and compression. To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance - a binary segmentation net providing a mask and a localization net providing a bounding box. Due to the recurrent component and the localization component, our method is able to take advantage of long-term temporal structures of the video data as well as rejecting outliers. We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them.
A non local Monge-Ampere equation

We introduce a non local analog to the Monge-Ampere operator and show some of its properties. We prove that a global problem involving this operator has C 1,1 solutions in the full space.
A Maximum Problem of S.-T. Yau for Variational p-Capacity

Through using the semidiameter (in connection to: the mean radius and surface radius) of a convex closed hypersurface in $\mathbb R^{n\ge 2}$ as an sharp upper bound of the variational $(1,n)\ni p$-capacity radius, this paper settles a restriction/variant of S.-T. Yau's \cite[Problem 59]{Yau} from the surface area to the variational $p$-capacity whose limit as $p\to 1$ actually induces the surface area.
Recursive Distributed Detection for Composite Hypothesis Testing: Nonlinear Observation Models in Additive Gaussian Noise

This paper studies recursive composite hypothesis testing in a network of sparsely connected agents. The network objective is to test a simple null hypothesis against a composite alternative concerning the state of the field, modeled as a vector of (continuous) unknown parameters determining the parametric family of probability measures induced on the agents’ observation spaces under the hypotheses. Specifically, under the alternative hypothesis, each agent sequentially observes an independent and identically distributed time-series consisting of a (nonlinear) function of the true but unknown parameter corrupted by Gaussian noise, whereas, under the null, they obtain noise only. Two distributed recursive generalized likelihood ratio test type algorithms of the consensus+innovations form are proposed, namely, $\mathcal {CIGLRT-L}$ and $\mathcal {CIGLRT-NL}$ , in which the agents estimate the underlying parameter and in parallel also update their test decision statistics by simultaneously processing the latest local sensed information and information obtained from neighboring agents. For $\mathcal {CIGLRT-NL}$ , for a broad class of nonlinear observation models and under a global observability condition, algorithm parameters which ensure asymptotically decaying probabilities of errors (probability of miss and probability of false detection) are characterized. For $\mathcal {CIGLRT-L}$ , a linear observation model is considered and upper bounds on large deviations decay exponent for the error probabilities are obtained.
A-graded methods for monomial ideals

Abstract We use Z d -gradings to study d-dimensional monomial ideals. The Koszul functor is employed to interpret the quasidegrees of local cohomology in terms of the geometry of distractions and to explicitly compute the multiplicities of exponents. These multigraded techniques originate from the study of hypergeometric systems of differential equations.
Resilient Monotone Sequential Maximization

Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and actuators fail. Thence, traditional sequential design paradigms become insufficient and, in contrast, resilient sequential designs that adapt against system-wide attacks, deletions, or failures become important. In general, resilient sequential design problems are computationally hard. Also, even though they often involve objective functions that are monotone and (possibly) submodular, no scalable approximation algorithms are known for their solution. In this paper, we provide the first scalable algorithm, that achieves the following characteristics: system-wide resiliency, i.e., the algorithm is valid for any number of denial-of-service attacks, deletions, or failures; adaptiveness, i.e., at each time step, the algorithm selects system elements based on the history of inflicted attacks, deletions, or failures; and provable approximation performance, i.e., the algorithm guarantees for monotone objective functions a solution close to the optimal. We quantify the algorithm's approximation performance using a notion of curvature for monotone (not necessarily submodular) set functions. Finally, we support our theoretical analyses with simulated experiments, by considering a control-aware sensor scheduling scenario, namely, sensing-constrained robot navigation.
Learning Multi-Layer Transform Models

Learned data models based on sparsity are widely used in signal processing and imaging applications. A variety of methods for learning synthesis dictionaries, sparsifying transforms, etc., have been proposed in recent years, often imposing useful structures or properties on the models. In this work, we focus on sparsifying transform learning, which enjoys a number of advantages. We consider multi-layer or nested extensions of the transform model, and propose efficient learning algorithms. Numerical experiments with image data illustrate the behavior of the multi-layer transform learning algorithm and its usefulness for image denoising. Multi-layer models provide better denoising quality than single layer schemes.
Summations of linear recurrent sequences

We give an extension of Sister Celine’s method of proving hypergeometric sum identities that allows it to handle a larger variety of input summands. In particular, we extend the summand to powers o...
Compassionately Conservative Balanced Cuts for Image Segmentation

The Normalized Cut (NCut) objective function, widely used in data clustering and image segmentation, quantifies the cost of graph partitioning in a way that biases clusters or segments that are balanced towards having lower values than unbalanced partitionings. However, this bias is so strong that it avoids any singleton partitions, even when vertices are very weakly connected to the rest of the graph. Motivated by the BA¼hler-Hein family of balanced cut costs, we propose the family of Compassionately Conservative Balanced (CCB) Cut costs, which are indexed by a parameter that can be used to strike a compromise between the desire to avoid too many singleton partitions and the notion that all partitions should be balanced. We show that CCB-Cut minimization can be relaxed into an orthogonally constrained lt-minimization problem that coincides with the problem of computing Piecewise Flat Embeddings (PFE) for one particular index value, and we present an algorithm for solving the relaxed problem by iteratively minimizing a sequence of reweighted Rayleigh quotients (IRRQ). Using images from the BSDS500 database, we show that image segmentation based on CCB-Cut minimization provides better accuracy with respect to ground truth and greater variability in region size than NCut-based image segmentation.
On some problems concerning symmetrization operators

In [G. Bianchi, R. J. Gardner and P. Gronchi, Symmetrization in Geometry, Adv. Math., vol. 306 (2017), 51-88], a systematic study of symmetrization operators on convex sets and their properties is conducted. In the end of their article, the authors pose several open questions. The primary goal of this manuscript is to study these questions.
Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition

Tensor decompositions are used in various data mining applications from social network to medical applications and are extremely useful in discovering latent structures or concepts in the data. Many real-world applications are dynamic in nature and so are their data. To deal with this dynamic nature of data, there exist a variety of online tensor decomposition algorithms. A central assumption in all those algorithms is that the number of latent concepts remains fixed throughout the entire stream. However, this need not be the case. Every incoming batch in the stream may have a different number of latent concepts, and the difference in latent concepts from one tensor batch to another can provide insights into how our findings in a particular application behave and deviate over time. In this paper, we define “concept” and “concept drift” in the context of streaming tensor decomposition, as the manifestation of the variability of latent concepts throughout the stream. Furthermore, we introduce SeekAndDestroy (The method name is after (and a tribute to) Metallica’s song from their first album (who also owns the copyright for the name)), an algorithm that detects concept drift in streaming tensor decomposition and is able to produce results robust to that drift. To the best of our knowledge, this is the first work that investigates concept drift in streaming tensor decomposition. We extensively evaluate SeekAndDestroy on synthetic datasets, which exhibit a wide variety of realistic drift. Our experiments demonstrate the effectiveness of SeekAndDestroy, both in the detection of concept drift and in the alleviation of its effects, producing results with similar quality to decomposing the entire tensor in one shot. Additionally, in real datasets, SeekAndDestroy outperforms other streaming baselines, while discovering novel useful components. Code related to this paper is available at: https://github.com/ravdeep003/conceptDrift.
Rational generating series for affine permutation pattern avoidance

We consider the set of affine permutations that avoid a fixed permutation pattern. Crites has given a simple characterization for when this set is infinite. We find the generating series for this set using the Coxeter length statistic and prove that it can always be represented as a rational function. We also give a characterization of the patterns for which the coefficients of the generating series are periodic. The proofs exploit a new polyhedral encoding for the affine symmetric group.
Capacity Considerations for Secondary Networks in TV White Space

The so-called “TV white spaces” (TVWS)—representing unused TV channels in any given location as the result of the transition to digital broadcasting—is designated for unlicensed use [1] , [2] , [3] by the US Federal Communications Commission (FCC). This presents significant new opportunities within the context of emerging 4G networks for developing new wireless access technologies that meet the goals of the US National Broadband Plan [4] (notably true broadband access for an increasing fraction of the population). There are multiple challenges in realizing this goal; the most fundamental being the fact that the available WS capacity is currently not accurately known, since it depends on a multiplicity of factors—including system parameters of existing incumbents (broadcasters), propagation characteristics of local terrain as well as FCC rules. In this paper, we explore the capacity of white space networks by developing a detailed model that includes all the major variables, and is cognizant of FCC regulations that provide constraints on incumbent protection. Real terrain information and propagation models for the primary broadcaster and adjacent channel interference from TV transmitters are included to estimate their impact on achievable WS capacity. The model is later used to explore various trade-offs between network capacity and system parameters and suggests possible amendments to FCC’s incumbent protection rules in the favor of furthering white space capacity.
Counterexamples of Kodaira vanishing for smooth surfaces of general type in positive characteristic

Abstract We generalize the construction of Raynaud [14] of smooth projective surfaces of general type in positive characteristic that violate the Kodaira vanishing theorem. This corrects an earlier paper [19] of the same purpose. These examples are smooth surfaces fibered over a smooth curve whose direct images of the relative dualizing sheaves are not nef, and they violate Kollar's vanishing theorem. Further pathologies on these examples include the existence of non-trivial vector fields and that of non-closed global differential 1-forms.
A near-optimal algorithm for approximating the John Ellipsoid

We develop a simple and efficient algorithm for approximating the John Ellipsoid of a symmetric polytope. Our algorithm is near optimal in the sense that our time complexity matches the current best verification algorithm. We also provide the MATLAB code for further research.
Wavelet-spectral analysis of droplet-laden isotropic turbulence

The spectrum of turbulence kinetic energy for homogeneous turbulence is generally computed using the Fourier transform of the velocity field from physical three-dimensional space to wavenumber ) by increasing the dissipation rate at these wavenumbers.
On the Sample Complexity of the Linear Quadratic Regulator

This paper addresses the optimal control problem known as the linear quadratic regulator in the case when the dynamics are unknown. We propose a multistage procedure, called Coarse-ID control, that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then designs a controller using both the model and uncertainty estimate. Our technique uses contemporary tools from random matrix theory to bound the error in the estimation procedure. We also employ a recently developed approach to control synthesis called System Level Synthesis that enables robust control design by solving a quasi-convex optimization problem. We provide end-to-end bounds on the relative error in control cost that are optimal in the number of parameters and that highlight salient properties of the system to be controlled such as closed-loop sensitivity and optimal control magnitude. We show experimentally that the Coarse-ID approach enables efficient computation of a stabilizing controller in regimes where simple control schemes that do not take the model uncertainty into account fail to stabilize the true system.
The Number of Fixed Points of AND-OR Networks with Chain Topology

AND-OR networks are Boolean networks where each coordinate function is either the AND or OR logical operator. We study the number of fixed points of these Boolean networks in the case that they have a wiring diagram with chain topology. We find closed formulas for subclasses of these networks and recursive formulas in the general case. Our results allow for an effective computation of the number of fixed points in the case that the topology of the Boolean network is an open chain (finite or infinite) or a closed chain.
Markov Processes and Some PCF Quadratic Polynomials

For any $$n\ge 1$$ , let $$T_n$$ be the complete binary rooted tree of height n, and $$f(x)=(x+a)^2-a-1$$ such that $$a\ne \pm b^2$$ for any $$b\in \mathbb {Z}$$ . In [5], Jones and Boston empirically observed that iteratively applying a certain Markov process on the factorization types of f gives rise to certain permutation groups $$M_n(f)\le \text {Aut}(T_n)$$ for $$n\le 5$$ . We prove a refined version of this phenomenon for all n, and for all the irreducible post-critically finite quadratic polynomials with integer coefficients, except for certain conjugates of $$x^2-2$$ . We do this by constructing these groups explicitly. Although there have already been some conjectures relating the Markov processes to the dynamics of quadratic polynomials, our results are the first to prove such a connection. If $$f(x)\in \mathbb {Z}[x]$$ is a post-critically finite quadratic polynomial, and $$G_n(f)$$ is the Galois group of $$f^n$$ over $$\mathbb {Q}(i)$$ , then we conjecture that for all $$n\ge 1$$ , $$M_n(f)$$ contains a subgroup which is conjugate in $$Aut(T_n)$$ to $$G_n(f)$$ , analogous to the role of Mumford-Tate groups in classical arithmetic geometry. We provide evidence that this is implied by a purely group theoretical statement.
Maize yield under a changing climate: the hidden role of vapor pressure deficit

Abstract Temperatures over the next century are expected to rise to levels detrimental to crop growth and yield. As the atmosphere warms without additional water vapor input, vapor pressure deficit (VPD) increases as well. Increased temperatures and accompanied elevated VPD levels can both lead to negative impacts on crop yield. The independent importance of VPD, however, is often neglected or conflated with that from temperature due to a tight correlation between the two climate factors. We used a coupled process-based crop (MAIZSIM) and soil (2DSOIL) model to gain a mechanistic understanding of the independent roles temperature and VPD play in crop yield projections, as well as their interactions with rising CO2 levels and changing precipitation patterns. We found that by separating out the VPD effect from rising temperatures, VPD increases had a greater negative impact on yield (12.9 ± 1.8%, increase in VPD associated with 2 °C warming) compared to that from warming (8.5 ± 1.4%, the direct effect of 2 °C warming). The negative impact of these two factors varied with precipitation levels and influenced yield through separate mechanisms. Warmer temperatures caused yield loss mainly through shortening the growing season, while elevated VPD increased water loss and triggered several water stress responses such as reduced photosynthetic rates, lowered leaf area development, and shortened growing season length. Elevated CO2 concentrations partially alleviated yield loss under warming or increased VPD conditions through water savings, but the impact level varied with precipitation levels and was most pronounced under drier conditions. These results demonstrate the key role VPD plays in crop growth and yield, displaying a magnitude of impact comparative to temperature and CO2. A mechanistic understanding of the function of VPD and its relation with other climate factors and management practices is critical to improving crop yield projections under a changing climate.
Catalytic resonance theory: superVolcanoes, catalytic molecular pumps, and oscillatory steady state

Catalytic reactions on surfaces with forced oscillations in physical or electronic properties undergo controlled acceleration consistent with the selected parameters of frequency, amplitude, and external stimulus waveform. In this work, the general reaction of reversible A-to-B chemistry is simulated by varying the catalytic (heat of reaction, transition state and intermediate energies) and oscillation parameters (frequency, amplitude, endpoints, and waveform) to evaluate the influence on the overall catalytic turnover frequency and steady state extent of conversion. Variations of catalytic cycle energies are shown to comprise a superVolcano of superimposed individual Balandin–Sabatier volcano plots, with variations in linear scaling relationships leading to unique turnover frequency response to forced oscillation of the catalyst surface. Optimization of catalytic conditions identified a band of forced oscillation frequencies leading to resonance and rate enhancement as high as 10 000× above the static Sabatier maximum. Dynamic catalytic reactions conducted at long times achieved oscillatory steady state differing from equilibrium consistent with the imposed surface oscillation amplitude acting as a ‘catalytic pump’ relative to the Gibbs free energy of reaction.
An academic meets industry—Rethinking computer-based education and personalized systems of instruction

Education is a versatile animal. After belonging exclusively in the domain of the home for millenia, public education in the United States became institutionalized about 1800. Today, the school system in many cultures is often equated to "the" educational system. However, industrial institutions are also •becoming acutely aware that education is a vital component of their structure, and in some cases, of their market.
HITCHIN TYPE MODULI STACKS IN AUTOMORPHIC REPRESENTATION THEORY

In the study of automorphic representations over a function field, Hitchin moduli stack and its variants naturally appear and their geometry helps the comparison of trace formulae. We give a survey on applications of this observation to a relative fundamental lemma, the arithmetic fundamental lemma and to the higher Gross-Zagier formula.
Computational Astrocyence: Astrocytes encode inhibitory activity into the frequency and spatial extent of their calcium elevations

Deciphering the complex interactions between neurotransmission and astrocytic Ca2+ elevations is a target promising a comprehensive understanding of brain function. While the astrocytic response to excitatory synaptic activity has been extensively studied, how inhibitory activity results to intracellular Ca2+ waves remains elusive. In this study, we developed a compartmental astrocytic model that exhibits distinct levels of responsiveness to inhibitory activity. Our model suggested that the astrocytic coverage of inhibitory terminals defines the spatial and temporal scale of their Ca2+ elevations. Understanding the interplay between the synaptic pathways and the astrocytic responses will help us identify how astrocytes work independently and cooperatively with neurons, in health and disease.
Towards haptic learning on a smartwatch.

Haptic technology can be used as a tool for learning. Can even the haptic elements in a smartwatch teach a new skill? Here we present a case of using a smartwatch for passive tactile learning. We use the Sony Smartwatch 3 to teach users Morse code while they wear the watch but focus on unrelated tasks. An initial hypothesis forecasted that the stimulation from the smartwatch, typically used for message alerts, would be too subtle to enable haptic learning; however, we find significant improvements in six participants using the technique. Furthermore, we expose participants to two different durations of stimulation and find different results.
Fully Automated Pancreas Segmentation with Two-Stage 3D Convolutional Neural Networks.

Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image analysis. In this work, we proposed a fully automated two stage framework for pancreas segmentation based on convolutional neural networks (CNN). In the first stage, a U-Net is trained for the down-sampled 3D volume segmentation. Then a candidate region covering the pancreas is extracted from the estimated labels. Motivated by the superior performance reported by renowned region based CNN, in the second stage, another 3D U-Net is trained on the candidate region generated in the first stage. We evaluated the performance of the proposed method on the NIH computed tomography (CT) dataset, and verified its superiority over other state-of-the-art 2D and 3D approaches for pancreas segmentation in terms of dice-sorensen coefficient (DSC) accuracy in testing. The mean DSC of the proposed method is 85.99%.
Uniformity and stability of the LSST focal plane

The Large Synoptic Survey Telescope (LSST) focal plane consists of 21 autonomous modules [raft tower modules (RTMs)], each of which contains nine thick, fully depleted 4k × 4k CCDs with associated control and readout electronics. To enable LSST’s repetitive short-exposure cadence while maintaining high duty factor and low read noise, the readout is highly parallelized into 3024 independent video channels (16 per CCD and 144 per RTM). Two vendors supplied the LSST sensors; the devices have compatible mechanical and electrical interfaces and meet the same electro-optic specifications, but each RTM is constructed with sensors from a single supplier. The full complement of rafts was assembled at Brookhaven National Laboratory during January 2017 to January 2019. Each unit underwent extensive electro-optic and metrology characterization at operating temperature, the results of which are presented here along with a discussion of uniformity and stability.
A Survey of Magnetic Field Strengths in the Envelopes of Molecular Clouds via the 18 cm OH Zeeman Effect

We present the results of an extensive Arecibo observational survey of magnetic field strengths in the inter-core regions of molecular clouds to determine their role in the evolution and collapse of molecular clouds as a whole. Sensitive 18 cm OH Zeeman observations of absorption lines from Galactic molecular gas in the direction of extragalactic continuum sources yielded 38 independent measurements of magnetic field strengths. Zeeman detections were achieved at the three sigma level toward 9 clouds, while the others revealed sensitive upper limits to the magnetic field strength. Our results suggest that total field strengths in the inter-core regions of GMCs are about 15 microgauss.
Adapting free-space fast multipole method for layered media Green's function: algorithm and analysis

Abstract In this paper, we present a numerical algorithm for an accurate and efficient computation of the convolution of the frequency domain layered media Green's function with a given density function. Instead of compressing the convolution matrix directly as in the classical fast multipole method, fast direct solvers, and fast H -matrix algorithms, this new algorithm considers a translated form of the original matrix so that many existing building blocks from the highly optimized free-space fast multipole method can be easily adapted to the Sommerfeld integral representations of the layered media Green's function. An asymptotic analysis is performed on the Sommerfeld integrals for large orders to provide an estimate of the decay rate in the new “multipole” and “local” expansions. To avoid the highly oscillatory integrand in the original Sommerfeld integral representations when the source and target are close to each other, or when they are both close to the interface in the scattered field, mathematically equivalent alternative direction integral representations are introduced. The convergence of the multipole and local expansions formulas and quadrature rules for the original and alternative direction integral representations are numerically validated.
Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals

This paper studies reinforcement learning (RL) under malicious falsification on cost signals and introduces a quantitative framework of attack models to understand the vulnerabilities of RL. Focusing on Q-learning, we show that Q-learning algorithms converge under stealthy attacks and bounded falsifications on cost signals. We characterize the relation between the falsified cost and the Q-factors as well as the policy learned by the learning agent which provides fundamental limits for feasible offensive and defensive moves. We propose a robust region in terms of the cost within which the adversary can never achieve the targeted policy. We provide conditions on the falsified cost which can mislead the agent to learn an adversary’s favored policy. A numerical case study of water reservoir control is provided to show the potential hazards of RL in learning-based control systems and corroborate the results.
Orbits and resonances of the regular moons of Neptune

Abstract We report integrated orbital fits for the inner regular moons of Neptune based on the most complete astrometric data set to date, with observations from Earth-based telescopes, Voyager 2, and the Hubble Space Telescope covering 1981–2016. We summarize the results in terms of state vectors, mean orbital elements, and orbital uncertainties. The estimated masses of the two innermost moons, Naiad and Thalassa, are GMNaiad = 0.0080 ± 0.0043 km3 s−2 and GMThalassa = 0.0236 ± 0.0064 km3 s−2, corresponding to densities of 0.80 ± 0.48 g cm−3 and 1.23 ± 0.43 g cm−3, respectively. Our analysis shows that Naiad and Thalassa are locked in an unusual type of orbital resonance. The resonant argument 73 λ Thalassa − 69 λ Naiad − 4 Ω Naiad ≈ 0 librates around 180° with an average amplitude of ~66° and a period of ~1.9 years for the nominal set of masses. This is the first fourth-order resonance discovered between the moons of the outer planets. More high precision astrometry is needed to better constrain the masses of Naiad and Thalassa, and consequently, the amplitude and the period of libration. We also report on a 13:11 near-resonance of Hippocamp and Proteus, which may lead to a mass estimate of Proteus provided that there are future observations of Hippocamp. Our fit yielded a value for Neptune's oblateness coefficient of J2 = 3409.1 ± 2.9 × 10−6.
Spinning Radiation from Topological Insulators

We show that thermal radiation from a topological insulator carries a nonzero average spin angular momentum.
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field.

Abstract Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds.
Law of two-sided exit by a spectrally positive strictly stable process

Abstract For a spectrally positive strictly stable process with index in (1, 2), we obtain (i) the sub-probability density of its first exit time from an interval by hitting the interval’s lower end before jumping over its upper end, and (ii) the joint distribution of the time, undershoot, and jump of the process when it makes the first exit the other way around. The density of the exit time is expressed in terms of the roots of a Mittag-Leffler function. Some theoretical applications of the results are given.
Engineered Chromatin Remodeling Proteins for Precise Nucleosome Positioning

Summary Regulation of chromatin structure is essential for controlling access of DNA to factors that require association with specific DNA sequences. Here we describe the development and validation of engineered chromatin remodeling proteins (E-ChRPs) for inducing programmable changes in nucleosome positioning by design. We demonstrate that E-ChRPs function both in vitro and in vivo to specifically reposition target nucleosomes and entire nucleosomal arrays. We show that induced, systematic positioning of nucleosomes over yeast Ume6 binding sites leads to Ume6 exclusion, hyperacetylation, and transcriptional induction at target genes. We also show that programmed global loss of nucleosome-free regions at Reb1 targets is generally inhibitory with mildly repressive transcriptional effects. E-ChRPs are compatible with multiple targeting modalities, including the SpyCatcher and dCas9 moieties, resulting in high versatility and enabling diverse future applications. Thus, engineered chromatin remodeling proteins represent a simple and robust means to probe and disrupt DNA-dependent processes in different chromatin contexts.
Soft Limits of Yang-Mills Amplitudes and Conformal Correlators

We study tree-level celestial amplitudes in Yang-Mills theory — Mellin transforms of multi-gluon scattering amplitudes that convert them into the correlators of conformal primary fields on two-dimensional celestial sphere. By using purely field-theoretical methods, we show that the soft conformal limit of celestial amplitudes, in which one of the primary field operators associated to gauge bosons becomes a dimension one current, is dominated by the contributions of low-energy soft particles. This result confirms conclusions reached by using Yang-Mills theory formulated in curvilinear coordinates, as pioneered by Strominger. By using well-known collinear limits of Yang-Mills amplitudes, we derive the OPE rules for the primary fields and the holomorphic currents arising in the conformally soft limit. The Ward identities following from OPE have the same form as the identities derived by using soft theorems.
The velocity structure and turbulence at the center of the Orion Nebula

Using the Coude Feed System of the Kitt Peak National Observatory at its highest possible dispersion and a CCD detector with wide dynamic range it was possible to examine in detail the radial and turbulent velocities of the Orion Nebula at the seeing limit. For the first time, multiple components of the spectral lines were resolved and the Gaussian profiles fit to them. Using four different observing runs, two circular areas 2 minutes in diameter were observed, the first centered on the theta sup 1 Orionis and the second centered on theta sup 2 Orionis. The velocity resolution of 1 km/s combined with the angular resolution of 2 seconds allowed observation in detail at line splitting as well as the location and measurement of high internal motions, about -50 km/s with respect to the bulk of ionized matter. Because over 900 different positions were measured, modern statistical techniques were used for the analysis of turbulence, so that the dispersion relation and the structure function could be calculated and compared with predictions of the Kolmogorov theory. The random motions showed correlation with spatial separation, as expected for turbulence; but comparison with predictions of the classical theory for nebular turbulence showed poormore » agreement. If Kolmogorov theory applies, then the energy must be input at many scales. 104 references.« less
Cost Savings of Carpal Tunnel Release Performed In-Clinic Compared to an Ambulatory Surgery Center: Time-Driven Activity-Based-Costing.

Purpose: The purpose of our study was to investigate carpal tunnel release (CTR) performed in the clinic versus the ambulatory surgery center (ASC) to evaluate for potential cost savings. Methods: ...
Abstention, Protest, and Residual Votes in the 2016 Election

Objective: We analyze the significant increase in the residual vote rate in the 2016 presidential election. The residual vote rate, which is the percentage of ballots cast in a presidential election that contain no vote for president, rose nationwide from 0.99 to 1.41 percent between 2012 and 2016. Method: We use election return data and public opinion data to examine why the residual vote rate increased in 2016. Results: The primary explanation for this rise is an increase in abstentions, which we argue results primarily from disaffected Republican voters rather than alienated Democratic voters. In addition, other factors related to election administration and electoral competition explain variation in the residual vote rates across states, particularly the use of mail/absentee ballots and the lack of competition at the top of the ticket in nonbattleground states. However, we note that the rise in the residual vote rate was not due to changes in voting technologies. Conclusion: Our research has implications for the use of the residual vote as a metric for studying election administration and voting technologies.
Groups where the centers of the irreducible characters form a chain II

We consider groups where the centers of the irreducible characters form a chain. We obtain two alternate characterizations of these groups, and we obtain some information regarding the structure of these groups. Using our results, we are able to classify those groups where the kernels of the irreducible characters form a chain. We show that a result of Nenciu regarding nested GVZ groups is really a result about nested groups. We obtain an alternate proof of a theorem of Isaacs regarding the existence of p-groups with a given set of irreducible character degrees.
Free Movement and Affordable Housing : Public Preferences for Reform in Uzbekistan

Uzbekistan has one of the lowest rates of internal migration in the world, leading to persistent economic imbalances. Drawing from a unique monthly panel survey called Listening to the Citizens of Uzbekistan and a survey experiment, this paper focuses on two factors that prevent domestic mobility: (i) restrictive propiska registration policies, and (ii) the exceptionally high cost of urban housing. Registration rules prohibit migration to urban centers, and urban housing costs push up the cost of living to as much as 550 percent of the national average, levels severely unaffordable for almost all rural residents. But the proposed government reforms in 2019 to address these challenges are very popular. The results show that about 90 percent of people support lifting all registration restrictions and over 80 percent favor increasing urban housing construction. The results of the experiment show that reform popularity increases when propiska rules and housing costs are referenced in randomly assigned vignettes. However, views may also be sensitive to perceptions of fairness. Recent high-profile involuntary demolitions coincided with a doubling of the share responding that policies are unfair. The increase was further associated with declining optimism and lower support for the wider government national development program, beyond urbanization issues.
Understanding the silence of sexual harassment victims through the #WhyIDidntReport movement

Sexual violence is a serious problem across the globe. A lot of victims, particularly women, go through this experience. Unfortunately, not all of these violent incidents come to public. A large portion of victims don't disclose their experience. On the September of 2018, people started revealing in Twitter why they didn't report a sexual violence experience using a hashtag #WhyIDidntReport. We collect about 40K such tweets and conduct a large-scale supervised analysis of why victims don't report. Our study finds the extent to which people shared their reasons as well as categorizes the reasons into finer reasons. We also analyze user engaged with the victims and compare our findings with existing literature.
Constitutional Cohesion and the Right to Public Health

Despite years of significant legal improvements stemming from a renaissance in public health law, Americans still face major challenges and barriers in assuring their communal health. Reversals of legal reforms coupled with maligned policies and chronic underfunding contribute to diminished public health outcomes. Underlying preventable morbidity and mortality nationally is an interpretive reality of our existing constitutional infrastructure. In essence there is no general obligation of government to protect or promote the public’s health. Under principles of “constitutional cohesion,” structural facets and rights-based principles interwoven within the Constitution protect individuals and groups from governmental vices (i.e., oppression, overreaching, tyranny, and malfeasance). Structural impediments and rights infringements provide viable options to challenge governmental efforts inapposite to protecting the public’s health. Through corollary applications framed as auxiliary, creative, and ghost righting, courts are also empowered to recognize core duties or rights that the Constitution may not explicitly denote, but assuredly contains, to remedy identifiable vices. Notably, ghost righting charts a course for recognizing a constitutional right to public health that Americans are owed, and government must respect, to assure basic public health needs.
Approximation algorithms for tours of orientation-varying view cones:

This article considers the problem of finding a shortest tour to visit viewing sets of points on a plane. Each viewing set is represented as an inverted view cone with apex angle α and height h. Th...
Carbon fueling complex global value chains tripled in the period 1995–2012

Abstract Complex global value chains are those involving more than two countries and imply that a country imports products as capital goods or intermediate inputs to the production of its exports. When tracing the life-cycle greenhouse gas (GHG) emissions of traded products, for example for border carbon adjustments, such emissions are counted at each border crossing. The prevalence and dynamics of this phenomenon have been poorly understood. This paper shows that GHG emissions associated with the production of imports that enter export production have risen rapidly from 1995, peaking 2012 and declining slightly to 2016. They now constitute a total of 4.4 Pg CO2eq. or 10% of global emissions. The most important exported products in terms of emissions associated with imported inputs are chemicals, vehicles, machinery, and information and communications technology (ICT). Crude petroleum, iron and steel, chemicals, and ICT components are the imported products being used for this export production. A decomposition analysis indicates that in industrialized countries, the declining domestic value added in exports and increasing share of exports in GDP have contributed most to this development. In emerging economies, however, the growth of GDP itself has been an important driving factor, while declines in the energy intensity of export production have provided a weak counterbalance. The importance of transiting carbon raises questions of how climate policies affect industrial competitiveness and how a potential border tax regime would account for such emissions.
Establishing a Denominator for Palliative Care Quality Metrics for Patients with Advanced Cancer.

Background: Measurement and monitoring of palliative care quality metrics for patients with advanced cancer promote early integration of palliative care within the oncology clinic. Accurately identifying the subset of advanced cancer patients within a population of cancer patients who would most benefit from palliative care is critical to the development of palliative care-relevant quality improvement activities. Methods: We evaluated two automated approaches to identifying patients with solid tumors sufficiently advanced to warrant discussions of palliative care and advanced care planning. These approaches included (1) pattern matching of words indicating an advanced cancer in oncology notes, radiology imaging, and active problem lists and (2) International Classification of Diseases (ICD-10) codes. We randomly selected 586 charts of patients with active cancer who are patients in our health system to establish a gold standard for advanced cancer through expert chart review. We evaluated the sensitivity and specificity of these automated approaches to identify advanced cancer patients compared with the gold standard. Results: We found that the highest performing pattern matching method had a specificity of 76% and a sensitivity of 81%. Using our final ICD-10 algorithm, we achieved a specificity of 92% and a sensitivity of 68%. We improved our sensitivity to 76% while maintaining our specificity at 91% when we excluded patients assigned to oncologists who predominantly see hematological malignancies. Conclusions: We achieved high specificity and reasonable sensitivity for an advanced cancer quality metric denominator using an ICD-10 algorithm within an academic oncology practice. This concrete definition will help inform quality improvement efforts locally and beyond.
Measurement Error Models in Astronomy

I discuss the effects of measurement error on regression and density estimation. I review the statistical methods that have been developed to correct for measurement error that are most popular in astronomical data analysis, discussing their advantages and disadvantages. I describe functional models for accounting for measurement error in regression, with emphasis on the methods of moments approach and the modified loss function approach. I then describe structural models for accounting for measurement error in regression and density estimation, with emphasis on maximum-likelihood and Bayesian methods. As an example of a Bayesian application, I analyze an astronomical data set subject to large measurement errors and a non-linear dependence between the response and covariate. I conclude with some directions for future research.
Analytical method to aggregate multi-machine SFR model with applications in power system dynamic studies

The system frequency response (SFR) model describes the average network frequency response after a disturbance and has been applied to a wide variety of dynamic studies. However, the traditional literature does not provide a generic, analytical method for obtaining the SFR model parameters when the system contains multiple generators; instead, a numerical simulation-based approach or the operators’ experience are the common practice to obtain an aggregated model. In this paper, an analytical method is proposed for aggregating the multi-machine SFR model into a single-machine model. The verification study indicates that the proposed aggregated SFR model can accurately represent the multi-machine SFR model. Furthermore, the detailed system simulation illustrates that the SFR model can also accurately represent the average frequency response of large systems for power system dynamic studies. Finally, three applications of the proposed method are explored, with system frequency control, frequency stability, and dynamic model reduction. The results show the method is promising with broad potential applications.
Distinct neural mechanisms of social orienting and mentalizing revealed by independent measures of neural and eye movement typicality.

Extensive study of typically developing individuals and those on the autism spectrum has identified a large number of brain regions associated with our ability to navigate the social world. Although it is widely appreciated that this so-called “social brain” is composed of distinct, interacting systems, these component parts have yet to be clearly elucidated. Here we used measures of eye movement and neural typicality—based on the degree to which subjects deviated from the norm—while typically developing (N = 62) and individuals with autism (N = 36) watched a large battery of movies depicting social interactions. Our findings provide clear evidence for distinct, but overlapping, neural systems underpinning two major components of the “social brain,” social orienting, and inferring the mental state of others. Ramot et al. investigate the relationship between typicality of brain activity with typicality of eye movement patterns in autistic and typically developed individuals. They link the two measures using data derived from movie stimuli independent from social stimuli outside of the scanner.