cssi/SciSciGPT-SciSciNet huggingface dataset:
Toxoplasma gondii Co-opts the Unfolded Protein Response To Enhance Migration and Dissemination of Infected Host Cells

Toxoplasma gondii is an intracellular parasite that reconfigures its host cell to promote pathogenesis. One consequence of Toxoplasma parasitism is increased migratory activity of host cells, which facilitates dissemination. Here, we show that Toxoplasma triggers the unfolded protein response (UPR) in host cells through calcium release from the endoplasmic reticulum (ER). We further identify a novel role for the host ER stress sensor protein IRE1 in Toxoplasma pathogenesis. Upon infection, Toxoplasma activates IRE1, engaging its noncanonical role in actin remodeling through the binding of filamin A. By inducing cytoskeletal remodeling via IRE1 oligomerization in host cells, Toxoplasma enhances host cell migration in vitro and dissemination of the parasite to host organs in vivo Our study has identified novel mechanisms used by Toxoplasma to induce dissemination of infected cells, providing new insights into strategies for treatment of toxoplasmosis.IMPORTANCE Cells that are infected with the parasite Toxoplasma gondii exhibit heightened migratory activity, which facilitates dissemination of the infection throughout the body. In this report, we identify a new mechanism used by Toxoplasma to hijack its host cell and increase its mobility. We further show that the ability of Toxoplasma to increase host cell migration involves not the enzymatic activity of IRE1 but rather IRE1 engagement with actin cytoskeletal remodeling. Depletion of IRE1 from infected host cells reduces their migration in vitro and significantly hinders dissemination of Toxoplasma in vivo Our findings reveal a new mechanism underlying host-pathogen interactions, demonstrating how host cells are co-opted to spread a persistent infection around the body.
Deriving approximate functionals with asymptotics

Modern density functional approximations achieve moderate accuracy at low computational cost for many electronic structure calculations. Some background is given relating the gradient expansion of density functional theory to the WKB expansion in one dimension, and modern approaches to asymptotic expansions. A mathematical framework for analyzing asymptotic behavior for the sums of energies unites both corrections to the gradient expansion of DFT and hyperasymptotics of sums. Simple examples are given for the model problem of orbital-free DFT in one dimension. In some cases, errors can be made as small as 10-32 Hartree suggesting that, if these new ingredients can be applied, they might produce approximate functionals that are much more accurate than those in current use. A variation of the Euler-Maclaurin formula generalizes previous results.
Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes.

We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs). In contrast to most decision-theoretic planning frameworks, which assume fully known state transition models, we design a method that eliminates such a strong assumption, which is oftentimes extremely difficult to engineer in reality. To achieve this, we first apply the second-order Taylor expansion of the value function. The Bellman optimality equation is then approximated by a partial differential equation, which only relies on the first and second moments of the transition model. By combining the kernel representation of value function, we then design an efficient policy iteration algorithm whose policy evaluation step can be represented as a linear system of equations characterized by a finite set of supporting states. We have validated the proposed method through extensive simulations in both simplified and realistic planning scenarios, and the experiments show that our proposed approach leads to a much superior performance over several baseline methods.
Klf9 is a key feedforward regulator of the transcriptomic response to glucocorticoid receptor activity

The zebrafish has recently emerged as a model system for investigating the developmental roles of glucocorticoid signaling and the mechanisms underlying glucocorticoid-induced developmental programming. To assess the role of the Glucocorticoid Receptor (GR) in such programming, we used CRISPR-Cas9 to produce a new frameshift mutation, GR369-, which eliminates all potential in-frame initiation codons upstream of the DNA binding domain. Using RNA-seq to ask how this mutation affects the larval transcriptome under both normal conditions and with chronic cortisol treatment, we find that GR mediates most of the effects of the treatment, and paradoxically, that the transcriptome of cortisol-treated larvae is more like that of larvae lacking a GR than that of larvae with a GR, suggesting that the cortisol-treated larvae develop GR resistance. The one transcriptional regulator that was both underexpressed in GR369- larvae and consistently overexpressed in cortisol-treated larvae was klf9. We therefore used CRISPR-Cas9-mediated mutation of klf9 and RNA-seq to assess Klf9-dependent gene expression in both normal and cortisol-treated larvae. Our results indicate that Klf9 contributes significantly to the transcriptomic response to chronic cortisol exposure, mediating the upregulation of proinflammatory genes that we reported previously.
Cyclin N-Terminal Domain-Containing-1 Coordinates Meiotic Crossover Formation with Cell-Cycle Progression in a Cyclin-Independent Manner.

During mammalian meiotic prophase I, programmed DNA double-strand breaks are repaired by non-crossover or crossover events, the latter predominantly occurring via the class I crossover pathway and requiring the cyclin N-terminal domain-containing 1(CNTD1) protein. Using an epitope-tagged Cntd1 allele, we detect a short isoform of CNTD1 in vivo that lacks a predicted N-terminal cyclin domain and does not bind cyclin-dependent kinases. Instead, we find that the short-form CNTD1 variant associates with components of the replication factor C (RFC) machinery to facilitate crossover formation, and with the E2 ubiquitin conjugating enzyme, CDC34, to regulate ubiquitylation and subsequent degradation of the WEE1 kinase, thereby modulating cell-cycle progression. We propose that these interactions facilitate a role for CNTD1 as a stop-go regulator during prophase I, ensuring accurate and complete crossover formation before allowing metaphase progression and the first meiotic division.
Beyond Teaching Methods: Highlighting Physics Faculty's Strengths and Agency.

Working with instructors to improve physics teaching; physics education research should move from a teaching method centered paradigm to an asset-based agentic paradigm.
Bias in the Air: A Nationwide Exploration of Teachers’ Implicit Racial Attitudes, Aggregate Bias, and Student Outcomes:

Theory suggests that teachers’ implicit racial attitudes affect their students, but large-scale evidence on U.S. teachers’ implicit biases and their correlates is lacking. Using nationwide data fro...
Smallest Ellipsoid Containing $p$ -Sum of Ellipsoids With Application to Reachability Analysis

In this article the problem of ellipsoidal bounding of convex set-valued data, where the convex set is obtained by the $p$ -sum of finitely many ellipsoids, for any real $p\geq 1$ is studied. The notion of $p$ -sum appears in the Brunn–Minkowski–Firey theory in convex analysis, and generalizes several well-known set-valued operations, such as the Minkowski sum of the summand convex sets (here, ellipsoids). We derive an outer ellipsoidal parameterization for the $p$ -sum of a given set of ellipsoids, and compute the tightest such parameterization for two optimality criteria: minimum trace and minimum volume. For such optimal parameterizations, several known results in the system-control literature are recovered as special cases of our general formula. For the minimum volume criterion, our analysis leads to a fixed point recursion over a scalar that parameterizes the shape matrix of the outer ellipsoid. This recursion is proved to be contractive, and found to converge fast in practice. We apply these results to compute the forward reach sets for a linear control system subject to different convex set-valued uncertainty models for the initial condition and control, generated by varying $p\in [1,\infty ]$ . Our numerical results show that the proposed fixed point algorithm offers more than two orders of magnitude speed-up in computational time for $p=1$ , compared to the existing semidefinite programming approach without significant effect on the numerical accuracy. For $p>1$ , the reach set computation results reported here are novel. Our results are expected to be useful in real-time safety critical applications, such as decision making, for collision avoidance of autonomous vehicles, where the computational time scale for reach set calculation needs to be much smaller than the vehicular dynamics time scale.
On the extremal maximum agreement subtree problem

Abstract Given two phylogenetic trees with leaf set { 1 , … , n } the maximum agreement subtree problem asks what is the maximum size of the subset A ⊆ { 1 , … , n } such that the two trees are equivalent when restricted to A . The long-standing extremal version of this problem focuses on the smallest number of leaves, mast ( n ) , on which any two (binary and unrooted) phylogenetic trees with n leaves must agree. In this work, we prove that this number grows asymptotically as Θ ( log n ) ; thus closing the enduring gap between the lower and upper asymptotic bounds on mast ( n ) .
Task Decomposition for Iterative Learning Model Predictive Control

A task decomposition method for iterative learning model predictive control is presented. We consider a constrained nonlinear dynamical system and assume the availability of state-input pair datasets which solve a task T1. Our objective is to find a feasible model predictive control policy for a second task, T2, using stored data from T1. Our approach applies to tasks T2 which are composed of subtasks contained in T1. In this paper we propose a formal definition of subtasks and the task decomposition problem, and provide proofs of feasibility and iteration cost improvement over simple initializations. We demonstrate the effectiveness of the proposed method on autonomous racing and robotic manipulation experiments.
Trade-offs in Robustness to Perturbations of Bacterial Population Controllers.

Synthetic biology applications have the potential to have lasting impact; however, there is considerable difficulty in scaling up engineered genetic circuits. One of the current hurdles is resource sharing, where different circuit components become implicitly coupled through the host cell9s pool of resources, which may destroy circuit function. One potential solution around this problem is to distribute genetic circuit components across multiple cell strains and control the cell population size using a population controller. In these situations, perturbations in the availability of cellular resources, such as due to resource sharing, will affect the performance of the population controller. In this work, we model a genetic population controller implemented by a genetic circuit while considering perturbations in the availability of cellular resources. We analyze how these intracellular perturbations and extracellular disturbances to cell growth affect cell population size. We find that it is not possible to tune the population controller9s gain such that the population density is robust to both extracellular disturbances and perturbations to the pool of available resources.
Machine Learning‐Based Distributed Model Predictive Control of Nonlinear Processes

This work addresses the design of distributed model predictive control (DMPC) systems for nonlinear processes using machine learning models to predict nonlinear dynamic behavior. Specifically, sequential and iterative distributed model predictive control systems are designed and analyzed with respect to closed-loop stability and performance properties. Extensive open- loop data within a desired operating region are used to develop Long Short-Term Memory (LSTM) recurrent neural network models with a sufficiently small modeling error from the actual nonlinear process model. Subsequently, these LSTM models are utilized in Lyapunov- based DMPC to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. Using a nonlinear chemical process network exam- ple, the simulation results demonstrate the improved computational efficiency when the process is operated under sequential and iterative DMPCs while the closed-loop performance is very close to the one of a centralized MPC system.
Constrained Functional Value under General Convexity Conditions with Applications to Distributed Simulation

We show a general phenomenon of the constrained functional value for densities satisfying general convexity conditions, which generalizes the observation in [1] that the entropy per coordinate in a log-concave random vector in any dimension with given density at the mode has a range of just 1. Specifically, for general functions ϕ and ψ, we derive upper and lower bounds of density functionals taking the form ${I_\phi }(f) = \int_{{\mathbb{R}^n}} \phi (f(x))dx$ assuming the convexity of ψ-1 (f(x)) for the density, and establish the tightness of these bounds under mild conditions satisfied by most examples. We apply this result to the distributed simulation of continuous random variables, and establish an upper bound of the exact common information for β-concave joint densities, which is a generalization of the log-concave densities in [2].
Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction

Semi-supervised learning aims to learn prediction models from both labeled and unlabeled samples. There has been extensive research in this area. Among existing work, generative mixture models with Expectation-Maximization (EM) is a popular method due to clear statistical properties. However, existing literature on EM-based semi-supervised learning largely focuses on unstructured prediction, assuming that samples are independent and identically distributed. Studies on EM-based semi-supervised approach in structured prediction is limited. This paper aims to fill the gap through a comparative study between unstructured and structured methods in EM-based semi-supervised learning. Specifically, we compare their theoretical properties and find that both methods can be considered as a generalization of self-training with soft class assignment of unlabeled samples, but the structured method additionally considers structural constraint in soft class assignment. We conducted a case study on real-world flood mapping datasets to compare the two methods. Results show that structured EM is more robust to class confusion caused by noise and obstacles in features in the context of the flood mapping application.
Visualizing droplet dispersal for face shields and masks with exhalation valves

Several places across the world are experiencing a steep surge in COVID-19 infections. Face masks have become increasingly accepted as one of the most effective means for combating the spread of the disease when used in combination with social-distancing and frequent hand-washing. However, there is an increasing trend of people substituting regular cloth or surgical masks with clear plastic face shields and with masks equipped with exhalation valves. One of the factors driving this increased adoption is improved comfort compared to regular masks. However, there is a possibility that widespread public use of these alternatives to regular masks could have an adverse effect on mitigation efforts. To help increase public awareness regarding the effectiveness of these alternative options, we use qualitative visualizations to examine the performance of face shields and exhalation valves in impeding the spread of aerosol-sized droplets. The visualizations indicate that although face shields block the initial forward motion of the jet, the expelled droplets can move around the visor with relative ease and spread out over a large area depending on light ambient disturbances. Visualizations for a mask equipped with an exhalation port indicate that a large number of droplets pass through the exhale valve unfiltered, which significantly reduces its effectiveness as a means of source control. Our observations suggest that to minimize the community spread of COVID-19, it may be preferable to use high quality cloth or surgical masks that are of a plain design, instead of face shields and masks equipped with exhale valves.
Min-Max Theory for Cell Complexes

In the study of smooth functions on manifolds, min-max theory provides a mechanism for identifying critical values of a function. We introduce a discretized version of this theory associated to a d...
Analyzing the Sources of Misallocation in Indian Manufacturing: A Gross-Output Approach

It is well established that misallocation of factor resources lowers productivity. In this paper, I use data from both formal and informal firms to study distortions in input and output markets as ...
Oscillatory Behavior in Cobalt Electrodeposition with 3-Mercapto-1-Propanesulfonate

A potential oscillation was observed during galvanostatic deposition of Co in the presence of 3-mercapto-1-propanesulfonate (MPS) and a systematic study was conducted. A suppression effect on Co de...
Nucleate boiling simulation using interface tracking method

Abstract The development and validation of 3D multiphase computational fluid dynamics (M-CFD) models and physics-informed data-driven modeling require data of high-quality and high-resolution. Considering the difficulties in acquiring the corresponding experimental data in prototypical conditions, two-phase boiling simulations by Interface Tracking Method (ITM) based models can be used to generate high-resolution numerical data in a consistent and relatively economical manner. A boiling model is developed in one of the ITM-based multiphase-flow solvers, named PHASTA, to investigate the nucleate boiling phenomenon. The interaction between bubbles forming at adjacent nucleation sites is investigated with this ITM boiling model. Nucleate pool boiling simulations with multiple nucleation sites are presented in this paper. Theinfluences of site distance, neighboring bubble size and contact angle effect are investigated. The presented boiling simulations are conducted on 3D unstructured computational meshes. These simulation results improve our understanding of the physical mechanisms of the nucleate boiling phenomenon and provide high-resolution numerical data for M-CFD validation and advanced boiling model development.
On fast computation of directed graph Laplacian pseudo-inverse

Abstract The Laplacian matrix and its pseudo-inverse for a strongly connected directed graph is fundamental in computing many properties of a directed graph. Examples include random-walk centrality and betweenness measures, average hitting and commute times, and other connectivity measures. These measures arise in the analysis of many social and computer networks. In this short paper, we show how a linear system involving the Laplacian may be solved in time linear in the number of edges, times a factor depending on the separability of the graph. This leads directly to the column-by-column computation of the entire Laplacian pseudo-inverse in time quadratic in the number of nodes, i.e., constant time per matrix entry. The approach is based on “off-the-shelf” iterative methods for which global linear convergence is guaranteed, without recourse to any matrix elimination algorithm.
Effects of Non-Conformal Boundary on Entanglement Entropy

Spacetime boundaries with canonical Neuman or Dirichlet conditions preserve conformal invarience, but “mixed” boundary conditions which interpolate linearly between them can break conformal symmetry and generate interesting Renormalization Group flows even when a theory is free, providing soluble models with nontrivial scale dependence. We compute the (Rindler) entanglement entropy for a free scalar field with mixed boundary conditions in half Minkowski space and in Anti-de Sitter space. In the latter case we also compute an additional geometric contribution, which according to a recent proposal then collectively give the 1/N corrections to the entanglement entropy of the conformal field theory dual. We obtain some perturbatively exact results in both cases which illustrate monotonic interpolation between ultraviolet and infrared fixed points. This is consistent with recent work on the irreversibility of renormalization group, allowing some assessment of the aforementioned proposal for holographic entanglement entropy and illustrating the generalization of the g-theorem for boundary conformal field theory.
New Insights Into Learning With Correntropy-Based Regression.

Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively studied and explored. Its application to regression prob...
Featureless adaptive optimization accelerates functional electronic materials design

Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.
ARPM-net: A novel CNN-based adversarial method with Markov random field enhancement for prostate and organs at risk segmentation in pelvic CT images.

Purpose The research is to develop a novel CNN-based adversarial deep learning method to improve and expedite the multi-organ semantic segmentation of CT images and to generate accurate contours on pelvic CT images. Methods Planning CT and structure datasets for 120 patients with intact prostate cancer were retrospectively selected and divided for tenfold cross-validation. The proposed adversarial multi-residual multi-scale pooling Markov random field (MRF) enhanced network (ARPM-net) implements an adversarial training scheme. A segmentation network and a discriminator network were trained jointly, and only the segmentation network was used for prediction. The segmentation network integrates a newly designed MRF block into a variation of multi-residual U-net. The discriminator takes the product of the original CT and the prediction/ground-truth as input and classifies the input into fake/real. The segmentation network and discriminator network can be trained jointly as a whole, or the discriminator can be used for fine-tuning after the segmentation network is coarsely trained. Multi-scale pooling layers were introduced to preserve spatial resolution during pooling using less memory compared to atrous convolution layers. An adaptive loss function was proposed to enhance the training on small or low contrast organs. The accuracy of modeled contours was measured with the dice similarity coefficient (DSC), average Hausdorff distance (AHD), average surface Hausdorff distance (ASHD), and relative volume difference (VD) using clinical contours as references to the ground-truth. The proposed ARPM-net method was compared to several state-of-the-art deep learning methods. Results ARPM-net outperformed several existing deep learning approaches and MRF methods and achieved state-of-the-art performance on a testing dataset. On the test set with 20 cases, the average DSC on the prostate, bladder, rectum, left femur, and right femur were 0.88 ( ± 0.11), 0.97 ( ± 0.07), 0.86 ( ± 0.12), 0.97 ( ± 0.01), and 0.97 ( ± 0.01), respectively. The average HD (mm) on these organs were 1.58 ( ± 1.77), 1.91 ( ± 1.29), 3.14 ( ± 2.39), 1.76 ( ± 1.57), and 1.92 ( ± 1.01). The average surface HD (mm) on these organs are 2.11 ( ± 2.03), 2.36 ( ± 2.43), 3.05 ( ± 2.11), 1.99 ( ± 1.66), and 2.00 ( ± 2.07). Conclusion ARPM-net was designed for the automatic segmentation of pelvic CT images. With adversarial fine-tuning, ARPM-net produces state-of-the-art accurate contouring of multiple organs on CT images and has the potential to facilitate routine pelvic cancer radiation therapy planning process.
Magnetic skyrmion bubble motion driven by surface acoustic waves

We study the dynamical control of a magnetic skyrmion bubble by using counter-propagating surface acoustic waves (SAWs) in a ferromagnet. First, we determine the bubble mass and derive the force due to SAWs acting on a magnetic bubble using Thiele's method. The force that pushes the bubble is proportional to the strain gradient for the major strain component. We then study the dynamical pinning and motion of magnetic bubbles by SAWs in a nanowire. In a disk geometry, we propose a SAWs-driven skyrmion bubble oscillator with two resonant frequencies.
On the Topology of Initial Data Sets with Higher Genus Ends

In this note we study the topology of 3-dimensional initial data sets with horizons of a sort associated with asymptotically locally anti-de Sitter spacetimes. We show that, within this class, those initial data sets that contain no (immersed) marginally outer trapped surfaces in their interior must have simple topology: they are a product of a surface and an interval, or a mild variation thereof, depending on the connectedness of the horizon and on its genus relative to that of the end. The results obtained here extend results in Eichmair et al. (J Differ Geom 95:389–405, 2013) to the case of higher genus ends.
The shape and mechanics of curved-fold origami structures

We develop recursion equations to describe the three-dimensional shape of a sheet upon which a series of concentric curved folds have been inscribed. In the case of no stretching outside the fold, the three-dimensional shape of a single fold prescribes the shape of the entire origami structure. To better explore these structures, we derive continuum equations, valid in the limit of vanishing spacing between folds, to describe the smooth surface intersecting all the mountain folds. We find that this surface has negative Gaussian curvature with magnitude equal to the square of the fold's torsion. A series of open folds with constant fold angle generate a helicoid.
Schottky anomaly of deSitter black holes

The interplay of black hole and cosmological horizons introduces distinctive thermodynamic behavior for deSitter black holes, including well-known upper bounds for the mass and entropy. We point to a new such feature, a Schottky peak in the heat capacity of Schwarzschild-deSitter (SdS) black holes. With this behavior in mind, we explore statistical models for the underlying quantum degrees of freedom of SdS holes. While a simple two-state spin model gives Schottky behavior, in order to capture the non-equilibrium nature of the SdS system we consider a system with a large number of non-interacting spins. We examine to what extent constrained states of this system reproduce the thermodynamic properties of the black hole. We also review results of a recent study of particle production in SdS spacetimes in light of the Schottky anomaly and our spin models.
Partonic Interpretation of Generalized Parton Distributions

Dispersion relations have been applied to hard exclusive processes suggesting that generalized parton distributions can be fully determined from their values on a kinematical ridge. We show significant pitfalls associated that vitiate this approach. We suggest an improved treatment of this assumption. Secondly, in order for a partonic interpretation to hold throughout the accessible kinematic region, semi-disconnected diagrams should not contribute. We show, however, that this condition is not satisfied for non-forward kinematics at leading order. Lastly we present our recent flexible parameterization of GPDs and indicate its application to neutral pseudoscalar electroproduction.
OSCILLONS IN SCALAR FIELD THEORIES: APPLICATIONS IN HIGHER DIMENSIONS AND INFLATION

The basic properties of oscillons — localized, long-lived, time-dependent scalar field configurations — are briefly reviewed, including recent results demonstrating how their existence depends on the dimensionality of space–time. Their role on the dynamics of phase transitions is discussed, and it is shown that oscillons may greatly accelerate the decay of metastable vacuum states. This mechanism for vacuum decay — resonant nucleation — is then applied to cosmological inflation. A new inflationary model is proposed which terminates with fast bubble nucleation.
Entanglement Entropy: A Perturbative Calculation

We provide a framework for a perturbative evaluation of the reduced density matrix. The method is based on a path integral in the analytically continued spacetime. It suggests an alternative to the holographic and ‘standard’ replica trick calculations of entanglement entropy. We implement this method within solvable field theory examples to evaluate leading order corrections induced by small perturbations in the geometry of the background and entangling surface. Our findings are in accord with Solodukhin’s formula for the universal term of entanglement entropy for four dimensional CFTs.
ISOTROPIC LUMINOSITY INDICATORS IN A COMPLETE AGN SAMPLE

The [O IV] λ25.89 μm line has been shown to be an accurate indicator of active galactic nucleus (AGN) intrinsic luminosity in that it correlates well with hard (10-200 keV) X-ray emission. We present measurements of [O IV] for 89 Seyfert galaxies from the unbiased revised Shapley-Ames (RSA) sample. The [O IV] luminosity distributions of obscured and unobscured Seyferts are indistinguishable, indicating that their intrinsic AGN luminosities are quite similar and that the RSA sample is well suited for tests of the unified model. In addition, we analyze several commonly used proxies for AGN luminosity, including [O III] λ5007 A, 6 cm radio, and 2-10 keV X-ray emission. We find that the radio luminosity distributions of obscured and unobscured AGNs show no significant difference, indicating that radio luminosity is a useful isotropic luminosity indicator. However, the observed [O III] and 2-10 keV luminosities are systematically smaller for obscured Seyferts, indicating that they are not emitted isotropically.
Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network

We propose a new machine learning approach to Stokes inversion based on a convolutional neural network (CNN) and the Milne-Eddington (ME) method. The Stokes measurements used in this study were taken by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory. By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS. Experimental results show that our CNN method produces smoother and cleaner magnetic maps than the widely used ME method. Furthermore, the CNN method is 4~6 times faster than the ME method, and is able to produce vector magnetic fields in near real-time, which is essential to space weather forecasting. Specifically, it takes ~50 seconds for the CNN method to process an image of 720 x 720 pixels comprising Stokes profiles of GST/NIRIS. Finally, the CNN-inferred results are highly correlated to the ME-calculated results and are closer to the ME's results with the Pearson product-moment correlation coefficient (PPMCC) being closer to 1 on average than those from other machine learning algorithms such as multiple support vector regression and multilayer perceptrons (MLP). In particular, the CNN method outperforms the current best machine learning method (MLP) by 2.6% on average in PPMCC according to our experimental study. Thus, the proposed physics-assisted deep learning-based CNN tool can be considered as an alternative, efficient method for Stokes inversion for high resolution polarimetric observations obtained by GST/NIRIS.
A COMPARISON OF METHODS FOR DETERMINING THE AGE DISTRIBUTION OF STAR CLUSTERS: APPLICATION TO THE LARGE MAGELLANIC CLOUD

The age distribution of star clusters in nearby galaxies plays a crucial role in evaluating the lifetimes and disruption mechanisms of the clusters. Two very different results have been recently found for the age distribution χ(τ) of clusters in the Large Magellanic Cloud (LMC). We found that χ(τ) can be approximately described by a power law χ(τ) τγ, with γ –0.8, by counting clusters in the mass-age plane, i.e., by constructing χ(τ) directly from mass-limited samples. Gieles & Bastian inferred a value of γ 0, based on the slope of the relation between the maximum mass of clusters in equal intervals of log τ, hereafter, the M max method, an indirect technique that requires additional assumptions about the upper end of the mass function. However, our own analysis shows that the M max method gives a result consistent with our direct counting method for clusters in the LMC, namely, χ(τ) τ–0.8 for τ 109 yr. The reason for the apparent discrepancy is that our analysis includes many intermediate and high-mass clusters (M > 1.5 × 103 M ☉), which formed recently (τ 107 yr), and which are known to exist in the LMC, whereas Gieles & Bastian are missing such clusters. We compile recent results from the literature showing that the age distribution of young star clusters in more than a dozen galaxies, including dwarf and giant galaxies, isolated and interacting galaxies, and irregular and spiral galaxies, has a similar declining shape. We interpret this approximately universal shape as primarily due to the progressive disruption of star clusters over their first ~few × 108 yr, starting soon after formation, and discuss some observational and physical implications of this early disruption for stellar populations in galaxies.
Stochastic p -Bits for Invertible Logic

Digital electronics are based on deterministic units called bits that can have one of two values, 0 and 1. New theoretical work suggests that circuits built out of probabilistic units that fluctuate randomly in value between 0 and 1 can be used to perform multiple functions: A multiplier, for example, can also function as a factorizer.
Multiplicativity of Completely Bounded p-Norms Implies a Strong Converse for Entanglement-Assisted Capacity

The fully quantum reverse Shannon theorem establishes the optimal rate of noiseless classical communication required for simulating the action of many instances of a noisy quantum channel on an arbitrary input state, while also allowing for an arbitrary amount of shared entanglement of an arbitrary form. Turning this theorem around establishes a strong converse for the entanglement-assisted classical capacity of any quantum channel. This paper proves the strong converse for entanglement-assisted capacity by a completely different approach and identifies a bound on the strong converse exponent for this task. Namely, we exploit the recent entanglement-assisted “meta-converse” theorem of Matthews and Wehner, several properties of the recently established sandwiched Renyi relative entropy (also referred to as the quantum Renyi divergence), and the multiplicativity of completely bounded p-norms due to Devetak et al. The proof here demonstrates the extent to which the Arimoto approach can be helpful in proving strong converse theorems, it provides an operational relevance for the multiplicativity result of Devetak et al., and it adds to the growing body of evidence that the sandwiched Renyi relative entropy is the correct quantum generalization of the classical concept for all α > 1.
Complex dynamics in coevolution models with ratio-dependent functional response

Abstract We explore the complex dynamical behavior of two simple predator–prey models of biological coevolution that on the ecological level account for interspecific and intraspecific competition, as well as adaptive foraging behavior. The underlying individual-based population dynamics are based on a ratio-dependent functional response [Getz, W.M., 1984. Population dynamics: a per capita resource approach. J. Theor. Biol. 108, 623–643]. Analytical results for fixed-point population sizes in some simple communities are derived and discussed. In long kinetic Monte Carlo simulations we find quite robust, approximate 1 / f noise in species diversity and population sizes, as well as power-law distributions for the lifetimes of individual species and the durations of periods of relative evolutionary stasis. Adaptive foraging enhances coexistence of species and produces a metastable low-diversity phase and a stable high-diversity phase.
Modular inflation observables and j -inflation phenomenology

Modular inflation is the restriction to two fields of automorphic inflation, a general group based framework for multifield scalar field theories with curved target spaces, which can be parametrized by the comoving curvature perturbation ℛ and the isocurvature perturbation tensor S IJ . This paper describes the dynamics and observables of these per-turbations and considers in some detail the special case of modular inflation as an extensive class of two-field inflation theories with a conformally flat target space. It is shown that the nonmodular nature of derivatives of modular forms leads to CMB observables in modular invariant inflation theories that are in general constructed from almost holomorphic modular forms. The phenomenology of the model of j-inflation is compared to the recent observational constraints from the Planck satellite and the BICEP2/Keck Array data.
AN ACCURATE FLUX DENSITY SCALE FROM 1 TO 50 GHz

We develop an absolute flux density scale for centimeter-wavelength astronomy by combining accurate flux density ratios determined by the Very Large Array between the planet Mars and a set of potential calibrators with the Rudy thermophysical emission model of Mars, adjusted to the absolute scale established by the Wilkinson Microwave Anisotropy Probe. The radio sources 3C123, 3C196, 3C286, and 3C295 are found to be varying at a level of less than {approx}5% per century at all frequencies between 1 and 50 GHz, and hence are suitable as flux density standards. We present polynomial expressions for their spectral flux densities, valid from 1 to 50 GHz, with absolute accuracy estimated at 1%-3% depending on frequency. Of the four sources, 3C286 is the most compact and has the flattest spectral index, making it the most suitable object on which to establish the spectral flux density scale. The sources 3C48, 3C138, 3C147, NGC 7027, NGC 6542, and MWC 349 show significant variability on various timescales. Polynomial coefficients for the spectral flux density are developed for 3C48, 3C138, and 3C147 for each of the 17 observation dates, spanning 1983-2012. The planets Venus, Uranus, and Neptune are included in our observations, and we derive theirmore » brightness temperatures over the same frequency range.« less
Bulk reconstruction of metrics with a compact space asymptotically

Holographic duality implies that the geometric properties of the gravitational bulk theory should be encoded in the dual field theory. These naturally include the metric on dimensions that become compact near the conformal boundary, as is the case for any asymptotically locally AdSn × $$ \mathbbm{S} $$ k spacetime. Almost all previous work on metric reconstruction ignores these dimensions and would thus at most apply to dimensionally-reduced metrics. In this work, we generalize the approach to bulk reconstruction using light-cone cuts and propose a prescription to obtain the full higher-dimensional metric of generic spacetimes up to an overall conformal factor. We first extend the definition of light-cone cuts to include information about the asymptotic compact dimensions, and show that the full conformal metric can be recovered from these extended cuts. We then give a prescription for obtaining these extended cuts from the dual field theory. The location of the usual cuts can still be obtained from bulk-point singularities of correlators, and the new information in the extended cut can be extracted by using appropriate combinations of operators dual to Kaluza-Klein modes of the higher-dimensional bulk fields.
Review on Neutrino Telescopes

I will discuss the motivations for Neutrino Astronomy and its prospects given the current experimental scenario, which is the main focus of this paper. I will also go through the first results of the IceCube detector deep in the ice and of the ANTARES undersea telescope underlying complementary aspects, common and different challenges. It is an exciting time for this science since the first completed undersea detector is successfully taking data and the first cubic kilometer detector is going to be shortly more than half-way from its completion in Antarctica.
The structure of BPS equations for ambi-polar microstate geometries

Ambi-polar metrics, defined so as to allow the signature to change from +4 to -4 across hypersurfaces, are a mainstay in the construction of BPS microstate geometries. This paper elucidates the cohomology of these spaces so as to simplify greatly the construction of infinite families of fluctuating harmonic magnetic fluxes. It is argued that such fluxes should come from scalar, harmonic pre-potentials whose source loci are holomorphic divisors. This insight is obtained by exploring the Kahler structure of ambi-polar Gibbons-Hawking spaces and it is shown that differentiating the pre-potentials with respect to Kahler moduli yields solutions to the BPS equations for the electric potentials sourced by the magnetic fluxes. This suggests that harmonic analysis on ambi-polar spaces has a novel, and an extremely rich structure, that is deeply intertwined with the BPS equations. We illustrate our results using a family of two-centered solutions.
Multifield stochastic particle production: beyond a maximum entropy ansatz

We explore non-adiabatic particle production for $N_{\rm f}$ coupled scalar fields in a time-dependent background with stochastically varying effective masses, cross-couplings and intervals between interactions. Under the assumption of weak scattering per interaction, we provide a framework for calculating the typical particle production rates after a large number of interactions. After setting up the framework, for analytic tractability, we consider interactions (effective masses and cross couplings) characterized by series of Dirac-delta functions in time with amplitudes and locations drawn from different distributions. Without assuming that the fields are statistically equivalent, we present closed form results (up to quadratures) for the asymptotic particle production rates for the $N_{\rm f}=1$ and $N_{\rm f}=2$ cases. We also present results for the general $N_{\rm f} >2$ case, but with more restrictive assumptions. We find agreement between our analytic results and direct numerical calculations of the total occupation number of the produced particles, with departures that can be explained in terms of violation of our assumptions. We elucidate the precise connection between the maximum entropy ansatz (MEA) used in Amin & Baumann (2015) and the underlying statistical distribution of the self and cross couplings. We provide and justify a simple to use (MEA-inspired) expression for the particle production rate, which agrees with our more detailed treatment when the parameters characterizing the effective mass and cross-couplings between fields are all comparable to each other. However, deviations are seen when some parameters differ significantly from others. We show that such deviations become negligible for a broad range of parameters when $N_{\rm f}\gg 1$.
Quantum Griffiths Effects and Smeared Phase Transitions in Metals: Theory and Experiment

In this paper, we review theoretical and experimental research on rare region effects at quantum phase transitions in disordered itinerant electron systems. After summarizing a few basic concepts about phase transitions in the presence of quenched randomness, we introduce the idea of rare regions and discuss their importance. We then analyze in detail the different phenomena that can arise at magnetic quantum phase transitions in disordered metals, including quantum Griffiths singularities, smeared phase transitions, and cluster-glass formation. For each scenario, we discuss the resulting phase diagram and summarize the behavior of various observables. We then review several recent experiments that provide examples of these rare region phenomena. We conclude by discussing limitations of current approaches and open questions.
Low-Complexity Modeling of Partially Available Second-Order Statistics: Theory and an Efficient Matrix Completion Algorithm

State statistics of linear systems satisfy certain structural constraints that arise from the underlying dynamics and the directionality of input disturbances. In the present paper, we study the problem of completing partially known state statistics. Our aim is to develop tools that can be used in the context of control-oriented modeling of large-scale dynamical systems. For the type of applications we have in mind, the dynamical interaction between state variables is known while the directionality and dynamics of input excitation is often uncertain. Thus, the goal of the mathematical problem that we formulate is to identify the dynamics and directionality of input excitation in order to explain and complete observed sample statistics. More specifically, we seek to explain correlation data with the least number of possible input disturbance channels. We formulate this inverse problem as rank minimization, and for its solution, we employ a convex relaxation based on the nuclear norm. The resulting optimization problem is cast as a semidefinite program and can be solved using general-purpose solvers. For problem sizes that these solvers cannot handle, we develop a customized alternating minimization algorithm (AMA). We interpret AMA as a proximal gradient for the dual problem and prove sublinear convergence for the algorithm with fixed step-size. We conclude with an example that illustrates the utility of our modeling and optimization framework and draw contrast between AMA and the commonly used alternating direction method of multipliers (ADMM) algorithm.
Graphical cyclic supercharacters for composite moduli

Recent work has introduced the study of graphical properties of cyclic supercharacters, functions $\mathbb{Z}/n\mathbb{Z}\to \mathbb{C}$ whose values are exponential sums with close connections to Gauss sums and Gaussian periods. Plots of these functions exhibit striking features, some of which have been previously explained when the modulus $n$ is a power of an odd prime. After reviewing this material, we initiate the graphical study of images of cyclic supercharacters in the case of composite $n$.
High-precision Dynamical Masses of Pre-main-sequence Stars with ALMA and Gaia

Heising-Simons Foundation; Homer L. Dodge Endowed Chair; National Science Foundation Graduate Research Fellowship [2012115762]; NSF AAG grant [1311910]; NASA's Science Mission Directorate