cssi/SciSciGPT-SciSciNet huggingface dataset:
Central Moment Lattice Boltzmann Method using a Pressure-based Formulation for Multiphase Flows at High Density Ratios and including Effects of Surface Tension and Marangoni Stresses

Abstract Simulation of multiphase flows, which are ubiquitous in nature and engineering applications, require coupled capturing or tracking of the interfaces in conjunction with the solution of fluid motion often occurring at multiple scales. In this contribution, we will present unified cascaded LB methods based on central moments for the solution of the incompressible two-phase flows at high density ratios and for capturing of the interfacial dynamics. Based on a modified continuous Boltzmann equation (MCBE) for two-phase flows, where a kinetic transformation to the distribution function involving the pressure field is introduced to reduce the associated numerical stiffness at high density gradients, a central moment cascaded LB formulation using multiple relaxation times for computing the fluid motion will be constructed. In this LB scheme, the collision step is prescribed by the relaxation of various central moments to their equilibria that are reformulated in terms of the pressure field obtained via matching to the continuous equilibria based on the transformed Maxwell distribution. Furthermore, the differential treatments for the effects of the source term representing the change due to the pressure field and of the source term due to the interfacial tension force and body forces appearing in the MCBE on different moments are consistently accounted for in this cascaded LB solver that computes the pressure and velocity fields. In addition, another cascaded LB scheme will be developed to solve for the interfacial dynamics represented by a phase field model based on the conservative Allen-Cahn equation that evolves interfaces by advection and under the competing effects due to a diffusion term and a phase segregation flux term. The latter is introduced into the cascaded LB scheme via a modification to the moment equilibria. Based on numerical simulations of a variety of two-phase flow benchmark problems at high density ratios and involving the effects of surface tension and its tangential gradients (Marangoni stresses), we will validate our unified cascaded LB approach and also demonstrate improvements in numerical stability.
Superconductivity in Graphene induced by the rotated layer

Abstract We linearize the Hamiltonian for Graphene in the vicinity of the nodes and obtain a two dimensional Dirac massless Hamiltonian for each layer. Including the spin–orbit a mass is introduced. The attractive interaction is obtained as a result of rotation of one of the layer. The mass introduced gives rise to attraction for small angle rotation. Using the spinors representation in the vicinity of the nodal Dirac points the interactions are modified. The parity anomaly resulting from rotations modifies the “ A P S ” i n d e x theorem of “Gravitational” “Chern Simons”. We show directly that the combination of rotation and projection of the spinors in the vicinity of the Dirac nodes are modified in such way, that a repulsive real space interaction, becomes attractive in the restricted momentum space, in certain directions. The chemical potential controls the behavior.
MOSAIC: a joint modeling methodology for combined circadian and non-circadian analysis of multi-omics data.

Motivation Circadian rhythms are approximately 24-h endogenous cycles that control many biological functions. To identify these rhythms, biological samples are taken over circadian time and analyzed using a single omics type, such as transcriptomics or proteomics. By comparing data from these single omics approaches, it has been shown that transcriptional rhythms are not necessarily conserved at the protein level, implying extensive circadian post-transcriptional regulation. However, as proteomics methods are known to be noisier than transcriptomic methods, this suggests that previously identified arrhythmic proteins with rhythmic transcripts could have been missed due to noise and may not be due to post-transcriptional regulation. Results To determine if one can use information from less-noisy transcriptomic data to inform rhythms in more-noisy proteomic data, and thus more accurately identify rhythms in the proteome, we have created the Multi-Omics Selection with Amplitude Independent Criteria (MOSAIC) application. MOSAIC combines model selection and joint modeling of multiple omics types to recover significant circadian and non-circadian trends. Using both synthetic data and proteomic data from Neurospora crassa, we showed that MOSAIC accurately recovers circadian rhythms at higher rates in not only the proteome but the transcriptome as well, outperforming existing methods for rhythm identification. In addition, by quantifying non-circadian trends in addition to circadian trends in data, our methodology allowed for the recognition of the diversity of circadian regulation as compared to non-circadian regulation. Availability and implementation MOSAIC's full interface is available at https://github.com/delosh653/MOSAIC. An R package for this functionality, mosaic.find, can be downloaded at https://CRAN.R-project.org/package=mosaic.find. Supplementary information Supplementary data are available at Bioinformatics online.
Physics-informed deep learning for incompressible laminar flows

ABSTRACT Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics problems, whose basic concept is to embed physical laws to constrain/inform neural networks, with the need of less data for training a reliable model. This can be achieved by incorporating the residual of physics equations into the loss function. Through minimizing the loss function, the network could approximate the solution. In this paper, we propose a mixed-variable scheme of physics-informed neural network (PINN) for fluid dynamics and apply it to simulate steady and transient laminar flows at low Reynolds numbers. A parametric study indicates that the mixed-variable scheme can improve the PINN trainability and the solution accuracy. The predicted velocity and pressure fields by the proposed PINN approach are also compared with the reference numerical solutions. Simulation results demonstrate great potential of the proposed PINN for fluid flow simulation with a high accuracy.
Catheter Removal on the Same Day of Holmium Laser Enucleation of the Prostate: Outcomes of a Pilot Study

Abstract Objective To determine if same day catheter removal is feasible in a select population after holmium laser enucleation of the prostate (HoLEP). Methods We performed an analysis of patients undergoing HoLEP at our institution who underwent same-day catheter removal after HoLEP. All HoLEPs were performed with Moses 2.0 Optimized for BPH. Patients were dismissed from postoperative recovery unit to the clinic for catheter removal and voiding trial. Results To date, 30 patients have undergone same day catheter removal. Median age is 68.6 years (interquartile range [IQR] 61.8-73.3) and preoperative prostate volume 81 mL (IQR 53-114.8). Median enucleation time was 39.5 minutes (IQR 30.5-53), morcellation time was 5 minutes (IQR 4-12 minutes), and enucleated specimen weight was 52.5 g (IQR 33-81). Twenty-seven (90%) patients successfully voided on the same day without requiring catheter replacement. All patients were catheter free by POD1. For patients who successfully passed their voiding trial, the median time from the end of the procedure to hospital discharge was 2.6 hours (IQR [2.1-2.9]) and from the end of the HoLEP to catheter removal was 4.9 hours (IQR [3.5-6.0]). Conclusion We present for the first time that same day catheter removal is a feasible option in a select population of patients undergoing HoLEP. With more study, this has the potential for transforming the management of BPH, especially larger glands.
A survey on compressive sensing: classical results and recent advancements

Recovering sparse signals from linear measurements has demonstrated outstanding utility in a vast variety of real-world applications. Compressive sensing is the topic that studies the associated raised questions for the possibility of a successful recovery. This topic is well-nourished and numerous results are available in the literature. However, their dispersity makes it time-consuming for practitioners to quickly grasp its main ideas and classical algorithms, and further touch upon the recent advancements. In this survey, we overview vital classical tools and algorithms in compressive sensing and describe its significant recent advancements. We conclude by a numerical comparison of the performance of described approaches.
Low-Light Environment Neural Surveillance

We design and implement an end-to-end system for real-time crime detection in low-light environments. Unlike Closed-Circuit Television, which performs reactively, the Low-Light Environment Neural Surveillance provides real time crime alerts. The system uses a low-light video feed processed in real-time by an optical-flow network, spatial and temporal networks, and a Support Vector Machine to identify shootings, assaults, and thefts. We create a low-light action-recognition dataset, LENS-4, which will be publicly available. An IoT infrastructure set up via Amazon Web Services interprets messages from the local board hosting the camera for action recognition and parses the results in the cloud to relay messages. The system achieves 71.5% accuracy at 20 FPS. The user interface is a mobile app which allows local authorities to receive notifications and to view a video of the crime scene. Citizens have a public app which enables law enforcement to push crime alerts based on user proximity.
On macroscopic dimension of non-spin 4-manifolds

We prove that for 4-manifolds M with residually finite fundamental group and non-spin universal covering M, the inequality dimmcM ≤ 3 implies the inequality dimmcM ≤ 2. This allows us to complet...
Local glutamate-mediated dendritic plateau potentials change the state of the cortical pyramidal neuron

Dendritic spikes in thin dendritic branches (basal and oblique dendrites) are traditionally inferred from spikelets measured in the cell body. Here, we used laser-spot voltage-sensitive dye imaging in cortical pyramidal neurons (rat brain slices) to investigate the voltage waveforms of dendritic potentials occurring in response to spatially restricted glutamatergic inputs. Local dendritic potentials lasted 200-500 ms and propagated to the cell body, where they caused sustained 10- to 20-mV depolarizations. Plateau potentials propagating from dendrite to soma and action potentials propagating from soma to dendrite created complex voltage waveforms in the middle of the thin basal dendrite, comprised of local sodium spikelets, local plateau potentials, and backpropagating action potentials, superimposed on each other. Our model replicated these voltage waveforms across a gradient of glutamatergic stimulation intensities. The model then predicted that somatic input resistance (Rin) and membrane time constant (tau) may be reduced during dendritic plateau potential. We then tested these model predictions in real neurons and found that the model correctly predicted the direction of Rin and tau change but not the magnitude. In summary, dendritic plateau potentials occurring in basal and oblique branches put pyramidal neurons into an activated neuronal state ("prepared state"), characterized by depolarized membrane potential and smaller but faster membrane responses. The prepared state provides a time window of 200-500 ms, during which cortical neurons are particularly excitable and capable of following afferent inputs. At the network level, this predicts that sets of cells with simultaneous plateaus would provide cellular substrate for the formation of functional neuronal ensembles.NEW & NOTEWORTHY In cortical pyramidal neurons, we recorded glutamate-mediated dendritic plateau potentials with voltage imaging and created a computer model that recreated experimental measures from dendrite and cell body. Our model made new predictions, which were then tested in experiments. Plateau potentials profoundly change neuronal state: a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to firing triggered by other afferent inputs.
MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases

There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimer’s Disease (AD). Elucidating distinct subtypes of diseases allows a better understanding of neuropathogenesis and enables the possibility of developing targeted treatment programs. Recent semi-supervised clustering techniques have provided a data-driven way to understand disease heterogeneity. However, existing methods do not take into account that subtypes of the disease might present themselves at different spatial scales across the brain. Here, we introduce a novel method, MAGIC, to uncover disease heterogeneity by leveraging multi-scale clustering. We first extract multi-scale patterns of structural covariance (PSCs) followed by a semi-supervised clustering with double cyclic block-wise optimization across different scales of PSCs. We validate MAGIC using simulated heterogeneous neuroanatomical data and demonstrate its clinical potential by exploring the heterogeneity of AD using T1 MRI scans of 228 cognitively normal (CN) and 191 patients. Our results indicate two main subtypes of AD with distinct atrophy patterns that consist of both fine-scale atrophy in the hippocampus as well as large-scale atrophy in cortical regions. The evidence for the heterogeneity is further corroborated by the clinical evaluation of two subtypes, which indicates that there is a subpopulation of AD patients that tend to be younger and decline faster in cognitive performance relative to the other subpopulation, which tends to be older and maintains a relatively steady decline in cognitive abilities.
Polar Subspaces and Automatic Maximality

This paper is about certain linear subspaces of Banach SN spaces (that is to say Banach spaces which have a symmetric nonexpansive linear map into their dual spaces). We apply our results to monotone linear subspaces of the product of a Banach space and its dual. In this paper, we establish several new results and also give improved proofs of some known ones in both the general and the special contexts.
Hubble induced mass after inflation in spectator field models

Spectator field models such as the curvaton scenario and the modulated reheating are attractive scenarios for the generation of the cosmic curvature perturbation, as the constraints on inflation models are relaxed. In this paper, we discuss the effect of Hubble induced masses on the dynamics of spectator fields after inflation. We pay particular attention to the Hubble induced mass by the kinetic energy of an oscillating inflaton, which is generically unsuppressed but often overlooked. In the curvaton scenario, the Hubble induced mass relaxes the constraint on the property of the inflaton and the curvaton, such as the reheating temperature and the inflation scale. We comment on the implication of our discussion for baryogenesis in the curvaton scenario. In the modulated reheating, the predictions of models e.g. the non-gaussianity can be considerably altered. Furthermore, we propose a new model of the modulated reheating utilizing the Hubble induced mass which realizes a wide range of the local non-gaussianity parameter.
Suppression of sub-surface freezing in free-standing thin films of a coarse-grained model of water.

Freezing in the vicinity of water–vapor interfaces is of considerable interest to a wide range of disciplines, most notably the atmospheric sciences. In this work, we use molecular dynamics and two advanced sampling techniques, forward flux sampling and umbrella sampling, to study homogeneous nucleation of ice in free-standing thin films of supercooled water. We use a coarse-grained monoatomic model of water, known as mW, and we find that in this model a vapor–liquid interface suppresses crystallization in its vicinity. This suppression occurs in the vicinity of flat interfaces where no net Laplace pressure in induced. Our free energy calculations reveal that the pre-critical crystalline nuclei that emerge near the interface are thermodynamically less stable than those that emerge in the bulk. We investigate the origin of this instability by computing the average asphericity of nuclei that form in different regions of the film, and observe that average asphericity increases closer to the interface, which is consistent with an increase in the free energy due to increased surface-to-volume ratios.
Harnessing electro-optic correlations in an efficient mechanical converter

An optical network of superconducting quantum bits (qubits) is an appealing platform for quantum communication and distributed quantum computing, but developing a quantum-compatible link between the microwave and optical domains remains an outstanding challenge. Operating at T < 100 mK temperatures, as required for quantum electrical circuits, we demonstrate a mechanically mediated microwave–optical converter with 47% conversion efficiency, and use a classical feed-forward protocol to reduce added noise to 38 photons. The feed-forward protocol harnesses our discovery that noise emitted from the two converter output ports is strongly correlated because both outputs record thermal motion of the same mechanical mode. We also discuss a quantum feed-forward protocol that, given high system efficiencies, would allow quantum information to be transferred even when thermal phonons enter the mechanical element faster than the electro-optic conversion rate.
Optimizing Pulsar Timing Array Observational Cadences for Sensitivity to Low-frequency Gravitational-wave Sources

Observations of low-frequency gravitational waves (GWs) will require the highest possible timing precision from an array of the most spin-stable pulsars. We can improve the sensitivity of a pulsar timing array (PTA) to different GW sources by observing pulsars with low timing noise over years to decades and distributed across the sky. We discuss observing strategies for a PTA focused on a stochastic GW background such as from unresolved supermassive black hole binaries as well as focused on single continuous-wave sources. First, we describe the method to calculate a PTA's sensitivity to different GW-source classes. We then apply our method to the 45 pulsars presented in the North American Nanohertz Observatory for the GW 11 year data set. For expected amplitudes of the stochastic background, we find that all pulsars contribute significantly over the timescale of decades; the exception is for very pessimistic values of the stochastic-background amplitude. For individual single sources, we find that a number of pulsars contribute to the sensitivity of a given source, but that which pulsars contribute is different depending on the source, or versus an all-sky metric. Our results seem robust to the presence of red noise in pulsar arrival times. It is critical to obtain more robust pulsar-noise parameters as they heavily affect our results. Our results show that it is also imperative to locate and time as many high-precision pulsars as possible, as quickly as possible, to maximize the sensitivity of next-generation PTA detectors.
On products of generalized geometries

In this paper we address what generalized geometric structures are possible on products of spaces that each admit generalized geometries. In particular we consider, first, the product of two odd dimensional spaces that each admit a generalized almost contact structure, and then subsequently, the product of an odd dimensional space that admits a generalized almost contact structure and an even dimensional space that admits a generalized almost complex structure. We also draw attention to the relationship of the Courant bracket to the classical notion of normality for almost contact structures.
HOW ENVIRONMENT AFFECTS GALAXY METALLICITY THROUGH STRIPPING AND FORMATION HISTORY: LESSONS FROM THE ILLUSTRIS SIMULATION

Recent studies have found higher galaxy metallicities in richer environments. It is not yet clear, however, whether metallicity-environment dependencies are merely an indirect consequence of environmentally dependent formation histories, or of environment related processes directly affecting metallicity. Here, we present a first detailed study of metallicity-environment correlations in a cosmological hydrodynamical simulation, in particular the Illustris simulation. Illustris galaxies display similar relations to those observed. Utilizing our knowledge of simulated formation histories, and leveraging the large simulation volume, we construct galaxy samples of satellites and centrals that are matched in formation histories. This allows us to find that ~1/3 of the metallicity-environment correlation is due to different formation histories in different environments. This is a combined effect of satellites (in particular, in denser environments) having on average lower z=0 star formation rates (SFRs), and of their older stellar ages, even at a given z=0 SFR. Most of the difference, ~2/3, however, is caused by the higher concentration of star-forming disks of satellite galaxies, as this biases their SFR-weighted metallicities toward their inner, more metal-rich parts. With a newly defined quantity, the 'radially averaged' metallicity, which captures the metallicity profile but is independent of the SFR profile, the metallicities of satellites and centrals become environmentally independent once they are matched in formation history. We find that circumgalactic metallicity (defined as rapidly inflowing gas around the virial radius), while sensitive to environment, has no measurable effect on the metallicity of the star-forming gas inside the galaxies.
Predicting Solar Flares Using a Long Short-term Memory Network

We present a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a gamma-class flare within the next 24 hours. We consider three gamma classes, namely >=M5.0 class, >=M class, and >=C class, and build three LSTM models separately, each corresponding to a gamma class. Each LSTM model is used to make predictions of its corresponding gamma-class flares. The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples. Each data sample has 40 features including 25 magnetic parameters obtained from the Space-weather HMI Active Region Patches (SHARP) and related data products as well as 15 flare history parameters. We survey the flare events that occurred from 2010 May to 2018 May, using the GOES X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and select flares with identified ARs in the NCEI flare catalogs. These flare events are used to build the labels (positive vs. negative) of the data samples. Experimental results show that (i) using only 14-22 most important features including both flare history and magnetic parameters can achieve better performance than using all the 40 features together; (ii) our LSTM network outperforms related machine learning methods in predicting the labels of the data samples. To our knowledge, this is the first time that LSTMs have been used for solar flare prediction.
Dark Matter And The Habitability of Planets

In many models, dark matter particles can elastically scatter with nuclei in planets, causing those particles to become gravitationally bound. While the energy expected to be released through the subsequent annihilations of dark matter particles in the interior of the Earth is negligibly small (a few megawatts in the most optimistic models), larger planets that reside in regions with higher densities of slow moving dark matter could plausibly capture and annihilate dark matter at a rate high enough to maintain liquid water on their surfaces, even in the absence of additional energy from starlight or other sources. On these rare planets, it may be dark matter rather than light from a host star that makes it possible for life to emerge, evolve, and survive.
The number of parts in certain residue classes of integer partitions

We use the Circle Method to derive asymptotics for functions related to the number of parts of partitions in particular residue classes.
Warping of Accretion Disks with Magnetically Driven Outflows: A Possible Origin for Jet Precession

Current theoretical models for the outflows/jets from active galactic nuclei, X-ray binaries, and young stellar objects involve large-scale magnetic fields threading an underlying accretion disk. We suggest that such a disk is subjected to warping instability and retrograde precession driven by magnetic torques associated with the outflow. The growth timescale for the disk warp and the precession period are of order the radial infall time of the disk. These effects may be relevant to jet precession and other variabilities observed in many systems.
Weighted bounds for variational Fourier series

For 1<p<infty and for weight w in A_p, we show that the r-variation of the Fourier sums of any function in L^p(w) is finite a.e. for r larger than a finite constant depending on w and p. The fact that the variation exponent depends on w is necessary. This strengthens previous work of Hunt-Young and is a weighted extension of a variational Carleson theorem of Oberlin-Seeger-Tao-Thiele-Wright. The proof uses weighted adaptation of phase plane analysis and a weighted extension of a variational inequality of Lepingle.
Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe

Broadband photometry of galaxies measures an unresolved mix of complex stellar populations, gas, and dust. Interpreting these data is a challenge for models: many studies have shown that properties derived from modeling galaxy photometry are uncertain by a factor of two or more, and yet answering key questions in the field now requires higher accuracy than this. Here, we present a new model framework specifically designed for these complexities. Our model, Prospector-$\alpha$, includes dust attenuation and re-radiation, a flexible attenuation curve, nebular emission, stellar metallicity, and a 6-component nonparametric star formation history. The flexibility and range of the parameter space, coupled with MCMC sampling within the Prospector inference framework, is designed to provide unbiased parameters and realistic error bars. We assess the accuracy of the model with aperture-matched optical spectroscopy, which was excluded from the fits. We compare spectral features predicted solely from fits to the broadband photometry to the observed spectral features. Our model predicts H$\alpha$ luminosities with a scatter of $\sim$0.18 dex and an offset of $\sim$0.1 dex across a wide range of morphological types and stellar masses. This agreement is remarkable, as the H$\alpha$ luminosity is dependent on accurate star formation rates, dust attenuation, and stellar metallicities. The model also accurately predicts dust-sensitive Balmer decrements, spectroscopic stellar metallicities, PAH mass fractions, and the age- and metallicity-sensitive features D$_{\mathrm{n}}$4000 and H$\delta$. Although the model passes all these tests, we caution that we have not yet assessed its performance at higher redshift or the accuracy of recovered stellar masses.
Cheater-altruist synergy in immunopathogenic ecological public goods games

Much research has focused on the deleterious effects of free-riding in public goods games, and a variety of mechanisms that suppresses cheating behavior. Here we argue that under certain conditions cheating behavior can be beneficial to the population. In a public goods game, cheaters do not pay for the cost of the public goods, yet they receive the benefit. Although this free-riding harms the entire population in the long run, the success of cheaters may aid the population when there is a common enemy that antagonizes both cooperators and cheaters. Here we study models in which an immune system antagonizes a cooperating pathogen. We investigate three population dynamics models, and determine under what conditions the presence of cheaters help defeat the immune system. The mechanism of action is that a polymorphism of cheaters and altruists optimizes the average growth rate. Our results give support for a possible synergy between cooperators and cheaters in ecological public goods games.
Electrical networks and hyperplane arrangements

Abstract This paper introduces Dirichlet arrangements, a generalization of graphic hyperplane arrangements arising from electrical networks and order polytopes of finite posets. We generalize combinatorial results on graphic arrangements to Dirichlet arrangements, addressing characteristic polynomials and supersolvability in particular. We apply these results to visibility sets of order polytopes and fixed-energy harmonic functions on electrical networks.
Quantum multi-object search algorithm with the availability of partial information

Consider the unstructured search of an unknown number l of items in a large unsorted database of size N. The multi-object quantum search algorithm consists of two parts. The first part of the algorithm is to generalize Grover's single-object search algorithm to the multiobject case, and the second part is to solve a counting problem to determine l. In this paper we study the multi-object quantum search algorithm (in continuous time), but in a more structured way by taking into account the availability of partial information. The modeling of available partial information is done simply by the combination of several prescribed, possibly overlapping, information sets with varying weights to signify the reliability of each set. The associated statistics is estimated and the algorithm efficiency and complexity are analyzed. Our analysis shows that the search described here may not be more efficient than the unstructured (generalized) multi-object Grover search if there is "misplaced confidence". However, if the information sets have a "basic confidence" property in the sense that each information set contains at least one search item, then a quadratic speedup holds on a much smaller data space, which further expedites the quantum search for the first item.
Nonclassical properties and quantum resources of hierarchical photonic superposition states

We motivate and introduce a class of “hierarchical” quantum superposition states of N coupled quantum oscillators. Unlike other well-known multimode photonic Schrodinger-cat states such as entangled coherent states, the hierarchical superposition states are characterized as two-branch superpositions of tensor products of single-mode Schrodinger-cat states. In addition to analyzing the photon statistics and quasiprobability distributions of prominent examples of these nonclassical states, we consider their usefulness for highprecision quantum metrology of nonlinear optical Hamiltonians and quantify their mode entanglement. We propose two methods for generating hierarchical superpositions in N = 2 coupled microwave cavities, exploiting currently existing quantum optical technology for generating entanglement between spatially separated electromagnetic field modes.
Value iteration and optimization of multiclass queueing networks

This paper considers in parallel the scheduling problem for multiclass queueing networks, and optimization of Markov decision processes. It is shown that the value iteration algorithm may perform poorly when the algorithm is not initialized properly. The most typical case where the initial value function is taken to be zero may be a particularly bad choice. In contrast, if the value iteration algorithm is initialized with a stochastic Lyapunov function, then the following hold: (i) a stochastic Lyapunov function exists for each intermediate policy, and hence each policy is regular (a strong stability condition), (ii) intermediate costs converge to the optimal cost, and (iii) any limiting policy is average cost optimal. It is argued that a natural choice for the initial value function is the value function for the associated deterministic control problem based upon a fluid model, or the approximate solution to Poisson’s equation obtained from the LP of Kumar and Meyn. Numerical studies show that either choice may lead to fast convergence to an optimal policy.
STAR FORMATION TIMESCALES AND THE SCHMIDT LAW

We offer a simple parameterization of the rate of star formation in galaxies. In this new approach, we make explicit and decouple the timescales associated (1) with disruptive effects of the star formation event itself from (2) the timescales associated with the cloud assembly and collapse mechanisms leading up to star formation. The star formation law in near-by galaxies, as measured on sub-kiloparsec scales, has recently been shown by Bigiel et al. to be distinctly nonlinear in its dependence on total gas density. Our parameterization of the spatially resolved Schmidt-Sanduleak relation naturally accommodates that dependence. The parameterized form of the relation is ρ* ~ ρ g /(τ s + ρ–n g ), where ρ g is the gas density, is the efficiency of converting gas into stars, and ρ–n g captures the physics of cloud collapse. Accordingly at high gas densities, quiescent star formation is predicted to progress as ρ* ~ ρ g , while at low gas densities ρ* ~ ρ1+n g , as is now generally observed. A variable efficiency in locally converting gas into stars as well as the unknown plane thickness variations from galaxy to galaxy, and radially within a given galaxy, can readily account for the empirical scatter in the observed (surface density rather than volume density) relations, and also plausibly account for the noted upturn in the relation at very high apparent projected column densities.
Ward-takahashi identities and Noether's theorem in quantum field theory

The gap in the mathematical derivation of Noether’s theorem, and also of the Ward-Takahashi identities, caused by performing variation before quantization is closed by introduction of variational calculus for operator fields. It is demonstrated that both Noether’s theorem and the Ward-Takahashi identities retain full validity in quantum field theory.
Two Definite Integrals Involving Products of Four Legendre Functions

The definite integrals $$ \int _{-1}^1x[P_\nu (x)]^4\mathrm{d}x$$ and $$ \int _{0}^1x[P_\nu (x)]^2\{[P_\nu (x)]^2-[P_\nu (-x)]^2\}\mathrm{d}x$$ are evaluated in closed form, where $$ P_\nu $$ stands for the Legendre function of degree $$ \nu \in \mathbb C$$ . Special cases of these integral formulae have appeared in arithmetic studies of automorphic Green’s functions and Epstein zeta functions.
Quilted Floer cohomology

We generalize Lagrangian Floer cohomology to sequences of Lagrangian correspondences. For sequences related by the geometric composition of Lagrangian correspondences we establish an isomorphism of the Floer cohomologies. This provides the foundation for the construction of a symplectic 2–category as well as for the definition of topological invariants via decomposition and representation in the symplectic category. Here we give some first direct symplectic applications: Calculations of Floer cohomology, displaceability of Lagrangian correspondences and transfer of displaceability under geometric composition.
Neutrino mass hierarchy

The neutrino mass hierarchy, i.e., whether the ν_3 neutrino mass eigenstate is heavier or lighter than the ν_1 and ν_2 mass eigenstates, is one of the remaining undetermined fundamental features of the neutrino Standard Model. Its determination would represent an important step in the formulation of the generalized model, and would have a profound impact on the quest of the nature of neutrinos (Dirac or Majorana) and the search for a theory of flavor. In this review, we summarize the status of experimental and theoretical work in this field and explore the future opportunities that emerge in light of the recently discovered non-zero and relatively large third neutrino mixing angle θ_(13).
THE 5 GHz ARECIBO SEARCH FOR RADIO FLARES FROM ULTRACOOL DWARFS

We present the results of a 4.75 GHz survey of 33 brown dwarfs and one young exoplanetary system for flaring radio emission, conducted with the 305 m Arecibo radio telescope. The goal of this program was to detect and characterize the magnetic fields of objects cooler than spectral type L3.5, the coolest brown dwarf detected prior to our survey. We have also attempted to detect flaring radio emission from the HR 8799 planetary system, guided by theoretical work indicating that hot, massive exoplanets may have strong magnetic fields capable of generating radio emission at GHz frequencies. We have detected and confirmed radio flares from the T6.5 dwarf 2MASS J10475385+2124234. This detection dramatically extends the temperature range over which brown dwarfs appear to be at least sporadic radio-emitters, from 1900 K (L3.5) down to 900 K (T6.5). It also demonstrates that the utility of radio detection as a unique tool to study the magnetic fields of substellar objects extends to the coolest dwarfs, and, plausibly to hot, massive exoplanets. We have also identified a single, 3.6σ flare from the L1 dwarf, 2MASS J1439284+192915. This detection is tentative and requires confirmation by additional monitoring observations.
Neutrino flavor oscillations in a curved space-time

Using a WKB approximation of the Dirac equation in a curved spacetime, we obtain the expression for the phase shift between two neutrino mass eigenstates in a generic gravitational field. We apply this expression to two specific space-time geometries, namely the Kerr–Newman metric describing a rotating and charged black hole, and the Friedmann–Robertson–Walker metric.
Growth and Properties of Dislocated Two-dimensional Layered Materials

Two-dimensional (2D) layered materials hosting dislocations have attracted considerable research attention in recent years. In particular, screw dislocations can result in a spiral topology and an interlayer twist in the layered materials, significantly impacting the stacking order and symmetry of the layers. Moreover, the dislocations with large strain and heavily distorted atomic registry can result in a local modification of the structures around the dislocation. The dislocations thus provide a useful route to engineering optical, electrical, thermal, mechanical and catalytic properties of the 2D layered materials, which show great potential to bring new functionalities. This article presents a comprehensive review of the experimental and theoretical progress on the growth and properties of the dislocated 2D layered materials. It also offers an outlook on the future works in this promising research field.
Building light nuclei from neutrons, protons, and pions

In these lectures I first explain, in a rather basic fashion, the construction of effective field theories. I then discuss some recent developments in the application of such theories to two- and three-nucleon systems.
A Positive Mass Theorem for Manifolds with Boundary

We derive a positive mass theorem for asymptotically flat manifolds with boundary whose mean curvature satisfies a sharp estimate involving the conformal Green's function. The theorem also holds if the conformal Green's function is replaced by the standard Green's function for the Laplacian operator. As an application, we obtain an inequality relating the mass and harmonic functions that generalizes H. Bray's mass-capacity inequality in his proof of the Riemannian Penrose conjecture.
The Effect of Active Galactic Nuclei on the Mid-infrared Aromatic Features

We present Spitzer measurements of the aromatic (also known as polycyclic aromatic hydrocarbon) features for 35 Seyfert galaxies from the revised Shapley-Ames sample and find that the relative strengths of the features differ significantly from those observed in star-forming galaxies. Specifically, the features at 6.2, 7.7, and 8.6 μm are suppressed relative to the 11.3 μm feature in Seyferts. Furthermore, we find an anti-correlation between the L(7.7 μm)/L(11.3 μm) ratio and the strength of the rotational H2 emission, which traces shocked gas. This suggests that shocks suppress the short-wavelength features by modifying the structure of the aromatic molecules or destroying the smallest grains. Most Seyfert nuclei fall on the relationship between aromatic emission and [Ne II] emission for star-forming galaxies, indicating that aromatic-based estimates of the star formation rate are generally reasonable in galaxies hosting active galactic nuclei. For the outliers from this relationship, which have small L(7.7 μm)/L(11.3 μm) ratios and strong H2 emission, the 11.3 μm feature still provides a valid measure of the star formation rate.
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures

In this paper, we demonstrate a computationally efficient new approach based on deep learning (DL) techniques for analysis, design and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redefine the conventional many-to-one design problem in EM nanostructures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, we applied such an efficacious method to design a new class of on-demand reconfigurable optical metasurfaces based on phase-change materials (PCMs). The experimental results of the fabricated devices are in good agreement with those predicted by the proposed approach. We envision that the integration of such a DL-based technique with full-wave commercial software packages offers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding, and predicting the observed responses in such structures. It will thus enable to solve complex design problems that could not be solved with existing techniques.
A New Upper Bound for 1324-Avoiding Permutations

We prove that the number of 1324-avoiding permutations of length n is less than $(7+4\sqrt{3})^n$ . The novelty of our method is that we injectively encode such permutations by a pair of words of length n over a finite alphabet that avoid a given factor.
On the lack of type I x-ray bursts in black hole x-ray binaries: Evidence for the event horizon?

Type I X-ray bursts are very common in neutron star X-ray binaries, but no type I burst has been seen in the dozen or so binaries in which the accreting compact star is too massive to be a neutron star and is therefore identified as a black hole candidate. We have carried out a global linear stability analysis of the accumulating fuel on the surface of a compact star to identify the conditions under which thermonuclear bursts are triggered. Our analysis, which improves on previous calculations, reproduces the gross observational trends of bursts in neutron star systems. It further shows that, if black hole candidates have surfaces, they would very likely exhibit instabilities similar to those that lead to type I bursts on neutron stars. The lack of bursts in black hole candidates is thus significant and indicates that these objects have event horizons. We discuss possible caveats to this conclusion.
Non-commuting two-local Hamiltonians for quantum error suppression

Physical constraints make it challenging to implement and control many-body interactions. For this reason, designing quantum information processes with Hamiltonians consisting of only one- and two-local terms is a worthwhile challenge. Enabling error suppression with two-local Hamiltonians is particularly challenging. A no-go theorem of Marvian and Lidar (Phys Rev Lett 113(26):260504, 2014) demonstrates that, even allowing particles with high Hilbert space dimension, it is impossible to protect quantum information from single-site errors by encoding in the ground subspace of any Hamiltonian containing only commuting two-local terms. Here, we get around this no-go result by encoding in the ground subspace of a Hamiltonian consisting of non-commuting two-local terms arising from the gauge operators of a subsystem code. Specifically, we show how to protect stored quantum information against single-qubit errors using a Hamiltonian consisting of sums of the gauge generators from Bacon---Shor codes (Bacon in Phys Rev A 73(1):012340, 2006) and generalized-Bacon---Shor code (Bravyi in Phys Rev A 83(1):012320, 2011). Our results imply that non-commuting two-local Hamiltonians have more error-suppressing power than commuting two-local Hamiltonians. While far from providing full fault tolerance, this approach improves the robustness achievable in near-term implementable quantum storage and adiabatic quantum computations, reducing the number of higher-order terms required to encode commonly used adiabatic Hamiltonians such as the Ising Hamiltonians common in adiabatic quantum optimization and quantum annealing.
Distributed Detection in Ad Hoc Networks Through Quantized Consensus

We study the asymptotic performance of distributed detection in large scale connected sensor networks. Contrasting to the canonical parallel network where a single node has access to local decisions from all other nodes, each node can only exchange information with its direct neighbors in the present setting. We establish that, with each node employing an identical one-bit quantizer for local information exchange, a novel consensus reaching approach can achieve the optimal asymptotic performance of centralized detection as the network size scales. The statement is true under three different detection frameworks: 1) the Bayesian criterion where the maximum a posteriori detector is optimal; 2) the Neyman–Pearson criterion with a constant type-I error probability constraint; and 3) the Neyman–Pearson criterion with an exponential type-I error probability constraint. Leveraging recent development in distributed consensus reaching using bounded quantizers with possibly unbounded data (which are log-likelihood ratios of local observations in the context of distributed detection), we design a one-bit deterministic quantizer with a controllable threshold that leads to desirable consensus error bounds. The obtained bounds are key to establishing the optimal asymptotic detection performance. In addition, we examine the non-asymptotic performance of the proposed approach and show that the type-I and type-II error probabilities at each node can be made arbitrarily close to the centralized ones simultaneously when a continuity condition is satisfied.
Order generated by random many-body dynamics

New features related to collective properties generated in the systems driven by random dynamics are observed and their implications for further understanding of interplay between coherence and chaos are discussed.