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TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package., Comment: v2: updated funding acknowledgement

Neutral atoms become strongly interacting when their electrons are excited to loosely bound Rydberg states. We investigate the strongly correlated quantum phases of matter that emerge in two-dimensional atom arrays where three Rydberg levels are used to encode an effective spin-1 degree of freedom. Dipolar exchange between such spin-1 Rydberg atoms naturally yields two distinct models: (i) a two-species hardcore boson model, and (ii) upon tuning near a F\"orster resonance, a dipolar spin-1 XY model. Through extensive, large-scale infinite density matrix renormalization group calculations, we provide a broad roadmap predicting the quantum phases that emerge from these models on a variety of lattice geometries: square, triangular, kagome, and ruby. We identify a wealth of correlated states, including lattice supersolids and simplex phases, all of which can be naturally realized in near-term experiments., Comment: 5 pages, 4 figures

The COVID-19 pandemic has shown the urgent need for the development of efficient, durable, reusable and recyclable filtration media for the deep-submicron size range. Here we demonstrate a multifunctional filtration platform using porous metallic nanowire foams that are efficient, robust, antimicrobial, and reusable, with the potential to further guard against multiple hazards. We have investigated the foam microstructures, detailing how the growth parameters influence the overall surface area and characteristic feature size, as well as the effects of the microstructures on the filtration performance. Nanogranules deposited on the nanowires during electrodeposition are found to greatly increase the surface area, up to 20 m$^{2}$/g. Surprisingly, in the high surface area regime, the overall surface area gained from the nanogranules has little correlation with the improvement in capture efficiency. However, nanowire density and diameter play a significant role in the capture efficiency of PM$_{0.3}$ particles, as do the surface roughness of the nanowire fibers and their characteristic feature sizes. Antimicrobial tests on the Cu foams show a >99.9995% inactivation efficiency after contacting the foams for 30 seconds. These results demonstrate promising directions to achieve a highly efficient multifunctional filtration platform with optimized microstructures., Comment: 25 pages, 5 figures, 1 table; 11 page of supplementary information with 7 figures

The negatively charged boron vacancy ($\mathrm{V}_{\mathrm{B}}^-$) in hexagonal boron nitride (hBN) has garnered significant attention among defects in two-dimensional materials. This owes, in part, to its deterministic generation, well-characterized atomic structure, and optical polarizability at room temperature. We investigate the latter through extensive measurements probing both the ground and excited state polarization dynamics. We develop a semiclassical model based on these measurements that predicts a near-unity degree of spin polarization, surpassing other solid-state spin defects under ambient conditions. Building upon our model, we include the presence of nuclear spin degrees of freedom adjacent to the $\mathrm{V}_{\mathrm{B}}^-$ and perform a comprehensive set of Lindbladian numerics to investigate the hyperfine-induced polarization of the nuclear spins. Our simulations predict a number of important features that emerge as a function of magnetic field which are borne out by experiment.

Deep Reinforcement Learning (DRL) agents have demonstrated impressive success in a wide range of game genres. However, existing research primarily focuses on optimizing DRL competence rather than addressing the challenge of prolonged player interaction. In this paper, we propose a practical DRL agent system for fighting games named Sh\=ukai, which has been successfully deployed to Naruto Mobile, a popular fighting game with over 100 million registered users. Sh\=ukai quantifies the state to enhance generalizability, introducing Heterogeneous League Training (HELT) to achieve balanced competence, generalizability, and training efficiency. Furthermore, Sh\=ukai implements specific rewards to align the agent's behavior with human expectations. Sh\=ukai's ability to generalize is demonstrated by its consistent competence across all characters, even though it was trained on only 13% of them. Additionally, HELT exhibits a remarkable 22% improvement in sample efficiency. Sh\=ukai serves as a valuable training partner for players in Naruto Mobile, enabling them to enhance their abilities and skills., Comment: Accept at ICML 2024

We predict that the gapless $U(1)$ Dirac spin liquid naturally emerges in a two-dimensional array of quantum dipoles. In particular, we demonstrate that the dipolar XY model$\unicode{x2014}$realized in both Rydberg atom arrays and ultracold polar molecules$\unicode{x2014}$hosts a quantum spin liquid ground state on the kagome lattice. Large-scale density matrix renormalization group calculations indicate that this spin liquid exhibits signatures of gapless, linearly-dispersing spinons, consistent with the $U(1)$ Dirac spin liquid. We identify a route to adiabatic preparation via staggered on-site fields and demonstrate that this approach can prepare cold spin liquids within experimentally realistic time-scales. Finally, we propose a number of novel signatures of the Dirac spin liquid tailored to near-term quantum simulators, including termination-dependent edge modes and the Friedel response to a local perturbation., Comment: 5+13 pages, 4+10 figures

It is increasingly recognized that participation bias can pose problems for genetic studies. Recently, to overcome the challenge that genetic information of non-participants is unavailable, it is shown that by comparing the IBD (identity by descent) shared and not-shared segments among the participants, one can estimate the genetic component underlying participation. That, however, does not directly address how to adjust estimates of heritability and genetic correlation for phenotypes correlated with participation. Here, for phenotypes whose mean differences between population and sample are known, we demonstrate a way to do so by adopting a statistical framework that separates out the genetic and non-genetic correlations between participation and these phenotypes. Crucially, our method avoids making the assumption that the effect of the genetic component underlying participation is manifested entirely through these other phenotypes. Applying the method to 12 UK Biobank phenotypes, we found 8 have significant genetic correlations with participation, including body mass index, educational attainment, and smoking status. For most of these phenotypes, without adjustments, estimates of heritability and the absolute value of genetic correlation would have underestimation biases.

The measurements of morphological indicators of galaxies are often influenced by a series of observational effects. In this study, we utilize a sample of over 800 TNG50 simulated galaxies with log($M_*$/M$_\odot$)$>9$ at $0.51$\,$\mu$m. The morphological indicators of star-forming galaxies (SFGs) and quiescent galaxies (QGs) are significantly different. The morphologies of QGs exhibit a higher sensitivity to rest-frame wavelength than SFGs. After analyzing the evolution of morphological indicators in the rest-frame V-band (0.5-0.7\,$\mu$m) and rest-frame J-band (1.1-1.4\,$\mu$m), we find that the morphologies of QGs evolve substantially with both redshift and stellar mass. For SFGs, the $C$, $Gini$ and $M_{\rm 20}$ show a rapid evolution with stellar mass at log($M_*$/M$_\odot$)$\geq10.5$, while the $A_{\rm O}$, $D_{\rm O}$ and $A$ evolve with both redshift and stellar mass. Our comparison shows that TNG50 simulations effectively reproduce the morphological indicators we measured from {\it JWST} observations when the impact of dust attenuation is considered., Comment: 21 pages, 14 figures, 1 table. Accepted for publication in ApJ

At continuous phase transitions, quantum many-body systems exhibit scale-invariance and complex, emergent universal behavior. Most strikingly, at a quantum critical point, correlations decay as a power law, with exponents determined by a set of universal scaling dimensions. Experimentally probing such power-law correlations is extremely challenging, owing to the complex interplay between decoherence, the vanishing energy gap, and boundary effects. Here, we employ a Rydberg quantum simulator to adiabatically prepare critical ground states of both a one-dimensional ring and a two-dimensional square lattice. By accounting for and tuning the openness of our quantum system, which is well-captured by the introduction of a single phenomenological length scale, we are able to directly observe power-law correlations and extract the corresponding scaling dimensions. Moreover, in two dimensions, we observe a decoupling between phase transitions in the bulk and on the boundary, allowing us to identify two distinct boundary universality classes. Our work demonstrates that direct adiabatic preparation of critical states in quantum simulators can complement recent approaches to studying quantum criticality using the Kibble-Zurek mechanism or digital quantum circuits.

A univariate continuous function can always be decomposed as the sum of a non-increasing function and a non-decreasing one. Based on this property, we propose a non-parametric regression method that combines two spline-fitted monotone curves. We demonstrate by extensive simulations that, compared to standard spline-fitting methods, the proposed approach is particularly advantageous in high-noise scenarios. Several theoretical guarantees are established for the proposed approach. Additionally, we present statistics to test the monotonicity of a function based on monotone decomposition, which can better control Type I error and achieve comparable (if not always higher) power compared to existing methods. Finally, we apply the proposed fitting and testing approaches to analyze the single-cell pseudotime trajectory datasets, identifying significant biological insights for non-monotonically expressed genes through Gene Ontology enrichment analysis. The source code implementing the methodology and producing all results is accessible at https://github.com/szcf-weiya/MonotoneDecomposition.jl.

Star-forming galaxies (SFGs) adhere to a surprisingly tight scaling relation of dust attenuation parameterized by the infrared excess (IRX=$L_{\rm IR}/L_{\rm UV}$), being jointly determined by the star formation rate (SFR), galaxy size ($R_{\rm e}$), metallicity ($Z$/Z$_\odot$) and axial ratio ($b/a$). We examine how these galaxy parameters determine the effective dust attenuation and give rise to the universal IRX relation, utilizing a simple two-component star-dust geometry model in which dust in the dense and diffuse interstellar medium (ISM) follows exponential mass density profiles, connected with but not necessarily identical to the stellar mass profiles. Meanwhile, empirical relations are adopted to link galaxy properties, including the gas--star formation relation, the dust-to-stellar size relation, as well as the dust-to-gas ratio versus metallicity relation. By fitting a large sample of local SFGs with the model, we obtain the best-fitting model parameters as a function of metallicity, showing that the two-component geometry model is able to successfully reproduce the dependence of IRX on SFR, $R_{\rm e}$, $b/a$ at given $Z$/Z$_\odot$, as well as the dependence of power-law indices on metallicity. Moreover, we also retrieve constraints on the model geometry parameters, including the optical depth of birth clouds (BCs), BC-to-total dust mass fraction, BC covering factor of UV-emitting stars, and star-to-total dust disc radius ratio, which all evolve with galaxy metallicity. Finally, a consistent picture of how the star-dust geometry in SFGs evolves with galaxy metallicity is discussed., Comment: 20 pages, 10 figures, published in MNRAS (2024, Volume 528, Issue 1, pp.658-675); A PHTHON package IRX_TAU_TOT is available at https://github.com/LvZF/irx_tau_tot/ to calculate the total dust optical depth of a galaxy with given metallicity and best-fitting geometry parameters

Large-scale structure (LSS) surveys will increasingly provide stringent constraints on our cosmological models. Recently, the density-marked correlation function (MCF) has been introduced, offering an easily computable density-correlation statistic. Simulations have demonstrated that MCFs offer additional, independent constraints on cosmological models beyond the standard two-point correlation (2PCF). In this study, we apply MCFs for the first time to SDSS CMASS data, aiming to investigate the statistical information regarding clustering and anisotropy properties in the Universe and assess the performance of various weighting schemes in MCFs. Upon analyzing the CMASS data, we observe that, by combining different weights ($\alpha = [-0.2, 0, 0.2, 0.6]$), the MCFs provide a tight and independent constraint on the cosmological parameter $\Omega_m$, yielding $\Omega_m = 0.293 \pm0.006$ at the $1\sigma$ level, which represents a significant reduction in the statistical error by a factor of 3.4 compared to that from 2PCF. Our constraint is consistent with recent findings from the small-scale clustering of BOSS galaxies \cite{arXiv:2203.08999v2} within the 1$\sigma$ level. However, we also find that our estimate is lower than the Planck measurements by about 2.6$\sigma$, indicating the potential presence of new physics beyond the standard cosmological model if all the systematics are fully corrected. The method outlined in this study can be extended to other surveys and datasets, allowing for the constraint of other cosmological parameters. Additionally, it serves as a valuable tool for forthcoming emulator analysis on the Chinese Space Station Telescope (CSST)., Comment: 16 pages, 10 figures

Motivated by the experiment of electrostatic conveyor belt for indirect excitons [A. G. Winbow, \textit{et al.}, Phys. Rev. Lett. \textbf{106}, 196806 (2011)], we study the exciton patterns for understanding the exciton dynamics. By analyzing the exciton diffusion, we find that the patterns mainly come from the photoluminescence of two kinds of excitons. The patterns near the laser spot come from the hot excitons which can be regarded as the classical particles. However, the patterns far from the laser spot come from the cooled excitons or coherent excitons. Taking into account of the finite lifetime of Bosonic excitons and of the interactions between them, we build a time-dependent nonlinear Schr\"{o}dinger equation including the non-Hermitian dissipation to describe the coherent exciton dynamics. The real-time and imaginary-time evolutions are used alternately to solve the Schr\"{o}dinger equation in order to simulate the exciton diffusion accompanied with the exciton cooling in the moving lattices. By calculating the escape probability, we obtain the transport distances of the coherent excitons in the conveyor which are consistent with the experimental data. The cooling speed of excitons is found to be important in the coherent exciton transport. Moreover, the plateau in the average transport distance cannot be explained by the dynamical localization-delocalization transition induced by the disorders., Comment: 12 pages, 9 figures

We revisit the evolution of galaxy morphology in the COSMOS field over the redshift range $0.2\leq z \leq 1$, using a large and complete sample of 33,605 galaxies with a stellar mass of log($M_{\ast}$/M$_{\odot} )>9.5$ with significantly improved redshifts and comprehensive non-parametric morphological parameters. Our sample has 13,881 ($\sim41.3\%$) galaxies with reliable spectroscopic redshifts and has more accurate photometric redshifts with a $\sigma_{\rm NMAD} \sim 0.005$. This paper is the first in a series that investigates merging galaxies and their properties. We identify 3,594 major merging galaxies through visual inspection and find 1,737 massive galaxy pairs with log($M_\ast$/M$_\odot$)$>10.1$. Among the family of non-parametric morphological parameters including $C$, $A$, $S$, $Gini$, $M_{\rm 20}$, $A_{\rm O}$, and $D_{\rm O}$, we find that the outer asymmetry parameter $A_{\rm O}$ and the second-order momentum parameter $M_{\rm 20}$ are the best tracers of merging features than other combinations. Hence, we propose a criterion for selecting candidates of violently star-forming mergers: $M_{\rm 20}> -3A_{\rm O}+3$ at $0.2 -6A_{\rm O}+3.7$ at $0.6

We introduce a class of generic spike-and-slab priors for high-dimensional linear regression with grouped variables and present a Coordinate-ascent Variational Inference (CAVI) algorithm for obtaining an optimal variational Bayes approximation. Using parameter expansion for a specific, yet comprehensive, family of slab distributions, we obtain a further gain in computational efficiency. The method can be easily extended to fitting additive models. Theoretically, we present general conditions on the generic spike-and-slab priors that enable us to derive the contraction rates for both the true posterior and the VB posterior for linear regression and additive models, of which some previous theoretical results can be viewed as special cases. Our simulation studies and real data application demonstrate that the proposed method is superior to existing methods in both variable selection and parameter estimation. Our algorithm is implemented in the R package GVSSB., Comment: 64 pages, 6 figures

Despite the widespread utilization of Gaussian process models for versatile nonparametric modeling, they exhibit limitations in effectively capturing abrupt changes in function smoothness and accommodating relationships with heteroscedastic errors. Addressing these shortcomings, the heteroscedastic Gaussian process (HeGP) regression seeks to introduce flexibility by acknowledging the variability of residual variances across covariates in the regression model. In this work, we extend the HeGP concept, expanding its scope beyond regression tasks to encompass classification and state-space models. To achieve this, we propose a novel framework where the Gaussian process is coupled with a covariate-induced precision matrix process, adopting a mixture formulation. This approach enables the modeling of heteroscedastic covariance functions across covariates. To mitigate the computational challenges posed by sampling, we employ variational inference to approximate the posterior and facilitate posterior predictive modeling. Additionally, our training process leverages an EM algorithm featuring closed-form M-step updates to efficiently evaluate the heteroscedastic covariance function. A notable feature of our model is its consistent performance on multivariate responses, accommodating various types (continuous or categorical) seamlessly. Through a combination of simulations and real-world applications in climatology, we illustrate the model's prowess and advantages. By overcoming the limitations of traditional Gaussian process models, our proposed framework offers a robust and versatile tool for a wide array of applications., Comment: submitted to the Journal of the American Statistical Association (JASA)

Gamma rays from HESS J1849$-$000, a middle-aged TeV pulsar wind nebula (PWN), are observed by the Tibet air shower array and the muon detector array. The detection significance of gamma rays reaches $4.0\, \sigma$ and $4.4\, \sigma$ levels above 25 TeV and 100 TeV, respectively, in units of Gaussian standard deviation $\sigma$. The energy spectrum measured between $40\, {\rm TeV} < E < 320\, {\rm TeV}$ for the first time is described with a simple power-law function of ${\rm d}N/{\rm d}E = (2.86 \pm 1.44) \times 10^{-16}(E/40\, {\rm TeV})^{-2.24 \pm 0.41}\, {\rm TeV}^{-1}\, {\rm cm}^{-2}\, {\rm s}^{-1}$. The gamma-ray energy spectrum from the sub-TeV ($E < 1\, {\rm TeV}$) to sub-PeV ($100\, {\rm TeV} < E < 1\, {\rm PeV}$) ranges including the results of previous studies can be modeled with the leptonic scenario, inverse Compton scattering by high-energy electrons accelerated by the PWN of PSR J1849$-$0001. On the other hand, the gamma-ray energy spectrum can also be modeled with the hadronic scenario in which gamma rays are generated from the decay of neutral pions produced by collisions between accelerated cosmic-ray protons and the ambient molecular cloud found in the gamma-ray emitting region. The cutoff energy of cosmic-ray protons $E_{\rm p\, cut}$, cut is estimated at ${\rm log}_{10}(E_{\rm p,\, cut}/{\rm TeV}) = 3.73^{+2.98}_{-0.66}$, suggesting that protons are accelerated up to the PeV energy range. Our study thus proposes that HESS J1849$-$000 should be further investigated as a new candidate for a Galactic PeV cosmic-ray accelerator, PeVatron., Comment: 10 pages, 2 figures, Accepted for publication from the Astrophysical Journal

HESS J1843$-$033 is a very-high-energy gamma-ray source whose origin remains unidentified. This work presents, for the first time, the energy spectrum of gamma rays beyond $100\, {\rm TeV}$ from the HESS J1843$-$033 region using the data recorded by the Tibet air shower array and its underground muon detector array. A gamma-ray source with an extension of $0.34^{\circ} \pm 0.12^{\circ}$ is successfully detected above $25\, {\rm TeV}$ at $(\alpha,\, \delta) = (281.09^{\circ}\pm 0.10^{\circ},\, -3.76^{\circ}\pm 0.09^{\circ})$ near HESS J1843$-$033 with a statistical significance of $6.2\, \sigma$, and the source is named TASG J1844$-$038. The position of TASG J1844$-$038 is consistent with those of HESS J1843$-$033, eHWC J1842$-$035, and LHAASO J1843$-$0338. The measured gamma-ray energy spectrum in $25\, {\rm TeV} < E < 130\, {\rm TeV}$ is described with ${\rm d}N/{\rm d}E = (9.70\pm 1.89)\times 10^{-16} (E/40\, {\rm TeV})^{-3.26\pm 0.30}\, {\rm TeV}^{-1} {\rm cm}^{-2} {\rm s}^{-1}$, and the spectral fit to the combined spectra of HESS J1843$-$033, LHAASO J1843$-$0338, and TASG J1844$-$038 implies the existence of a cutoff at $49.5\pm 9.0\, {\rm TeV}$. Associations of TASG J1844-038 with SNR G28.6$-$0.1 and PSR J1844-0346 are also discussed in detail for the first time., Comment: 11 pages, 4 figures, 1 table

In this paper, we prove that functional sliced inverse regression (FSIR) achieves the optimal (minimax) rate for estimating the central space in functional sufficient dimension reduction problems. First, we provide a concentration inequality for the FSIR estimator of the covariance of the conditional mean, i.e., $\var(\E[\boldsymbol{X}\mid Y])$. Based on this inequality, we establish the root-$n$ consistency of the FSIR estimator of the image of $\var(\E[\boldsymbol{X}\mid Y])$. Second, we apply the most widely used truncated scheme to estimate the inverse of the covariance operator and identify the truncation parameter which ensures that FSIR can achieve the optimal minimax convergence rate for estimating the central space. Finally, we conduct simulations to demonstrate the optimal choice of truncation parameter and the estimation efficiency of FSIR. To the best of our knowledge, this is the first paper to rigorously prove the minimax optimality of FSIR in estimating the central space for multiple-index models and general $Y$ (not necessarily discrete).

We present a method for fitting monotone curves using cubic B-splines, which is equivalent to putting a monotonicity constraint on the coefficients. We explore different ways of enforcing this constraint and analyze their theoretical and empirical properties. We propose two algorithms for solving the spline fitting problem: one that uses standard optimization techniques and one that trains a Multi-Layer Perceptrons (MLP) generator to approximate the solutions under various settings and perturbations. The generator approach can speed up the fitting process when we need to solve the problem repeatedly, such as when constructing confidence bands using bootstrap. We evaluate our method against several existing methods, some of which do not use the monotonicity constraint, on some monotone curves with varying noise levels. We demonstrate that our method outperforms the other methods, especially in high-noise scenarios. We also apply our method to analyze the polarization-hole phenomenon during star formation in astrophysics. The source code is accessible at \texttt{\url{https://github.com/szcf-weiya/MonotoneSplines.jl}}.

Large language models (LLMs) such as ChatGPT and GPT-4 have recently demonstrated their remarkable abilities of communicating with human users. In this technical report, we take an initiative to investigate their capacities of playing text games, in which a player has to understand the environment and respond to situations by having dialogues with the game world. Our experiments show that ChatGPT performs competitively compared to all the existing systems but still exhibits a low level of intelligence. Precisely, ChatGPT can not construct the world model by playing the game or even reading the game manual; it may fail to leverage the world knowledge that it already has; it cannot infer the goal of each step as the game progresses. Our results open up new research questions at the intersection of artificial intelligence, machine learning, and natural language processing.

We investigate the shallow flow of viscous fluid into and out of a channel whose gap width increases as a power-law ($x^n$), where $x$ is the downstream axis. The fluid flows slowly, while injected at a rate in the form of $t^\alpha$, where $t$ is time and $\alpha$ is a constant. The invading fluid has higher viscosity than the ambient fluid, thus avoiding Saffman-Taylor instability. Similarity solutions of the first kind for the outflow problem are found using approximations of lubrication theory. Zheng et al [2014] studied the deep-channel case and found divergent behaviour of the similarity variable as $n\rightarrow1$ and $n\rightarrow3$, when fluid flows into and out of the channel respectively. No divergence is found in the shallow case presented here. The characteristic equilibration time for the numerically simulated constant-volume flow to converge to the similarity solution is calculated assuming inverse dependence on the ratio disagreement between the current front using the method of lines (MOL). The inverse power dependence between equilibration time and ratio disagreement is found for channels of different powers. A similarity solution of the second kind for the inflow problem is found using the phase plane formalism and the bisection method. An exponential decay relationship is found between $n$ and the degree $\delta$ of the similarity variable $xt^{-\delta}$, which does not show any divergent behaviour for large $n$. An asymptotic behaviour is found for $\delta$ that approaches $1/2$ as $n\rightarrow\infty$.

We report the magneto-transport measurements of thin film devices of the topological Weyl semimetal WTe2 with the applied current along and vertical to the in-plane directions. The device is composed of a Van der Waals thin film of WTe2 sandwiched between top and bottom Au electrodes.At low temperatures, we found a large unsaturated in-plane magnetoresistance and a saturated out-of-plane magnetoresistance when the external magnetic fields are applied perpendicular to the plane. By analysis of Shubnikov-de Haas oscillations, one oscillation peak is found in the out-of-plane magnetoresistance, in contrast to four oscillation peaks in the in-plane magnetoresistance.Our work provides new insight into the origin of the unsaturated magnetoresistance in WTe2 and may inspire non-planar engineering to reach higher integration in spintronics.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based neural architecture search method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. We introduce the Pseudo-Inverted Bottleneck Conv (PIBConv) block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower computational footprint (measured in GMACs) and parameter count, GradCAM comparisons show that our network can better detect distinctive features of target objects compared to DARTS. Code is available from https://github.com/mahdihosseini/PIBConv., Comment: 5 pages

We study the localization and topological transitions of the generalized non-Hermitian SSH models, where the non-Hermiticities are introduced by the complex quasiperiodic hopping and the nonreciprocal hopping. We elucidate the universality of the models and how many models can be mapped to them. Under the open boundary condition, two delocalization transitions are found due to the competition between the Anderson localization and the boundary localization from the nontrivial edge states and the non-Hermitian skin effect. Under the periodic boundary condition, only one delocalization transition is found due to the disappearance of the non-Hermitian skin effect. The winding numbers of energy and the Lyapunov exponents in analytical form are obtained to exactly characterize the two deloaclizateon transitions. It finds that the delocalization transitions don't accompany the topological transition. Furthermore, the large on-site non-Hermiticity and the large nonreciprocal hopping are all detrimental to the topological transitions. However, the large nonreciprocal hopping enhances the Anderson localizations. The above analyses are verified by calculating the energy gap and the inverse of the participation ratio numerically., Comment: 9 pages, 3 figures

Analysis of helicon wave heating and current drive capability in EXL-50 spherical torus plasmas has been conducted. It is found that the driven current increases with the launched parallel refractive index $n_{||}$ and peaks around $n_{||} = 4.0$ when the frequency of the helicon wave is between 300~MHz and 380~MHz. The helicon wave current drive efficiency shows a relatively stable upward trend with increasing plasma temperature. Moreover, the driven current decreases as the plasma density increases. We also analyzed the current drive with helicon waves of 150~MHz and 170~MHz and found that the driven current at a lower frequency was lower than that at a higher frequency. A positive proportional relationship exists between the driven current and $n_{||}$. Besides, as $n_{||}$ increases, the profile of the driven current becomes wider. Finally, the effect of the scrape-off layer (SOL) region on the helicon wave current drive was also investigated.

With respect to the $\rm H\beta$ full width at half-maximum ($\rm FWHM_{H\beta}$), the broad $\rm H\beta$ line dispersion ($\sigma_{\rm H\beta}$) was preferred as a velocity tracer to calculate the single-epoch supermassive black hole mass ($M_{\rm BH}$) suggested by \cite{Yu2020b}. For a compiled sample of 311 broad-line active galactic nuclei (AGN) with measured hard X-ray photon index ($z<0.7$), $\sigma_{\rm H\beta}$ and the optical Fe II relative strength ($R_{\rm Fe}$) are measured from their optical spectra, which are used to calculate $\sigma_{\rm H\beta}$-based virial $M_{\rm BH}$ and dimensionless accretion rate ($\dot{\mathscr{M}}$). With respect to $\rm FWHM_{\rm H\beta}$, it is found that the mean value of $\sigma_{\rm H\beta}$-based $M_{\rm BH}$ is on average larger by 0.26 dex, and the mean value of $\sigma_{\rm H\beta}$-based $\dot{\mathscr{M}}$ is on average smaller by 0.51 dex. It is found that there exists a non-linear relationship between the Eddington ratio ($L_{\rm Bol}/L_{\rm Edd}$) and $\dot{\mathscr{M}}$, i.e., $L_{\rm Bol}/L_{\rm Edd} \propto \dot{\mathscr{M}}^{0.56\pm 0.01}$. This non-linear relationship comes from the accretion efficiency $\eta$, which is smaller for AGN with higher $\dot{\mathscr{M}}$. We find a strong bivariate correlation of the fraction of energy released in the corona $F_{\rm X}$ with $\dot{\mathscr{M}}$ and \mbh, $F_{\rm X} \propto \dot{\mathscr{M}}^{-0.57\pm 0.05} M_{\rm BH}^{-0.54\pm 0.06}$. The flat slope of $-0.57\pm 0.05$ favours the shear stress tensor of the accretion disk being proportional to the geometric mean of gas pressure and total pressure. We find a strong bivariate relation of $\Gamma$ with $\dot{\mathscr{M}}$ and $F_{\rm X}$, $\Gamma \propto \dot{\mathscr{M}}^{-0.21\pm 0.02}F_{\rm X}^{0.02\pm 0.04}$. The hard X-ray spectrum becomes softer with increasing of $F_{\rm X}$, although the scatter is large., Comment: 18 pages, 9 figures, ApJ, in press

Atomic-scale magnetic field sensors based on nitrogen vacancy (NV) defects in diamonds are an exciting platform for nanoscale nuclear magnetic resonance (NMR) spectroscopy. The detection of NMR signals from a few zeptoliters to single molecules or even single nuclear spins has been demonstrated using NV-centers close to the diamond surface. However, fast molecular diffusion of sample molecules in and out of nanoscale detection volumes impedes their detection and limits current experiments to solid-state or highly viscous samples. Here, we show that restricting diffusion by confinement enables nanoscale NMR spectroscopy of liquid samples. Our approach uses metal-organic frameworks (MOF) with angstrom-sized pores on a diamond chip to trap sample molecules near the NV-centers. This enables the detection of NMR signals from a liquid sample, which would not be detectable without confinement. These results set the route for nanoscale liquid-phase NMR with high spectral resolution., Comment: Affiliations: Technical University of Munich, Walter Schottky Institute and Physics Department

Lab-on-a-chip (LOC) applications have emerged as invaluable physical and life sciences tools. The advantages stem from advanced system miniaturization, thus, requiring far less sample volume while allowing for complex functionality, increased reproducibility, and high throughput. However, LOC applications necessitate extensive sensor miniaturization to leverage these inherent advantages fully. Atom-sized quantum sensors are highly promising to bridge this gap and have enabled measurements of temperature, electric and magnetic fields on the nano- to microscale. Nevertheless, the technical complexity of both disciplines has so far impeded an uncompromising combination of LOC systems and quantum sensors. Here, we present a fully integrated microfluidic platform for solid-state spin quantum sensors, such as the nitrogen-vacancy (NV) center in diamond. Our platform fulfills all technical requirements, such as fast spin manipulation, enabling full quantum sensing capabilities, biocompatibility, and easy adaptability to arbitrary channel and chip geometries. To illustrate the vast potential of quantum sensors in LOC systems, we demonstrate various NV center-based sensing modalities for chemical analysis in our microfluidic platform, ranging from paramagnetic ion detection to high-resolution microscale NV-NMR. Consequently, our work opens the door for novel chemical analysis capabilities within LOC devices with applications in electrochemistry, high throughput reaction screening, bioanalytics, organ-on-a-chip, or single-cell studies., Comment: 16 pages, 8 figures

Unconventional fermions in the immensely studied topological semimetals are the source for rich exotic topological properties. Here, using symmetry analysis and first-principles calculations, we propose the coexistence of multiple topological nodal structure in LaSb2, including topological nodal surfaces, nodal lines and in particular eightfold degenerate nodal points, which have been scarcely observed in a single material. Further, utilizing high resolution angle-resolved photoemission spectroscopy in combination with Shubnikov-de Haas quantum oscillations measurements, we confirm the existence of nodal surfaces and eightfold degenerate nodal points in LaSb2, and extract the {\pi} Berry phase proving the non-trivial electronic band structure topology therein. The intriguing multiple topological nodal structure might play a crucial role in giving rise to the large linear magnetoresistance. Our work renews the insights into the exotic topological phenomena in LaSb2 and its analogous.