Miracle-dz/UniFAR-FTUs huggingface dataset:
Dirac electrons in the square-lattice Hubbard model with a 
d
-wave pairing field: The chiral Heisenberg universality class revisited

We numerically investigate the quantum criticality of the chiral Heisenberg universality class with the total number of fermion components $N$=8 in terms of the Gross-Neveu theory. Auxiliary-field quantum Monte Carlo simulations are performed for the square lattice Hubbard model in the presence of a $d$-wave pairing field, inducing Dirac cones in the single particle spectrum. This property makes the model particularly interesting because it turns out to belong to the same universality class of the Hubbard model on the honeycomb lattice, that is the canonical model for graphene, despite that the unit cells are apparently different (e.g. they contain one and two sites, respectively). We indeed show that the two phase transitions, expected to occur on the square and on the honeycomb lattices, have the same quantum criticality. We also argue that details of the models, the way of counting $N$ and the anisotropy of the Dirac cones, do not change the critical exponents. The present estimates of the exponents for the $N$=8 chiral Heisenberg universality class are $\nu$=1.05(5), $\eta_{\phi}$=0.75(4), and $\eta_{\psi}$=0.23(4), which are compared with the previous numerical estimations.
A Minimum Energy Filter for Localisation of an Unmanned Aerial Vehicle

Accurate localisation of unmanned aerial vehicles is vital for the next generation of automation tasks. This paper proposes a minimum energy filter for velocity-aided pose estimation on the extended special Euclidean group. The approach taken exploits the Lie-group symmetry of the problem to combine Inertial Measurement Unit (IMU) sensor output with landmark measurements into a robust and high performance state estimate. We propose an asynchronous discrete-time implementation to fuse high bandwidth IMU with low bandwidth discrete-time landmark measurements typical of real-world scenarios. The filter’s performance is demonstrated by simulation.
Superconductivity in the three-band model of cuprates: nodal direction characteristics and influence of intersite interactions

The three-band Emery model is applied to study the selected principal features of the d-wave superconducting phase in the copper-based compounds. The electron–electron correlations are taken into account by the use of the diagrammatic expansion of the Guztwiller wave function (DE-GWF method). The nodal Fermi velocity, Fermi momentum, and effective mass are all determined in the paired state and show relatively good agreement with the available experimental data, as well as with the corresponding single-band calculations. Additionally, the influence of the next-nearest neighbor oxygen–oxygen hopping and intersite Coulomb repulsion terms on the superconducting phase is analyzed.
Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation

The process of planning views to observe a scene is known as the Next Best View (NBV) problem. Approaches often aim to obtain high-quality scene observations while reducing the number of views, travel distance and computational cost. Considering occlusions and scene coverage can significantly reduce the number of views and travel distance required to obtain an observation. Structured representations (e.g., a voxel grid or surface mesh) typically use raycasting to evaluate the visibility of represented structures but this is often computationally expensive. Unstructured representations (e.g., point density) avoid the computational overhead of maintaining and raycasting a structure imposed on the scene but as a result do not proactively predict the success of future measurements. This paper presents proactive solutions for handling occlusions and considering scene coverage with an unstructured representation. Their performance is evaluated by extending the density-based Surface Edge Explorer (SEE). Experiments show that these techniques allow an unstructured representation to observe scenes with fewer views and shorter distances while retaining high observation quality and low computational cost.
ISCOs in AdS/CFT

We study stable circular orbits in spherically symmetric AdS black holes in various dimensions and their limiting innermost stable circular orbits. We provide analytic expressions for their size, angular velocity and angular momentum in a large black hole mass regime. The dual interpretation is in terms of meta-stable states not thermalising in typical thermal scales and whose existence is due to non-perturbative effects on the spatial curvature. Our calculations reproduce the binding energy known in the literature, but also include a binding energy in the radial fluctuations corresponding to near circular trajectories. We also describe how particles are placed on these orbits from integrated operators on the boundary: they tunnel inside in a way that can be computed from both complex geodesics in the black hole background and from the WKB approximation of the wave equation. We explain how these two computations are related.
Pauli Crystals-Interplay of Symmetries

Recently observed Pauli crystals are structures formed by trapped ultracold atoms with the Fermi statistics. Interactions between these atoms are switched off, so their relative positions are determined by joined action of the trapping potential and the Pauli exclusion principle. Numerical modeling is used in this paper to find the Pauli crystals in a two-dimensional isotropic harmonic trap, three-dimensional harmonic trap, and a two-dimensional square well trap. The Pauli crystals do not have the symmetry of the trap—the symmetry is broken by the measurement of positions and, in many cases, by the quantum state of atoms in the trap. Furthermore, the Pauli crystals are compared with the Coulomb crystals formed by electrically charged trapped particles. The structure of the Pauli crystals differs from that of the Coulomb crystals, this provides evidence that the exclusion principle cannot be replaced by a two-body repulsive interaction but rather has to be considered to be a specifically quantum mechanism leading to many-particle correlations.
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification

Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS classification. XCM is a new compact convolutional neural network which extracts information relative to the observed variables and time directly from the input data. Thus, XCM architecture enables a good generalization ability on both large and small datasets, while allowing the full exploitation of a faithful post hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions. We first show that XCM outperforms the state-of-the-art MTS classifiers on both the large and small public UEA datasets. Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability. Finally, we present how XCM can outperform the current most accurate state-of-the-art algorithm on a real-world application while enhancing explainability by providing faithful and more informative explanations.
Constraints on the nuclear symmetry energy from asymmetric-matter calculations with chiral 
NN
 and 
3N
 interactions

The nuclear symmetry energy is a key quantity in nuclear (astro)physics. It describes the isospin dependence of the nuclear equation of state (EOS), which is commonly assumed to be almost quadratic. In this work, we confront this standard quadratic expansion of the EOS with explicit asymmetric nuclear-matter calculations based on a set of commonly used Hamiltonians including two- and three-nucleon forces derived from chiral effective field theory. We study, in particular, the importance of non-quadratic contributions to the symmetry energy, including the non-analytic logarithmic term introduced by Kaiser [Phys.~Rev.~C \textbf{91}, 065201 (2015)]. Our results suggest that the quartic contribution to the symmetry energy can be robustly determined from the various Hamiltonians employed, and we obtain 1.00(8) MeV (or 0.55(8) MeV for the potential part) at saturation density, while the logarithmic contribution to the symmetry energy is relatively small and model-dependent. We finally employ the meta-model approach to study the impact of the higher-order contributions on the neutron-star crust-core transition density, and find a small 5\% correction.
The antesonic condition for the explosion of core-collapse supernovae – II. Rotation and turbulence

In the problem of steady free-fall onto a standing shockwave around acentral mass, the "antesonic" condition limits the regime of stable accretion to $c_T^2/v_\mathrm{esc}^2\leq3/16$, where $c_T$ is the isothermal sound speed in the subsonic post-shock flow, and $v_\mathrm{esc}$ is the escape velocity at the shock radius. Above this limit, it is impossible to satisfy both the Euler equation and theshock jump conditions, and the system transitions to a wind. This physics explains the existence of a critical neutrino luminosity in steady-state models ofaccretion in the context of core-collapse supernovae. Here, we extend the antesonic condition to flows with rotation and turbulence using a simple one-dimensional formalism. Both effects decrease the critical post-shock sound speed required for explosion. While quite rapid rotation is required for a significant change to the critical condition, we show that the level of turbulence typically achieved in supernova simulations can greatly impact the critical value of $c_T^2/v_\mathrm{esc}^2$. A core angular velocity corresponding to a millisecond rotation period after contraction of the proto-neutron star results in only a $\sim\! 5$ per-cent reduction of the critical curve. In contrast, near-sonic turbulence with specific turbulent kinetic energy $K/c_T^2=0.5-1$, leads to a decrease in the critical value of $c_T^2/v_{\rm esc}^2$ by $\sim\! 20-40$ per-cent. This analysis provides a framework for understanding the role of post-shock turbulence in instigating explosions in models that would otherwise fail and helps explain why multi-dimensional simulations explode more easily than their one-dimensional counterparts.
Technical Note: A prototype clinical proton radiography system

Purpose: To demonstrate a proton imaging system based on well-established fast scintillator technology to achieve high performance with low cost and complexity, with the potential of a straightforward translation into clinical use. Methods: The system tracks individual protons through one (X, Y) scintillating fiber tracker plane upstream and downstream of the object and into a 13 cm-thick scintillating block residual energy detector. The fibers in the tracker planes are multiplexed into silicon photomultipliers (SiPMs) to reduce the number of electronics channels. The light signal from the residual energy detector is collected by 16 photomultiplier tubes (PMTs). Only four signals from the PMTs are output from each event, which allows for fast signal readout. A robust calibration method of the PMT signal to residual energy has been developed to obtain accurate proton images. The development of patient-specific scan patterns using multiple input energies allows for an image to be produced with minimal excess dose delivered to the patient. Results: The calibration of signals in the energy detector produces accurate residual range measurements limited by intrinsic range straggling. The use of patient-specific scan patterns using multiple input energies enables imaging with a compact range detector. Conclusions: We have developed a prototype clinical proton radiography system for pretreatment imaging in proton radiation therapy. We have optimized the system for use with pencil beam scanning systems and have achieved a reduction of size and complexity compared to previous designs.
Structured heterosymmetric quantum droplets

We predict that Lee-Huang-Yang effect makes it possible to create stable quantum droplets (QDs) in binary Bose-Einstein condensates with a hetero-symmetric or hetero-multipole structure, i.e., different vorticities or multipolarities in their components. The QDs feature flat-top shapes when either chemical potential \mu_1,2 of the droplet approaches an edge of a triangular existence domain in the (\mu_1,\mu_2) plane. QDs with different vorticities of their components are stable against azimuthal perturbations, provided that the norm of one component is large. We also present multipole states, in which the interaction with a strong fundamental component balances the repulsion between poles with opposite signs in the other component, leading to the formation of stable bound states. Extended stability domains are obtained for dipole QDs; tripole ones exist but are unstable, while quadrupoles are stable in a narrow region. The results uncover the existence of much richer families of stable binary QDs in comparison to states with identical components.
Semi-Anchored Detector for One-Stage Object Detection

A standard one-stage detector is comprised of two tasks: classification and regression. Anchors of different shapes are introduced for each location in the feature map to mitigate the challenge of regression for multi-scale objects. However, the performance of classification can degrade due to the highly class-imbalanced problem in anchors. Recently, many anchor-free algorithms have been proposed to classify locations directly. The anchor-free strategy benefits the classification task but can lead to sup-optimum for the regression task due to the lack of prior bounding boxes. In this work, we propose a semi-anchored framework. Concretely, we identify positive locations in classification, and associate multiple anchors to the positive locations in regression. With ResNet-101 as the backbone, the proposed semi-anchored detector achieves 43.6% mAP on COCO data set, which demonstrates the state-of-art performance among one-stage detectors.
Superconductivity of underdoped PrFeAs(O,F) investigated via point-contact spectroscopy and nuclear magnetic resonance

Underdoped PrFeAs(O,F), one of the less known members of the 1111 family of iron-based superconductors, was investigated in detail by means of transport, SQUID magnetometry, nuclear magnetic resonance (NMR) measurements and point-contact Andreev-reflection spectroscopy (PCARS). PCARS measurements on single crystals evidence the multigap nature of PrFeAs(O,F) superconductivity, shown to host at least two isotropic gaps, clearly discernible in the spectra, irrespective of the direction of current injection (i.e., along the ab planes or along the c axis). Additional features at higher energy can be interpreted as signatures of a strong electron-boson coupling, as demonstrated by a model which combines Andreev reflection with the Eliashberg theory. Magnetic resonance measurements in the normal phase indicate the lack of a magnetic order in underdoped PrFeAs(O,F), while $^{75}$As NMR spin-lattice relaxation results suggest the presence of significant electronic spin fluctuations, peaking above $T_{c}$ and expected to mediate the superconducting pairing.
Bias Variance Tradeoff in Analysis of Online Controlled Experiments

Many organizations utilize large-scale online controlled experiments (OCEs) to accelerate innovation. Having high statistical power to detect small differences between control and treatment accurately is critical, as even small changes in key metrics can be worth millions of dollars or indicate user dissatisfaction for a very large number of users. For large-scale OCE, the duration is typically short (e.g. two weeks) to expedite changes and improvements to the product. In this paper, we examine two common approaches for analyzing usage data collected from users within the time window of an experiment, which can differ in accuracy and power. The open approach includes all relevant usage data from all active users for the entire duration of the experiment. The bounded approach includes data from a fixed period of observation for each user (e.g. seven days after exposure) after the first time a user became active in the experiment window.
SkyMapper colours of Seyfert galaxies and Changing-Look AGN

We study the utility of broad-band colours in the SkyMapper Southern Survey for selecting Seyfert galaxies at low luminosity. We find that the $u-v$ index, built from the ultraviolet $u$ and violet $v$ filters, separates normal galaxies, starburst galaxies and type-1 AGN. This $u-v$ index is not sensitive to age or metallicity in a stellar population but is instead a quenching-and-bursting indicator in galaxies and detects power-law continua in type-1 AGN. Using over 25,000 galaxies at $z<0.1$ from 6dFGS, we find a selection cut based on $u-v$ and central $u$ band brightness that identifies type-1 AGN. By eyeballing 6dFGS spectra we classify new Seyfert galaxies of type 1 to 1.8. Our sample includes eight known Changing-Look AGN, two of which show such strong variability that they move across the selection cut during the five years of SkyMapper observations in DR3, along mixing sequences of nuclear and host galaxy light. We identify 46 Changing-Look AGN candidates in our sample, one of which has been reported as a type-IIn supernova. We show that this transient persists for at least five years and marks a flare in a Seyfert-1 period of a new Changing-Look AGN.
Frequencies, chaos, and resonances: A study of orbital parameters of nearby thick-disc and halo stars

Aims. We study the distribution of nearby thick-disc and halo stars in subspaces defined by their characteristic orbital parameters. Our aim is to establish the origin of the structure reported in particular in the Rmax − zmax space. Methods. To this end, we computed the orbital parameters and frequencies of stars for a generic and for a Stäckel Milky Way potential. Results. We find that for both the thick-disc and halo populations, very similar prominent substructures are apparent for the generic Galactic potential, while no substructure is seen for the Stäckel model. This indicates that the origin of these features is not merger-related, but due to the non-integrability of the generic potential. This conclusion is strengthened by our frequency analysis of the orbits of stars, which reveals the presence of prominent resonances, with ∼30% of the halo stars associated with resonance families. In fact, the stars in resonances define the substructures seen in the spaces of characteristic orbital parameters. Intriguingly, we find that some stars in our sample and in debris streams are on the same resonance as the Sagittarius dwarf. Conclusions. Our study constitutes a step towards disentangling the imprint of merger debris from substructures driven by internal dynamics. Given their prominence, these resonant-driven overdensities could potentially be useful in constraining the exact form of the Galactic potential.
Second Order Optimization for Adversarial Robustness and Interpretability

Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique aimed at learning features robust to such attacks and is widely regarded as a very effective defense. However, the computational cost of such training can be prohibitive as the network size and input dimensions grow. Inspired by the relationship between robustness and curvature, we propose a novel regularizer which incorporates first and second order information via a quadratic approximation to the adversarial loss. The worst case quadratic loss is approximated via an iterative scheme. It is shown that using only a single iteration in our regularizer achieves stronger robustness than prior gradient and curvature regularization schemes, avoids gradient obfuscation, and, with additional iterations, achieves strong robustness with significantly lower training time than AT. Further, it retains the interesting facet of AT that networks learn features which are well-aligned with human perception. We demonstrate experimentally that our method produces higher quality human-interpretable features than other geometric regularization techniques. These robust features are then used to provide human-friendly explanations to model predictions.
Nonperturbative Quantization Approach for QED on the Hopf Bundle

We consider the Dirac equation and Maxwell’s electrodynamics in R×S3 spacetime, where a three-dimensional sphere is the Hopf bundle S3→S2. In both cases, discrete spectra of classical solutions are obtained. Based on the solutions obtained, the quantization of free, noninteracting Dirac and Maxwell fields is carried out. The method of nonperturbative quantization of interacting Dirac and Maxwell fields is suggested. The corresponding operator equations and the infinite set of the Schwinger–Dyson equations for Green’s functions is written down. We write a simplified set of equations describing some physical situations to illustrate the suggested scheme of nonperturbative quantization. Additionally, we discuss the properties of quantum states and operators of interacting fields.
Universal consistency of Wasserstein k-NN classifier

The Wasserstein distance provides a notion of dissimilarities between probability measures, which has recent applications in learning of structured data with varying size such as images and text documents. In this work, we analyze the $k$-nearest neighbor classifier ($k$-NN) under the Wasserstein distance and establish the universal consistency on families of distributions. Using previous known results on the consistency of the $k$-NN classifier on infinite dimensional metric spaces, it suffices to show that the families is a countable union of finite dimensional components. As a result, we are able to prove universal consistency of $k$-NN on spaces of finitely supported measures, the space of finite wavelet series and the spaces of Gaussian measures with commuting covariance matrices.
Histological Studies on Testes Development of the Mud Crab, Scylla serrata

The testicular development cycle of Scylla serrata can be divided into 5 periods: the spermatogonium stage, the spermatocyte stage, the spermatid stage, the sperm stage and the resting stage. Based on the division of the development cycle, the diameter of the seminiferous tubule and the ratio of its mature sections can reflect the process of the testicular development. For the first time, the ratio of the mature sections is presented as a characteristic index of the testicular development.