Miracle-dz/UniFAR-FTUs huggingface dataset:
Infinite Time Solutions of Numerical Schemes for Advection Problems

This paper addresses the question whether there are numerical schemes for constant-coefficient advection problems that can yield convergent solutions for an infinite time horizon. The motivation is that such methods may serve as building blocks for long-time accurate solutions in more complex advection-dominated problems. After establishing a new notion of convergence in an infinite time limit of numerical methods, we first show that linear methods cannot meet this convergence criterion. Then we present a new numerical methodology, based on a nonlinear jet scheme framework. We show that these methods do satisfy the new convergence criterion, thus establishing that numerical methods exist that converge on an infinite time horizon, and demonstrate the long-time accuracy gains incurred by this property.
mirisim: a simulator for the Mid-Infrared Instrument on JWST

The Mid-Infrared Instrument (MIRI) on the James Webb Space Telescope (JWST), has imaging, four coronagraphs and both low and medium resolution spectroscopic modes . Being able to simulate MIRI observations will help commissioning of the instrument, as well as get users familiar with representative data. We designed the MIRI instrument simulator (MIRISim) to mimic the on-orbit performance of the MIRI imager and spectrometers using the Calibration Data Products (CDPs) developed by the MIRI instrument team. The software encorporates accurate representations of the detectors, slicers, distortions, and noise sources along the light path including the telescope's radiative background and cosmic rays. The software also includes a module which enables users to create astronomical scenes to simulate. MIRISim is a publicly available Python package that can be run at the command line, or from within Python. The outputs of MIRISim are detector images in the same uncalibrated data format that will be delivered to MIRI users. These contain the necessary metadata for ingestion by the JWST calibration pipeline.
Identification of Complex Mixtures for Raman Spectroscopy Using a Novel Scheme Based on a New Multi-Label Deep Neural Network

With a noisy environment caused by fluorescence and additive white noise as well as complicated spectra fingerprints, the identification of complex mixture materials remains a significant challenge in Raman spectroscopy application. This paper proposes a new scheme based on a constant wavelet transform (CWT) and a deep network for classifying complex mixture. The scheme first transforms the noisy Raman spectra to a two-dimensional scale map using CWT. Then, the multi-label deep neural network model (MLDNN) is simulated for material classification. The proposed model accelerates the feature extraction and expands the feature graph using the global averaging pooling layer. The Sigmoid function is implemented in the last layer of the model. The MLDNN model was trained, validated, and tested with data collected from the samples prepared from palm oil substances. During the training and validating process, data augmentation is applied to overcome the imbalance of data and enrich Raman spectra’s diversity. From the test results, MLDNN successfully surpassed VGG16, VGG19, ResNet50, DenseNet121, InceptionResNetV2, MobileNet50 in average precision, F1 macro averaging and F1 micro averaging, which were 0.9900, 0.9874 and 0.9874, respectively. In Hamming loss, one error, coverage, ranking loss, the MLDNN model reached 0.014, 0.020, 1.738 and 0.000, respectively. The values of other models in Hamming loss, one error, coverage, ranking loss are higher than those of MLDNN. In the comparison of AUC value, MLDNN outperforms other models on each label. The average detection time of the MLDNN is 5.3123 s, which is much faster than those of VGG16, VGG19, ResNet50, DenseNet121, InceptionResNetV2 and MobileNetV2, which are 7.1245, 7.5046, 8.6300, 12.3294, 16.1131 and 6.6451, respectively.
Off-Policy Interval Estimation with Lipschitz Value Iteration

Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data. When applied to high-stakes scenarios such as medical diagnosis or financial decision-making, it is crucial to provide provably correct upper and lower bounds of the expected reward, not just a classical single point estimate, to the end-users, as executing a poor policy can be very costly. In this work, we propose a provably correct method for obtaining interval bounds for off-policy evaluation in a general continuous setting. The idea is to search for the maximum and minimum values of the expected reward among all the Lipschitz Q-functions that are consistent with the observations, which amounts to solving a constrained optimization problem on a Lipschitz function space. We go on to introduce a Lipschitz value iteration method to monotonically tighten the interval, which is simple yet efficient and provably convergent. We demonstrate the practical efficiency of our method on a range of benchmarks.
Single germanium vacancy centers in nanodiamonds with bulk-like spectral stability

Motivated by the success of group IV colour centres in nanodiamonds (NDs) for hybrid technology requiring a single photon source, we study single germanium-vacancy (GeV$^-$) centres in NDs at room temperature with size rangingfrom 10 to 50 nm and with remarkable spectral properties. We characterize their zero-phonon line (ZPL), study their internal population dynamics and compare their emission properties in the framework of a three level model with intensity dependent de-shelving. Furthermore, we characterize their lifetime, polarization and brightness. We find amaximum photon emission count rate of 1.6 MHz at saturation. We also report a polarization visibility of 92% from the fluorescence light, which potentially makes GeV$^-$ centres good candidates for quantum key distribution (QKD)requiring polarized single photons. We show that the GeV$^-$ in NDs presented in this work have a comparable spectral stability compared to their bulk counterpart which is needed for future applications using NDs.
Flare Rates, Rotation Periods, and Spectroscopic Activity Indicators of a Volume-complete Sample of Mid- to Late-M Dwarfs within 15 pc

We present a study of flare rates, rotation periods, and spectroscopic activity indicators of 125 single stars within 15 parsecs and with masses between 0.1 and 0.3 M⊙ observed during the first year of the TESS mission, with the goal of elucidating the relationship between these various magnetically connected phenomena. We gathered multiepoch high-resolution spectra of each target, and we measured equivalent widths of the activity indicators helium I D3, Hα, and the calcium infrared triplet line at 8542.09 Å. We present 18 new rotation periods from MEarth photometry and 19 new rotation periods from TESS photometry. We present a catalog of 1392 flares. After correcting for sensitivity, we find the slope of the flare frequency distribution for all stars to have a standard value of α = 1.98 ± 0.02. We determine R31.5, the rate of flares per day with energies above E = 3.16 × 1031 ergs in the TESS bandpass. We find that below a critical value of Hα EW = −0.71 Å, log R31.5 increases linearly with increasing Hα emission; above this value, log R31.5 declines rapidly. The stars divide into two groups: 26% have Hα in emission, high flare rates with typical values of log R31.5 = −1.30 ± 0.08, and have Rossby numbers <0.50. The remaining 74% show little to no Hα in emission and exhibit log R31.5 < −3.86, with the majority of these stars not showing a single flare during the TESS observations.
Diabetic Retinopathy detection through integration of Deep Learning classification framework

This document represents a brief account of ongoing project for Diabetic Retinopathy Detection (DRD) through integration of state-of the art Deep Learning methods. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in multiple fields of computer vision including medical imaging, and we bring their power to the diagnosis of eye fundus images. For training our models we used publicly available Kaggle data set. For testing we used portion of Kaggle data withheld from training and Messidor-2 reference standard. Neither withheld Kaggle images, nor Messidor-2 were used for training. For Messidor-2 we achieved sensitivity 99%, specificity 71%, and AUC 0.97. These results close to recent state-of-the-art models trained on much larger data sets and surpass average results of diabetic retinopathy screening when performed by trained optometrists. With continuous development of our Deep Learning models we expect to further increase the accuracy of the method and expand it to cataract and glaucoma diagnostics.
Group-regularized ridge regression via empirical Bayes noise level cross-validation.

Features in predictive models are not exchangeable, yet common supervised models treat them as such. Here we study ridge regression when the analyst can partition the features into $K$ groups based on external side-information. For example, in high-throughput biology, features may represent gene expression, protein abundance or clinical data and so each feature group represents a distinct modality. The analyst's goal is to choose optimal regularization parameters $\lambda = (\lambda_1, \dotsc, \lambda_K)$ -- one for each group. In this work, we study the impact of $\lambda$ on the predictive risk of group-regularized ridge regression by deriving limiting risk formulae under a high-dimensional random effects model with $p\asymp n$ as $n \to \infty$. Furthermore, we propose a data-driven method for choosing $\lambda$ that attains the optimal asymptotic risk: The key idea is to interpret the residual noise variance $\sigma^2$, as a regularization parameter to be chosen through cross-validation. An empirical Bayes construction maps the one-dimensional parameter $\sigma$ to the $K$-dimensional vector of regularization parameters, i.e., $\sigma \mapsto \widehat{\lambda}(\sigma)$. Beyond its theoretical optimality, the proposed method is practical and runs as fast as cross-validated ridge regression without feature groups ($K=1$).
Multiplicity among the cool supergiants in the Magellanic Clouds

The characterization of multiplicity of high-mass stars is of fundamental importance to understand their evolution, the diversity of observed core-collapse supernovae and the formation of gravitational wave progenitor systems. Despite that, until recently, one of the final phases of massive star evolution – the cool supergiant phase – has received comparatively little attention. In this study, we aim to explore the multiplicity among the cool supergiant (CSG) population in the Large and Small Magellanic Clouds (LMC and SMC, respectively). To do this we compile extensive archival radial velocity (RV) measurements for over 1000 CSGs from the LMC and SMC, spanning a baseline of over 40 yr. By statistically correcting the RV measurements of each stellar catalogue to the Gaia DR2 reference frame we are able to effectively compare these diverse observations. We identify 45 CSGs where RV variations cannot be explained through intrinsic variability, and are hence considered binary systems. We obtain a minimum binary fraction of $15\pm 4{{\ \rm per\ cent}}$ for the SMC and of $14\pm 5{{\ \rm per\ cent}}$ for the LMC, restricting our sample to objects with at least 6 and 5 observational epochs, respectively. Combining these results, we determine a minimum binary fraction of $15\pm 3{{\ \rm per\ cent}}$ for CSGs. These results are in good agreement with previous results which apply a correction to account for observational biases. These results add strength to the hypothesis that the binary fraction of CSGs is significantly lower than their main-sequence counterparts. Going forward, we stress the need for long-baseline multi-epoch spectroscopic surveys to cover the full parameter space of CSG binary systems.
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport

We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network. This approach is based on smooth shells, the current state-of-the-art axiomatic correspondence method, which requires an a priori stochastic search over the space of initial poses. Our goal is to replace this costly preprocessing step by directly learning good initializations from the input surfaces. To that end, we systematically derive a fully differentiable, hierarchical matching pipeline from entropy regularized optimal transport. This allows us to combine it with a local feature extractor based on smooth, truncated spectral convolution filters. Finally, we show that the proposed unsupervised method significantly improves over the state-of-the-art on multiple datasets, even in comparison to the most recent supervised methods. Moreover, we demonstrate compelling generalization results by applying our learned filters to examples that significantly deviate from the training set.
Learned infinite elements

We study the numerical solution of scalar time-harmonic wave equations on unbounded domains which can be split into a bounded interior domain of primary interest and an exterior domain with separable geometry. To compute the solution in the interior domain, approximations to the Dirichlet-to-Neumann (DtN) map of the exterior domain have to be imposed as transparent boundary conditions on the artificial coupling boundary. Although the DtN map can be computed by separation of variables, it is a nonlocal operator with dense matrix representations, and hence computationally inefficient. Therefore, approximations of DtN maps by sparse matrices, usually involving additional degrees of freedom, have been studied intensively in the literature using a variety of approaches including different types of infinite elements, local non-reflecting boundary conditions, and perfectly matched layers. The entries of these sparse matrices are derived analytically, e.g. from transformations or asymptotic expansions of solutions to the differential equation in the exterior domain. In contrast, in this paper we propose to `learn' the matrix entries from the DtN map in its separated form by solving an optimization problem as a preprocessing step. Theoretical considerations suggest that the approximation quality of learned infinite elements improves exponentially with increasing number of infinite element degrees of freedom, which is confirmed in numerical experiments. These numerical studies also show that learned infinite elements outperform state-of-the-art methods for the Helmholtz equation. At the same time, learned infinite elements are much more flexible than traditional methods as they, e.g., work similarly well for exterior domains involving strong reflections, for example, for the atmosphere of the Sun, which is strongly inhomogeneous and exhibits reflections at the corona.
Scalable Attack-Resistant Obfuscation of Logic Circuits

Hardware IP protection has been one of the most critical areas of research in the past years. Recently, attacks on hardware IPs (such as reverse engineering or cloning) have evolved as attackers have developed sophisticated techniques. Therefore, hardware obfuscation has been introduced as a powerful tool to protect IPs against piracy attacks. However, many recent attempts to break existing obfuscation methods have been successful in unlocking the IP and restoring its functionality. In this paper, we propose SARO, a Scalable Attack-Resistant Obfuscation that provides a robust functional and structural design transformation process. SARO treats the target circuit as a graph, and performs a partitioning algorithm to produce a set of sub-graphs, then applies our novel Truth Table Transformation (T3) process to each partition. We also propose the $T3_{metric}$, which is developed to quantify the structural and functional design transformation level caused by the obfuscation process. We evaluate SARO on ISCAS85 and EPFL benchmarks, and provide full security and performance analysis of our proposed framework.
Life History and Potential Hosts of Zapatella davisae (Hymenoptera: Cynipidae), a Recent Invader on Black Oak in the Northeastern United States

Abstract Quercus velutina (Black Oak) is a dominant deciduous tree in Cape Cod, Martha's Vineyard, and Nantucket, MA. In recent years, Black Oak trees in these regions have experienced severe canopy loss due to the infestation of a stem-gall wasp, Zapatella davisae. In addition to infestations first documented in the Cape Cod region in 2012, Zapatella davisae has been present in Long Island, NY, since the 1990s. We investigated the life cycle of Z. davisae, specifically emergence patterns and timing of development. We evaluated differences in the severity of the infestations in Long Island and Cape Cod. We also identified towns in Cape Cod that contained Z. davisae infestations to better estimate the geographic extent of the infestation in the region. We found that Zapatella davisae completes 1 generation per year and emerges throughout the month of May. The severity of the infestation was the greatest in Cape Cod, an indication that something is controlling the population in Long Island. We concluded that Z. davisae is widespread and present in all towns in Cape Cod, Nantucket, and Martha's Vineyard. Our research will lay the foundation for future biological control efforts and will help arborists and landowners make management decisions regarding Z. davisae in the Cape Cod region and Long Island.
Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning

Despite empirical success, the theory of reinforcement learning (RL) with value function approximation remains fundamentally incomplete. Prior work has identified a variety of pathological behaviours that arise in RL algorithms that combine approximate on-policy evaluation and greedification. One prominent example is policy oscillation, wherein an algorithm may cycle indefinitely between policies, rather than converging to a fixed point. What is not well understood however is the quality of the policies in the region of oscillation. In this paper we present simple examples illustrating that in addition to policy oscillation and multiple fixed points -- the same basic issue can lead to convergence to the worst possible policy for a given approximation. Such behaviours can arise when algorithms optimize evaluation accuracy weighted by the distribution of states that occur under the current policy, but greedify based on the value of states which are rare or nonexistent under this distribution. This means the values used for greedification are unreliable and can steer the policy in undesirable directions. Our observation that this can lead to the worst possible policy shows that in a general sense such algorithms are unreliable. The existence of such examples helps to narrow the kind of theoretical guarantees that are possible and the kind of algorithmic ideas that are likely to be helpful. We demonstrate analytically and experimentally that such pathological behaviours can impact a wide range of RL and dynamic programming algorithms; such behaviours can arise both with and without bootstrapping, and with linear function approximation as well as with more complex parameterized functions like neural networks.
Can supernova shells feed supermassive black holes in galactic nuclei?

Aims. We simulate shells created by supernovae expanding into the interstellar medium of the nuclear region of a galaxy, and analyze how the shell evolution is influenced by the supernova position relative to the galactic center, by the interstellar matter density, and by the combined gravitational pull of the nuclear star cluster and supermassive black hole (SMBH). Methods. We adopted simplified hydrodynamical simulations using the infinitesimally thin layer approximation in 3D (code RING) and determined whether and where the shell expansion may bring new gas into the inner parsec around the SMBH. Results. The simulations show that supernovae occurring within a conical region around the rotational axis of the galaxy can feed the central accretion disk surrounding the SMBH. For ambient densities between 103 and 105 cm−3, the average mass deposited into the central parsec by individual supernovae varies between 10 and 1000 solar masses depending on the ambient density and the spatial distribution of supernova events. Supernovae occurring in the aftermath of a starburst event near a galactic center can supply two to three orders of magnitude more mass into the central parsec, depending on the magnitude of the starburst. The deposited mass typically encounters and joins an accretion disk. The fate of that mass is then divided between the growth of the SMBH and an energetically driven outflow from the disk.
The rotational influence on solar convection

This paper considers the dominant dynamical, thermal and rotational balances within the solar convection zone. The reasoning is such that: Coriolis forces balance pressure gradients. Background vortex stretching, baroclinic torques and nonlinear advection balance jointly. Turbulent fluxes convey what part of the solar luminosity that radiative diffusion cannot. These four relations determine estimates for the dominant length scales and dynamical amplitudes strictly in terms of known physical quantities. We predict that the dynamical Rossby number for convection is less than unity below the near-surface shear layer, indicating strong rotational constraint. We also predict a characteristic convection length scale of roughly 30 Mm throughout much of the convection zone. These inferences help explain recent observations that reveal weak flow amplitudes at 100-200 Mm scales.
The statistical reliability of 267-GHz JCMT observations of Venus: no significant evidence for phosphine absorption

In the light of the recent announcement of the discovery of the potential biosignature phosphine in the atmosphere of Venus I present an independent reanalysis of the original JCMT data to assess the statistical reliability of the detection. Two line detection methods are explored, low order polynomial fits and higher order multiple polynomial fits. A non-parametric bootstrap analysis reveals that neither line detection method is able to recover a statistically significant detection. Similar to the results of other reanalyses of ALMA Venus spectra, the polynomial fitting process results in false positive detections in the JCMT spectrum. There is thus no significant evidence for phosphine absorption in the JCMT Venus spectra.