Miracle-dz/UniFAR-FTUs huggingface dataset:
Expansion dynamics in two-dimensional Bose-Hubbard lattices: BEC and thermal cloud.

We study the temporal expansion of an ultracold Bose gas in two-dimensional, square optical lattices. The gas is described by the Bose-Hubbard model deep in the superfluid regime, with initially all bosons condensed in the central site of the lattice. We use the previously developed nonequilibrium propagator method for capturing the time evolution of an interacting bosonic system, where the many-body Hamiltonian is represented in an appropriate local basis and the corresponding field operators are separated into the classical (BEC) part and quantum mechanical fluctuations. After a quench, i.e. after a sudden switch of the lattice nearest-neighbor hopping, the expanding, bosonic cloud separates spatially into a fast, ballistic forerunner and a slowly expanding central part controlled by selftrapping. We show that the forerunner expansion is driven by the coherent dynamics of the BEC and that its velocity is consistent with the Lieb-Robinson bound. For smaller lattices we analyze how quasiparticle collisions lead to enhanced condensate depletion and oscillation damping.
Density estimation of Asian bears using photographic capture–recapture sampling based on chest marks

Abstract Assessing the conservation status of species of concern is greatly aided by unbiased estimates of population size. Population size is one of the primary parameters determining urgency of conservation action, and it provides baseline data against which to measure progress toward recovery. Asiatic black bears (Ursus thibetanus) and sun bears (Helarctos malayanus) are vulnerable to extinction, but no statistically rigorous population density estimates exist for wild bears of either species. We used a camera-based approach to estimate density of these sympatric bear species. First, we tested a technique to photograph bear chest marks using 3 camera traps mounted on trees facing each other in a triangular arrangement with bait in the center. Second, we developed criteria to identify individual sun bears and black bears based on chest-mark patterns and tested the level of congruence among 5 independent observers using a set of 234 photographs. Finally, we camera-trapped wild bears at 2 study areas (Khlong E-Tow, 33 km2, and Khlong Samor-Pun, 40 km2) in Khao Yai National Park, Thailand, and used chest marks to identify individual bears and thereby derive capture histories for bears of each species. Average congruence among observers' identifications of individual bears was 78.4% for black bear and 92.9% for sun bear across sites. At Khlong E-Tow, we recorded 13 black bears (8 M, 4 F, 1 unknown sex) and 8 sun bears (1 M, 5 F, 2 unknown sex). At Khlong Samor-Pun, we recorded 10 black bears (6 M, 4 F) and 6 sun bears (4 M, 2 F). We used a spatially explicit capture–recapture method, resulting in density estimates of 8.0 (SE = 3.04) and 5.8 (SE = 2.31) black bears per 100 km2 and 5.9 (SE = 3.07) and 4.3 (SE = 2.32) sun bears per 100 km2 for each study area, respectively. Our camera trap design and chest-mark identification criteria can be used to estimate density of sun bears and black bears, enhancing knowledge of the conservation status of these threatened and little-known bear species.
Does blood type affect the COVID-19 infection pattern?

Among the many aspects that characterize the COVID-19 pandemic, two seem particularly challenging to understand: i) the great geographical differences in the degree of virus contagiousness and lethality that were found in the different phases of the epidemic progression, and, ii) the potential role of the infected people’s blood type in both the virus infectivity and the progression of the disease. A recent hypothesis could shed some light on both aspects. Specifically, it has been proposed that, in the subject-to-subject transfer, SARS-CoV-2 conserves on its capsid the erythrocytes’ antigens of the source subject. Thus these conserved antigens can potentially cause an immune reaction in a receiving subject that has previously acquired specific antibodies for the source subject antigens. This hypothesis implies a blood type-dependent infection rate. The strong geographical dependence of the blood type distribution could be, therefore, one of the factors at the origin of the observed heterogeneity in the epidemics spread. Here, we present an epidemiological deterministic model where the infection rules based on blood types are taken into account, and we compare our model outcomes with the exiting worldwide infection progression data. We found an overall good agreement, which strengthens the hypothesis that blood types do play a role in the COVID-19 infection.
Spawning related movement of shovelnose sturgeon in the Missouri River above Fort Peck Reservoir, Montana

Summary The hypotheses of this study were (i) that shovelnose sturgeon would make upstream movements to spawn, (ii) movement of spawning fish would be greater in a year with higher discharge, and (iii) that spawning fish would have greater movements than reproductively inactive fish. Shovelnose sturgeon Scaphirhynchus platorynchus (Rafinesque, 1820) in five reproductive categories (e.g. males, confirmed spawning females, potentially spawning females, atretic females, and reproductively inactive females) were tracked in 2008 and 2009. All reproductive categories, except reproductively inactive females, exhibited large-scale movements and had omnidirectional movements. No differences in movement rates were observed in confirmed spawning females between years despite a 45% higher peak discharge in 2008 (839 m3 s−1) than in 2009 (578 m3 s−1). A peak discharge was obtained at a faster rate in 2008 (165 m3 s−1 day−1) than in 2009 (39 m3 s−1 day−1), and high discharge was of greater duration in 2008. Reproductively inactive females did not exhibit large-scale movements and their movement rate differed from other reproductive categories. Shovelnose sturgeon spawned in both years, despite highly varying hydrographs between years.
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI

Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks. In this approach, predictions from a diverse set of learners are combined to obtain a joint decision. Recently, various methods have been explored in literature for estimating decision uncertainties using ensemble learning; however, determining which metrics are a better fit for certain decision-making applications remains a challenging task. In this paper, we study the following key research question in the selection of uncertainty metrics: when does an uncertainty metric outperforms another? We answer this question via a rigorous analysis of two commonly used uncertainty metrics in ensemble learning, namely ensemble mean and ensemble variance. We show that, under mild assumptions on the ensemble learners, ensemble mean is preferable with respect to ensemble variance as an uncertainty metric for decision making. We empirically validate our assumptions and theoretical results via an extensive case study: the diagnosis of referable diabetic retinopathy.
Monitoring and explainability of models in production

The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and data monitoring, detecting outliers and data drift using statistical techniques, and providing explanations of historic predictions. We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.
How Does GAN-based Semi-supervised Learning Work?

Generative adversarial networks (GANs) have been widely used and have achieved competitive results in semi-supervised learning. This paper theoretically analyzes how GAN-based semi-supervised learning (GAN-SSL) works. We first prove that, given a fixed generator, optimizing the discriminator of GAN-SSL is equivalent to optimizing that of supervised learning. Thus, the optimal discriminator in GAN-SSL is expected to be perfect on labeled data. Then, if the perfect discriminator can further cause the optimization objective to reach its theoretical maximum, the optimal generator will match the true data distribution. Since it is impossible to reach the theoretical maximum in practice, one cannot expect to obtain a perfect generator for generating data, which is apparently different from the objective of GANs. Furthermore, if the labeled data can traverse all connected subdomains of the data manifold, which is reasonable in semi-supervised classification, we additionally expect the optimal discriminator in GAN-SSL to also be perfect on unlabeled data. In conclusion, the minimax optimization in GAN-SSL will theoretically output a perfect discriminator on both labeled and unlabeled data by unexpectedly learning an imperfect generator, i.e., GAN-SSL can effectively improve the generalization ability of the discriminator by leveraging unlabeled information.
Yield, Dry Matter Partitioning, and Storage Quality of Hardneck Garlic as Affected by Soil Amendments and Scape Removal

Two on-farm field studies were conducted in 1996 and repeated in 1997 to determine the effects of soil amendments and scape (flower stalk) removal on yield, dry matter partitioning, and storage quality of hardneck garlic ( Allium sativum L.). One study site was on a loamy sand soil with low organic matter and fertility and the other site was on a sandy loam soil with high organic matter and fertility. Soil amendment treatments tested at both sites were: 1) no amendment, 2) composted manure, and 3) inorganic fertilizer according to soil test recommendations. A fourth treatment, dried, composted turkey-manure-based fertilizer, was included at the low organic matter site. Scapes were removed at the curled stage from plants in half of the harvest rows. Scapes from the remainder of the harvest row plants were allowed to mature until harvest. In 1997, bulbs from each treatment were stored at 0 to 3 °C or 19 to 21 °C for 6 months. Soil amendment treatments had no effect on total garlic bulb yield, dry mass partitioning, or stored bulb weight loss at the sandy loam, high organic matter site. Manure compost, fertilizer, and composted turkey manure soil amendments reduced the yield of smaller bulbs compared with the control at the loamy sand, low organic matter site. The proportion of bulbs >5 cm was highest with the manure compost treatment. At the low organic matter site, scape removal resulted in a 15% increase in bulb yield and an increase in bulb size compared with leaving scapes on until harvest ( P = 0.05). At the high organic matter site, scape removal increased bulb yield by 5% (P = 0.10). Scape removal increased dry matter partitioning to the bulbs, but had no effect on total (scape + shoot + bulb) aboveground dry matter production. The increase in bulb dry mass when scapes were removed was offset by an increase in scape dry mass when scapes were left on. Bulb weight loss in storage was less at 0 to 3 °C than 19 to 21 °C. Soil amendments only affected bulb storage quality at the loamy sand, low soil organic matter site. The effect of scape removal on bulb weight loss
Monophyly of the conifer species in the Ceratocystis coerulescens complex based on DNA sequence data

Ceratocystis sensu stricto includes numer- ous species of insect-vectored, wood-staining and plant pathogenic fungi. Among these, Ceratocystis coe- rulescens is a well-known cause of blue-stain in spruce and pine. Previous investigations, using morphologi- cal characteristics and isozyme comparisons, have shown that C. coerulescens encompasses at least five morphological types. The aim of this study was thus to compare isolates of C. coerulescens sensu lato and morphologically similar species, including C. larici- cola, C. polonica, C. virescens, C. eucalypti, Chalara australis and Ch. neocaledoniae, on the basis of DNA sequence data. Using the polymerase chain reaction (PCR), a 600 base pair fragment within the ribosomal DNA operon was amplified, and the PCR products were sequenced. The analyzed sequence included the 5.8S rRNA gene and the internal transcribed spacers (ITS) 1 and 2. Relationships were deter- mined by parsimony analysis. Using C. fimbriata as the outgroup taxon, the five morphological types previously known as C. coerulescens and the two other taxa from conifers formed a strongly-supported monophyletic group that includes all the Ceratocystis species occurring primarily on conifers. The species from hardwood trees, C. eucalypti, Ch. australis and Ch. neocaledoniae, also formed a monophyletic group, sister to the conifer group. The fourth species from hardwoods, C. virescens, formed a group basal to the two sister groups.
fenicsR13

We present a mixed finite element solver for the linearized regularized 13-moment equations of non-equilibrium gas dynamics. The Python implementation builds upon the software tools provided by the FEniCS computing platform. We describe a new tensorial approach utilizing the extension capabilities of FEniCS’ Unified Form Language to define required differential operators for tensors above second degree. The presented solver serves as an example for implementing tensorial variational formulations in FEniCS, for which the documentation and literature seem to be very sparse. Using the software abstraction levels provided by the Unified Form Language allows an almost one-to-one correspondence between the underlying mathematics and the resulting source code. Test cases support the correctness of the proposed method using validation with exact solutions. To justify the usage of extended gas flow models, we discuss typical application cases involving rarefaction effects. We provide the documented and validated solver publicly.
Discrete integrable systems and Pitman's transformation

We survey recent work that relates Pitman's transformation to a variety of classical integrable systems, including the box-ball system, the ultra-discrete and discrete KdV equations, and the ultra-discrete and discrete Toda lattice equations. It is explained how this connection enables the dynamics of the integrable systems to be initiated from infinite configurations, which is important in the study of invariant measures. In the special case of spatially independent and identically distributed configurations, progress on the latter topic is also reported.
Local Editing in LZ-End Compressed Data

This paper presents an algorithm for the modification of data compressed using LZ-End, a derivate of LZ77, without prior decompression. The performance of the algorithm and the impact of the modifications on the compression ratio is evaluated. Finally, we discuss the importance of this work as a first step towards local editing in Lempel-Ziv compressed data.
Effective potential of scalar–tensor gravity

Effective potential of a scalar field induced by weak gravity is studied. The set of operators providing the leading contribution and preserving the second order of field equations is found. It is shown that only a mass term and a specific Brans–Dicke-like interaction are relevant within such a setup. An explicit form of the potential is found. The model has room for a natural inflationary scenario similar to the well-known case of the Starobinsky inflation. Possible implications for the standard model are highlighted.
A successful search for intervening 21 cm H i absorption in galaxies at 0.4 < z <1.0 with the Australian square kilometre array pathfinder (ASKAP)

We have used the Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope to search for intervening 21 cm neutral hydrogen (H i) absorption along the line of sight to 53 bright radio continuum sources. Our observations are sensitive to H i column densities typical of Damped Lyman Alpha absorbers (DLAs) in cool gas with an H i spin temperature below about 300–500 K. The six-dish Boolardy Engineering Test Array (BETA) and twelve-antenna Early Science array (ASKAP-12) covered a frequency range corresponding to redshift 0.4 < z < 1.0 and 0.37 < z < 0.77, respectively, for the H i line. Fifty of the 53 radio sources observed have reliable optical redshifts, giving a total redshift path Δz = 21.37. This was a spectroscopically untargeted survey, with no prior assumptions about the location of the lines in redshift space. Four intervening H i lines were detected, two of them new. In each case, the estimated H i column density lies above the DLA limit for H i spin temperatures above 50–80 K, and we estimate a DLA number density at redshift z ∼ 0.6 of $n(z)=0.19^{+0.15 }_{ -0.09}$. This value lies somewhat above the general trend of n(z) with redshift seen in optical DLA studies. Although the current sample is small, it represents an important proof of concept for the much larger 21 cm First Large Absorption Survey in H i (FLASH) project to be carried out with the full 36-antenna ASKAP telescope, probing a total redshift path $\Delta z\sim \, 50,000$.
The new generation planetary population synthesis (NGPPS). I. Bern global model of planet formation and evolution, model tests, and emerging planetary systems

Aims. Comparing theoretical models of exoplanet formation with observations allows one to make key step forward towards an understanding of planetary systems. It however requires a model able to (i) predict all the necessary observable quantities (not only masses and orbits, but also radii, luminosities, magnitudes, or evaporation rates) and (ii) address the large range in relevant planetary masses (from Mars mass to super-Jupiters) and distances (from stellar-grazing to wide orbits). Methods. We have developed a combined global planetary formation and evolution model that solves as directly as possible the underlying differential equations for the structure and evolution of the gas disc, the dynamical state of the planetesimals, the internal structure of the planets yielding their gas accretion rate and internal structure, the accretion rate of planetesimals, disc-driven orbital migration, and the gravitational interaction of concurrently forming planets via a full N-body calculation. Importantly, the model now also follows the long-term evolution of the planets on Gigayear timescales after formation including the effects of cooling and contraction, atmospheric escape, bloating, and stellar tides. Results. To test the model, we compared it with classical scenarios of Solar System formation. For the terrestrial planets, we find that we obtain a giant impact phase provided enough embryos (~100) are initially emplaced in the disc. For the giant planets, we find that Jupiter-mass planets must accrete their core shortly before the dispersal of the gas disc to prevent strong inward migration that would bring them to the inner edge of the disc. Conclusions. he model can form planetary systems with a wide range of properties. We find that giant-planet bearing systems are more diverse than the ones with only terrestrial planets.
The Computational Limits of Deep Learning

Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image recognition, voice recognition, translation, and other tasks. But this progress has come with a voracious appetite for computing power. This article reports on the computational demands of Deep Learning applications in five prominent application areas and shows that progress in all five is strongly reliant on increases in computing power. Extrapolating forward this reliance reveals that progress along current lines is rapidly becoming economically, technically, and environmentally unsustainable. Thus, continued progress in these applications will require dramatically more computationally-efficient methods, which will either have to come from changes to deep learning or from moving to other machine learning methods.
A Latent Mixture Model for Heterogeneous Causal Mechanisms in Mendelian Randomization

Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usually assume most genetic variants are valid instrumental variables that identify a common causal effect. There is a general lack of awareness that this effect homogeneity assumption can be violated when there are multiple causal pathways involved, even if all the instrumental variables are valid. In this article, we introduce a latent mixture model MR-PATH that groups instruments that yield similar causal effect estimates together. We develop a Monte-Carlo EM algorithm to fit this mixture model, derive approximate confidence intervals for uncertainty quantification, and adopt a modified Bayesian Information Criterion (BIC) for model selection. We verify the efficacy of the Monte-Carlo EM algorithm, confidence intervals, and model selection criterion using numerical simulations. We identify potential mechanistic heterogeneity when applying our method to estimate the effect of high-density lipoprotein cholesterol on coronary heart disease and the effect of adiposity on type II diabetes.
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation

We study the exploration-exploitation dilemma in the linear quadratic regulator (LQR) setting. Inspired by the extended value iteration algorithm used in optimistic algorithms for finite MDPs, we propose to relax the optimistic optimization of \ofulq and cast it into a constrained \textit{extended} LQR problem, where an additional control variable implicitly selects the system dynamics within a confidence interval. We then move to the corresponding Lagrangian formulation for which we prove strong duality. As a result, we show that an $\epsilon$-optimistic controller can be computed efficiently by solving at most $O\big(\log(1/\epsilon)\big)$ Riccati equations. Finally, we prove that relaxing the original \ofu problem does not impact the learning performance, thus recovering the $\tilde{O}(\sqrt{T})$ regret of \ofulq. To the best of our knowledge, this is the first computationally efficient confidence-based algorithm for LQR with worst-case optimal regret guarantees.
Chemical composition of the leaf essential oils of Murraya koenigii (L.) Spreng and Murraya paniculata (L.) Jack

The chemical composition of the leaf oils of Murraya koenigii (L.) Spreng and M. paniculata (L.) Jack from Bangladesh was studied by gas chromatography mass spectroscopy (GC-MS) . M. koenigii oil contained 39 compounds of which the major is 3-carene (54.2%) followed by caryophyllene (9.5%). Oil of M. paniculata contained 58 compounds of which the major are caryophyllene oxide (16.6%), b -caryophyllene (11.8%), spathulenol (10.2%), b -elemene (8.9%), germacrene D (6.9%) and cyclooctene, 4-methylene-6-(1-propenylidene) (6.4%). The compositions of both oils varied qualitatively and quantitatively.
Constraining Annihilating Dark Matter Mass by the Radio Continuum Spectral Data of a High-redshift Galaxy Cluster

In the past decade, the properties of annihilating dark matter models were examined by various kinds of data, including the data of gamma-rays, radio waves, X-rays, positrons, electrons, antiprotons and neutrinos. In particular, most of the studies focus on the data of our Galaxy, nearby galaxies (e.g., M31 galaxy) or nearby galaxy clusters (e.g., Fornax cluster). In this article, we examine the archival radio continuum spectral data of a relatively high-redshift galaxy cluster (A697 cluster) to constrain the properties of annihilating dark matter. We find that leptophilic annihilation channels (e+ e−, μ+ μ−, and τ+ τ−) can give very good fits to the radio continuum spectrum of the A697 cluster.