Miracle-dz/UniFAR-FTUs huggingface dataset:
Variability of Magnetically Dominated Jets from Accreting Black Holes

Structured jets have recently been invoked to explain the complex emission of gamma-ray bursts (GRBs), such as GW170817. Based on accretion simulations, the jets are expected to have a structure that is more complex than a simple top-hat structure. Also, the structure of the launch regions of blazar jets should influence their large-scale evolution. This was recently revealed by the interactions of jet components in TXS 0506+056, where the jet was observed at a viewing angle close to zero. Observational studies have also shown an anticorrelation between the jet variability, measured, e.g., by its minimum variability timescale, and the Lorentz factor, which spans several orders of magnitude and covers both blazars and GRBs samples. Motivated by those observational properties of black hole sources, we investigate the accretion inflow and outflow properties by means of numerical gamma-ray MHD simulations. We perform axisymmetric calculations of the structure and evolution of a central engine, composed of a magnetized torus around a Kerr black hole that is launching a nonuniform jet. We probe the jet energetics at different points along the line of sight, and we measure the jet-time variability as localized in these specific regions. We quantify our results by computing the minimum variability timescales and power density spectra. We reproduce the MTS–Γ correlation and we attribute it to the black hole’s spin as the main driving parameter of the engine. We also find that the power density spectral slope is not strongly affected by the black hole’s spin, while it differs for various viewing angles.
Large-time behavior of global solutions for barotropic compressible Navier-Stoles-Cahn-Hilliard equations

This paper is concerned with a diffuse interface model for the gas-liquid phase transition. The model consists of the barotropic compressible Navier-Stokes equations and a modified Cahn-Hilliard equation. For the initial boundary value problem in torus T (d = 2, 3), we prove that there exists a global unique smooth solution under the assumptions of small initial perturbations. Moreover, we show the large-time behavior for density, velocity and mass concentration difference of the mixture fluids. MSC 2020: 35B40, 35B65, 35L65, 76N05, 76N10, 76T10.
Dark Energy Survey year 3 results: Constraints on cosmological parameters and galaxy-bias models from galaxy clustering and galaxy-galaxy lensing using the redMaGiC sample

We constrain cosmological and galaxy-bias parameters using the combination of galaxy clustering and galaxy-galaxy lensing measurements from the Dark Energy Survey Year-3 data. We describe our modeling framework, and choice of scales analyzed, validating their robustness to theoretical uncertainties in small-scale clustering by analyzing simulated data. Using a linear galaxy bias model and redMaGiC galaxy sample, we obtain constraints on the matter density to be $\Omega_{\rm m} = 0.325^{+0.033}_{-0.034}$. We also implement a non-linear galaxy bias model to probe smaller scales that includes parameterization based on hybrid perturbation theory and find that it leads to a 17% gain in cosmological constraining power. We perform robustness tests of our methodology pipeline and demonstrate the stability of the constraints to changes in the theoretical model. Using the redMaGiC galaxy sample as foreground lens galaxies, we find the galaxy clustering and galaxy-galaxy lensing measurements to exhibit significant signals akin to de-correlation between galaxies and mass on large scales, which is not expected in any current models. This likely systematic measurement error biases our constraints on galaxy bias and the $S_8$ parameter. We find that a scale-, redshift- and sky-area-independent phenomenological de-correlation parameter can effectively capture the impact of this systematic error. We trace the source of this de-correlation to a color-dependent photometric issue and minimize its impact on our result by changing the selection criteria of redMaGiC galaxies. Using this new sample, our constraints on the $S_8$ parameter are consistent with previous studies, and we find a small shift in the $\Omega_{\rm m}$ constraints compared to the fiducial redMaGiC sample. We constrain the mean host halo mass of the redMaGiC galaxies in this new sample to be approximately $1.6 \times 10^{13} M_{\odot}/h$.
New conserved tensors and Brans–Dicke-type field equation, using integrability condition

In this paper, we explore some new off-shell and on-shell conserved quantities for a scalar field in Minkowski space, using integrability condition. The off-shell conserved tensors are related to the kinematics of the field, while a linear combination of the off-shell and the on-shell conserved tensors ends up with the energy–momentum tensor for the scalar field. In the curved background, using Ricci and Bianchi identities, scalar–tensor theory of gravity, that resembles somewhat with Brans–Dicke-type field equations, emerges without requiring the principle of equivalence. Further, starting from the curvature scalar and using these identities, the field equations for modified gravity (Einstein–Hilbert action in the presence of higher-order terms) follow.
An Unsupervised Optical Flow Estimation for Lidar Image Sequences

In recent years, the LiDAR images, as a 2D compact representation of 3D LiDAR point clouds, are widely applied in various tasks, e.g., 3D semantic segmentation, LiDAR point cloud compression (PCC). Among these works, the optical flow estimation for LiDAR image sequences has become a key issue, especially for the motion estimation of the inter prediction in PCC. However, the existing optical flow estimation models are likely to be unreliable for LiDAR images. In this work, we first propose a light-weight flow estimation model for LiDAR image sequences. The key novelty of our method lies in two aspects. One is that for the different characteristics (with the spatial-variation feature distribution) of the LiDAR images w.r. t. the normal color images, we introduce the attention mechanism into our model to improve the quality of the estimated flow. The other one is that to tackle the lack of large-scale LiDAR-image annotations, we present an unsupervised method, which directly minimizes the inconsistency between the reference image and the reconstructed image based on the estimated optical flow. Extensive experimental results have shown that our proposed model outperforms other mainstream models on the KITTI dataset, with much fewer parameters.
Efficient and Accurate Gradients for Neural SDEs

Neural SDEs combine many of the best qualities of both RNNs and SDEs: memory efficient training, high-capacity function approximation, and strong priors on model space. This makes them a natural choice for modelling many types of temporal dynamics. Training a Neural SDE (either as a VAE or as a GAN) requires backpropagating through an SDE solve. This may be done by solving a backwards-in-time SDE whose solution is the desired parameter gradients. However, this has previously suffered from severe speed and accuracy issues, due to high computational cost and numerical truncation errors. Here, we overcome these issues through several technical innovations. First, we introduce the \textit{reversible Heun method}. This is a new SDE solver that is \textit{algebraically reversible}: eliminating numerical gradient errors, and the first such solver of which we are aware. Moreover it requires half as many function evaluations as comparable solvers, giving up to a $1.98\times$ speedup. Second, we introduce the \textit{Brownian Interval}: a new, fast, memory efficient, and exact way of sampling \textit{and reconstructing} Brownian motion. With this we obtain up to a $10.6\times$ speed improvement over previous techniques, which in contrast are both approximate and relatively slow. Third, when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function. This reduces computational cost (giving up to a $1.87\times$ speedup) and removes the numerical truncation errors associated with gradient penalty. Altogether, we outperform the state-of-the-art by substantial margins, with respect to training speed, and with respect to classification, prediction, and MMD test metrics. We have contributed implementations of all of our techniques to the torchsde library to help facilitate their adoption.
Curse of Dimensionality in Unconstrained Private Convex ERM

We consider the lower bounds of differentially private empirical risk minimization for general convex functions in this paper. For convex generalized linear models (GLMs), the well-known tight bound of DP-ERM in the constrained case is ˜Θ( √ p ǫn ) , while recently, Song et al. [2021] ﬁnd the tight bound of DP-ERM in the unconstrained case is ˜Θ( √ rank ǫn ) where p is the dimension, n is the sample size and rank is the rank of the feature matrix of the GLM objective function. As rank ≤ min { n, p } , a natural and important question arises that whether we can evade the curse of dimensionality for over-parameterized models where n ≪ p , for more general convex functions beyond GLM. We answer this question negatively by giving the ﬁrst and tight lower bound of unconstrained private ERM for the general convex function, matching the current upper bound ˜ O ( √ pnǫ ) for un-constrained private ERM. We also give an Ω( pnǫ ) lower bound for unconstrained pure-DP ERM which recovers the result in the constrained case.
Evidence for Black Holes in Green Peas from WISE Colors and Variability

We explore the presence of active galactic nuclei (AGNs)/black holes in Green Pea galaxies (GPs), motivated by the presence of high-ionization emission lines such as He ii and [Ne iii] in their optical spectra. In order to identify AGN candidates, we used mid-infrared (MIR) photometric observations from the all-sky Wide-field Infrared Survey Explorer (WISE) mission for a sample of 1004 GPs. Considering only >5σ detections with no contamination from neighboring sources in AllWISE, we select 31 GPs out of 134 as candidate AGNs based on a stringent three-band WISE color diagnostic. Using multi-epoch photometry in W1 and W2 bands based on time-resolved unWISE coadd images, we find two sources exhibiting variability in both the WISE bands among 112 GPs with W1 ≤16 mag and no contamination from neighboring sources in unWISE. These two variable sources were selected as AGNs by the WISE three-band color diagnostic as well. Compared to variable AGN fractions observed among low-mass galaxy samples in previous studies, we find a higher fraction (∼1.8%) of MIR variable sources among GPs, which demonstrates the uniqueness and importance of studying these extreme objects. Through this work, we demonstrate that MIR diagnostics are promising tools to select AGNs that may be missed by other selection techniques (including optical emission-line ratios and X-ray emission) in star formation-dominated, low-mass, low-metallicity galaxies.
Single Cell RNA Sequencing Reveals a Unique Monocyte Population in Bronchoalveolar Lavage Cells of Mice Challenged with Afghanistan Particulate Matter and Allergen.

Upon returning from deployment to Afghanistan, a substantial number of United States military personnel report Deployment Related Lung Disease (DRLD) symptoms, including those consistent with an asthma-like airways disease. DRLD is thought to be caused by prolonged inhalation of toxic desert particulate matter, which can persist in the post-deployment setting such as exposure to common household allergens. The goal of this study was to define the transcriptomic responses of lung leukocytes of mice exposed to Afghanistan desert particulate matter (APM) and house dust mite (HDM). C57BL/6 mice (n = 15/group) were exposed to filtered air or aerosolized APM for 12 days, followed by intranasal PBS or HDM allergen challenges for 24 hours. Bronchoalveolar lavage (BAL) cells were collected for single cell RNA sequencing (scRNAseq), and assessment of inflammation and airway hyperresponsiveness (AHR). Unsupervised clustering of BAL cell scRNAseq data revealed a unique monocyte population induced only by both APM and allergen treatments. This population of monocytes is characterized by expression of genes involved in allergic asthma, including Alox15. We validated Alox15 expression in monocytes via immunostaining of lung tissue. APM pre-exposure, followed by HDM challenge, led to significantly increased total respiratory system resistance compared to filtered air controls. Using this mouse model to mimic DRLD, we demonstrated that inhalation of airborne PM during deployment may prime airways to be more responsive to allergen exposure after returning home, which may be linked to dysregulated immune responses such as induction of a unique lung monocyte population.
Mechanistic insights into the anti-depressant effect of emodin: an integrated systems pharmacology study and experimental validation

Depression is a complex neuropsychiatric disease involved multiple targets and signaling pathways. Systems pharmacology studies could potentially present a comprehensive molecular mechanism to delineate the anti-depressant effect of emodin (EMO). In this study, we investigated the anti-depressant effects of EMO in the chronic unpredictable mild stress (CUMS) rat model of depression and gained insights into the underlying mechanisms using systems pharmacology and molecular simulation analysis. Forty-three potential targets of EMO for treatment of depression were obtained. GO biological process analysis suggested that the biological functions of these targets mainly involve the regulation of reactive oxygen species metabolic process, response to lipopolysaccharide, regulation of inflammatory response, etc. KEGG pathway enrichment analysis showed that the PI3K-Akt signaling pathway, insulin resistance, IL-17 signaling pathway were the most significantly enriched signaling pathways. The molecular docking analysis revealed that EMO might have a strong combination with ESR1, AKT1 and GSK3B. Immunohistochemical staining and Western blotting showed that 2 weeks’ EMO treatment (80 mg/kg/day) reduced depression related microglial activation, neuroinflammation and altered PI3K-Akt signaling pathway. Our findings provide a systemic pharmacology basis for the anti-depressant effects of EMO.
Exploring assessment in STEM inquiry learning classrooms

ABSTRACT This article introduces this special issue by addressing the types of assessment practices that are possible within STEM inquiry classrooms, where the focus is on noticing, developing, and improving inquiry competencies. Three domains are explored: informal formative assessment occurring in classroom interactions; structured formative assessment when teachers or peers collect information at a planned time; ongoing evolution of teachers’ knowledge and practices. We analyse how classroom formative assessment practices develop alongside and support inquiry learning. We report on how STEM teachers might be supported in transforming their teaching practices and assessment methods in tandem. We focus on the role of classroom discussion to challenge students’ ideas and make explicit their reasoning. We highlight how interactions between teachers and students support students in being more active in the assessment process. Combining inquiry learning with adapted assessment practices requires teachers to make significant changes to their practices. They need to be aware of how students develop their learning. Such professional moves are complex because implementing formative and summative assessments in a complementary manner remains a challenge for teachers and researchers. We emphasise how the collaboration between researchers and teachers support teachers in making changes to their professional knowledge, beliefs, and classroom practices.
The impact of temperature anomalies on commodity futures

ABSTRACT Recent evidence points to global warming and climate change as the biggest issues of the century; thus, the analysis of the weather-commodity futures prices relationship has crucial importance. This paper considers the relationship between weather anomalies, proxied by the Global Historical Surface Temperature Anomalies (HadCRUT4), and futures prices of agricultural products, energy commodities, industrial, and precious metals. Analyzing the monthly data between December 1982 and November 2020, the outcomes of the novel Granger causality test suggest unidirectional causality from the temperature anomalies to commodity futures prices. The findings imply that global temperature anomalies impact the expectations about the agricultural- and energy-related economic activities, including the use of commercial and organic fertilizers and fossil fuel combustion, respectively.
Book Review: The Legal Mind: A New Introduction to Legal Epistemology

their investigation’s apolitical credentials. The application to conduct the test was filed by the prosecution almost as an afterthought. Far from being a well-thought-out strategy, the police belatedly sought to hide the violence of their prosecution under the cover of ‘scientific methods of interrogation’. As Lokaneeta’s insightful work shows us, violence and contingency remain at the heart of the state, even as science and the law constantly work together to maintain the facade of coherence and rationality. The book charts a way to understand the modes through which state violence operates, and the ways in which it can be revealed.
Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data

Abstract Recent work has demonstrated the use of sparse sensors in combination with the proper orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity fields in a variety of flows. The present work investigates the fidelity of such techniques applied to a stalled NACA 0012 aerofoil at $ {Re}_c=75,000 $ at an angle of attack $ \alpha ={12}^{\circ } $ as measured experimentally using planar time-resolved particle image velocimetry. In contrast to many previous studies, the flow is absent of any dominant shedding frequency and exhibits a broad range of singular values due to the turbulence in the separated region. Several reconstruction methodologies for linear state estimation based on classical compressed sensing and extended POD methodologies are presented as well as nonlinear refinement through the use of a shallow neural network (SNN). It is found that the linear reconstructions inspired by the extended POD are inferior to the compressed sensing approach provided that the sparse sensors avoid regions of the flow with small variance across the global POD basis. Regardless of the linear method used, the nonlinear SNN gives strikingly similar performance in its refinement of the reconstructions. The capability of sparse sensors to reconstruct separated turbulent flow measurements is further discussed and directions for future work suggested. Impact Statement Sparse reconstruction of full-field information using a limited subset of data is widely relevant to data-centric engineering applications; from reconstructing human faces with limited pixels to predicting laminar and turbulent flow fields from limited sensors. The focus of the present study is of the latter example with high relevance to active flow control in aerospace and related industry. There are multiple data-driven methodologies for obtaining flow field reconstructions from sparse measurements ranging from the linear unsupervised proper orthogonal decomposition to the use of nonlinear supervised NNs. The feasibility of such methods to flow fields that are highly turbulent as well as obtained via experiment remains an open area of research. The present study reveals the capability of these techniques to create a time-invariant library that can predict instantaneous states of the flow from sparse measurements alone (provided that these states are within the bounds of the applied training data). The proposed linear methods, as well as the NN architecture, provide well-characterized frameworks for future efforts in sparse sensing and state estimation applications: particularly for highly nonlinear underlying systems such as turbulent flow.
Crystal structures of alphavirus nonstructural protein 4 (nsP4) reveal an intrinsically dynamic RNA-dependent RNA polymerase fold

Alphaviruses such as Ross River virus (RRV), chikungunya virus, Sindbis virus (SINV), and Venezuelan equine encephalitis virus are mosquito-borne pathogens that can cause arthritis or encephalitis diseases. Nonstructural protein 4 (nsP4) of alphaviruses possesses RNA-dependent RNA polymerase (RdRp) activity essential for viral RNA replication. No 3D structure has been available for nsP4 of any alphaviruses despite its importance for understanding alphaviral RNA replication and for the design of antiviral drugs. Here, we report crystal structures of the RdRp domain of nsP4 from both RRV and SINV determined at resolutions of 2.6 and 1.9 Å. The structure of the alphavirus RdRp domain appears most closely related to RdRps from pestiviruses, noroviruses, and picornaviruses. Hydrogendeuterium exchange mass spectrometry (HDX-MS) and nuclear magnetic resonance (NMR) methods, showed that in solution, nsP4 is highly dynamic with an intrinsically disordered N-terminal domain. Both full-length nsP4 and the RdRp domain were capable to catalyze RNA polymerization. Structure-guided mutagenesis using a trans-replicase system identified nsP4 regions critical for viral RNA replication. Key Points Crystal structures of alphavirus nsP4 RNA polymerase domain from RRV and SINV. nsP4 protein is highly dynamic with an intrinsically disordered N-terminal domain. Optimized RNA elongation activity assay to facilitate antiviral discovery.
Larger is not better: No mate preference by European Common Frog (Rana temporaria) males

According to classical sexual selection theory, females are the choosy sex in most species. Choosiness is defined as the individual effort to invest energy and time to assess potential mates. In explosive breeding anurans, high intrasexual competition between males leads to a sexual coercion ruled mating system, where males could have evolved preferences for specific female traits. In the current study, we tested male mating preference in the explosive breeding European Common Frog without intrasexual competition. We hypothesized that males show preferences towards larger female body size in the absence of male competition. We conducted mate choice experiments, placing a male and two differently sized females in a box and recorded their mating behavior. Males did not show any preference considering female body size, neither in the attempt to grab a female nor during the formation of pairs. We witnessed a high failure rate of male mating attempts, which might make the evolution of mate choice too costly. However, small males are faster in attempting females, which could be an alternative strategy to get access to females, because their larger competitors have an advantage during scramble competition. Nonetheless, in successfully formed pairs, the females were on average larger than the males, an observation which deviated from our null-model where pairs should be of similar size if mating would be random. This indicates that selection takes place, independent from male mating preference or scramble competition.
A machine learning approach for MRI-based classification of individuals with mild cognitive impairment

Diagnosis criteria for Mild Cognitive Impairment (MCI) rely on cognitive symptoms and do not require specific biomarker evidence of pathology. Nevertheless, patients may still have subtle brain changes that are identifiable with neuroimaging. To classify MCI patients vs. healthy age-matched controls, two algorithms—Multilayer Perceptron (MLP) and Support Vector Machine (SVM)—were applied to structural MRI data from the OASIS-1 cohort (https://www.oasis-brains.org/). Despite their comparable performance in some measures, the MLP algorithm was preferable due to superior recall of MCI cases.