Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetorotational core collapse of possible GRB progenitors – II. Formation of protomagnetars and collapsars

We assess the variance of the post-collapse evolution remnants of compact, massive, low-metallicity stars, under small changes in the degrees of rotation and magnetic field of selected pre-supernova cores. These stellar models are commonly considered progenitors of long gamma-ray bursts. The fate of the proto-neutron star (PNS) formed after collapse, whose mass may continuously grow due to accretion, critically depends on the poloidal magnetic field strength at bounce. Should the poloidal magnetic field be sufficiently weak, the PNS collapses to a black hole (BH) within a few seconds. Models on this evolutionary track contain promising collapsar engines. Poloidal magnetic fields smooth over large radial scales (e.g. dipolar fields) or slightly augmented with respect to the original pre-supernova core yield long-lasting PNSs. In these models, BH formation is avoided or staved off for a long time, hence, they may produce proto-magnetars (PMs). Some of our PM candidates have been run for $\lesssim 10\,$s after core bounce, but they have not entered the Kelvin-Helmholtz phase yet. Among these models, some display episodic events of spin-down during which we find properties broadly compatible with the theoretical expectations for PMs ($M_{PNS} \approx 1.85\,M_\odot - 2.5\,M_\odot$, $\bar{P}_{PNS} \approx 1.5 - 4\,$ms, and $b^{\rm surf}_{PNS} \lesssim 10^{15}\,$G) and their very collimated supernova ejecta has nearly reached the stellar surface with (still growing) explosion energies $\gtrsim 2\times 10^{51}\,$erg.
SN 2020bqj: A Type Ibn supernova with a long-lasting peak plateau

Context. Type Ibn supernovae (SNe Ibn) are a rare class of stripped envelope supernovae interacting with a helium-rich circumstellar medium (CSM). The majority of the SNe Ibn reported in the literature display a surprising homogeneity in their fast-evolving lightcurves and are typically found in actively starforming spiral galaxies. Aims. We present the discovery and the study of SN 2020bqj (ZTF20aalrqbu), a SN Ibn with a long-duration peak plateau lasting 40 days and hosted by a faint low-mass galaxy. We aim to explain its peculiar properties using an extensive photometric and spectroscopic data set. Methods. We compare the photometric and spectral evolution of SN 2020bqj with regular SNe Ibn from the literature, as well as with other outliers in the SN Ibn subclass. We fit the bolometric and multi-band lightcurves with powering mechanism models such as radioactive decay and CSM interaction. We also model the host galaxy of SN 2020bqj. Results. The risetime, peak magnitude and spectral features of SN 2020bqj are consistent with those of most SNe Ibn, but the SN is a clear outlier in the subclass based on its bright, long-lasting peak plateau and the low mass of its faint host galaxy. We show through modeling that the lightcurve of SN 2020bqj can be powered predominantly by shock heating from the interaction of the SN ejecta and a dense CSM, combined with radioactive decay. The peculiar Type Ibn SN 2011hw is a close analog to SN 2020bqj in terms of lightcurve and spectral evolution, suggesting a similar progenitor and CSM scenario. In this scenario a very massive progenitor star in the transitional phase between a luminous blue variable and a compact Wolf-Rayet star undergoes core-collapse, embedded in a dense helium-rich CSM with an elevated opacity compared to normal SNe Ibn, due to the presence of residual hydrogen. This scenario is consistent with the observed properties of SN 2020bqj and the modeling results. Conclusions. SN 2020bqj is a compelling example of a transitional SN Ibn/IIn based on not only its spectral features, but also its lightcurve, host galaxy properties and the inferred progenitor properties. The strong similarity with SN 2011hw suggests this subclass may be the result of a progenitor in a stellar evolution phase that is distinct from those of progenitors of regular SNe Ibn.
An Explainable 3D Residual Self-Attention Deep Neural Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis Using Structural MRI

Computer-aided early diagnosis of Alzheimer’s disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided a cost-effective and objective way for early prevention and treatment of disease progression, leading to improved patient care. In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of MR images to improve diagnostic performance; 2) An explainable method using Gradient-based Localization Class Activation mapping (Grad-CAM) has been introduced to improve the interpretability of the proposed method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis. Our proposed 3D ResAttNet method has been evaluated on a large cohort of subjects from real datasets for two changeling classification tasks (i.e. Alzheimer’s disease (AD) vs. Normal cohort (NC) and progressive MCI (pMCI) vs. stable MCI (sMCI)). The experimental results show that the proposed approach has a competitive advantage over the state-of-the-art models in terms of accuracy performance and generalizability. The explainable mechanism in our approach is able to identify and highlight the contribution of the important brain parts (e.g., hippocampus, lateral ventricle and most parts of the cortex) for transparent decisions
Aggression in the Workplace Makes Social Distancing Difficult

Background: The coronavirus disease (COVID-19) is still spreading. While social distancing has attracted attention as a measure to prevent the spread of infection, some occupations find it difficult to practice it. Therefore, we decided to investigate the differences in the ease of practicing social distancing depending on the occupation using the data on O*NET, an occupational information site.Methodology: Eight factors were extracted by performing exploratory factor analysis based on certain rules while eliminating arbitrariness as much as possible: adverse conditions, leadership, information processing, response to aggression, mechanical movement, autonomy, communication with the outside, and horizontal teamwork.Results: Adverse conditions, response to aggression, and horizontal teamwork had a positive correlation with physical proximity, while information processing, mechanical movement, autonomy, and communication with the outside had a negative correlation with it. Furthermore, multiple regression analysis showed that response to aggression, not just teamwork, as assumed in previous studies, had the greatest influence on physical proximity.Conclusion: To maintain social distancing, it is necessary to constrain aggression in the workplace.
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes

Self-supervised depth estimators have recently shown results comparable to the supervised methods on the challenging single image depth estimation (SIDE) task, by exploiting the geometrical relations between target and reference views in the training data. However, previous methods usually learn forward or backward image synthesis, but not depth estimation, as they cannot effectively neglect occlusions between the target and the reference images. Previous works rely on rigid photometric assumptions or the SIDE network to infer depth and occlusions, resulting in limited performance. On the other hand, we propose a method to "Forget About the LiDAR" (FAL), for the training of depth estimators, with Mirrored Exponential Disparity (MED) probability volumes, from which we obtain geometrically inspired occlusion maps with our novel Mirrored Occlusion Module (MOM). Our MOM does not impose a burden on our FAL-net. Contrary to the previous methods that learn SIDE from stereo pairs by regressing disparity in the linear space, our FAL-net regresses disparity by binning it into the exponential space, which allows for better detection of distant and nearby objects. We define a two-step training strategy for our FAL-net: It is first trained for view synthesis and then fine-tuned for depth estimation with our MOM. Our FAL-net is remarkably light-weight and outperforms the previous state-of-the-art methods with 8x fewer parameters and 3x faster inference speeds on the challenging KITTI dataset. We present extensive experimental results on the KITTI, CityScapes, and Make3D datasets to verify our method's effectiveness. To the authors' best knowledge, the presented method performs the best among all the previous self-supervised methods until now.
Orbital angular momentum due to modes interference

We present generalized expressions to calculate the orbital angular momentum for invariant beams using scalars potentials. The solutions can be separated into transversal electric TE, transversal magnetic TM and transversal electromagnetic TE/TM polarization modes. We show that the superposition of non-paraxial vectorial beams with axial symmetry can provide a well defined orbital angular momentum and that the modes superposition affects the angular momentum flux density. The results are illustrated and analyzed for Bessel beams.
Probing the atmosphere of HD189733b with the Na i and K i lines

High spectral resolution transmission spectroscopy is a powerful tool to characterize exoplanet atmospheres. Especially for hot Jupiters, this technique is highly relevant, due to their high-altitude absorption, e.g. from resonant sodium (Na i) and potassium (K i) lines. We resolve the atmospheric K i absorption on HD189733b with the aim to compare the resolved K i line and previously obtained high-resolution Na i-D line observations with synthetic transmission spectra. The line profiles suggest atmospheric processes leading to a line broadening of the order of ∼10 km/s for the Na i-D lines and only a few km/s for the K i line. The investigation hints that either the atmosphere of HD189733b lacks a significant amount of K i or the alkali lines probe different atmospheric regions with different temperature, which could explain the differences we see in the resolved absorption lines.
Describe What to Change: A Text-guided Unsupervised Image-to-image Translation Approach

Manipulating visual attributes of images through human-written text is a very challenging task. On the one hand, models have to learn the manipulation without the ground truth of the desired output. On the other hand, models have to deal with the inherent ambiguity of natural language. Previous research usually requires either the user to describe all the characteristics of the desired image or to use richly-annotated image captioning datasets. In this work, we propose a novel unsupervised approach, based on image-to-image translation, that alters the attributes of a given image through a command-like sentence such as "change the hair color to black". Contrarily to state-of-the-art approaches, our model does not require a human-annotated dataset nor a textual description of all the attributes of the desired image, but only those that have to be modified. Our proposed model disentangles the image content from the visual attributes, and it learns to modify the latter using the textual description, before generating a new image from the content and the modified attribute representation. Because text might be inherently ambiguous (blond hair may refer to different shadows of blond, e.g. golden, icy, sandy), our method generates multiple stochastic versions of the same translation. Experiments show that the proposed model achieves promising performances on two large-scale public datasets: CelebA and CUB. We believe our approach will pave the way to new avenues of research combining textual and speech commands with visual attributes.
Water worlds in N-body simulations with fragmentation in systems without gaseous giants

Aims. We analyze the formation and evolution of terrestrial-like planets around solar-type stars in the absence of gaseous giants. In particular, we focus on the physical and dynamical properties of those that survive in the system’s habitable zone (HZ). This investigation is based on a comparative study between N-body simulations that include fragmentation and others that consider all collisions as perfect mergers. Methods. We use an N-body code, presented in a previous paper, that allows planetary fragmentation. We carry out three sets of 24 simulations for 400 Myr. Two sets are developed adopting a model that includes hit-and-run collisions and planetary fragmentation, each one with different values of the individual minimum mass allowed for the fragments. For the third set, we considered that all collisions lead to perfect mergers. Results. The planetary systems produced in N-body simulations with and without fragmentation are broadly similar, though with some differences. In simulations with fragmentation, the formed planets have lower masses since part of them is distributed among collisional fragments. Additionally, those planets presented lower eccentricities, presumably due to dynamical friction with the generated fragments. Lastly, perfect mergers and hit-and-run collisions are the most common outcome. Regardless of the collisional treatment adopted, most of the planets that survive in the HZ start the simulation beyond the snow line, having very high final water contents. Such planets are called water worlds. The fragments’ contribution to their final mass and water content is negligible. Finally, the individual minimum mass for fragments may play an important role in the planets’ collisional history. Conclusions. Collisional models that incorporate fragmentation and hit-and-run collisions lead to a more detailed description of the physical properties of the terrestrial-like planets formed. We conclude that planetary fragmentation is not a barrier to the formation of water worlds in the HZ. The results shown in this work suggest that further refinement is necessary to have a more realistic model of planetary formation.
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Federated learning is a challenging optimization problem due to the heterogeneity of the data across different clients. Such heterogeneity has been observed to induce client drift and significantly degrade the performance of algorithms designed for this setting. In contrast, centralized learning with centrally collected data does not experience such drift, and has seen great empirical and theoretical progress with innovations such as momentum, adaptivity, etc. In this work, we propose a general framework Mime which mitigates client-drift and adapts arbitrary centralized optimization algorithms (e.g.\ SGD, Adam, etc.) to federated learning. Mime uses a combination of control-variates and server-level statistics (e.g. momentum) at every client-update step to ensure that each local update mimics that of the centralized method. Our thorough theoretical and empirical analyses strongly establish Mime's superiority over other baselines.
DR2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression

Visual tracking has yielded promising applications with unmanned aerial vehicle (UAV). In literature, the advanced discriminative correlation filter (DCF) type trackers generally distinguish the foreground from the background with a learned regressor which regresses the implicit circulated samples into a fixed target label. However, the predefined and unchanged regression target results in low robustness and adaptivity to uncertain aerial tracking scenarios. In this work, we exploit the local maximum points of the response map generated in the detection phase to automatically locate current distractors1. By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity. Substantial experiments conducted on three challenging UAV benchmarks demonstrate both excellent performance and extraordinary speed (~50fps on a cheap CPU) of our tracker.
On Einstein's Effective Viscosity Formula

In his PhD thesis, Einstein derived an explicit first-order expansion for the effective viscosity of a Stokes fluid with a random suspension of small rigid particles at low density. This formal derivation is based on two assumptions: (i) there is a scale separation between the size of particles and the observation scale, and (ii) particles do not interact with one another at first order. While the first assumption was addressed in a companion work in terms of homogenization theory, the second one is reputedly more subtle due to the long-range character of hydrodynamic interactions. In the present contribution, we provide a rigorous justification of Einstein's first-order expansion at low density in the most general setting. This is pursued to higher orders in form of a cluster expansion, where the summation of hydrodynamic interactions crucially requires suitable renormalizations, and we justify in particular a celebrated result by Batchelor and Green on the next-order correction. In addition, we address the summability of the cluster expansion in two specific settings (random deletion and geometric dilation of a fixed point set). Our approach relies on an intricate combination of combinatorial arguments, PDE analysis, and probability theory.
Adversarial Examples on Object Recognition

Deep neural networks are at the forefront of machine learning research. However, despite achieving impressive performance on complex tasks, they can be very sensitive: Small perturbations of inputs can be sufficient to induce incorrect behavior. Such perturbations, called adversarial examples, are intentionally designed to test the network’s sensitivity to distribution drifts. Given their surprisingly small size, a wide body of literature conjectures on their existence and how this phenomenon can be mitigated. In this article, we discuss the impact of adversarial examples on security, safety, and robustness of neural networks. We start by introducing the hypotheses behind their existence, the methods used to construct or protect against them, and the capacity to transfer adversarial examples between different machine learning models. Altogether, the goal is to provide a comprehensive and self-contained survey of this growing field of research.
Supervised Machine Learning Techniques: An Overview with Applications to Banking

This article provides an overview of supervised machine learning (ML) with a focus on applications in banking. The supervised ML techniques covered include bagging (random forest), boosting (gradient boosting machine) and neural networks. We begin with an introduction to ML tasks and techniques. This is followed by a description of tree‐based ensemble algorithms, including bagging with random forest and boosting with gradient boosting machines, as well as feedforward neural networks. We then provide an extensive discussion of hyper‐parameter optimisation techniques. Interpretability of ML results is an important topic in banking and other regulated industries, and it is also covered in some depth. The paper concludes with a comparison of the features of different ML algorithms and a discussion of their use in practice. An application from credit risk modelling in banking is used throughout the paper to illustrate the techniques and interpret the results of the algorithms.
Conductivity of superconductors in the flux flow regime

We develop a theory of conductivity of type-II superconductors in the flux flow regime taking into account random spatial fluctuations of the system parameters, such as the gap magnitude $\Delta$(r) and the diffusion coefficient D(r). We find a contribution to the conductivity that is proportional to the inelastic relaxation time $\tau_{in}$, which is much longer than the elastic relaxation time. This new contribution is due to Debye-type relaxation, and it can be much larger than the conventional flux flow conductivity due to Bardeen and Stephen. The new contribution is expected to dominate in clean superconductors at low temperatures and in magnetic fields much smaller than $H_{c2}$.
TGK-Planner: An Efficient Topology Guided Kinodynamic Planner for Autonomous Quadrotors

In this letter, we propose a lightweight yet effective Topology Guided Kinodynamic planner (TGK-Planner) for quadrotor aggressive flights with limited onboard computing resources. The proposed system follows the traditional hierarchical planning workflow, with novel designs to improve the robustness and efficiency in both the pathfinding and trajectory optimization sub-modules. Firstly, we propose the topology guided graph, which roughly captures the topological structure of the environment and guides the state sampling of a sampling-based kinodynamic planner. In this way, we significantly improve the efficiency of finding a safe and dynamically feasible trajectory. Then, we refine the smoothness and continuity of the trajectory in an optimization framework, which incorporates the homotopy constraint to guarantee the safety of the trajectory. The optimization program is formulated as a sequence of quadratic programmings (QPs) and can be iteratively solved in a few milliseconds. Finally, the proposed system is integrated into a fully autonomous quadrotor and validated in various simulated and real-world scenarios. Benchmark comparisons show that our method outperforms state-of-the-art methods with regard to efficiency and trajectory quality. Moreover, we will release our code as an open-source package.11Code is released at https://github.com/ZJU-FAST-Lab/TGK-Planner.
Effecf of Trichoderma Spp. on the growth of Colletotrichum capsici isolated from cotton seed

The Seed-borne pathogen Colleterichum capsici was isolated from cotton seed. The bioagents viz., Trichoderma viride, T. hamatum, T. harzianum, T. longiforum and T. koningil were tested for their antagonistic property against C. capsici by direct bit placement method. All the biological agents were significantly superior over control in checking the growth of C. capsici. Among the biological agents, T. hamatum was significantly very effective in checking the growth of tested C. capsici under in-vitro conditions.
Bit Parallel 6T SRAM In-memory Computing with Reconfigurable Bit-Precision

This paper presents 6T SRAM cell-based bit-parallel in-memory computing (IMC) architecture to support various computations with reconfigurable bit-precision. In the proposed technique, bit-line computation is performed with a short WL followed by BL boosting circuits, which can reduce BL computing delays. By per-forming carry-propagation between each near-memory circuit, bit-parallel complex computations are also enabled by iterating operations with low latency. In addition, reconfigurable bit-precision is also supported based on carry-propagation size. Our 128KB in/near memory computing architecture has been implemented using a 28nm CMOS process, and it can achieve 2.25GHz clock frequency at 0.9V with 5.2% of area overhead. The proposed architecture also achieves 0.68, 8.09 TOPS/W for the parallel addition and multiplication, respectively. In addition, the proposed work also supports a wide range of supply voltage, from 0.6V to 1.1V.