Miracle-dz/UniFAR-FTUs huggingface dataset:
Stellar Tidal Disruption Events with Abundances and Realistic Structures (STARS): Library of Fallback Rates

We present the STARS library, a grid of tidal disruption event (TDE) simulations interpolated to provide the mass fallback rate (dM/dt) to the black hole for a main-sequence star of any stellar mass, stellar age, and impact parameter. We use a one-dimensional stellar evolution code to construct stars with accurate stellar structures and chemical abundances, then perform tidal disruption simulations in a three-dimensional adaptive-mesh hydrodynamics code with a Helmholtz equation of state, in unprecedented resolution: from 131 to 524 cells across the diameter of the star. The interpolated library of fallback rates is available on GitHub (github.com/jamielaw-smith/STARS_library) and version 1.0. 0 is archived on Zenodo; one can query the library for any stellar mass, stellar age, and impact parameter. We provide new fitting formulae for important disruption quantities ( ) as a function of stellar mass, stellar age, and impact parameter. Each of these quantities varies significantly with stellar mass and stellar age, but we are able to reduce all of our simulations to a single relationship that depends only on stellar structure, characterized by a single parameter , and impact parameter β. We also find that, in general, more centrally concentrated stars have steeper dM/dt rise slopes and shallower decay slopes. For the same ΔM, the dM/dt shape varies significantly with stellar mass, promising the potential determination of stellar properties from the TDE light curve alone. The dM/dt shape depends strongly on stellar structure and to a certain extent stellar mass, meaning that fitting TDEs using this library offers a better opportunity to determine the nature of the disrupted star and the black hole.
Coevolution of black hole accretion and star formation in galaxies up to z = 3.5

Aims. We study the coevolution between the black hole accretion rate (BHAR) and the star formation rate (SFR) in different phases of galaxy life: main-sequence star-forming galaxies, quiescent galaxies, and starburst galaxies at different cosmic epochs. Methods. We exploited the unique combination of depth and area in the COSMOS field and took advantage of the X-ray data from the Chandra COSMOS-Legacy survey and the extensive multiwavelength ancillary data presented in the COSMOS2015 catalog, including in particular the UVista Ultra-deep observations. These large datasets allowed us to perform an X-ray stacking analysis and combine it with detected sources in a broad redshift interval (0.1 < z < 3.5) with unprecedented statistics for normal star-forming, quiescent, and starburst galaxies. The X-ray luminosity was used to predict the black holeAR, and a similar stacking analysis on far-infrared Herschel maps was used to measure the corresponding obscured SFR for statistical samples of sources in different redshifts and stellar mass bins. Results. We focus on the evolution of the average SFR-stellar mass (M*) relation and compare it with the BHAR-M* relation. This extends previous works that pointed toward the existence of almost linear correlations in both cases. We find that the ratio between BHAR and SFR does not evolve with redshift, although it depends on stellar mass. For the star-forming populations, this dependence on M* has a logarithmic slope of ∼0.6 and for the starburst sample, the slope is ∼0.4. These slopes are both at odds with quiescent sources, where the dependence remains constant (log(BHAR/SFR) ∼ −3.4). By studying the specific BHAR and specific SFR, we find signs of downsizing for M* and black hole mass (MBH) in galaxies in all evolutionary phases. The increase in black hole mass-doubling timescale was particularly fast for quiescents, whose super-massive black holes grew at very early times, while accretion in star-forming and starburst galaxies continued until more recent times. Conclusions. Our results support the idea that the same physical processes feed and sustain star formation and black hole accretion in star-forming galaxies while the starburst phase plays a lesser role in driving the growth of the supermassive black holes, especially at high redshift. Our integrated estimates of the M* − MBH relation at all redshifts are consistent with independent determinations of the local M* − MBH relation for samples of active galactic nuclei. This adds key evidence that the evolution in the BHAR/SFR is weak and its normalization is relatively lower than that of local dynamical M* − MBH relations.
Analytical prediction of electrowetting-induced jumping motion for droplets on textured hydrophobic substrates: Effects of the wetting states

In electrowetting, an applied electric voltage can induce spreading, sliding, or even jumping of an individual droplet by changing the intrinsic balance of the three-phase interfacial tensions. This technique has been widely used for manipulating droplets in microfluidics and by lab-on-a-chip devices in recent decades. In the present paper, we present an analytical prediction of the jumping velocity for droplets undergoing electrowetting on textured hydrophobic surfaces with different wetting states. In particular, we consider wetting a liquid droplet on a textured hydrophobic substrate with a voltage applied between the droplet and the substrate. Once the voltage is turned off, the energy stored in the droplet during electrowetting is released and could even result in the detachment of the droplet. The effects of the initial and electrowetting states, i.e., the Cassie–Baxter state and the Wenzel state, on the jumping velocity of droplets are systematically discussed. Based on energy conservation, the energy conversion between the surface energy, the elastic energy of the contact line, and the kinetic energy of droplets due to internal viscous dissipation in different wetting states is analyzed. Closed-form formulas for the jumping velocity of different droplet wetting states are systematically derived. Finally, a unified form for predicting the electrowetting-induced jumping velocity of droplets on both flat and textured substrates with different wetting states is obtained. It can describe the jumping motion under various wetting conditions, which is validated by some experimental results. This work provides theoretical insights into the accurate control of the electrowetting-induced jumping motion of droplets on textured hydrophobic surfaces.
Molecular Distributions of the Protostellar Envelope and the Outflow of IRAS 15398–3359: Principal Component Analysis

Using the Atacama Large Millimeter/submillimeter Array, we have imaged 15 molecular-line emissions and the dust continuum emission around the Class 0 protostellar source IRAS 15398−3359. The outflow structure is mainly traced by the H2CO (Ka = 0 and 1), CCH, and CS emissions. These lines also trace the disk/envelope structure around the protostar. The H2CO (Ka = 2 and 3), CH3OH, and SO emissions are concentrated toward the protostar, while the DCN emission is more extended around the protostar. We have performed principal component analysis (PCA) for these distributions on two different scales, the outflow and the disk/envelope structure. For the latter case, the molecular-line distributions are classified into two groups, according to the contribution of the second principal component, one having a compact distribution around the protostar and the other showing a rather extended distribution over the envelope. Moreover, the second principal component value tends to increase as an increasing quantum number of H2CO (Ka = 0, 1, 2, and 3), reflecting the excitation condition: the distribution is more compact for higher excitation lines. These results indicate that PCA is effective at extracting the characteristic features of the molecular-line distributions around the protostar in an unbiased way. In addition, we identify four blobs in the outflow structure in the H2CO lines, some of which can also be seen in the CH3OH, CS, CCH, and SO emissions. The gas temperature derived from the H2CO lines ranges from 43–63 K, which suggests shocks due to the local impact of the outflow on clumps of the ambient gas.
Online Monitoring of Global Attitudes Towards Wildlife

Human factors are increasingly recognised as central to conservation of biodiversity. Despite this, there are no existing systematic efforts to monitor global trends in perceptions of wildlife. With traditional news reporting now largely online, the internet presents a powerful means to monitor global attitudes towards species. In this work we develop a method using the Global Database of Events, Language, and Tone (GDELT) to scan global news media, allowing us to identify and download conservation-related articles. Applying supervised machine learning techniques, we filter irrelevant articles to create a continually updated global dataset of news coverage for seven target taxa: lion, tiger, saiga, rhinoceros, pangolins, elephants, and orchids, and observe that over two-thirds of articles matching a simple keyword search were irrelevant. We examine coverage of each taxa in different regions, and find that elephants, rhinos, tigers, and lions receive the most coverage, with daily peaks of around 200 articles. Mean sentiment was positive for all taxa, except saiga for which it was neutral. Coverage was broadly distributed, with articles from 73 countries across all continents. Elephants and tigers received coverage in the most countries overall, whilst orchids and saiga were mentioned in the smallest number of countries. We further find that sentiment towards charismatic megafauna is most positive in non-range countries, with the opposite being true for pangolins and orchids. Despite promising results, there remain substantial obstacles to achieving globally representative results. Disparities in internet access between low and high income countries and users is a major source of bias, with the need to focus on a diversity of data sources and languages, presenting sizable technical challenges.
Spectral Clustering using Eigenspectrum Shape Based Nystrom Sampling

Spectral clustering has shown a superior performance in analyzing the cluster structure. However, its computational complexity limits its application in analyzing large-scale data. To address this problem, many low-rank matrix approximating algorithms are proposed, including the Nystrom method - an approach with proven approximate error bounds. There are several algorithms that provide recipes to construct Nystrom approximations with variable accuracies and computing times. This paper proposes a scalable Nystrom-based clustering algorithm with a new sampling procedure, Centroid Minimum Sum of Squared Similarities (CMS3), and a heuristic on when to use it. Our heuristic depends on the eigen spectrum shape of the dataset, and yields competitive low-rank approximations in test datasets compared to the other state-of-the-art methods
Deep-VFX: Deep Action Recognition Driven VFX for Short Video

Human motion is a key function to communicate information. In the application, short-form mobile video is so popular all over the world such as Tik Tok. The users would like to add more VFX so as to pursue creativity and personlity. Many special effects are added on the short video platform. These gives the users more possibility to show off these personality. The common and traditional way is to create the template of VFX. However, in order to synthesis the perfect, the users have to tedious attempt to grasp the timing and rhythm of new templates. It is not easy-to-use especially for the mobile app. This paper aims to change the VFX synthesis by motion driven instead of the traditional template matching. We propose the AI method to improve this VFX synthesis. In detail, in order to add the special effect on the human body. The skeleton extraction is essential in this system. We also propose a novel form of LSTM to find out the user's intention by action recognition. The experiment shows that our system enables to generate VFX for short video more easier and efficient.
Collaborative Localization of Aerial and Ground Mobile Robots through Orthomosaic Map

With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization approach considering the cooperative SLAM of an aerial-ground system. The proposed approach aims to help precisely matching the map constructed by two independent systems who have large scale variance of view points of the same route and eventually enables the ground mobile robot to localize itself in the global map given by the drone. It contains dense mapping with Elevation Map and software “Metashape”, map matching with a proposed template matching algorithm, weighted normalized cross correlation (WNCC) and localization with particle filter. The approach enables map matching for cooperative SLAM with a feasibility of multiple scene sensors, varies from stereo cameras to lidars, and is insensitive to the synchronization of the two systems. We demonstrate the accuracy, robustness, and the speed of the approach under experiments of the Aero-Ground Dataset [1].
Cache-Enabling UAV Communications: Network Deployment and Resource Allocation

In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization problem, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.
Zero-Dimensional Organic-Inorganic Hybrid Material with Ultra-Narrow-Red Emission at Room Temperature

Recently, low-dimensional organic-inorganic hybrid halide compounds have aroused great attention in the optoelectronic field, due to the unique topology and optical properties. Herein, we report an Mn4+ doped [N(CH3)4]2TiF6 zero-dimensional organic-inorganic hybrid phosphor, which could not only exhibit very narrow and pure red emission, but also maintain efficient emission intensity at room temperature. The crystal structure, photoluminescence properties and temperature sensing application are discussed. The excellent temperature dependent luminescent properties are attributed to the rigid structure and isolated MnF62- octahedra in the total crystal framework. These results will help design suitable materials and devices in both warm white light emitting diodes and optical sensors.
Three-dimensional heat transfer effects in external layers of a magnetized neutron star

Determination of a magnetic field structure on a neutron star (NS) surface is an important problem of a modern astrophysics. In a presence of strong magnetic fields a thermal conductivity of a degenerate matter is anisotropic. In this paper we present 3D anisotropic heat transfer simulations in outer layers of magnetized NSs, and construct synthetic thermal light curves. We have used a different from previous works tensorial thermal conductivity coefficient of electrons, derived from the analytical solution of the Boltzmann equation by the Chapman-Enskog method. We have obtained a NS surface temperature distribution in presence of dipole-plus-quadrupole magnetic fields. We consider a case, in which magnetic axes of a dipole and quadrupole components of the magnetic field are not aligned. To examine observational manifestations of such fields we have generated thermal light curves for the obtained temperature distributions using a composite black-body model. It is shown, that the simplest (only zero-order spherical function in quadrupole component) non-coaxial dipole-plus-quadrupole magnetic field distribution can significantly affect the thermal light curves, making pulse profiles non-symmetric and amplifying pulsations in comparison to the pure-dipolar field.
Thermodynamical Evolution of Supra-arcade Downflows

Supra-arcade downflows (SADs) are dark, teardrop-shaped features descending upon flare arcades. They are thought to be the results of magnetic reconnection, but the detailed formation mechanism of SADs and their relationship to flare energy release are still unclear. In this work, we explore the thermodynamical properties of SADs in the 2011 October 22 limb flare using the observations of the Solar Dynamics Observatory/Atmospheric Imaging Assembly and an improved Differential Emission Measure (DEM) technique. Different heating events around SADs are identified and the propagation speeds of plasma heating are calculated. The first heating event starts with the increase of the emission measure at temperatures higher than 5 MK, about 2.8 minutes before the arrival of the first studied SAD. Its propagation speed is about 140 km s−1, a little faster than the speed of the SAD. However, the other two events have fast propagation speeds more than 700 km s−1. We suspect that the first heating event can be explained by adiabatic compression, but the others may have different causes. Besides, we observed that SADs can push away their surrounding spikes. The formation of SADs is further explained on the basis of patchy and bursty magnetic reconnection that reconnection outflows may push away surrounding plasma and leave dark lanes behind them. The reliability of the DEM results, heating and cooling mechanisms, and other SAD explanations are discussed.
Far-infrared Photometric Redshifts: A New Approach to a Highly Uncertain Enterprise

This paper presents a new approach to deriving far-infrared (FIR) photometric redshifts for galaxies based on their reprocessed emission from dust at rest-frame FIR through millimeter wavelengths. FIR photometric redshifts (“FIR-z”) have been used over the past decade to derive redshift constraints for highly obscured galaxies that lack photometry at other wavelengths like the optical/near-IR. Most literature FIR-z fits are performed through minimization to a single galaxy’s FIR template spectral energy distribution (SED). The use of a single galaxy template, or modest set of templates, can lead to an artificially low uncertainty estimate on FIR-z's because real galaxies display a wide range in intrinsic dust SEDs. I use the observed distribution of galaxy SEDs (for well-constrained samples across ) to motivate a new FIR through millimeter photometric redshift technique called MMpz. The MMpz algorithm asserts that galaxies are most likely drawn from the empirically observed relationship between rest-frame peak wavelength, , and total IR luminosity, L ; the derived photometric redshift accounts for the measurement uncertainties and intrinsic variation in SEDs at the inferred L , as well as heating from the cosmic microwave background at . The MMpz algorithm has a precision of , similar to single-template fits, while providing a more accurate estimate of the FIR-z uncertainty with reduced chi-squared of order , compared to alternative FIR photometric redshift techniques (with ).
DEAL: Deep Evidential Active Learning for Image Classification

Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models for image classification. However, large labeled data sets are generally needed for the training and validation of such models. In many domains, unlabeled data is available but labeling is expensive, for instance when specific expert knowledge is required. Active Learning (AL) is one approach to mitigate the problem of limited labeled data. Through selecting the most informative and representative data instances for labeling, AL can contribute to more efficient learning of a model. Recent AL methods for CNNs propose different solutions for the selection of such instances. However, they do not perform consistently well and are often computationally expensive. In this paper, we propose a novel AL algorithm that efficiently learns from unlabeled data by capturing high prediction uncertainty. By replacing the softmax standard output of a CNN with the parameters of a Dirichlet density, the model learns to identify data instances that contribute efficiently to improving model performance during training. We demonstrate in several experiments with publicly available data that our method consistently outperforms other state-of-the-art AL approaches. It can be easily implemented and does not require extensive computational resources for training. Additionally, we are able to show the benefits of the approach on the real-world medical use case of pneumonia detection in chest X-ray images. We encourage readers to consult the extended manuscript on the arXiv.
Topology Optimization of Plasmonics with the FDTD method in Frequency Domain

We present a new method for calculating sensitivity in plasmonics with the FDTD method by direct differentiation of the time-marching system in frequency domain. This new method supports general frequency-domain objective functions, does not relay on implementation details of the FDTD method, works with general isotropic materials and can be incorporated into density-based topology optimization. We demonstrate the accuracy of this method for plasmonics. We also present a framework to carry out density-based topology optimization using the new sensitivity formula. We use non-linear material interpolation to counter the non-physical field amplification, adopt filtering-and-projection regularization to ensure manufacturability of the design. As examples of this approach, successful reconstruction of electric fields of a plasmonic bowtie aperture and optimization of absorption within a lossy medium produced by a plasmonic antenna with the framework are presented.
How to Increase Interest in Studying Functional Programming via Interdisciplinary Application

Functional programming represents a modern tool for applying and implementing software. The state of the art in functional programming reports an increasing number of methodologies in this paradigm. However, extensive interdisciplinary applications are missing. Our goal is to increase student interest in pursuing further studies in functional programming with the use of an application: the ray tracer. We conducted a teaching experience, with positive results and student feedback, described here in this paper.