Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics and interactions of Quincke roller clusters: From orbits and flips to excited states

Active matter systems may be characterized by the conversion of energy into active motion, e.g., the self-propulsion of microorganisms. Artificial active colloids form models that exhibit essential properties of more complex biological systems but are amenable to laboratory experiments. While most experimental models consist of spheres, active particles of different shapes are less understood. Furthermore, interactions between these anisotropic active colloids are even less explored. Here, we investigate the motion of active colloidal clusters and the interactions between them. We focus on self-assembled dumbbells and trimers powered by an external dc electric field. For dumbbells, we observe an activity-dependent behavior of spinning, circular, and orbital motions. Moreover, collisions between dumbbells lead to the hierarchical self-assembly of tetramers and hexamers, both of which form rotational excited states. On the other hand, trimers exhibit flipping motion that leads to trajectories reminiscent of a honeycomb lattice.
Effect of optically thin cooling curves on condensation formation: Case study using thermal instability

Context. Non-gravitationally induced condensations are observed in many astrophysical environments. In solar physics, common phenomena are coronal rain and prominences. These structures are formed due to energy loss by optically thin radiative emission. Instead of solving the full radiative transfer equations, precomputed cooling curves are typically used in numerical simulations. In the literature, a wide variety of cooling curves exist, and they are quite often used as unquestionable ingredients. Aims. We here determine the e ﬀ ect of the optically thin cooling curves on the formation and evolution of condensations. We also investigate the e ﬀ ect of numerical settings. This includes the resolution and the low-temperature treatment of the cooling curves, for which the optically thin approximation is not valid. Methods. We performed a case study using thermal instability as a mechanism to form in situ condensations. We compared 2D numerical simulations with di ﬀ erent cooling curves using interacting slow magnetohydrodynamic (MHD) waves as trigger for the thermal instability. Furthermore, we discuss a bootstrap measure to investigate the far non-linear regime of thermal instability. In the appendix, we include the details of all cooling curves implemented in MPI-AMRVAC and brieﬂy discuss a hydrodynamic variant of the slow MHD waves setup for thermal instability. Results. For all tested cooling curves, condensations are formed. The di ﬀ erences due to the change in cooling curve are twofold. First, the growth rate of the thermal instability is di ﬀ erent, leading to condensations that form at di ﬀ erent times. Second, the morphology of the formed condensation varies widely. After the condensation forms, we ﬁnd fragmentation that is a ﬀ ected by the low-temperature treatment of the cooling curves. Condensations formed using cooling curves that vanish for temperatures lower than 20000K appear to be more stable against dynamical instabilities. We also show the need for high-resolution simulations. The bootstrap procedure allows us to continue the simulation into the far non-linear regime, where the condensation fragments dynamically align with the background magnetic ﬁeld. The non-linear regime and fragmentation in the hydrodynamic case di ﬀ er greatly from the low-beta MHD case. Conclusions. We advocate the use of modern cooling curves, based on accurate computations and current atomic parameters and solar abundances. Our bootstrap procedure can be used in future multi-dimensional simulations to study ﬁne-structure dynamics in solar prominences.
A Sub-2% Distance to M31 from Photometrically Homogeneous Near-infrared Cepheid Period–Luminosity Relations Measured with the Hubble Space Telescope

We present period–luminosity relations (PLRs) for 55 Cepheids in M31 with periods ranging from 4 to 78 days observed with the Hubble Space Telescope using the same three-band photometric system recently used to calibrate their luminosities. Images were taken with the Wide Field Camera 3 in two optical filters (F555W and F814W) and one near-infrared filter (F160W) using the Drift and Shift (DASH) mode of operation to significantly reduce overheads and observe widely separated Cepheids in a single orbit. We include additional F160W epochs for each Cepheid from the Panchromatic Hubble Andromeda Treasury and use light curves from the Panoramic Survey Telescope and Rapid Response System of the Andromeda galaxy project to determine mean magnitudes. Combined with a 1.28% absolute calibration of Cepheid PLRs in the Large Magellanic Cloud from Riess et al. in the same three filters, we find a distance modulus to M31 of μ 0 = 24.407 ± 0.032, corresponding to 761 ± 11 kpc and 1.49% uncertainty including all error sources, the most precise determination of its distance to date. We compare our results to past measurements using Cepheids and the tip of the red giant branch. This study also provides the groundwork for turning M31 into a precision anchor galaxy in the cosmic distance ladder to measure the Hubble constant together with efforts to measure a fully geometric distance to M31.
Considerations for Optimizing the Photometric Classification of Supernovae from the Rubin Observatory

The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all SNe discovered. Thus, photometric classification is crucial, but its accuracy depends on the not-yet-finalized observing strategy of Rubin Observatory’s Legacy Survey of Space and Time (LSST). We quantitatively analyze the impact of the LSST observing strategy on SNe classification using simulated multiband light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). First, we augment the simulated training set to be representative of the photometric redshift distribution per SNe class, the cadence of observations, and the flux uncertainty distribution of the test set. Then we build a classifier using the photometric transient classification library snmachine, based on wavelet features obtained from Gaussian process fits, yielding a similar performance to the winning PLAsTiCC entry. We study the classification performance for SNe with different properties within a single simulated observing strategy. We find that season length is important, with light curves of 150 days yielding the highest performance. Cadence also has an important impact on SNe classification; events with median inter-night gap <3.5 days yield higher classification performance. Interestingly, we find that large gaps (>10 days) in light-curve observations do not impact performance if sufficient observations are available on either side, due to the effectiveness of the Gaussian process interpolation. This analysis is the first exploration of the impact of observing strategy on photometric SN classification with LSST.
Understanding Heating in Active Region Cores through Machine Learning. II. Classifying Observations

Constraining the frequency of energy deposition in magnetically closed active region cores requires sophisticated hydrodynamic simulations of the coronal plasma and detailed forward modeling of the optically thin line-of-sight integrated emission. However, understanding which set of model inputs best matches a set of observations is complicated by the need for any proposed heating model to simultaneously satisfy multiple observable constraints. In this paper, we train a random forest classification model on a set of forward-modeled observable quantities, namely the emission measure slope, the peak temperature of the emission measure distribution, and the time lag and maximum cross-correlation between multiple pairs of AIA channels. We then use our trained model to classify the heating frequency in every pixel of active region NOAA 1158 using the observed emission measure slopes, peak temperatures, time lags, and maximum cross-correlations, and are able to map the heating frequency across the entire active region. We find that high-frequency heating dominates in the inner core of the active region while intermediate-frequency dominates closer to the periphery of the active region. Additionally, we assess the importance of each observed quantity in our trained classification model and find that the emission measure slope is the dominant feature in deciding with which heating frequency a given pixel is most consistent. The technique presented here offers a very promising and widely applicable method for assessing observations in terms of detailed forward models given an arbitrary number of observable constraints.
Imitate TheWorld: A Search Engine Simulation Platform

Recent E-commerce applications benefit from the growth of deep learning techniques. However, we notice that many works attempt to maximize business objectives by closely matching offline labels which follow the supervised learning paradigm. This results in models obtain high offline performance in terms of Area Under Curve (AUC) and Normalized Discounted Cumulative Gain (NDCG), but cannot consistently increase the revenue metrics such as purchases amount of users. Towards the issues, we build a simulated search engine AESim that can properly give feedback by a well-trained discriminator for generated pages, as a dynamic dataset. Different from previous simulation platforms which lose connection with the real world, ours depends on the real data in AliExpress Search: we use adversarial learning to generate virtual users and use Generative Adversarial Imitation Learning (GAIL) to capture behavior patterns of users. Our experiments also show AESim can better reflect the online performance of ranking models than classic ranking metrics, implying AESim can play a surrogate of AliExpress Search and evaluate models without going online.
Large unidirectional magnetoresistance in metallic heterostructures in the spin transfer torque regime

A large unidirectional magnetoresistance (UMR) ratio of UMR/Rxx ~ 0.36% is found in W/CoFeB metallic bilayer heterostructures at room temperature. Three different regimes in terms of the current dependence of UMR ratio are identified: A spin-dependent-scattering mechanism regime at small current densities J ~ 10 9 A/m (UMR ratio J  ), a spin-magnon-interaction mechanism regime at intermediate J ~ 10 A/m (UMR ratio 3 J  ), and a spin-transfer torque (STT) regime at J ~ 10 11 A/m (UMR ratio independent of J). We verify the direct correlation between this large UMR and the transfer of spin angular momentum from the W layer to the CoFeB layer by both field-dependent and current-dependent UMR characterizations. Numerical simulations further confirm that the large STT-UMR stems from the tilting of the magnetization affected by the spin Hall effect-induced spin-transfer torques. An alternative approach to estimate damping-like spin torque efficiencies from magnetic heterostructures is also proposed.
Flexible Covariate Adjustments in Regression Discontinuity Designs

Empirical regression discontinuity (RD) studies often use covariates to increase the precision of their estimates. In this paper, we propose a novel class of estimators that use such covariate information more efficiently than the linear adjustment estimators that are currently used widely in practice. Our approach can accommodate a possibly large number of either discrete or continuous covariates. It involves running a standard RD analysis with an appropriately modified outcome variable, which takes the form of the difference between the original outcome and a function of the covariates. We characterize the function that leads to the estimator with the smallest asymptotic variance, and show how it can be estimated via modern machine learning, nonparametric regression, or classical parametric methods. The resulting estimator is easy to implement, as tuning parameters can be chosen as in a conventional RD analysis. An extensive simulation study illustrates the performance of our approach.
Influence of high temperature aerobic composting treatment on the form of heavy metals in pig manure

The influence of high temperature aerobic composting treatment on the change of heavy metal combined form in pig manure was studied using Tessier sequential extraction method. After aerobic composting treatment, the total concentrations of Pb, Cu, Zn, Ni, Cr, Cd, As in pig manure increased. The concentrations of residual forms increased generally; only the concentrations of sulfide and organic combined form As and Zn decreased. The concentrations of exchangeable form Cu, Zn, Cr and As decreased markedly. Composting treatment could decrease the distribution coefficients of exchangeable form and carbonate form Pb, Ni, Cu, Cr, Zn, As and Fe-Mn oxides combined form Pb, Cu, Cr and As. Therefore, it could decrease the effectiveness of heavy metals in pig manure. Composting treatment is a possible way to reduce the risk of heavy metal pollution in the utilisation of pig manure on land.
An Energy-Efficient Edge Computing Paradigm for Convolution-Based Image Upsampling

State-of-the-art deep learning solutions for image upsampling are currently trained using either resize or sub-pixel convolution to learn kernels that generate high fidelity images with minimal artifacts. However, performing inference with these learned convolution kernels requires memory-intensive feature map transformations that dominate time and energy costs in real-time applications. To alleviate this pressure on memory bandwidth, we propose a novel energy-efficient edge computing paradigm that confines the use of resize or sub-pixel convolution to training in the cloud by transforming learned convolution kernels to deconvolution kernels before deploying them for inference as a functionally equivalent deconvolution. These kernel transformations, intended as a one-time cost when shifting from training to inference, enable a systems designer to use each algorithm in their optimal context by preserving the image fidelity learned when training in the cloud while minimizing data transfer penalties during inference at the edge. We compare the inference properties of these convolution-based image upsampling algorithms and introduce a novel deconvolution inference algorithm, which we refer to as REVD2. To demonstrate the benefits of our approach, we upsample images selected from the BSD300 dataset using a pre-trained single-image super resolution network provided by the PyTorch model zoo. Using quantitative models of incurred time and energy costs to analyze this deep neural network, we estimate that using REVD2 for inference at the edge improves system latency by <inline-formula> <tex-math notation="LaTeX">$2.1\times $ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">$2.8\times $ </tex-math></inline-formula> and energy efficiency by <inline-formula> <tex-math notation="LaTeX">$2.1\times $ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">$2.7\times $ </tex-math></inline-formula> when respectively compared to sub-pixel or resize convolution counterparts.
Learning Locomotion Controllers for Walking Using Deep FBSDE

In this paper, we propose a deep forward-backward stochastic differential equation (FBSDE) based control algorithm for locomotion tasks. We also include state constraints in the FBSDE formulation to impose stable walking solutions or other constraints that one may want to consider (e.g., energy). Our approach utilizes a deep neural network (i.e., LSTM) to solve, in general, high-dimensional Hamilton-Jacobi-Bellman (HJB) equation resulting from the stated optimal control problem. As compared to traditional methods, our proposed method provides a higher computational efficiency in real-time; thus yielding higher frequency implementation of the closed-loop controllers. The efficacy of our approach is shown on a linear inverted pendulum model (LIPM) for walking. Even though we are deploying a simplified model of walking, the methodology is applicable to generalized and complex models for walking and other control/optimization tasks in robotic systems. Simulation studies have been provided to show the effectiveness of the proposed methodology.
New magnetic chemically peculiar stars and candidates in the ATLAS first catalogue of variable stars

The number of known variable stars has increased by several magnitudes over the last decade, and automated classification routines are becoming increasingly important to cope with this development. Here we show that the ‘upside-down CBH variables’, which were proposed as a potentially new class of variable stars by Heinze et al. in the ATLAS First Catalogue of Variable Stars, are, at least to a high percentage, made up of α2 Canum Venaticorum (ACV) variables – that is, photometrically variable magnetic chemically peculiar (CP2/He-peculiar) stars – with distinct double-wave light curves. Using suitable selection criteria, we identified 264 candidate ACV variables in the ATLAS variable star catalogue. 62 of these objects were spectroscopically confirmed with spectra from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (all new discoveries except for nine stars) and classified on the MK system. The other 202 stars are here presented as ACV star candidates that require spectroscopic confirmation. The vast majority of our sample of stars are main-sequence objects. Derived masses range from 1.4 M⊙ to 5 M⊙, with half our sample stars being situated in the range from 2 M⊙ to 2.4 M⊙, in good agreement with the spectral classifications. Most stars belong to the thin or thick disc; four objects, however, classify as members of the halo population. With a peak magnitude distribution at around 14th magnitude, the here presented stars are situated at the faint end of the known Galactic mCP star population. Our study highlights the need to consider rare variability classes, like ACV variables, in automated classification routines.
Tight Bounds for Approximate Near Neighbor Searching for Time Series under the Fréchet Distance

We study the $c$-approximate near neighbor problem under the continuous Fr\'echet distance: Given a set of $n$ polygonal curves with $m$ vertices, a radius $\delta>0$, and a parameter $k \leq m$, we want to preprocess the curves into a data structure that, given a query curve $q$ with $k$ vertices, either returns an input curve with Fr\'echet distance at most $c\cdot \delta$ to $q$, or returns that there exists no input curve with Fr\'echet distance at most $\delta$ to $q$. We focus on the case where the input and the queries are one-dimensional polygonal curves -- also called time series -- and we give a comprehensive analysis for this case. We obtain new upper bounds that provide different tradeoffs between approximation factor, preprocessing time, and query time. Our data structures improve upon the state of the art in several ways. We show that for any $0<\varepsilon \leq 1$ an approximation factor of $(1+\varepsilon)$ can be achieved within the same asymptotic time bounds as the previously best result for $(2+\varepsilon)$. Moreover, we show that an approximation factor of $(2+\varepsilon)$ can be obtained by using preprocessing time and space $O(nm)$, which is linear in the input size, and query time in $O(\frac{1}{\varepsilon})^{k+2}$, where the previously best result used preprocessing time in $n \cdot O(\frac{m}{\varepsilon k})^k$ and query time in $O(1)^k$. We complement our upper bounds with matching conditional lower bounds based on the Orthogonal Vectors Hypothesis. Interestingly, some of our lower bounds already hold for any super-constant value of $k$. This is achieved by proving hardness of a one-sided sparse version of the Orthogonal Vectors problem as an intermediate problem, which we believe to be of independent interest.
On the Extended TSP Problem

We initiate the theoretical study of Ext-TSP , a problem that originates in the area of profile-guided binary optimization. Given a graph G = ( V, E ) with positive edge weights w : E → R + , and a non-increasing discount function f ( · ) such that f (1) = 1 and f ( i ) = 0 for i > k , for some parameter k that is part of the problem definition. The problem is to sequence the vertices V so as to maximize P ( u,v ) ∈ E f ( | d u − d v | ) · w ( u, v ), where d v ∈ { 1 , . , | V |} is the position of vertex v in the sequence. We show that Ext-TSP is APX-hard to approximate in general and we give a ( k + 1)-approximation algorithm for general graphs and a PTAS for some sparse graph classes such as planar or treewidth-bounded graphs. Interestingly, the problem remains challenging even on very simple graph classes; indeed, there is no exact n o ( k ) time algorithm for trees unless the ETH fails. We complement this negative result with an exact n O ( k ) time algorithm for trees. Acknowledgements We would like to thank Vahid Liaghat for fruitful discussions on the Ext-TSP problem.
Flow onset for a single bubble in a yield-stress fluid

Abstract We use computational methods to determine the minimal yield stress required in order to hold static a buoyant bubble in a yield-stress liquid. The static limit is governed by the bubble shape, the dimensionless surface tension ($\gamma$) and the ratio of the yield stress to the buoyancy stress ($Y$). For a given geometry, bubbles are static for $Y > Y_c$, which we determine for a range of shapes. Given that surface tension is negligible, long prolate bubbles require larger yield stress to hold static compared with oblate bubbles. Non-zero $\gamma$ increases $Y_c$ and for large $\gamma$ the yield-capillary number ($Y/\gamma$) determines the static boundary. In this limit, although bubble shape is important, bubble orientation is not. Two-dimensional planar and axisymmetric bubbles are studied.
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems

Accurately learning the temporal behavior of dynamical systems requires models with well-chosen learning biases. Recent innovations embed the Hamiltonian and Lagrangian formalisms into neural networks and demonstrate a significant improvement over other approaches in predicting trajectories of physical systems. These methods generally tackle autonomous systems that depend implicitly on time or systems for which a control signal is known a priori. Despite this success, many real world dynamical systems are nonautonomous, driven by time-dependent forces and experience energy dissipation. In this study, we address the challenge of learning from such nonautonomous systems by embedding the port-Hamiltonian formalism into neural networks, a versatile framework that can capture energy dissipation and time-dependent control forces. We show that the proposed port-Hamiltonian neural network can efficiently learn the dynamics of nonlinear physical systems of practical interest and accurately recover the underlying stationary Hamiltonian, time-dependent force, and dissipative coefficient. A promising outcome of our network is its ability to learn and predict chaotic systems such as the Duffing equation, for which the trajectories are typically hard to learn.