Miracle-dz/UniFAR-FTUs huggingface dataset:
Settling and clustering of particles of moderate mass density in turbulence

We present a numerical study of settling and clustering of small inertial particles in homogeneous and isotropic turbulence, in the dilute regime in which particles do not interact with each other or affect the fluid. Particles are denser than the fluid, but not in the limit of being much heavier than the displaced fluid. At fixed Reynolds and Stokes numbers we vary the fluid-to-particle mass ratio and the gravitational acceleration. The effect of varying one or the other is similar but not quite the same. We report non-monotonic behavior of the particles’ velocity skewness and kurtosis with the second parameter, and an associated anomalous behavior of the settling velocity when compared to the free-fall Stokes velocity, including some cases of loitering. Clustering increases for increasing gravitational acceleration, and for decreasing fluid-to-particle mass ratio.
The dynamics of the globular cluster NGC 3201 out to the Jacobi radius

As part of a chemodynamical survey of five nearby globular clusters with 2dF/AAOmega on the Anglo-Australian Telescope (AAT), we have obtained kinematic information for the globular cluster NGC 3201. Our new observations confirm the presence of a significant velocity gradient across the cluster which can almost entirely be explained by the high proper motion of the cluster (${\sim}9\, \mathrm{mas\, yr^{-1}}$). After subtracting the contribution of this perspective rotation, we found a remaining rotation signal with an amplitude of ${\sim}1\, \mathrm{km\, s^{-1}}$ around a different axis to what we expect from the tidal tails and the potential escapers, suggesting that this rotation is internal and can be a remnant of its formation process. At the outer part, we found a rotational signal that is likely a result from potential escapers. The proper motion dispersion at large radii reported by Bianchini et al. ($3.5\pm 0.9\, \mathrm{km\, s^{-1}}$) has previously been attributed to dark matter. Here, we show that the LOS dispersion between 0.5 and 1 Jacobi radius is lower ($2.01\pm 0.18\, \mathrm{km\, s^{-1}}$), yet above the predictions from an N-body model of NGC 3201 that we ran for this study ($1.48\pm 0.14\, \mathrm{km\, s^{-1}}$). Based on the simulation, we find that potential escapers cannot fully explain the observed velocity dispersion. We also estimate the effect on the velocity dispersion of different amounts of stellar-mass black holes and unbound stars from the tidal tails with varying escape rates and find that these effects cannot explain the difference between the LOS dispersion and the N-body model. Given the recent discovery of tidal tail stars at large distances from the cluster, a dark matter halo is an unlikely explanation. We show that the effect of binary stars, which is not included in the N-body model, is important and can explain part of the difference in dispersion. We speculate that the remaining difference must be the result of effects not included in the N-body model, such as initial cluster rotation, velocity anisotropy, and Galactic substructure.
A Catalog of High-velocity C iv Mini-broad Absorption Lines in the VLT-UVES and Keck-HIRES Archives

We present a catalog of high-velocity C iv λ1548,1551 mini-broad absorption lines (mini-BALs) in the archives of the Very Large Telescope-UV Visual Echelle Spectrograph and Keck-High Resolution Echelle Spectrometer. We identify C iv mini-BALs based on smooth rounded BAL-like profiles with velocity blueshifts <−4000 km s−1 and widths in the range 70 ≲ FWHM(1548) ≲ 2000 km s−1. We find 105 mini-BALs in 44 quasars from a total sample of 638 quasars. The fraction of quasars with at least one mini-BAL meeting our criteria is roughly ∼9% after correcting for incomplete velocity coverage. All of the systems are highly ionized based on the strong presence of N v and O vi and/or the absence of Si ii and C ii when within the wavelength coverage. Two of the mini-BAL systems in our catalog, plus three others at smaller velocity shifts, have P v λ1118,1128 absorption indicating highly saturated C iv absorption and total hydrogen column densities ≳1022 cm−2. Most of the mini-BALs are confirmed to have optical depths ≳1 with partial covering of the quasar continuum source. The covering fractions are as small as 0.06 in C iv and 0.03 in Si iv, corresponding to outflow absorbing structures <0.002 pc across. When multiple lines are measured, the lines of less abundant ions tend to have narrower profiles and smaller covering fractions indicative of inhomogeneous absorbers where higher column densities occur in smaller clumps. This picture might extend to BAL outflows if the broader and generally deeper BALs form in either the largest clumps or collections of many mini-BAL-like clumps that blend together in observed quasar spectra.
AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset

Screwdriving is one of the most popular industrial processes. As such, it is increasingly common to automate that procedure by using various robots. Even though the automation increases the efficiency of the screwdriving process, if the process is not monitored correctly, faults may occur during operation, which can impact the effectiveness and quality of assembly. Machine Learning (ML) has the potential to detect those undesirable events and limit their impact. In order to do so, first a dataset that fully describes the operation of an industrial robot performing automated screwdriving must be available. This report describes a dataset created using a UR3e series robot and OnRobot Screwdriver. We create different scenarios and introduce 4 types of anomalies to the process while all available robot and screwdriver sensors are continuously recorded. The resulting data contains 2042 samples of normal and anomalous robot operation. Brief ML benchmarks using this data are also provided, showcasing the data's suitability and potential for further analysis and experimentation.
Multi-Modal Ensemble Models for Predicting Video Memorability

Modeling media memorability has been a consistent challenge in the field of machine learning. The Predicting Media Memorability task in MediaEval2020 is the latest benchmark among similar challenges addressing this topic. Building upon techniques developed in previous iterations of the challenge, we developed ensemble methods with the use of extracted video, image, text, and audio features. Critically, in this work we introduce and demonstrate the efficacy and high generalizability of extracted audio embeddings as a feature for the task of predicting media memorability.
Volumes of definable sets in o-minimal expansions and affine GAGA theorems

In this mostly expository note, I give a very quick proof of the deﬁnable Chow theorem of Peterzil and Starchenko using the Bishop-Stoll theorem and a volume estimate for deﬁnable sets due to Nguyen and Valette. The volume estimate says that any d -dimensional deﬁnable subset of S ⊆ R n in an o-minimal expansion of the ordered ﬁeld of real numbers satisﬁes the inequality H d ( { x ∈ S : k x k < r } ) ≤ Cr d , where H d denotes the d -dimensional Hausdorﬀ measure on R n and C is a constant depending on S . A closely related volume estimate for subanalytic sets goes back to Kurdyka and Raby. Since this note is intended to be helpful to algebraic geometers not versed in o-minimal structures and deﬁnable sets, I review these notions and also prove the main volume estimate from scratch.
Thermal Characterizations of Chitosan and Polyacrylonitrile Semi-Interpenetrating Polymer Networks

The thermal characterization of the semi-interpenetrating polymer networks (IPNs) was investigated by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and dielectric analysis (DEA). The melting temperature (Tm) of semi-IPNs were observed with increasing chitosan content using DSC. DEA was employed to ascertain the glass transition temperature (Tg) of semi-IPNs. In the results of DEA, three relaxation peaks appeared around 100, 200 and 290 'C for the semi-IPNs, and the semi-IPNs exhibited two Tgs indicating the presence of phase separation in the semi-IPN. The thermal decomposition of semi-IPNs was investigated using TGA and appeared near 208 0C.
Symplectic Gaussian Process Dynamics

Dynamics model learning is challenging and at the same time an active ﬁeld of research. Due to potential safety critical downstream applications, such as control tasks, there is a need for theoretical guarantees. While GPs induce rich theoretical guarantees as function approximators in space, they do not explicitly cope with the time aspect of dynamical systems. However, propagating system properties through time is exactly what classical numerical integrators were designed for. We introduce a recurrent sparse Gaussian process based variational inference scheme that is able to discretize the underlying system with any explicit or implicit single or multistep integrator, thus leveraging properties of numerical integrators. In particular we discuss Hamiltonian problems coupled with symplectic integrators producing volume preserving predictions.
Horizon Thermodynamics in D-Dimensional f(R) Black Hole

We consider whether the new horizon-first law works in higher-dimensional f(R) theory. We firstly obtain the general formulas to calculate the entropy and the energy of a general spherically-symmetric black hole in D-dimensional f(R) theory. For applications, we compute the entropies and the energies of some black hokes in some interesting higher-dimensional f(R) theories.
MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation

Despite major advances in open-ended text generation, there has been limited progress in designing evaluation metrics for this task. We propose MAUVE —a metric for open-ended text generation, which directly compares the distribution of machine-generated text to that of human language. MAUVE measures the mean area under the divergence curve for the two distributions, exploring the trade-off be-tween two types of errors: those arising from parts of the human distribution that the model distribution approximates well, and those it does not. We present experiments across two open-ended generation tasks in the web text domain and the story domain, and a variety of decoding algorithms and model sizes. Our results show that evaluation under MAUVE indeed reﬂects the more natural behavior with respect to model size, compared to prior metrics. MAUVE ’s ordering of the decoding algorithms also agrees with that of generation perplexity, the most widely used metric in open-ended text generation; however, MAUVE presents a more principled evaluation metric for the task as it considers both model and human text. 1
Universal systole bounds for arithmetic locally symmetric spaces

The systole of a closed Riemannian manifold is the minimal length of a non-contractible closed loop. We give a uniform lower bound for the systole for large classes of simple arithmetic locally symmetric orbifolds. We establish new bounds for the translation length of a semisimple element x in SL_n(R) in terms of its associated Mahler measure. We use these geometric methods to prove the existence of extensions of number fields in which fixed sets of primes have certain prescribed splitting behavior.
Data-driven Approach to Parameterize SCAN+U for an Accurate Description of 3d Transition Metal Oxide Thermochemistry

Semi-local DFT methods exhibit significant errors for the phase diagrams of transition-metal oxides that are caused by an incorrect description of molecular oxygen and the large self-interaction error in materials with strongly localized electronic orbitals. Empirical and semiempirical corrections based on the DFT+U method can reduce these errors, but the parameterization and validation of the correction terms remains an on-going challenge. We develop a systematic methodology to determine the parameters and to statistically assess the results by considering thermochemical data across a set of transition metal compounds. We consider three interconnected levels of correction terms: (1) a constant oxygen binding correction, (2) Hubbard-U correction, and (3) DFT/DFT+U compatibility correction. The parameterization is expressed as a unified optimization problem. We demonstrate this approach for 3d transition metal oxides, considering a target set of binary and ternary oxides. With a total of 37 measured formation enthalpies taken from the literature, the dataset is augmented by the reaction energies of 1,710 unique reactions that were derived from the formation energies by systematic enumeration. To ensure a balanced dataset across the available data, the reactions were grouped by their similarity using clustering and suitably weighted. The parameterization is validated using leave-one-out cross-validation (CV), a standard technique for the validation of statistical models. We apply the methodology to the SCAN density functional. Based on the CV score, the error of binary (ternary) oxide formation energies is reduced by 40% (75%) to 0.10 (0.03) eV/atom. The method and tools demonstrated here can be applied to other classes of materials or to parameterize the corrections to optimize DFT+U performance for other target physical properties.
pseudo-Bayesian Neural Networks for detecting Out of Distribution Inputs

Conventional Bayesian Neural Networks (BNNs) are known to be capable of providing multiple outputs for a single input, the variations in which can be utilised to detect Out of Distribution (OOD) inputs. BNNs are difﬁcult to train due to their sensitivity towards the choice of priors. To alleviate this issue, we propose pseudo-BNNs where instead of learning distributions over weights, we use point estimates and perturb weights at the time of inference. We modify the cost function of conventional BNNs and use it to learn parameters for the purpose of injecting right amount of random perturbations to each of the weights of a neural network with point estimate. In order to effectively seg-regate OOD inputs from In Distribution (ID) inputs using multiple outputs, we further propose two measures, derived from the index of dispersion and entropy of probability distributions, and combine them with the proposed pseudo-BNNs. Overall, this combination results in a principled technique to detect OOD samples at the time of inference. We evaluate our technique on a wide variety of neural network architectures and image classiﬁcation datasets. We observe that our method achieves state of the art results and beats the related previous work on various metrics such as FPR at 95% TPR, AUROC, AUPR and Detection Error by just using 2 to 5 samples of weights per input.
A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data

This paper demonstrates the predictive superiority of discrete wavelet transform (DWT) over previously used methods of feature extraction in the diagnosis of epileptic seizures from EEG data. Classification accuracy, specificity, and sensitivity are used as evaluation metrics. We specifically show the immense potential of 2 combinations (DWT-db4 combined with SVM and DWT-db2 combined with RF) as compared to others when it comes to diagnosing epileptic seizures either in the balanced or the imbalanced dataset. The results also highlight that MFCC performs less than all the DWT used in this study and that, The mean-differences are statistically significant respectively in the imbalanced and balanced dataset. Finally, either in the balanced or the imbalanced dataset, the feature extraction techniques, the models, and the interaction between them have a statistically significant effect on the classification accuracy.
Origin and Evolution of Ices in Star-Forming Regions: A VLT-ISAAC 3-5 microns Spectroscopic Survey

The VLT has opened up the possibility to perform spectroscopic surveys of large number s of young low-mass stars which are still deeply embedded in their parental clouds. O ur infrared spectra show a rich variety of features due to ices and gas-phase molecule s, each of which trace different aspects of the physical and chemical state of the obj ects. Highlights include fundamental new insight into the structure of interstellar i ces; the first detection of solid methanol in low-mass protostars, a key ingredient fo r building more complex organic molecules; direct evidence for significant freeze-out in edge-on circumstellar discs; and sensitive limits on minor ice components such as a mmonia and deuterated water.
Optimal Sequential Detection of Signals With Unknown Appearance and Disappearance Points in Time

This article addresses a sequential changepoint detection problem, assuming that the duration of change may be finite and unknown. This problem is of importance for many applications, e.g., for signal and image processing where signals appear and disappear at unknown points in time or space. In contrast to the conventional optimality criterion in quickest change detection that requires minimization of the expected delay to detection for a given average run length to a false alarm, we focus on a reliable maximin change detection criterion of maximizing the minimal probability of detection in a given time (or space) window for a given local maximal probability of false alarm in the prescribed window. We show that the optimal detection procedure is a modified CUSUM procedure. We then compare operating characteristics of this optimal procedure with popular in engineering the Finite Moving Average (FMA) detection algorithm and the ordinary CUSUM procedure using Monte Carlo simulations, which show that typically the later algorithms have almost the same performance as the optimal one. At the same time, the FMA procedure has a substantial advantage – independence to the intensity of the signal, which is usually unknown. Finally, the FMA algorithm is applied to detecting faint streaks of satellites in optical images.
Capacity and quantum geometry of parametrized quantum circuits

To harness the potential of noisy intermediate-scale quantum devices, it is paramount to find the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are parametrized quantum circuits that can be effectively implemented on current devices. Here, we evaluate the capacity and trainability of these circuits using the geometric structure of the parameter space via the effective quantum dimension, which reveals the expressive power of circuits in general as well as of particular initialization strategies. We assess the expressive power of various popular circuit types and find striking differences depending on the type of entangling gates used. Particular circuits are characterized by scaling laws in their expressiveness. We identify a transition in the quantum geometry of the parameter space, which leads to a decay of the quantum natural gradient for deep circuits. For shallow circuits, the quantum natural gradient can be orders of magnitude larger in value compared to the regular gradient; however, both of them can suffer from vanishing gradients. By tuning a fixed set of circuit parameters to randomized ones, we find a region where the circuit is expressive, but does not suffer from barren plateaus, hinting at a good way to initialize circuits. We show an algorithm that prunes redundant parameters of a circuit without affecting its effective dimension. Our results enhance the understanding of parametrized quantum circuits and can be immediately applied to improve variational quantum algorithms.
Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT)

Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using a hyperbolic and interesting words, sometimes unfinished sentence in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model M-BERT that acted as a embedding layer is then combined with a 100 nodes hidden layer and topped with a sigmoid classifier was trained to detect clickbait headlines. With a total of 6632 headlines as a training dataset, the classifier performed remarkably well. Evaluated with 5-fold cross validation, it has an accuracy score of 0.914, f1-score of 0.914, precision score of 0.916, and ROC-AUC of 0.92. The usage of multilingual BERT in Indonesian text classification task was tested and is possible to be enhanced further. Future possibilities, societal impact, and limitations of the clickbait detection are discussed.
Adaptive Variational Quantum Imaginary Time Evolution Approach for Quantum Chemistry Calculations

An adaptive variational quantum imaginary time evolution (AVQITE) approach is introduced that yields eﬃcient representations of ground states for interacting Hamiltonians on near-term quantum computers. It is based on McLachlan’s variational principle applied to imaginary time evolution of variational wave functions, and avoids diﬃcult nonconvex high-dimensional optimization that plagues other variational approaches such as the variational quantum eigensolver. The variational parameters evolve according to equations of motions that minimize the diﬀerence to the exact imaginary time evolution, which is quantiﬁed by the McLachlan distance. Rather than working with a ﬁxed variational ansatz, where the minimal McLachlan distance is constrained by the quality of the ansatz, the adaptive method iteratively expands the ansatz along the dynamical path to ensure the McLachlan distance remains below a chosen threshold. AVQITE is used to prepare ground states of H 4 , H 2 O and BeH 2 molecules and yields compact variational ans¨atze and ground state energies beyond chemical accuracy. Finally, quantum Lanczos calculations can also be naturally performed alongside AVQITE without additional quantum resource costs.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.