Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive lasso and Dantzig selector for spatial point processes intensity estimation

Lasso and Dantzig selector are standard procedures able to perform variable selection and estimation simultaneously. This paper is concerned with extending these procedures to spatial point process intensity estimation. We propose adaptive versions of these procedures, develop efficient computational methodologies and derive asymptotic results for a large class of spatial point processes under an original setting where the number of parameters, i.e. the number of spatial covariates considered, increases with the expected number of data points. Both procedures are compared theoretically, in a simulation study, and in a real data example.
Training Deep Architectures Without End-to-End Backpropagation: A Brief Survey

—This tutorial paper surveys training alternatives to end-to-end backpropagation (E2EBP) — the de facto standard for training deep architectures. Modular training refers to strictly local training without both the forward and the backward pass, i.e., dividing a deep architecture into several nonoverlapping modules and training them separately without any end-to-end operation. Between the fully global E2EBP and the strictly local modular training, there are “weakly modular” hybrids performing training without the backward pass only. These alternatives can match or surpass the performance of E2EBP on challenging datasets such as ImageNet, and are gaining increased attention primarily because they offer practical advantages over E2EBP, which will be enumerated herein. In particular, they allow for greater modularity and transparency in deep learning workﬂows, aligning deep learning with the mainstream computer science engineering that heavily exploits modularization for scalability. Modular training has also revealed novel insights about learning and has further implications on other important research domains. Speciﬁcally, it induces natural and effective solutions to some important practical problems such as data efﬁciency and transferability estimation.
Synthesis and hydrophilic properties of Mo doped TiO2 thin films

Amorphous undoped TiO2 and Mo-doped TiO2 thin films were obtained by spray pyrolysis. By heat treatment, they became polycrystalline consisting in anatase, or mixed anatase/rutile phases, if deposited on glass or silicon, respectively. Mo enters in the TiO2 matrix as Mo5+, determining a slight red shift of the absorption edge. Based on Fourier transmission infrared analysis, performed on the studied films, before and after irradiation, providing information on the relationship between hydrophilicity and the amount of the adsorbed hydroxyl groups, we certify once again that the anatase phase (present in proportion of 100% in the films deposited on glass) is superior concerning the hydrophilic properties. Mo enhances wettability for the films deposited on silicon, compared to the undoped one, even if determines a rise in the rutile weight percentage, as a combined effect with the substrate nature. The decomposition of the oleic acid confirms that titania films with higher hydrophilic performances are also g.
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals

Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entities that are multiple hops away in the Knowl- edge Base (KB) from the entities in the question. A major challenge is the lack of supervision signals at intermediate steps. Therefore, multi-hop KBQA algorithms can only receive the feedback from the final answer, which makes the learning unstable or ineffective. To address this challenge, we propose a novel teacher-student approach for the multi-hop KBQA task. In our approach, the stu- dent network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network. The major novelty lies in the design of the teacher network, where we utilize both forward and backward reasoning to enhance the learning of intermediate entity distributions. By considering bidi- rectional reasoning, the teacher network can produce more reliable intermediate supervision signals, which can alleviate the issue of spurious reasoning. Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our approach on the KBQA task.
On the RND under Heston's Stochastic Volatility Model

We consider Heston's (1993) stochastic volatility model for valuation of European options to which (semi) closed form solutions are available and are given in terms of characteristic functions. We prove that the class of scale-parameter distributions with mean being the forward spot price satisfies Heston's solution. Thus, we show that any member of this class could be used for the direct risk-neutral valuation of the option price under Heston's SV model. In fact, we also show that any RND with mean being the forward spot price that satisfies Hestons' option valuation solution, must be a member of a scale-family of distributions in that mean. As particular examples, we show that one-parameter versions of the {\it Log-Normal, Inverse-Gaussian, Gamma, Weibull} and the {\it Inverse-Weibull} distributions are all members of this class and thus provide explicit risk-neutral densities (RND) for Heston's pricing model. We demonstrate, via exact calculations and Monte-Carlo simulations, the applicability and suitability of these explicit RNDs using already published Index data with a calibrated Heston model (S&amp;P500, Bakshi, Cao and Chen (1997), and ODAX, Mrázek and Pospíšil (2017)), as well as current option market data (AMD).
Analysis of the Alignment of Non-Random Patterns of Spin Directions in Populations of Spiral Galaxies

Observations of non-random distribution of galaxies with opposite spin directions have recently attracted considerable attention. Here, a method for identifying cosinedependence in a dataset of galaxies annotated by their spin directions is described in the light of different aspects that can impact the statistical analysis of the data. These aspects include the presence of duplicate objects in a dataset, errors in the galaxy annotation process, and non-random distribution of the asymmetry that does not necessarily form a dipole or quadrupole axes. The results show that duplicate objects in the dataset can artificially increase the likelihood of cosine dependence detected in the data, but a very high number of duplicate objects is required to lead to a false detection of an axis. Inaccuracy in galaxy annotations has relatively minor impact on the identification of cosine dependence when the error is randomly distributed between clockwise and counterclockwise galaxies. However, when the error is not random, even a small bias of 1% leads to a statistically significant cosine dependence that peaks at the celestial pole. Experiments with artificial datasets in which the distribution was not random showed strong cosine dependence even when the data did not form a full dipole axis alignment. The analysis when using the unmodified data shows asymmetry profile similar to the profile shown in multiple previous studies using several different telescopes.
Early-life Income Shocks and Old-Age Cause-Specific Mortality

This paper investigates the causal relationship between income shocks during the first years of life and adulthood mortality due to specific causes of death. Using all death records in the United States during 1968-2004 for individuals who were born in the first half of the 20th century, we document a sizable and statistically significant association between income shocks early in life, proxied by GDP per capita fluctuations, and old age cause-specific mortality. Conditional on individual characteristics and controlling for a broad array of current and early-life conditions, we find that a 1 percent decrease in the aggregate business cycle in the year of birth is associated with 2.2, 2.3, 3.1, 3.7, 0.9, and 2.1 percent increase in the likelihood of mortality in old ages due to malignant neoplasms, Diabetes Mellitus, cardiovascular diseases, Influenza, chronic respiratory diseases, and all other diseases, respectively.
Bayesian U-Net for Segmenting Glaciers in Sar Imagery

Fluctuations of the glacier calving front have an important influence over the ice flow of whole glacier systems. It is therefore important to precisely monitor the position of the calving front. However, the manual delineation of SAR images is a difficult, laborious and subjective task. Convolutional neural networks have previously shown promising results in automating the glacier segmentation in SAR images, making them desirable for further exploration of their possibilities. In this work, we propose to compute uncertainty and use it in an Uncertainty Optimization regime as a novel two-stage process. By using dropout as a random sampling layer in a U-Net architecture, we create a probabilistic Bayesian Neural Network. With several forward passes we create a sampling distribution, which can estimate the model uncertainty for each pixel in the segmentation mask. The additional uncertainty map information can serve as a guideline for the experts in the manual annotation of the data. Furthermore, feeding the uncertainty map to the network leads to 95.24 % Dice similarity, which is an overall improvement in the segmentation performance compared to the state-of-the-art deterministic U-Net-based glacier segmentation pipelines.
A Luminous Quasar at Redshift 7.642

Distant quasars are unique tracers to study the formation of the earliest supermassive black holes (SMBHs) and the history of cosmic reionization. Despite extensive efforts, only two quasars have been found at z ≥ 7.5, due to a combination of their low spatial density and the high contamination rate in quasar selection. We report the discovery of a luminous quasar at z = 7.642, J0313−1806, the most distant quasar yet known. This quasar has a bolometric luminosity of 3.6 × 1013 L ⊙. Deep spectroscopic observations reveal a SMBH with a mass of (1.6 ± 0.4) × 109 M ⊙ in this quasar. The existence of such a massive SMBH just ∼670 million years after the big bang challenges significantly theoretical models of SMBH growth. In addition, the quasar spectrum exhibits strong broad absorption line (BAL) features in C iv and Si iv, with a maximum velocity close to 20% of the speed of light. The relativistic BAL features, combined with a strongly blueshifted C iv emission line, indicate that there is a strong active galactic nucleus (AGN)-driven outflow in this system. Atacama Large Millimeter/submillimeter Array observations detect the dust continuum and [C ii] emission from the quasar host galaxy, yielding an accurate redshift of 7.6423 ± 0.0013 and suggesting that the quasar is hosted by an intensely star-forming galaxy, with a star formation rate of ∼200 M ⊙ yr−1 and a dust mass of ∼7 × 107 M ⊙. Follow-up observations of this reionization-era BAL quasar will provide a powerful probe of the effects of AGN feedback on the growth of the earliest massive galaxies.
Good Students Play Big Lottery Better

Lottery ticket hypothesis [1] suggests that a dense neural network contains a sparse sub-network that can match the test accuracy of the original dense net when trained in isolation from (the same) random initialization. However, the hypothesis failed to generalize to larger dense networks such as ResNet-50. As a remedy, recent studies [2, 3] demonstrate that a sparse sub-network can still be obtained by using a rewinding technique, which is to re-train it from early-phase training weights or learning rates of the dense model, rather than from random initialization. Is rewinding the only or the best way to scale up lottery tickets ? This paper proposes a new, simpler and yet powerful technique for re-training the sub-network, called "Knowledge Distillation ticket" ( KD ticket ). Rewinding exploits the value of inheriting knowledge from the early training phase to improve lottery tickets in large networks. In comparison, KD ticket addresses a complementary possibility - inheriting useful knowledge from the late training phase of the dense model. It is achieved by leveraging the soft labels generated by the trained dense model to re-train the sub-network, instead of the hard labels. Extensive experiments are conducted using several large deep networks (e.g ResNet-50 and ResNet-110) on CIFAR-10 and ImageNet datasets. Without bells and whistles, when applied by itself, KD ticket performs on par or better than rewinding, while being nearly free of hyperparameters or ad-hoc selection. KD ticket can be further applied together with rewinding, yielding state-of-the-art results for large-scale lottery tickets.
Geomagnetic semblance and dipolar–multipolar transition in top-heavy double-diffusive geodynamo models

Convection in the liquid outer core of the Earth is driven by thermal and chemical perturbations. The main purpose of this study is to examine the impact of double-diffusive convection on magnetic field generation by means of 3-D global geodynamo models, in the so-called ‘top-heavy’ regime of double-diffusive convection, when both thermal and compositional background gradients are destabilizing. Using a linear eigensolver, we begin by confirming that, compared to the standard single-diffusive configuration, the onset of convection is facilitated by the addition of a second buoyancy source. We next carry out a systematic parameter survey by performing 79 numerical dynamo simulations. We show that a good agreement between simulated magnetic fields and the geomagnetic field can be attained for any partitioning of the convective input power between its thermal and chemical components. On the contrary, the transition between dipole-dominated and multipolar dynamos is found to strongly depend on the nature of the buoyancy forcing. Classical parameters expected to govern this transition, such as the local Rossby number—a proxy of the ratio of inertial to Coriolis forces—or the degree of equatorial symmetry of the flow, fail to capture the dipole breakdown. A scale-dependent analysis of the force balance instead reveals that the transition occurs when the ratio of inertial to Lorentz forces at the dominant length scale reaches 0.5, regardless of the partitioning of the buoyancy power. The ratio of integrated kinetic to magnetic energy Ek/Em provides a reasonable proxy of this force ratio. Given that Ek/Em ≈ 10−4 − 10−3 in the Earth’s core, the geodynamo is expected to operate far from the dipole–multipole transition. It hence appears that the occurrence of geomagnetic reversals is unlikely related to dramatic and punctual changes of the amplitude of inertial forces in the Earth’s core, and that another mechanism must be sought.
Wolf-Rayet Galaxies in SDSS-IV MaNGA. II. Metallicity Dependence of the High-mass Slope of the Stellar Initial Mass Function

As hosts of living high-mass stars, Wolf-Rayet (WR) regions or WR galaxies are ideal objects for constraining the high-mass end of the stellar initial mass function (IMF). We construct a large sample of 910 WR galaxies/regions that cover a wide range of stellar metallicity (from Z ∼ 0.001 to 0.03) by combining three catalogs of WR galaxies/regions previously selected from the SDSS and SDSS-IV/MaNGA surveys. We measure the equivalent widths of the WR blue bump at ∼4650 Å for each spectrum. They are compared with predictions from stellar evolutionary models Starburst99 and BPASS, with different IMF assumptions (high-mass slope α of the IMF ranging from 1.0 to 3.3). Both singular evolution and binary evolution are considered. We also use a Bayesian inference code to perform full spectral fitting to WR spectra with stellar population spectra from BPASS as fitting templates. We then make a model selection among different α assumptions based on Bayesian evidence. These analyses have consistently led to a positive correlation of the IMF high-mass slope α with stellar metallicity Z, i.e., with a steeper IMF (more bottom-heavy) at higher metallicities. Specifically, an IMF with α = 1.00 is preferred at the lowest metallicity (Z ∼ 0.001), and an Salpeter or even steeper IMF is preferred at the highest metallicity (Z ∼ 0.03). These conclusions hold even when binary population models are adopted.
Asymptotic interplay of states and adaptive coupling gains in the Lohe Hermitian sphere model

<p style='text-indent:20px;'>We study emergent dynamics of the Lohe Hermitian sphere (LHS) model with the same free flows under the dynamic interplay between state evolution and adaptive couplings. The LHS model is a complex counterpart of the Lohe sphere (LS) model on the unit sphere in Euclidean space, and when particles lie in the Euclidean unit sphere embedded in <inline-formula><tex-math id="M1">\begin{document}$ \mathbb C^{d+1} $\end{document}</tex-math></inline-formula>, it reduces to the Lohe sphere model. In the absence of interactions between states and coupling gains, emergent dynamics have been addressed in [<xref ref-type="bibr" rid="b23">23</xref>]. In this paper, we further extend earlier results in the aforementioned work to the setting in which the state and coupling gains are dynamically interrelated via two types of coupling laws, namely anti-Hebbian and Hebbian coupling laws. In each case, we present two sufficient frameworks leading to complete aggregation depending on the coupling laws, when the corresponding free flow is the same for all particles. </p>
Evolutionary map of the Universe (EMU): Compact radio sources in the scorpio field towards the galactic plane

We present observations of a region of the Galactic plane taken during the Early Science Program of the Australian Square Kilometre Array Pathfinder (ASKAP). In this context, we observed the scorpio field at 912 MHz with an uncompleted array consisting of 15 commissioned antennas. The resulting map covers a square region of ∼40 deg2, centred on (l, b) = (343.5°, 0.75°), with a synthesized beam of 24 × 21 arcsec2 and a background rms noise of 150–200 μJy beam−1, increasing to 500–600 μJy beam−1 close to the Galactic plane. A total of 3963 radio sources were detected and characterized in the field using the caesar source finder. We obtained differential source counts in agreement with previously published data after correction for source extraction and characterization uncertainties, estimated from simulated data. The ASKAP positional and flux density scale accuracy were also investigated through comparison with previous surveys (MGPS, NVSS) and additional observations of the scorpio field, carried out with ATCA at 2.1 GHz and 10 arcsec spatial resolution. These allowed us to obtain a measurement of the spectral index for a subset of the catalogued sources and an estimated fraction of (at least) 8 per cent of resolved sources in the reported catalogue. We cross-matched our catalogued sources with different astronomical data bases to search for possible counterparts, finding ∼150 associations to known Galactic objects. Finally, we explored a multiparametric approach for classifying previously unreported Galactic sources based on their radio-infrared colours.
Corrosion inhibition by Allium sativum (garlic) extract

The inhibition efficiency (IE) of an aqueous extract of garlic (GE) in controlling corrosion of carbon steel in well water in the absence and presence of Zn2+ has been evaluated by mass loss method. The formulation consisting of 2 mL GE and 25 ppm Zn2+ offers 65% IE to carbon steel immersed in well water. Addition of malic acid (MA) increases IE of the GE – Zn2+ system. The IE of GE-Zn2+ and GEZn2+-MA system decreases with increase in immersion period (IP) and it increases with increase in pH from 3 to 11. Polarization study reveals that this formulation controls the anodic reaction predominantly. FTIR spectra reveal that the protective film consists of Fe2+- allicin complex and Zn (OH)2. The nature of the protective film formed on the metal surface has been characterized by Scanning Electron Microscopy (SEM).
Measuring Turbulence with Young Stars in the Orion Complex

Stars form in molecular clouds in the interstellar medium (ISM) with a turbulent kinematic state. Newborn stars therefore should retain the turbulent kinematics of their natal clouds. Gaia DR2 and APOGEE-2 surveys in combination provide three-dimensional (3D) positions and 3D velocities of young stars in the Orion Molecular Cloud Complex. Using the full 6D measurements, we compute the velocity structure functions (VSFs) of the stars in six different groups within the Orion Complex. We find that the motions of stars in all diffuse groups exhibit strong characteristics of turbulence. Their first-order VSFs have a power-law exponent ranging from ∼0.2–0.5 on scales of a few to a few tens of parsecs, generally consistent with Larson’s relation. On the other hand, dense star clusters, such as the Orion Nebula Cluster (ONC), have experienced rapid dynamical relaxation, and have lost the memory of the initial turbulent kinematics. The VSFs of several individual groups and the whole Complex all show features supporting local energy injection from supernovae. The measured strength of turbulence depends on the location relative to the supernova epicenters and the formation history of the groups. Our detection of turbulence traced by young stars introduces a new method of probing the turbulent kinematics of the ISM. Unlike previous gas-based studies with only projected measurements accessible to observations, we utilize the full 6D information of stars, presenting a more complete picture of the 3D interstellar turbulence.
System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data

The most important goal of customer service is to keep the customer satisfied. However, service resources are always limited and must prioritize specific customers. Therefore, it is essential to identify customers who potentially become unsatisfied and might lead to escalations. Data science on IoT data (especially log data) for machine health monitoring and analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. This paper presents a data-driven decision support system framework that combines IoT and enterprise data to model customer sentiment and predicts escalations. The proposed framework includes a fully automated and interpretable machine learning pipeline using state-of-the-art methods. The framework is applied in a real-world case study with a major medical device manufacturer providing data from a fleet of thousands of high-end medical devices. An anonymized version of this industrial benchmark is released for the research community based on the presented case study, which has interesting and challenging properties. In our extensive experiments, we achieve a Recall@50 of 50.0 % for the task of predicting customer escalations. In addition, we show that combining IoT and enterprise data can improve prediction results and ease troubleshooting. Additionally, we propose a practical workflow for end-users when applying the proposed framework.
Equalized Recovery State Estimators for Linear Systems With Delayed and Missing Observations

This letter presents a dynamic state observer design for discrete-time linear time-varying systems that robustly achieves equalized recovery despite delayed or missing observations, where the set of all temporal patterns for the missing or delayed data is modeled by a finite-length language. By introducing a mapping of the language onto a reduced event-based language, we design a state estimator that adapts based on the history of available data at each step, and satisfies equalized recovery for all patterns in the reduced language. In contrast to existing equalized recovery estimators, the proposed design considers the equalized recovery level as a decision variable, which enables us to directly obtain the global minimum for the intermediate recovery levels, resulting in improved estimation performance. Finally, we demonstrate the effectiveness of the proposed observer when compared to existing approaches using several illustrative examples.