Miracle-dz/UniFAR-FTUs huggingface dataset:
Fr\'echet random forests for metric space valued regression with non euclidean predictors

Random forests are a statistical learning method widely used in many areas of scientific research because of its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forest approaches are not flexible enough to handle heterogeneous data such as curves, images and shapes. In this paper, we introduce Fr\'echet trees and Fr\'echet random forests, which allow to handle data for which input and output variables take values in general metric spaces. To this end, a new way of splitting the nodes of trees is introduced and the prediction procedures of trees and forests are generalized. Then, random forests out-of-bag error and variable importance score are naturally adapted. A consistency theorem for Fr\'echet regressogram predictor using data-driven partitions is given and applied to Fr\'echet purely uniformly random trees. The method is studied through several simulation scenarios on heterogeneous data combining longitudinal, image and scalar data. Finally, one real dataset about air quality is used to illustrate the use of the proposed method in practice.
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions

The connection between continuous-time dynamics and discrete-time algorithms has led to the introduction of many methods in optimization. We add to this body of work by introducing a family of descent dynamics and descent algorithms with matching non-asymptotic convergence guarantees. We use this framework to introduce a new first-order algorithm, called rescaled gradient descent (RGD), and show that RGD achieves a faster convergence rate than gradient descent provided the function is strongly smooth - a natural generalization of the standard smoothness assumption on the objective function. When the objective function is convex, we present two frameworks for accelerating descent methods, one in the style of Nesterov and the other in the style of Monteiro and Svaiter. Rescaled gradient descent can be accelerated under the same strong smoothness assumption using both frameworks. We provide several examples of strongly smooth loss functions in machine learning and numerical experiments that verify our theoretical findings.
Ex-Twit: Explainable Twitter Mining on Health Data

Since most machine learning models provide no explanations for the predictions, their predictions are obscure for the human. The ability to explain a model's prediction has become a necessity in many applications including Twitter mining. In this work, we propose a method called Explainable Twitter Mining (Ex-Twit) combining Topic Modeling and Local Interpretable Model-agnostic Explanation (LIME) to predict the topic and explain the model predictions. We demonstrate the effectiveness of Ex-Twit on Twitter health-related data.
Hubble Frontier Field photometric catalogues of Abell 370 and RXC J2248.7−4431: multiwavelength photometry, photometric redshifts, and stellar properties

This paper presents multiwavelength photometric catalogues of the last two Hubble Frontier Fields (HFFs), the massive galaxy clusters Abell 370 and RXC J2248.7−4431. The photometry ranges from imaging performed on the Hubble Space Telescope to ground-based Very Large Telescope and Spitzer/Infrared Array Camera, in collaboration with the ASTRODEEP team, and using the ASTRODEEP pipeline. While the main purpose of this paper is to release the catalogues, we also perform, as a proof of concept, a brief analysis of z > 6 objects selected using dropout method, as well as spectroscopically confirmed sources and multiple images in both clusters. While dropout methods yield a sample of high-z galaxies, the addition of longer wavelength data reveals that as expected the samples have substantial contamination at the ∼30–45 per cent level by dusty galaxies at lower redshifts. Furthermore, we show that spectroscopic redshifts are still required to unambiguously determine redshifts of multiply imaged systems. Finally, the now publicly available ASTRODEEP catalogues were combined for all HFFs and used to explore stellar properties of a large sample of 20 000 galaxies across a large photometric redshift range. The powerful magnification provided by the HFF clusters allows for an exploration of the properties of galaxies with intrinsic stellar masses as low as M* ≳ 107 M⊙ and intrinsic star formation rates ∼0.1–1 M⊙ yr−1 at z > 6.
Observational signatures of outbursting protostars - I: From hydrodynamic simulations to observations

Accretion onto protostars may occur in sharp bursts. Accretion bursts during the embedded phase of young protostars are probably most intense, but can only be inferred indirectly through long-wavelength observations. We perform radiative transfer calculations for young stellar objects (YSOs) formed in hydrodynamic simulations to predict the long wavelength, sub-mm and mm, flux responses to episodic accretion events, taking into account heating from the young protostar and from the interstellar radiation field. We find that the flux increase due to episodic accretion events is more prominent at sub-mm wavelengths than at mm wavelengths; e.g. a factor of ∼570 increase in the luminosity of the young protostar leads to a flux increase of a factor of 47 at 250 $\mu$m but only a factor of 10 at 1.3 mm. Heating from the interstellar radiation field may reduce further the flux increase observed at longer wavelengths. We find that during FU Ori-type outbursts the bolometric temperature and luminosity may incorrectly classify a source as a more evolved YSO due to a larger fraction of the radiation of the object being emitted at shorter wavelengths.
BOUNDS ON THE CONDITIONAL AND AVERAGE TREATMENT EFFECT WITH UNOBSERVED CONFOUNDING FACTORS.

For observational studies, we study the sensitivity of causal inference when treatment assignments may depend on unobserved confounders. We develop a loss minimization approach for estimating bounds on the conditional average treatment effect (CATE) when unobserved confounders have a bounded effect on the odds ratio of treatment selection. Our approach is scalable and allows flexible use of model classes in estimation, including nonparametric and black-box machine learning methods. Based on these bounds for the CATE, we propose a sensitivity analysis for the average treatment effect (ATE). Our semiparametric estimator extends/bounds the augmented inverse propensity weighted (AIPW) estimator for the ATE under bounded unobserved confounding. By constructing a Neyman orthogonal score, our estimator of the bound for the ATE is a regular root-n estimator so long as the nuisance parameters are estimated at the opn-1/4 rate. We complement our methodology with optimality results showing that our proposed bounds are tight in certain cases. We demonstrate our method on simulated and real data examples, and show accurate coverage of our confidence intervals in practical finite sample regimes with rich covariate information.
Resolving starlight: a quantum perspective

The wave-particle duality of light introduces two fundamental problems to imaging, namely, the diffraction limit and the photon shot noise. Quantum information theory can tackle them both in one holistic formalism: model the light as a quantum object, consider any quantum measurement, and pick the one that gives the best statistics. While Helstrom pioneered the theory half a century ago and first applied it to incoherent imaging, it was not until recently that the approach offered a genuine surprise on the age-old topic by predicting a new class of superior imaging methods. For the resolution of two sub-Rayleigh sources, the new methods have been shown theoretically and experimentally to outperform direct imaging and approach the true quantum limits. Recent efforts to generalize the theory for an arbitrary number of sources suggest that, despite the existence of harsh quantum limits, the quantum-inspired methods can still offer significant improvements over direct imaging for subdiffraction objects, potentially benefiting many applications in astronomy as well as fluorescence microscopy.
Formation of Blue-cored Dwarf Early-type Galaxies in a Cluster Environment: A Kinematical Perspective

The presence of blue cores in some dwarf early-type galaxies (dEs) in galaxy clusters suggests the scenario of late-type galaxy infall and subsequent transformation into red, quiescent dEs. We present Gemini Multi-Object Spectrograph long-slit spectroscopy of two dEs with blue cores (dE(bc)s), EVCC 591 and EVCC 516, located at the core and outskirts of the Virgo cluster, respectively. We obtained their internal kinematics along the major axis out to at least ∼1 effective radius. The EVCC 591 shows evidence of a kinematically decoupled core (KDC) with a size of 2″ (160 pc), exhibiting an inverted pattern for velocity with respect to the main body of its host galaxy. The rotation curve of the stellar component in the inner region of EVCC 591 is steeper than that in the rest of the galaxy. On the other hand, the overall velocity profiles of the stellar and ionized gas components of EVCC 516 show no signature of significant rotation. The occurrence of a KDC and zero rotation in the internal kinematics along with the central star formation support the scenario of gas-rich dwarf–dwarf mergers in the formation of these two dE(bc)s. Furthermore, evolution of dE(bc)s in a cluster environment into ordinary dEs with KDCs is possible based on their structural properties. We suggest that at least some of the dE(bc)s in the Virgo cluster were formed through dwarf–dwarf mergers in lower-density environments before they subsequently fell into the cluster; they were then quenched by subsequent effects within the cluster environment.
Design and Applications of a Fluorescent Labeling Technique for Lipid and Surfactant Preformed Vesicles

Amphiphilic molecules such as surfactants, lipids, and block copolymers can be assembled into bilayers and form vesicles. Fluorescent membrane labeling methods require the use of dye molecules that can be inserted into the bilayers at different stages of synthesis. To our knowledge, there is no generalized method for labeling preformed vesicles. Herein, we develop a versatile protocol that is suitable to both surfactant and lipid preformed vesicles and requires no separation or purification steps. On the basis of the lipophilic carbocyanine green dye PKH67, the methodology is assessed on zwitterionic phosphatidylcholine vesicles. To demonstrate its versatility, it is applied to dispersions of anionic or cationic vesicles, such as a drug administrated to premature infants with respiratory distress syndrome, or a vesicle formulation used as a fabric softener for home care applications. By means of fluorescence microscopy, we then visualize the interaction mechanisms of nanoparticles crossing live cell membranes and of surfactants adsorbed on a cotton fabric. These results highlight the advantages of a membrane labeling technique that is simple and applicable to a large number of soft matter systems.
Quark-flavor dependence of the shear viscosity in a quasiparticle model

We study the temperature-dependence of the shear viscosity to entropy density ratio in pure Yang-Mills theory and in QCD with light and strange quarks within kinetic theory in the relaxation time approximation. As effective degrees of freedom in a deconfined phase we consider quasiparticle excitations with quark and gluon quantum numbers and dispersion relations that depend explicitly on the temperature. The quasiparticle relaxation times are obtained by computing the microscopic two-body scattering amplitudes for the elementary scatterings among the quasiparticles. For pure Yang-Mills theory we show that the shear viscosity to entropy density ratio exhibits a characteristic non-monotonicity with a minimum at the first-order phase transition. In the presence of dynamical quarks the ratio smoothens while still exhibiting a minimum near confinement. Furthermore, there is a significant increase of the shear viscosity to entropy density ratio in QCD resulting from the quark contributions. This observation differs from previously reported estimates based on functional methods but is in line with perturbative QCD expectations at higher temperatures.
University of Birmingham Variability in Cumulative Habitual Sleep Duration Predicts Waking Functional Connectivity

Objective: We examined whether interindividual differences in habitual sleep patterns, quantified as the cumulative habitual total sleep time (cTST) over a 2-w period, were reflected in waking measurements of intranetwork and internetwork functional connectivity (FC) between major nodes of three intrinsically connected networks (ICNs): default mode network (DMN), salience network (SN), and central executive network (CEN). Design: Resting state functional magnetic resonance imaging (fMRI) study using seed-based FC analysis combined with 14-d wrist actigraphy, sleep diaries, and subjective questionnaires. Data were statistically analyzed using multiple linear regression. Setting: Fourteen consecutive days of wrist actigraphy in participant’s home environment and fMRI scanning on day 14 at the Birmingham University Imaging Centre. Participants: N = 33 healthy adults, mean age 34.3, standard deviation = +/- 11.6 y. Interventions: None. Measurements: Seed-based FC analysis on ICNs from resting-state fMRI data and multiple linear regression analysis performed for each ICN seed and target. cTST was used to predict FC (controlling for age). Results: cTST was specific predictor of intranetwork FC when the mesial prefrontal cortex (MPFC) region of the DMN was used as a seed for FC , with a positive correlation between FC and cTST observed. No significant relationship between FC and cTST was seen for any pair of nodes not including the MPFC. Internetwork FC between the DMN (MPFC) and SN (right anterior insula) was also predicted by cTST, with a negative correlation observed between FC and cTST. Conclusion: This study improves understanding of the relationship between intranetwork and internetwork functional connectivity of ICNs in relation to habitual sleep quality and duration. The cumulative amount of sleep that participants achieved over a 14-d period was significantly predictive of intranetwork and internetwork FC of ICNs, an observation that may underlie the link between sleep status and cognitive performance.
Global Optimality Guarantees For Policy Gradient Methods

Policy gradient methods, which have powered a lot of recent success in reinforcement learning, search for an optimal policy in a parameterized policy class by performing stochastic gradient descent on the cumulative expected cost-to-go under some initial state distribution. Although widely used, these methods lack theoretical guarantees as the optimization objective is typically nonconvex even for simple control problems, and hence are understood to only converge to a stationary point. In “Global Optimality Guarantees for Policy Gradient Methods,” J. Bhandari and D. Russo identify structural properties of the underlying MDP that guarantee that despite nonconvexity, the optimization objective has no suboptimal stationary points, ensuring asymptotic convergence of policy gradient methods to globally optimal policies. Under stronger conditions, authors show the policy gradient objective to satisfy a Polyak-lojasiewicz (gradient dominance) condition that yields fast convergence rates. In addition, when some of the said conditions are relaxed, authors provide bounds on the suboptimality gap of any stationary point. The results rely on a key connection with policy iteration, a classic dynamic programming algorithm which solves a single period optimization problem at every step. The authors show how structure in the single period optimization problems solved by policy iteration translate into nice properties of the multiperiod policy gradient objective, making it amenable for first-order methods to find globally optimal solutions. The authors also instantiate their framework for several classical control problems including tabular and linear MDPs, linear quadratic control, optimal stopping, and finite-horizon inventor control problems.
Typical dynamics and fluctuation analysis of slow–fast systems driven by fractional Brownian motion

This article studies typical dynamics and fluctuations for a slow-fast dynamical system perturbed by a small fractional Brownian noise. Based on an ergodic theorem with explicit rates of convergence, which may be of independent interest, we characterize the asymptotic dynamics of the slow component to two orders (i.e., the typical dynamics and the fluctuations). The limiting distribution of the fluctuations turns out to depend upon the manner in which the small-noise parameter is taken to zero relative to the scale-separation parameter. We study also an extension of the original model in which the relationship between the two small parameters leads to a qualitative difference in limiting behavior. The results of this paper provide an approximation to two orders of dynamical systems perturbed by small fractional Brownian noise and subject to multiscale effects.
IMMUNIZATION OF ARCTIC FOXES (ALOPEX LAGOPUS) WITH ORAL RABIES VACCINE

Arctic foxes (Alopex lagopus) were successfully immunized against rabies using an orally-administered, liquid SAD-BHK21 live virus vaccine in a sausage bait. Immunization was determined by serologic response and by resistance to challenge with an arctic rabies virus strain. Virus was not shed in saliva following oral vaccination, indicating that arctic foxes would not infect other foxes after ingesting this vaccine. High antibody levels were present in all experimental foxes 2 wk following initial vaccination. A booster vaccination at 56 wk induced a significant serologic response within 1 wk, suggesting an anamnestic response but titers began to decline within 8 wk in most foxes. Foxes were observed for 16 mo following the challenge and exhibited no symptoms of rabies. The SAD-BHK21 rabies vaccine in a sausage bait system has a strong potential for vaccinating wild populations of arctic fox.
Restore: Retrospective Fault Localization Enhancing Automated Program Repair

Fault localization is a crucial step of automated program repair, because accurately identifying program locations that are most closely implicated with a fault greatly affects the effectiveness of the patching process. An ideal fault localization technique would provide precise information while requiring moderate computational resources—to best support an efficient search for correct fixes. In contrast, most automated program repair tools use standard fault localization techniques—which are not tightly integrated with the overall program repair process, and hence deliver only subpar efficiency. In this paper, we present retrospective fault localization: a novel fault localization technique geared to the requirements of automated program repair. A key idea of retrospective fault localization is to reuse the outcome of failed patch validation to support mutation-based dynamic analysis—providing accurate fault localization information without incurring onerous computational costs. We implemented retrospective fault localization in a tool called Restore—based on the Jaid Java program repair system. Experiments involving faults from the Defects4J standard benchmark indicate that retrospective fault localization can boost automated program repair: Restore efficiently explores a large fix space, delivering state-of-the-art effectiveness (41 Defects4J bugs correctly fixed, 8 of which no other automated repair tool for Java can fix) while simultaneously boosting performance (speedup over 3 compared to Jaid). Retrospective fault localization is applicable to any automated program repair techniques that rely on fault localization and dynamic validation of patches.
Velocities of Flare Kernels and the Mapping Norm of Field Line Connectivity

We report on observations of flare ribbon kernels during the 2012 August 31 filament eruption. In the 1600 and 304 Å channels of the Atmospheric Imaging Assembly, flare kernels were observed to move along flare ribbons at velocities v∥ of up to 450 km s−1. Kernel velocities were found to be roughly anticorrelated with strength of the magnetic field. An apparent slipping motion of the flare loops was observed in the 131 Å only for the slowest kernels moving through the strong-B region. In order to interpret the observed relation between BLOS and v∥, we examined the distribution of the norm N, a quantity closely related to the slippage velocity. We calculated the norm N of the quasi-separatrix layers (QSLs) in MHD model of a solar eruption adapted to the magnetic environment that qualitatively agrees to that of the observed event. We found that both the modeled N and velocities of kernels reach their highest values in the same weak-field regions, one located in the curved part of the ribbon hook and the other in the straight part of the conjugate ribbon located close to a parasitic polarity. Contrariwise, lower values of the kernel velocities are seen at the tip of the ribbon hook, where the modeled N is low. Because the modeled distribution of N matches the observed dynamics of kernels, this supports the notion that the kernel motions can be interpreted as a signature of QSL reconnection during the eruption.
Validating Causal Inference Models via Influence Functions

The problem of estimating causal effects of treatments from observational data falls beyond the realm of supervised learning — because counter-factual data is inaccessible, we can never observe the true causal effects. In the absence of “super-vision”, how can we evaluate the performance of causal inference methods? In this paper, we use inﬂuence functions — the functional derivatives of a loss function — to develop a model validation procedure that estimates the estimation error of causal inference methods. Our procedure utilizes a Taylor-like expansion to approximate the loss function of a method on a given dataset in terms of the inﬂuence functions of its loss on a “synthesized”, proximal dataset with known causal effects. Under minimal regularity assumptions, we show that our procedure is √ n -consistent and efﬁcient. Experiments on 77 benchmark datasets show that using our procedure, we can accurately predict the comparative performances of state-of-the-art causal inference methods applied to a given observational study.