Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Exploration in Factored Average-Reward MDPs

We consider a regret minimization task under the average-reward criterion in an unknown Factored Markov Decision Process (FMDP). More specifically, we consider an FMDP where the state-action space $\mathcal X$ and the state-space $\mathcal S$ admit the respective factored forms of $\mathcal X = \otimes_{i=1}^n \mathcal X_i$ and $\mathcal S=\otimes_{i=1}^m \mathcal S_i$, and the transition and reward functions are factored over $\mathcal X$ and $\mathcal S$. Assuming known factorization structure, we introduce a novel regret minimization strategy inspired by the popular UCRL2 strategy, called DBN-UCRL, which relies on Bernstein-type confidence sets defined for individual elements of the transition function. We show that for a generic factorization structure, DBN-UCRL achieves a regret bound, whose leading term strictly improves over existing regret bounds in terms of the dependencies on the size of $\mathcal S_i$'s and the involved diameter-related terms. We further show that when the factorization structure corresponds to the Cartesian product of some base MDPs, the regret of DBN-UCRL is upper bounded by the sum of regret of the base MDPs. We demonstrate, through numerical experiments on standard environments, that DBN-UCRL enjoys a substantially improved regret empirically over existing algorithms.
A Search for FeH in Hot-Jupiter Atmospheres with High-dispersion Spectroscopy

Most of the molecules detected thus far in exoplanet atmospheres, such as water and CO, are present for a large range of pressures and temperatures. In contrast, metal hydrides exist in much more specific regimes of parameter space, and so can be used as probes of atmospheric conditions. Iron hydride (FeH) is a dominant source of opacity in low-mass stars and brown dwarfs, and evidence for its existence in exoplanets has recently been observed at low resolution. We performed a systematic search of archival CARMENES near-infrared data for signatures of FeH during transits of 12 exoplanets. These planets span a large range of equilibrium temperatures (600 4000 K) and surface gravities (2.5 3.5). We did not find a statistically significant FeH signal in any of the atmospheres, but obtained potential low-confidence signals (signal-to-noise ratio ∼ 3) in two planets, WASP-33b and MASCARA-2b. Previous modeling of exoplanet atmospheres indicate that the highest volume mixing ratios (VMRs) of 10−7 to 10−9 are expected for temperatures between 1800 and 3000 K and log . The two planets for which we find low-confidence signals are in the regime where strong FeH absorption is expected. We performed injection and recovery tests for each planet and determined that FeH would be detected in every planet for VMRs , and could be detected in some planets for VMRs as low as . Additional observations are necessary to conclusively detect FeH and assess its role in the temperature structures of hot-Jupiter atmospheres.
The intrinsic reddening of the Magellanic Clouds as traced by background galaxies – II. The Small Magellanic Cloud

We present a map of the total intrinsic reddening across ≃34 deg2 of the Small Magellanic Cloud (SMC) derived using optical (ugriz) and near-infrared (IR; YJKs) spectral energy distributions (SEDs) of background galaxies. The reddening map is created using a subsample of 29 274 galaxies with low levels of intrinsic reddening based on the lephare χ2 minimization SED-fitting routine. We find statistically significant enhanced levels of reddening associated with the main body of the SMC compared with regions in the outskirts [ΔE(B − V) ≃ 0.3 mag]. A comparison with literature reddening maps of the SMC shows that, after correcting for differences in the volume of the SMC sampled, there is good agreement between our results and maps created using young stars. In contrast, we find significant discrepancies between our results and maps created using old stars or based on longer wavelength far-IR dust emission that could stem from biased samples in the former and uncertainties in the far-IR emissivity and the optical properties of the dust grains in the latter. This study represents one of the first large-scale categorizations of extragalactic sources behind the SMC and as such we provide the lephare outputs for our full sample of ∼500 000 sources.
On Target Segmentation for Direct Speech Translation

Recent studies on direct speech translation show continuous improvements by means of data augmentation techniques and bigger deep learning models. While these methods are helping to close the gap between this new approach and the more traditional cascaded one, there are many incongruities among different studies that make it difficult to assess the state of the art. Surprisingly, one point of discussion is the segmentation of the target text. Character-level segmentation has been initially proposed to obtain an open vocabulary, but it results on long sequences and long training time. Then, subword-level segmentation became the state of the art in neural machine translation as it produces shorter sequences that reduce the training time, while being superior to word-level models. As such, recent works on speech translation started using target subwords despite the initial use of characters and some recent claims of better results at the character level. In this work, we perform an extensive comparison of the two methods on three benchmarks covering 8 language directions and multilingual training. Subword-level segmentation compares favorably in all settings, outperforming its character-level counterpart in a range of 1 to 3 BLEU points.
Nash equilibrium seeking under partial-decision information over directed communication networks

We consider the Nash equilibrium problem in a partial-decision information scenario. Specifically, each agent can only receive information from some neighbors via a communication network, while its cost function depends on the strategies of possibly all agents. In particular, while the existing methods assume undirected or balanced communication, in this paper we allow for non-balanced, directed graphs. We propose a fully-distributed pseudo-gradient scheme, which is guaranteed to converge with linear rate to a Nash equilibrium, under strong monotonicity and Lipschitz continuity of the game mapping. Our algorithm requires global knowledge of the communication structure, namely of the Perron-Frobenius eigenvector of the adjacency matrix and of a certain constant related to the graph connectivity. Therefore, we adapt the procedure to setups where the network is not known in advance, by computing the eigenvector online and by means of vanishing step sizes.
Nonadiabatic Turbulence Driving during Gravitational Collapse

We investigate the generation of turbulence during the prestellar gravitational contraction of a turbulent spherical core. We define the ratio g of the one-dimensional turbulent velocity dispersion to the gravitational velocity to then analytically estimate g under the assumptions of (a) equipartition or virial equilibrium between the gravitational ( ) and turbulent kinetic ( ) energies and (b) stationarity of transfer from gravitational to turbulent energy (implying cst). In the equipartition and virial cases, we find and , respectively; in the stationary case we find , where η is an efficiency factor, is the energy injection scale of the turbulence, and R is the core’s radius. Next, we perform AMR simulations of the prestellar collapse of an isothermal, transonic turbulent core at two different resolutions, and a nonturbulent control simulation. We find that the turbulent simulations collapse at the same rate as the nonturbulent one, so that the turbulence generation does not significantly slow down the collapse. We also find that (a) the simulations approach near balance between the rates of energy injection from the collapse and of turbulence dissipation; (b) , close to the “virial” value (turbulence is 30% ∼ 40% of nonthermal linewidth); (c) the injection scale is , and (d) the “turbulent pressure” scales as , an apparently nearly adiabatic scaling. We propose that this scaling and the nearly virial values of the turbulent velocity dispersion may be reconciled with the nondelayed collapse rate if the turbulence is dissipated as soon as it is generated.
Analyze the Effects of Weighting Functions on Cost Function in the Glove Model

When dealing with the large vocabulary size and corpus size, the run-time for training Glove model is long, it can even be up to several dozen hours for data, which is approximately 500MB in size. As a result, finding and selecting the optimal parameters for the weighting function create many difficulties for weak hardware. Of course, to get the best results, we need to test benchmarks many times. In order to solve this problem, we derive a weighting function, which can save time for choosing parameters and making benchmarks. It also allows one to obtain nearly similar accuracy at the same given time without concern for experimentation.
The POLAR gamma-ray burst polarization catalog

Context.Despite over 50 years of research, many open questions remain about the origin and nature of gamma-ray bursts (GRBs). Linear polarization measurements of the prompt emission of these extreme phenomena have long been thought to be key to answering a range of these questions. The POLAR detector was designed to produce the first set of detailed and reliable linear polarization measurements in the 50 − 500 keV energy range. During late 2016 and early 2017, POLAR detected a total of 55 GRBs. The analysis results of 5 of these GRBs have been reported, and were found to be consistent with a low or unpolarized flux. However, previous reports by other collaborations found high levels of linear polarization, including some as high as 90%. Aims.We study the linear polarization for the 14 GRBs observed by POLAR for which statistically robust inferences are possible. Additionally, time-resolved polarization studies are performed on GRBs with sufficient apparent flux.Methods. A publicly available polarization analysis tool, developed within the Multi-Mission Maximum Likelihood framework (3ML), was used to produce statistically robust results. The method allows spectral and polarimetric data from POLAR to be combined with spectral data from theFermiGamma-ray Burst Monitor (Fermi-GBM) and theNeil Gehrels SwiftObservatory to jointly model the spectral and polarimetric parameters.Results. The time-integrated analysis finds all results to be compatible with low or zero polarization with the caveat that, when time-resolved analysis is possible within individual pulses, we observe moderate linear polarization with a rapidly changing polarization angle. Therefore, time-integrated polarization results, while pointing to lower polarization, are potentially an artifact of summing over the changing polarization signal and thus washing out the true moderate polarization. We therefore caution against overinterpretation of any time-integrated results inferred herein and encourage the community to wait for more detailed polarization measurements from forthcoming missions such as POLAR-2 and LEAP.
Coherent phonons and the interplay between charge density wave and Mott phases in 
1T−TaSe2

$1T\ensuremath{-}\mathrm{Ta}{\mathrm{Se}}_{2}$ is host to coexisting strongly correlated phases including charge density waves (CDWs) and an unusual Mott transition at low temperature. Here, we investigate coherent phonon oscillations in $1T\ensuremath{-}\mathrm{Ta}{\mathrm{Se}}_{2}$ using a combination of time- and angle-resolved photoemission spectroscopy (TR-ARPES) and time-resolved reflectivity (TRR). Perturbation by a femtosecond laser pulse triggers a modulation of the valence band binding energy at the $\phantom{\rule{0.83328pt}{0ex}}\overline{\mathrm{\ensuremath{\Gamma}}}\phantom{\rule{0.83328pt}{0ex}}$ point, related to the Mott gap, that is consistent with the in-plane CDW amplitude mode frequency. By contrast, TRR measurements show a modulation of the differential reflectivity comprised of multiple frequencies belonging to the distorted CDW lattice modes. Comparison of the temperature dependence of coherent and spontaneous phonons across the CDW transition shows that the amplitude mode intensity is more easily suppressed during perturbation of the CDW state by the optical excitation compared to other modes. Our results clearly identify the relationship of the in-plane CDW amplitude mode with the Mott phase in $1T\ensuremath{-}\mathrm{Ta}{\mathrm{Se}}_{2}$ and highlight the importance of lattice degrees of freedom.
Dependence of the dynamical properties of light-cone simulation dark matter halos on their environment

Aims. We study the dependence of the dynamical properties of massive (M ≥ 1.5 × 1013 M⊙ h−1) dark matter halos on their environment in a whole-sky ΛCDM light-cone simulation extending to z ∼ 0.65. The properties of interest for this study are the halo shape (parametrized via its principal axes), spin and virialization status, the alignment of halo spin and shape, as well as the shape-shape and spin-spin alignments among halo neighbors. Methods. We define the halo environment using the notion of halo isolation status determined by the distance to its nearest neighbor. This defines a maximum spherical region around each halo devoid of other halos, above the catalog threshold mass. We consider “close halo pairs” to be pairs separated by a distance that is lower than a specific threshold. In order to decontaminate our results from the known dependence of halo dynamical properties on mass, we used a random sampling procedure to compare properties of similar halo abundance distributions. Results. We find that: (a) there is a strong dependence on the part of the halo properties with regard to their environment, confirming that isolated halos are more aspherical and more prolate with lower spin values; (b) correlations between halo properties exist and are mostly independent of the halo environment; (c) halo spins are aligned with the minor axis, regardless of halo shape; and (d) close halo neighbors have their major axes statistically aligned, while they show a slight but statistically significant preference for anti-parallel spin directions. The latter result is enhanced for the case of close halo pairs in low-density environments. Furthermore, we find a tendency for the spin vectors to be oriented perpendicular to the line that connects such close halo pairs.
Phonon redshift and Hubble friction in an expanding BEC.

We revisit the theoretical analysis of an expanding ring-shaped Bose-Einstein condensate. Starting from the action and integrating over dimensions orthogonal to the phonon's direction of travel, we derive an effective one-dimensional wave equation for azimuthally-travelling phonons. This wave equation shows that expansion redshifts the phonon frequency at a rate determined by the effective azimuthal sound speed, and damps the amplitude of the phonons at a rate given by V ˙ / V , where V is the volume of the background condensate. This behavior is analogous to the redshifting and "Hubble friction" for quantum fields in the expanding universe and, given the scalings with radius determined by the shape of the ring potential, is consistent with recent experimental and theoretical results. The action-based dimensional reduction methods used here should be applicable in a variety of settings, and are well suited for systematic perturbation expansions.
Importance Weighted Policy Learning and Adaption

The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones. In the meta reinforcement learning literature much recent work has focused on the problem of optimizing the learning process itself. In this paper we study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning. The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior, or default behavior that constrains the space of solutions and serves as a bias for exploration; as well as a representation for the value function, both of which are easily learned from a number of training tasks in a multi-task scenario. Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
Design Equations for a Closely-Spaced Two-Element Interferometer Including Internal Noise Coupling

We present design equations for a two-element closely-spaced interferometer for measuring the noise temperature of a uniform sky. Such an interferometer is useful for observing highly diffuse radio sources such as the Milky Way and Cosmological signals. We develop a simple equivalent circuit based on radiophysics and antenna theory to describe the interactions between key design parameters such as antenna self and mutual impedance and noise parameters of the receiver; the latter is considered internal noise. This approach straightforwardly facilitates design studies as the response of the uniform signal and the systematic error due to internal noise coupling can be analyzed using the same equivalent circuit. The equivalent circuit shows that mutual coherence due to internal noise coupling is non-negligible and an inherent property of a closely-spaced interferometer. A realistic example design involving two closely-spaced horizontal dipoles over a lossy ground for Cosmological signal detection from 50 to 100 MHz is discussed as an illustration.
Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion

<p>We evaluate the impact of uncertainties in surface wind and sea ice cohesion on sea ice forecasts by the neXtSIM sea ice model. neXtSIM includes the Maxwell-elasto-brittle rheology describing the ice dynamics. Ensemble forecasts are done every 10 days from January to April 2008. The ensembles are generated by perturbing the wind forcing and ice cohesion field both separately and jointly. The wind forcing, an external forcing of the model, is perturbed continuously during the forecast. While the sea-ice cohesion, an internal parameter of the model, is randomized on the initial field of each sea ice forecast. The model uncertainties are assessed statistically using ensemble forecasts, in which virtual drifters are seeded over the Arctic Ocean. We analyze the spread of Lagrangian sea ice trajectories of the ensemble of virtual drifters and compare them with the IABP buoys. We demonstrate that the wind perturbations usually contribute more to the forecast uncertainty, but the ice cohesion perturbations significantly increase the degree of anisotropy in the spread and become occasionally important during strong wind events. </p>
All-Weather sub-50-cm Radar-Inertial Positioning

Deploying automated ground vehicles beyond the confines of sunny and dry climes will require sub-lane-level positioning techniques that use radio waves, rather than near-visible light radiation. Like human sight, LiDAR and optical cameras perform poorly in low-visibility conditions. We present and demonstrate a novel technique for robust, sub-50-cm, urban ground vehicle positioning based on all-weather sensors. The technique incorporates a computationally-efficient, globally-optimal radar scan registration algorithm within a larger estimation pipeline that fuses data from commercially-available, low-cost, automotive radars, low-cost inertial sensors, vehicle motion constraints, and, when available, precise GNSS measurements. We evaluate the performance of the presented technique on an extensive and realistic urban dataset derived from all-weather sensors. Comparison against ground truth shows that during 60 min of GNSS-denied driving in the urban center of Austin, TX, the technique maintains 95th-percentile errors below 50 cm in horizontal position and 0.5 in heading.
Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time

We consider the problem of monotone, submodular maximization over a ground set of size $n$ subject to cardinality constraint $k$. For this problem, we introduce streaming algorithms with linearquery complexity and linear number of arithmetic operations; these algorithms are the first deterministic algorithms for submodular maximization that require a linear number of arithmetic operations. Specifically, for any $c \ge 1, \epsilon > 0$, we propose a single-pass, deterministic streaming algorithm with ratio $1/(4c)-\epsilon$, query complexity $\lceil n / c \rceil + c$, memory complexity $O(k \log k)$, and $O(n)$ total running time. As $k \to \infty$, the ratio converges to $(1 - 1/e)/(c + 1)$. In addition, we propose a deterministic, multi-pass streaming algorithm with $O(1 / \epsilon)$ passes that achieves ratio $1-1/e - \epsilon$ in $O(n/\epsilon)$ queries, $O(k \log (k))$ memory, and $O(n)$ time. We prove a lower bound that implies no constant-factor approximation exists using $o(n)$ queries, even if queries to infeasible sets are allowed. An experimental analysis demonstrates that our algorithms require fewer queries (often substantially less than $n$) to achieve better objective value than the current state-of-the-art algorithms.
OrthoReg: Robust Network Pruning Using Orthonormality Regularization

Network pruning in Convolutional Neural Networks (CNNs) has been extensively investigated in recent years. To determine the impact of pruning a group of filters on a network's accuracy, state-of-the-art pruning methods consistently assume filters of a CNN are independent. This allows the importance of a group of filters to be estimated as the sum of importances of individual filters. However, overparameterization in modern networks results in highly correlated filters that invalidate this assumption, thereby resulting in incorrect importance estimates. To address this issue, we propose OrthoReg, a principled regularization strategy that enforces orthonormality on a network's filters to reduce inter-filter correlation, thereby allowing reliable, efficient determination of group importance estimates, improved trainability of pruned networks, and efficient, simultaneous pruning of large groups of filters. When used for iterative pruning on VGG-13, MobileNet-V1, and ResNet-34, OrthoReg consistently outperforms five baseline techniques, including the state-of-the-art, on CIFAR-100 and Tiny-ImageNet. For the recently proposed Early-Bird Ticket hypothesis, which claims networks become amenable to pruning early-on in training and can be pruned after a few epochs to minimize training expenditure, we find OrthoReg significantly outperforms prior work. Code available at this https URL.