Miracle-dz/UniFAR-FTUs huggingface dataset:
The REQUIEM Survey. I. A Search for Extended Lyα Nebular Emission Around 31 z > 5.7 Quasars

The discovery of quasars a few hundred megayears after the Big Bang represents a major challenge to our understanding of black holes as well as galaxy formation and evolution. Quasars' luminosity is produced by extreme gas accretion onto black holes, which have already reached masses of MBH > 109 M⊙ by z ∼ 6. Simultaneously, their host galaxies form hundreds of stars per year, using up gas in the process. To understand which environments are able to sustain the rapid formation of these extreme sources, we started a Very Large Telescope/Multi-Unit Spectroscopic Explorer (MUSE) effort aimed at characterizing the surroundings of a sample of 5.7 < z < 6.6 quasars, which we have dubbed the Reionization Epoch QUasar InvEstigation with MUSE (REQUIEM) survey. We here present results of our searches for extended Lyα halos around the first 31 targets observed as part of this program. Reaching 5σ surface brightness limits of 0.1–1.1 × 10−17 erg s−1 cm−2 arcsec−2 over a 1 arcsec2 aperture, we were able to unveil the presence of 12 Lyα nebulae, eight of which are newly discovered. The detected nebulae show a variety of emission properties and morphologies with luminosities ranging from 8 × 1042 to 2 × 1044 erg s−1, FWHMs between 300 and 1700 km s−1, sizes <30 pkpc, and redshifts consistent with those of the quasar host galaxies. As the first statistical and homogeneous investigation of the circumgalactic medium of massive galaxies at the end of the reionization epoch, the REQUIEM survey enables the study of the evolution of the cool gas surrounding quasars in the first 3 Gyr of the universe. A comparison with the extended Lyα emission observed around bright ( M 1450 ≲ −25 mag) quasars at intermediate redshift indicates little variations on the properties of the cool gas from z ∼ 6 to z ∼ 3, followed by a decline in the average surface brightness down to z ∼ 2.
Online Spectral Approximation in Random Order Streams

This paper studies spectral approximation for a positive semidefinite matrix in the online setting. It is known in [Cohen et al. APPROX 2016] that we can construct a spectral approximation of a given $n \times d$ matrix in the online setting if an additive error is allowed. In this paper, we propose an online algorithm that avoids an additive error with the same time and space complexities as the algorithm of Cohen et al., and provides a better upper bound on the approximation size when a given matrix has small rank. In addition, we consider the online random order setting where a row of a given matrix arrives uniformly at random. In this setting, we propose time and space efficient algorithms to find a spectral approximation. Moreover, we reveal that a lower bound on the approximation size in the online random order setting is $\Omega (d \epsilon^{-2} \log n)$, which is larger than the one in the offline setting by an $\mathrm{O}\left( \log n \right)$ factor.
A LITERATURE REVIEW OF AI FOR CLASSIC VIDEO GAMES USING REINFORCEMENT LEARNING

Deep reinforcement learning is the road to build artificially intelligent machines that can perform tasks similar to that of human beings, without any prior training. It is essential for training an agent to make smart decisions under uncertain conditions and to take small actions in order to achieve a higher overarching goal. In this paper, some preliminary research is done in order to understand how reinforcement learning and deep learning techniques can be combined, along with an approximation function called Q-learning, to train an agent to play a classic game. This deep neural network model will successfully learn to control policies directly from high-dimensional sensory input using reinforcement learning. The challenge is that the agent only sees the pixels and the rewards, similar to a human player. The main deep neural net used was Convolutional Neural Networks (CNNs). ARTIFICIAL INTELLIGENCE FOR CLASSIC VIDEO GAMES USING REINFORCEMENT LEARNING 3
NIEL Dose Analysis on triple and single junction InGaP/GaAs/Ge solar cells irradiated with electrons, protons and neutrons

Triple junction (InGaP/GaAs/Ge) and single junction (SJ) solar cells were irradiated with electrons, protons and neutrons. The degradation of remaining factors was analyzed as function of the induced Displacement Damage Dose (DDD) calculated by means of the SR-NIEL (Screened Relativistic Non Ionizing Energy Loss) approach. In particular, the aim of this work is to analyze the variation of the solar cells remaining factors due to neutron irradiation with respect to those previously obtained with electrons and protons. The current analysis confirms that the degradation of the Pmax electrical parameter is related by means of the usual semi-empirical expression to the displacement dose, independently of type of the incoming particle. Isc and Voc parameters were also measured as a function of the displacement damage dose. Furthermore, a DLTS analysis was carried out on diodes –with the same epitaxial structure as the middle sub-cell - irradiated with neutrons.
MetH: A family of high-resolution and variable-shape image challenges

This work is partially supported by the Intel-BSC Exascale Lab agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2017-SGR-1414).
DomainScope: A disease network based on protein domain connections

Protein domains are highly conserved functional units of proteins. Because they carry functionally significant information, the majority of the coding disease variants are located on domains. Additionally, domains are specific units of the proteins that can be targeted for drug delivery purposes. Here, using information about variants sites associated with diseases, a disease network was built, based on their sharing the same domain and domain variation site. The result was 49,990 disease pairs linked by domain variant site and 533,687 disease pairs that share the same mutated domain. These pairs were compared to disease pairs made using previous methods such as gene identity and gene variant site identity, which revealed that over 8,000 of these pairs were not only missing from the gene pairings but also not found commonly together in literature. The disease network was analyzed from their disease subject categories, which when compared to the gene-based disease network revealed that the domain method results in higher number of connections across disease categories versus within a disease category. Further, a study into the drug repurposing possibilities of the disease network created using domain revealed that 16,902 of the disease pairs had a drug reported for one disease but not the other, highlighting the drug repurposing potential of this new methodology.
Six Degree-of-Freedom Hovering using LIDAR Altimetry via Reinforcement Meta-Learning

We optimize a six degrees of freedom hovering policy using reinforcement meta-learning. The policy maps flash LIDAR measurements directly to on/off spacecraft body-frame thrust commands, allowing hovering at a fixed position and attitude in the asteroid body-fixed reference frame. Importantly, the policy does not require position and velocity estimates, and can operate in environments with unknown dynamics, and without an asteroid shape model or navigation aids. Indeed, during optimization the agent is confronted with a new randomly generated asteroid for each episode, insuring that it does not learn an asteroid's shape, texture, or environmental dynamics. This allows the deployed policy to generalize well to novel asteroid characteristics, which we demonstrate in our experiments. The hovering controller has the potential to simplify mission planning by allowing asteroid body-fixed hovering immediately upon the spacecraft's arrival to an asteroid. This in turn simplifies shape model generation and allows resource mapping via remote sensing immediately upon arrival at the target asteroid.
BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification

We propose BeamSeg, a joint model for segmentation and topic identification of documents from the same domain. The model assumes that lexical cohesion can be observed across documents, meaning that segments describing the same topic use a similar lexical distribution over the vocabulary. The model implements lexical cohesion in an unsupervised Bayesian setting by drawing from the same language model segments with the same topic. Contrary to previous approaches, we assume that language models are not independent, since the vocabulary changes in consecutive segments are expected to be smooth and not abrupt. We achieve this by using a dynamic Dirichlet prior that takes into account data contributions from other topics. BeamSeg also models segment length properties of documents based on modality (textbooks, slides, etc.). The evaluation is carried out in three datasets. In two of them, improvements of up to 4.8% and 7.3% are obtained in the segmentation and topic identifications tasks, indicating that both tasks should be jointly modeled.
REPLICATION-BASED EMULATION OF THE RESPONSE DISTRIBUTION OF STOCHASTIC SIMULATORS USING GENERALIZED LAMBDA DISTRIBUTIONS

Due to limited computational power, performing uncertainty quantification analyses with complex computational models can be a challenging task. This is exacerbated in the context of stochastic simulators, the response of which to a given set of input parameters, rather than being a deterministic value, is a random variable with unknown probability density function (PDF). Of interest in this paper is the construction of a surrogate that can accurately predict this response PDF for any input parameters. We suggest using a flexible distribution family -- the generalized lambda distribution -- to approximate the response PDF. The associated distribution parameters are cast as functions of input parameters and represented by sparse polynomial chaos expansions. To build such a surrogate model, we propose an approach based on a local inference of the response PDF at each point of the experimental design based on replicated model evaluations. Two versions of this framework are proposed and compared on analytical examples and case studies.
Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification

The graph-based semisupervised label propagation (LP) algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, the available methods typically calculate the weights and estimate the labels in the original input space, which typically contains noise and corruption. Thus, the encoded similarities and manifold smoothness may be inaccurate for label estimation. In this article, we present effective schemes for resolving these issues and propose a novel and robust semisupervised classification algorithm, namely the triple matrix recovery-based robust auto-weighted label propagation framework (ALP-TMR). Our ALP-TMR introduces a TMR mechanism to remove noise or mixed signs from the estimated soft labels and improve the robustness to noise and outliers in the steps of assigning weights and predicting the labels simultaneously. Our method can jointly recover the underlying clean data, clean labels, and clean weighting spaces by decomposing the original data, predicted soft labels, or weights into a clean part plus an error part by fitting noise. In addition, ALP-TMR integrates the auto-weighting process by minimizing the reconstruction errors over the recovered clean data and clean soft labels, which can encode the weights more accurately to improve both data representation and classification. By classifying samples in the recovered clean label and weight spaces, one can potentially improve the label prediction results. Extensive simulations verified the effectivenss of our ALP-TMR.
On the Magnetic Current Density in the Maxwell Equations Based on the Noether Theorem

Despite the search for supersymmetry based on abelian and non-abelian Yang-Mills gauge field theory, the Maxwell equations, as the earliest gauge field theory, are non-symmetric because of the undefined term of magnetic current density. This article reports on the theoretical quantization of this term based on spontaneous symmetry breaking in the spatial geometry of a gauge group (G-group) of quantum charged (QC) particles. A locally supersymmetric background-independent spatial geometry of the G-group is developed based on the commutative string field interaction (SFI) between infinite number of QC particles and the grand monopole and the noncommutative SFI of each pair of adjacent QC particles in the G-group. Two adjoint and disjoint currents are associated with the commutative and noncommutative SFIs, respectively, based on the spin of a QC particle. The adjoint and disjoint currents are associated with a zero resistivity between a QC particle and the grand monopole and an infinite resistivity between each pair of adjacent QC particles correlated to their microscopic commutative and noncommutative SFIs in the G-group, respectively. This article demonstrates that the two corresponding resistivities are finite (greater than zero and less than infinity) for the macroscopic commutative and noncommutative SFIs of the G-group. Therefore, the adjoint and disjoint currents are related to the classical macroscopic currents known as electric and magnetic currents, respectively. Because the microscopic adjoint current associated with the commutative SFI has already been related to the macroscopic electric current density in the Maxwell equations, it is proposed that the microscopic disjoint current associated with the noncommutative SFI is related to the undefined magnetic current density in these equations.
Fast and Flexible Image Blind Denoising via Competition of Experts

Fast and flexible processing are two essential requirements for a number of practical applications of image denoising. Current state-of-the-art methods, however, still require either high computational cost or limited scopes of the target. We introduce an efficient ensemble network trained via a competition of expert networks, as an application for image blind denoising. We realize automatic division of unlabeled noisy datasets into clusters respectively optimized to enhance denoising performance. The architecture is scalable, can be extended to deal with diverse noise sources/levels without increasing the computation time. Taking advantage of this method, we save up to approximately 90% of computational cost without sacrifice of the denoising performance compared to single network models with identical architectures. We also compare the proposed method with several existing algorithms and observe significant outperformance over the standard denoising algorithm BM3D in terms of computational efficiency.
Moist Shallow Water Response to Localized Tropical Forcing: Initial Value Problems

The response of spherical moist and dry shallow water systems to localized tropical imbalances are examined. The nonlinear dry response consists of a combination of Rossby, Kelvin and mixed Rossby-Gravity waves, depending on the nature of the initial condition. Remarkably, most of the power in the nonlinear solution follows linear dispersion curves quite accurately. In contrast, with a meridionally varying saturation profile, the long time moist solution consists of only westward propagating modes that are of a large scale, low frequency and confined to the subtropics. When the saturation profile is also allowed to vary with longitude, apart from a westward quadrupole, there is a distinct eastward propagating intraseasonal response at long times. In fact, the early eastward response is dominated by a Kelvin wave that changes into slow, predominantly rotational wave packets which arc out to the midlatitudes and return to the tropics. This carries over to a realistic saturation profile (derived from reanalysis based precipitable water) in the boreal summer, but with the eastward response restricted to the northern hemisphere. In the boreal winter, this component consists of a subtropically confined quadrupole in addition to weaker midlatitudinal disturbances. The nature of the eastward propagation is well described to first order by moist potential vorticity considerations. Finally, in all the moist simulations, the tropical equivalent depth closely matches linear rapid condensation estimates.
Utility Analysis of Network Architectures for 3D Point Cloud Processing

In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different network architectures. We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs. In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency. We conduct comparative studies based on such metrics to verify the hypotheses. We further use the verified hypotheses to revise architectures of existing DNNs to improve their utilities. Experiments demonstrate the effectiveness of our method.
A Configuration-Space Decomposition Scheme for Learning-based Collision Checking

Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish collision free subspace from in-collision subspace in C. In this paper, we propose a novel configuration space decomposition method and show two nice properties resulted from this decomposition. Using these two properties, we build a composite classifier that works compatibly with previous machine learning methods by using them as the elementary classifiers. Experimental results are presented, showing that our composite classifier outperforms state-of-the-art single classifier methods by a large margin. A real application of motion planning in a multi-robot system in plant phenotyping using three UR5 robotic arms is also presented.
Impact of Clouds and Hazes on the Simulated JWST Transmission Spectra of Habitable Zone Planets in the TRAPPIST-1 System

The TRAPPIST-1 system, consisting of an ultracool host star having seven known Earth-sized planets, will be a prime target for atmospheric characterization with the James Webb Space Telescope (JWST). However, the detectability of atmospheric molecular species may be severely impacted by the presence of clouds and/or hazes. In this work, we perform 3D general circulation model (GCM) simulations with the LMD-G model supplemented by 1D photochemistry simulations at the terminator with the Atmos model to simulate several possible atmospheres for TRAPPIST-1e, 1f, and 1g: (1) modern Earth, (2) Archean Earth, and (3) CO2-rich atmospheres. The JWST synthetic transit spectra were computed using the GSFC Planetary Spectrum Generator. We find that the TRAPPIST-1e, 1f, and 1g atmospheres, with clouds and/or hazes, could be detected using JWST’s NIRSpec Prism from the CO2 absorption line at 4.3 μm in less than 15 transits at 3σ or less than 35 transits at 5σ. However, our analysis suggests that other gases would require hundreds (or thousands) of transits to be detectable. We also find that H2O, mostly confined in the lower atmosphere, is very challenging to detect for these planets or similar systems if the planets’ atmospheres are not in a moist greenhouse state. This result demonstrates that the use of GCMs, self-consistently taking into account the effect of clouds and subsaturation, is crucial to evaluate the detectability of atmospheric molecules of interest, as well as for interpreting future detections in a more global (and thus robust and relevant) approach.
Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks

In this paper, we present the first study that compares different models of Bayesian Neural Networks (BNNs) to predict the posterior distribution of the cosmological parameters directly from the Cosmic Microwave Background temperature and polarization maps. We focus our analysis on four different methods to sample the weights of the network during training: Dropout, DropConnect, Reparameterization Trick (RT), and Flipout. We find out that Flipout outperforms all other methods regardless of the architecture used, and provides tighter constraints for the cosmological parameters. Additionally, we describe existing strategies for calibrating the networks and propose new ones. We show how tuning the regularization parameter for the scale of the approximate posterior on the weights in Flipout and RT we can produce unbiased and reliable uncertainty estimates, i.e., the regularizer acts as a hyper parameter analogous to the dropout rate in Dropout. The best performances are nevertheless achieved with a more convenient method, in which the network parameters are let free during training to achieve the best uncalibrated performances, and then the confidence intervals are calibrated in a subsequent phase. Furthermore, we claim that the correct calibration of these networks does not change the behavior for the aleatoric and epistemic uncertainties provided for BNNs when the size of the training dataset changes. The results reported in the paper can be extended to other cosmological datasets in order to capture features that can be extracted directly from the raw data, such as non-Gaussianity or foreground emissions.
Reconstructed global monthly land air temperature dataset (1880–2017)

Land surface air temperature is an essential climate variable for understanding rapid global environmental changes. Sparse network coverage prior to the 1950s is a significant source of uncertainty in global climate change evaluations. Recognizing the importance of spatial coverage, more stations are continually being added to global climate networks. A challenge is how to best use the information introduced by the new station observations to enhance our understanding and assessment of global climate states and changes, particularly for times prior to the mid‐20th century. In this study, Data INterpolating Empirical Orthogonal Functions (DINEOF) were used to reconstruct mean monthly air temperatures from the Global Historical Climatology Network‐monthly (GHCNm version 4) over the land surface from 1880 through 2017. The final reconstructed air temperature dataset covers about 95% of the global land surface area, improving the spatial coverage by ~80% during 1880–1900 and by 10%–20% since the 1950s. Validation tests show that the mean absolute error of the reconstructed data is less than 0.82°C. Comparison with the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model output shows that the reconstructed dataset substantially reduces the bias in global datasets caused by sparse station coverage, particularly before the 1950s.