Miracle-dz/UniFAR-FTUs huggingface dataset:
Building the Largest Spectroscopic Sample of Ultracompact Massive Galaxies with the Kilo Degree Survey

Ultracompact massive galaxies (ucmgs), i.e., galaxies with stellar masses and effective radii , are very rare systems, in particular at low and intermediate redshifts. Their origin as well as their number density across cosmic time are still under scrutiny, especially because of the paucity of spectroscopically confirmed samples. We have started a systematic census of ucmg candidates within the ESO Kilo Degree Survey, together with a large spectroscopic follow-up campaign to build the largest possible sample of confirmed ucmgs. This is the third paper of the series and the second based on the spectroscopic follow-up program. Here, we present photometrical and structural parameters of 33 new candidates at redshifts and confirm 19 of them as ucmgs, based on their nominal spectroscopically inferred and . This corresponds to a success rate of , nicely consistent with our previous findings. The addition of these 19 newly confirmed objects allows us to fully assess the systematics on the system selection—and to finally reduce the number density uncertainties. Moreover, putting together the results from our current and past observational campaigns and some literature data, we build the largest sample of ucmgs ever collected, comprising 92 spectroscopically confirmed objects at . This number raises to 116, allowing for a 3σ tolerance on the and thresholds for the ucmg definition. For all these galaxies, we have estimated the velocity dispersion values at the effective radii, which have been used to derive a preliminary mass–velocity dispersion correlation.
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM). Given an input text with masked tokens, we rely on conventional masks to learn inter-relations between corrupted tokens and context via autoencoding, and pseudo masks to learn intra-relations between masked spans via partially autoregressive modeling. With well-designed position embeddings and self-attention masks, the context encodings are reused to avoid redundant computation. Moreover, conventional masks used for autoencoding provide global masking information, so that all the position embeddings are accessible in partially autoregressive language modeling. In addition, the two tasks pre-train a unified language model as a bidirectional encoder and a sequence-to-sequence decoder, respectively. Our experiments show that the unified language models pre-trained using PMLM achieve new state-of-the-art results on a wide range of natural language understanding and generation tasks across several widely used benchmarks.
Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management

Radio Resource Management in 5G mobile communication is a challenging problem for which Recurrent Neural Networks (RNN) have shown promising results. Accelerating the compute-intensive RNN inference is therefore of utmost importance. Programmable solutions are desirable for effective 5G-RRM coping with the rapidly evolving landscape of RNN variations. In this paper, we investigate RNN inference acceleration by tuning both the instruction set and micro-architecture of a micro-controller-class open-source RISC-V core. We couple HW extensions with software optimizations to achieve an overall improvement in throughput and energy efficiency of 15× and 10× w.r. t. the baseline core on a wide range of RNNs used in various RRM tasks.1
Generalized Sliced Distances for Probability Distributions

Probability metrics have become an indispensable part of modern statistics and machine learning, and they play a quintessential role in various applications, including statistical hypothesis testing and generative modeling. However, in a practical setting, the convergence behavior of the algorithms built upon these distances have not been well established, except for a few specific cases. In this paper, we introduce a broad family of probability metrics, coined as Generalized Sliced Probability Metrics (GSPMs), that are deeply rooted in the generalized Radon transform. We first verify that GSPMs are metrics. Then, we identify a subset of GSPMs that are equivalent to maximum mean discrepancy (MMD) with novel positive definite kernels, which come with a unique geometric interpretation. Finally, by exploiting this connection, we consider GSPM-based gradient flows for generative modeling applications and show that under mild assumptions, the gradient flow converges to the global optimum. We illustrate the utility of our approach on both real and synthetic problems.
A major optical and X-ray outburst from the Magellanic Bridge source RX J0209.6–7427

RX J0209.6–7427 is an X-ray source in the Magellanic Bridge that was first detected in 1993, but not seen again till 2019. It has been identified as a member of the Be/X-ray binary class, a category of objects that are well established as bright, often-unpredictable transients. Such systems are rarely known in the Bridge, possibly because they lie outside the area most commonly studied by X-ray telescopes. Whatever be the reason for the sparse number of such systems in the Bridge, they can provide useful tools for trying to understand the result of the tidal dynamics of the two Magellanic Clouds. In this paper, the nature of the object is explored with the help of new data obtained during the latest outburst. In particular, the first optical spectrum of the counterpart is presented to help classify the star, plus measurements of the Balmer emission lines over several years are used to investigate changes in the size and structure of the circumstellar disc.
Exploring the differences of integrated and spatially resolved analysis using integral field unit data: the case of Abell 14

We present a new approach to study planetary nebulae using integral field spectroscopy. VLT@VIMOS datacube of the planetary nebula Abell 14 is analysed in three different ways by extracting: (i) the integrated spectrum, (ii) 1-dimensional simulated long slit spectra for different position angles and (iii) spaxel-by-spaxel spectra. These data are used to built emission-line diagnostic diagrams and explore the ionization structure and excitation mechanisms combining data from 1- and 3- dimensional photoionization models. The integrated and 1D simulated spectra are suitable for developing diagnostic diagrams, while the spaxel spectra can lead to misinterpretation of the observations. We find that the emission-line ratios of Abell 14 are consistent with UV photo-ionized emission, however there are some pieces of evidence of an additional thermal mechanism. The chemical abundances confirm its previous classification as a Type I planetary nebula, without spatial variation. We find, though, variation in the ionization correction factors (ICFs) as a function of the slit position angle. The star at the geometric centre of Abell 14 has an A5 spectral type with an effective temperature of Teff = 7909$\pm$135 K and surface gravity log(g) = 1.4$\pm$0.1 cm s$^{-2}$. Hence, this star cannot be responsible for the ionization state of the nebula. Gaia parallaxes of this star yield distances between 3.6 and 4.5 kpc in good agreement with the distance derived from a 3-dimensional photoionization modelling of Abell 14, indicating the presence of a binary system at the centre of the planetary nebula.
Distortion of Magnetic Fields in BHR 71

The magnetic field structure of a star-forming Bok globule BHR 71 was determined based on near-infrared polarimetric observations of background stars. The magnetic field in BHR 71 was mapped from 25 stars. By using a simple 2D parabolic function, the plane-of-sky magnetic axis of the core was found to be θmag = 125° ± 11°. The plane-of-sky mean magnetic field strength of BHR 71 was found to be Bpos = 8.8–15.0 μG, indicating that the BHR 71 core is magnetically supercritical with λ = 1.44–2.43. Taking into account the effect of thermal/turbulent pressure and the plane-of-sky magnetic field component, the critical mass of BHR 71 was Mcr = 14.5–18.7 M⊙, which is consistent with the observed core mass of Mcore ≈ 14.7 M⊙. We conclude that BHR 71 is in a condition close to a kinematically critical state, and the magnetic field direction lies close to the plane of sky. Since BHR 71 is a star-forming core, a significantly subcritical condition (i.e., the magnetic field direction deviating from the plane of sky) is unlikely, and collapsed from a condition close to a kinematically critical state. There are two possible scenarios to explain the curved magnetic fields of BHR 71, one is an hourglass-like field structure due to mass accumulation and the other is the Inoue & Fukui mechanism, which proposes the interaction of the core with a shock wave to create curved magnetic fields wrapping around the core.
Automatic Perturbation Analysis on General Computational Graphs

Linear relaxation based perturbation analysis for neural networks, which aims to compute tight linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense. However, the majority of linear relaxation based methods only consider feed-forward ReLU networks. While several works extended them to relatively complicated networks, they often need tedious manual derivations and implementation which are arduous and error-prone. Their limited flexibility makes it difficult to handle more complicated tasks. In this paper, we take a significant leap by developing an automatic perturbation analysis algorithm to enable perturbation analysis on any neural network structure, and its computation can be done automatically in a similar manner as the back-propagation algorithm for gradient computation. The main idea is to express a network as a computational graph and then generalize linear relaxation algorithms such as CROWN as a graph algorithm. Our algorithm itself is differentiable and integrated with PyTorch, which allows to optimize network parameters to reshape bounds into desired specifications, enabling automatic robustness verification and certified defense. In particular, we demonstrate a few tasks that are not easily achievable without an automatic framework. We first perform certified robust training and robustness verification for complex natural language models which could be challenging with manual derivation and implementation. We further show that our algorithm can be used for tasks beyond certified defense - we create a neural network with a provably flat optimization landscape and study its generalization capability, and we show that this network can preserve accuracy better after aggressive weight quantization. Code is available at this https URL.
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain

We study the estimation of risk-sensitive policies in reinforcement learning (RL) problems defined by a Markov Decision Process (MDPs) whose state and action spaces are countably finite. Prior efforts are predominately afflicted by computational challenges associated with the fact that risk-sensitive MDPs are time-inconsistent, and hence modifications of Bellman’s equations are required, which often do not permit efficient fixed point schemes. To ameliorate this issue, we propose a new definition of risk, which we call caution, as a penalty function added to the dual of the linear programming (LP) formulation of tabular RL. Caution quantifies the distributional risk of a policy, and is a function of the policy’s long-term state occupancy measure. When the risk is a convex function of the occupancy measure, we propose to solve this problem in an online model-free manner with a stochastic variant of primal-dual method that uses Kullback-Lieber (KL) divergence as its proximal term. We establish that the sample complexity required to attain near-optimal solutions matches tight dependencies on the cardinality of the spaces, but also depends on the infinity norm of the risk measure’s gradient. Moreover, when the risk is non-convex, we propose a block-coordinate augmentation of the aforementioned approach, and establish its convergence to KKT points. Experiments demonstrate that this approach improves the reliability of reward accumulation without additional computation as compared to risk-neutral LP solvers, both for convex and non-convex risks, respectively, for the cases of KL divergence to a prior, and the variance.
Generalized emergent dark energy: observational Hubble data constraints and stability analysis

Recently \citet{PEDE:2019ApJ} proposed a phenomenologically emergent dark energy (PEDE) which consider that the dark energy density evolves as $\widetilde{\Omega}_{\rm{DE}}(z)\,=\,\Omega_{\rm{DE,0}}\left[ 1 - {\rm{tanh}}\left( {\log}_{10}(1+z) \right) \right]$ with the advantage that it does not have degree of freedom. Later on, \citet{PEDE:2020} proposed a generalized model by adding one degree of freedom to the PEDE model, encoded in the parameter $\Delta$. Motivated by these proposals, we constrain the parameter space ($h,\Omega_m$) and ($h,\Omega_m, \Delta$) for PEDE and Generalized Emergent Dark Energy (GEDE) respectively, by employing the most recent observational (non-) homogeneous Hubble data. Additionally, we reconstruct the deceleration and jerk parameters and estimate yield values at $z=0$ of $q_0 = -0.784^{+0.028}_{-0.027}$ and $j_0 = 1.241^{+0.164}_{-0.149}$ for PEDE and $q_0 = -0.730^{+0.059}_{-0.067}$ and $j_0 = 1.293^{+0.194}_{-0.187}$ for GEDE using the homogeneous sample. We report values on the deceleration-acceleration transition redshift with those reported in the literature within $2\sigma$ CL. Furthermore, we perform a stability analysis of the PEDE and GEDE models to study the global evolution of the Universe around their critical points. Although the PEDE and GEDE dynamics are similar to the standard model, our stability analysis indicates that in both models there is an accelerated phase at early epochs of the Universe.
Statistical Applications of Random Matrix Theory: Comparison of Two Populations

This paper investigates a statistical procedure for testing the equality of two independent estimated covariance matrices when the number of potentially dependent data vectors is large and proportional to the size of the vectors, that is, the number of variables. Inspired by the spike models used in random matrix theory, we concentrate on the largest eigenvalues of the matrices in order to determine significance. To avoid false rejections we must guard against residual spikes and need a sufficiently precise description of the behaviour of the largest eigenvalues under the null hypothesis. In this paper we propose some "invariant" theorems that allows us to extend the test of arXiv:submit/3065871 for perturbation of order $1$ to some general tests for order $k$. The statistics introduced in this paper allow the user to test the equality of two populations based on high-dimensional multivariate data. Simulations show that these tests have more power of detection than standard multivariate approaches.
Self-Tuning Deep Reinforcement Learning

Reinforcement learning (RL) algorithms often require expensive manual or automated hyperparameter searches in order to perform well on a new domain. This need is particularly acute in modern deep RL architectures which often incorporate many modules and multiple loss functions. In this paper, we take a step towards addressing this issue by using metagradients (Xu et al., 2018) to tune these hyperparameters via differentiable cross validation, whilst the agent interacts with and learns from the environment. We present the Self-Tuning Actor Critic (STAC) which uses this process to tune the hyperparameters of the usual loss function of the IMPALA actor critic agent(Espeholt et. al., 2018), to learn the hyperparameters that define auxiliary loss functions, and to balance trade offs in off policy learning by introducing and adapting the hyperparameters of a novel leaky V-trace operator. The method is simple to use, sample efficient and does not require significant increase in compute. Ablative studies show that the overall performance of STAC improves as we adapt more hyperparameters. When applied to 57 games on the Atari 2600 environment over 200 million frames our algorithm improves the median human normalized score of the baseline from 243% to 364%.
How to smooth a crinkled map of spacetime: Uhlenbeck compactness for $L^\infty$ connections and optimal regularity for GR shock waves by the RT-equations.

We present authors' new theory of the RT-equations, nonlinear elliptic partial differential equations which determine the coordinate transformations which smooth connections $\Gamma$ to optimal regularity, one derivative smoother than the Riemann curvature tensor ${\rm Riem}(\Gamma)$. As one application we extend Uhlenbeck compactness from Riemannian to Lorentzian geometry; and as another application we establish that regularity singularities at GR shock waves can always be removed by coordinate transformation. This is based on establishing a general multi-dimensional existence theory for the RT-equations, by application of elliptic regularity theory in $L^p$ spaces. The theory and results in this paper apply to arbitrary $L^\infty$ connections on the tangent bundle $T\mathcal{M}$ of arbitrary manifolds $\mathcal{M}$, including Lorentzian manifolds of General Relativity.
The ARF-AID system: Methods that preserve endogenous protein levels and facilitate rapidly inducible protein degradation v1

The ARF-AID (Auxin Response Factor-Auxin Inducible Degron) system is a re-engineered auxin-inducible protein degradation system. Inducible degron systems are widely used to specifically and rapidly deplete proteins of interest in cell lines and organisms. An advantage of inducible degradation is that the biological system under study remains intact and functional until perturbation. This feature necessitates that the endogenous levels of the protein are maintained. However, endogenous tagging of genes with AID can result in chronic, auxin-independent proteasome-mediated degradation. The additional expression of the ARF-PB1 domain in the re-engineered ARF-AID system prevents chronic degradation of AID-tagged proteins while preserving rapid degradation of tagged proteins. Here we describe the protocol for engineering human cell lines to implement the ARF-AID system for specific and inducible protein degradation. These methods are adaptable and can be extended from cell lines to organisms.
Test sets for tautologies in modular quantum logic

As defined by Dunn, Moss, and Wang, an universal test set in an ortholattice $L$ is a subset $T$ such that each term takes value $1$, only, if it does so under all substitutions from $T$. Generalizing their result for ortholattices of subspaces of finite dimensional Hilbert spaces, we show that no infinite modular ortholattice of finite dimension admits a finite universal test set. On the other hand, answering a question of the same authors, we provide a countable universal test set for the ortholattice of projections of any type II$_1$ von Neumann algebra factor as well as for von Neumann's algebraic construction of a continuous geometry. These universal test sets consist of elements having rational normalized dimension with denominator a power of $2$.
A LIST-MODE OSEM-BASED ATTENUATION AND SCATTER COMPENSATION METHOD FOR SPECT

Reliable attenuation and scatter compensation (ASC) is a pre-requisite for quantification and beneficial for visual interpretation tasks in SPECT. In this paper, we develop a reconstruction method that uses the entire SPECT emission data, i.e. data in both the photopeak and scatter windows, acquired in list-mode format and including the energy attribute of the detected photon, to perform ASC. We implemented a GPU-based version of this method using an ordered subsets expectation maximization (OSEM) algorithm. The method was objectively evaluated using realistic simulation studies on the task of estimating uptake in the striatal regions of the brain in a 2-D dopamine transporter (DaT)-scan SPECT study. We observed that inclusion of data from the scatter window and using list-mode data yielded improved quantification compared to using data only from the photopeak window or using binned data. These results motivate further development of list-mode-based ASC methods that include scatter-window data for SPECT.
Do optimization methods in deep learning applications matter?

Author(s): Ozyildirim, Buse Melis; Kiran, Mariam | Abstract: With advances in deep learning, exponential data growth and increasing model complexity, developing efficient optimization methods are attracting much research attention. Several implementations favor the use of Conjugate Gradient (CG) and Stochastic Gradient Descent (SGD) as being practical and elegant solutions to achieve quick convergence, however, these optimization processes also present many limitations in learning across deep learning applications. Recent research is exploring higher-order optimization functions as better approaches, but these present very complex computational challenges for practical use. Comparing first and higher-order optimization functions, in this paper, our experiments reveal that Levemberg-Marquardt (LM) significantly supersedes optimal convergence but suffers from very large processing time increasing the training complexity of both, classification and reinforcement learning problems. Our experiments compare off-the-shelf optimization functions(CG, SGD, LM and L-BFGS) in standard CIFAR, MNIST, CartPole and FlappyBird experiments.The paper presents arguments on which optimization functions to use and further, which functions would benefit from parallelization efforts to improve pretraining time and learning rate convergence.
LARgE Survey – II. The dark matter haloes and the progenitors and descendants of ultramassive passive galaxies at cosmic noon

We use a 27.6 deg2 survey to measure the clustering of gzKs-selected quiescent galaxies at z ∼ 1.6, focusing on ultramassive quiescent galaxies. We find that z ∼ 1.6 Ultra-Massive Passively Evolving Galaxies (UMPEGs), which have Ks(AB) < 19.75 (stellar masses of M⋆$\gtrsim10^{11.4}\,\mathrm{ M}_{\odot }$ and mean <M⋆> = 1011.5 M⊙), cluster more strongly than any other known galaxy population at high redshift. Comparing their correlation length, r0 = 29.77 ± 2.75h−1Mpc, with the clustering of dark matter (DM) haloes in the Millennium XXL N-body simulation suggests that these z ∼ 1.6 UMPEGs reside in DM haloes of mass Mh ∼ 1014.1h−1M⊙. Such very massive z ∼ 1.6 haloes are associated with the ancestors of z ∼ 0 massive galaxy clusters such as the Virgo and Coma clusters. Given their extreme stellar masses and lack of companions with comparable mass, we surmise that these UMPEGs could be the already-quenched central massive galaxies of their (proto)clusters. We conclude that with only a modest amount of further growth in their stellar mass, z ∼ 1.6 UMPEGs could be the progenitors of some of the massive central galaxies of present-day massive galaxy clusters observed to be already very massive and quiescent near the peak epoch of the cosmic star formation.