Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term NIR variability in the UKIDSS Ultra Deep Survey: a new probe of AGN activity at high redshift

We present the first attempt to select active galactic nuclei (AGN) using long-term near-infrared (NIR) variability. By analysing the K-band light curves of all the galaxies in the UKIRT Infrared Deep Sky Survey (UKIDSS) Ultra Deep Survey, the deepest NIR survey over ∼1 deg2, we have isolated 393 variable AGN candidates. A comparison to other selection techniques shows that only half of the variable sources are also selected using either deep Chandra X-ray imaging or Infrared Array Camera (IRAC) colour selection, suggesting that using NIR variability can locate AGN that are missed by more standard selection techniques. In particular, we find that long-term NIR variability identifies AGN at low luminosities and in host galaxies with low stellar masses, many of which appear relatively X-ray quiet.
On the Shape of the Local Bubble

The shape of the local bubble is modeled in the framework of the thin layer approximation. The asymmetric shape of the local bubble is simulated by introducing axial profiles for the density of the interstellar medium, such as exponential, Gaussian, inverse square dependence and Navarro--Frenk--White. The availability of some observed asymmetric profiles for the local bubble allows us to match theory and observations via the observational percentage of reliability. The model is compatible with the presence of radioisotopes on Earth.
Drug transport in cell preparations with diffusional dosing and temporal ratiometry.

by PRASAD ORUGANTI The introduction of specific molecules into single cells is a widely used approach to probe cellular mechanisms. For example, to better understand the complex phenomenon of cancer drug resistance due to sequestration, binding and efflux, drug molecules are introduced into cancer cells. To probe cell-cell communication tracer molecules are delivered into c onnected cells and their junctional permeabilities are estimated. Existing methods for delivering molecules into cells include the injection of discrete boluses of material using mechanical or electro-mechanical principles, and continuous perfusion from a patch pipette in the whole cell configuration. However with these methods delivery cannot be quantified, or can introduce stress to the cells due to volume addition, or can cause excessive washout of cytosolic content. In this work we report on the use of a diffusional microburet that is a micropipette with a diffusion port at its tip to deliver molecules into an impaled target cell,
Transferrable AlGaN/GaN HEMTs to Arbitrary Substrates via a Two-dimensional Boron Nitride Release Layer.

Mechanical transfer of high performing thin film devices onto arbitrary substrates represents an exciting opportunity to improve device performance, explore non-traditional manufacturing approaches, and paves the way for soft, conformal, and flexible electronics. Using a two-dimensional (2D) boron nitride (BN) release layer, we demonstrate the transfer of AlGaN/GaN high-electron mobility transistors (HEMTs) to arbitrary substrates through both direct van der Waals (vdW) bonding and with a polymer adhesive interlayer. No device degradation was observed due to the transfer process, and a significant reduction in device temperature (327 °C to 132 °C at 600 mW) was observed when directly bonded to a silicon carbide (SiC) wafer relative to the starting wafer. With the use of a benzocyclobutene (BCB) adhesion interlayer, devices were easily transferred and characterized on Kapton and ceramic films, representing an exciting opportunity for integration onto arbitrary substrates. Upon reduction of this polymer adhesive layer thickness, the AlGaN/GaN HEMTs transferred onto a BCB/SiC substrate resulted in comparable peak temperatures during operation at powers as high as 600 mW to the as-grown wafer, revealing that by optimizing interlayer characteristics such as thickness and thermal conductivity, transferrable devices on polymer layers can still improve performance outputs.
Microlensing Analysis for the Gravitational Lens Systems SDSS0924+0219, Q1355-2257, and SDSS1029+2623

We use spectroscopic observations of the gravitationally lensed systems SDSS0924+0219(BC), Q1355-2257(AB), and SDSS1029+2623(BC) to analyze microlensing and dust extinction in the observed components. We detect chromatic microlensing effects in the continuum and microlensing in the broad emission line profiles of the systems SDSS0924+0219(BC) and Q1355-2257(AB). Using magnification maps to simulate microlensing and modeling the emitting region as a Gaussian intensity profile with size rs ∝ λp, we obtain the probability density functions for a logarithmic size prior to . In the case of SDSS0924+0219, we obtain lt-d (at 1σ), which is larger than the range of other estimates, and p = 0.8 ± 0.2 (at 1σ), which is smaller than predicted by the thin disk theory, but still in agreement with previous results. In the case of Q1355-2257 we obtain (at 1σ) lt-d, which is also larger than the theoretical prediction, and p = 2.0 ± 0.7, which is in agreement with the theory within errors. SDSS1029+2326 spectra show evidence of extinction, probably produced by a galaxy in the vicinity of image C. Fitting an extinction curve to the data we estimate ΔE ∼ 0.2 in agreement with previous results. We found no evidence of microlensing for this system.
Random weighting to approximate posterior inference in LASSO regression

We consider a general-purpose approximation approach to Bayesian inference in which repeated optimization of a randomized objective function provides surrogate samples from the joint posterior distribution. In the context of LASSO regression, we repeatedly assign independently-drawn standard-exponential random weights to terms in the objective function, and optimize to obtain the surrogate samples. We establish the asymptotic properties of this method under different regularization parameters $\lambda_n$. In particular, if $\lambda_n = o(\sqrt{n})$, then the random-weighting (weighted bootstrap) samples are equivalent (up to the first order) to the Bayesian posterior samples. If $\lambda_n = \mathcal{O} \left( n^c \right)$ for some $1/2 < c < 1$, then these samples achieve conditional model selection consistency. We also establish the asymptotic properties of the random-weighting method when weights are drawn from other distributions, and also if weights are assigned to the LASSO penalty terms.
Field/isolated lenticular galaxies with high SN values: the case of NGC 4546 and its globular cluster system

Abstract We present a photometric study of the field lenticular galaxy NGC 4546 using Gemini/GMOS imaging in g′r′i′z′. We perform a 2D image decomposition of the surface brightness distribution of the galaxy using galfit, finding that four components adequately describe it. The subtraction of this model from our images and the construction of a colour map allow us to examine in great detail the asymmetric dust structures around the galactic centre. In addition, we perform a detailed analysis of the globular cluster (GC) system of NGC 4546. Using a Gaussian Mixture Model algorithm in the colour-colour plane we detected hints of multiple groups of GC candidates: the classic blue and red subpopulations, a group with intermediate colours that present a concentrated spatial distribution towards the galaxy, and an additional group towards the red end of the colour distribution. We estimate a total GC population for NGC 4546 of 390 ± 60 members and specific frequency SN = 3.3 ± 0.7, which is relatively high compared to the typical value for galaxies of similar masses and environment. We suggest that the unusual GC population substructures were possibly formed during the interaction that led to the formation of the young ultra-compact dwarf (NGC 4546-UCD1) found in this system. Finally, we estimate the distance modulus of NGC 4546 by analyzing its luminosity function, resulting in (m − M) = 30.75 ± 0.12 mag (14.1 Mpc).
Parton distribution functions of the charged pion within the xFitter framework

We present the first open-source analysis of parton distribution functions (PDFs) of charged pions using xFitter, an open-source QCD fit framework to facilitate PDF extraction and analyses. Our calculations are implemented at next-to-leading order (NLO) using APPLgrids generated by MCFM generator. Using currently available Drell-Yan and photon production data, we find the valence distribution is well constrained; however, the considered data are not sensitive enough to unambiguously determine sea and gluon distributions. Fractions of momentum carried by the valence, sea and gluon components are discussed, and we compare with the results of JAM collaboration and the GRV group.
One-Shot Bayes Opt with Probabilistic Population Based Training

Selecting optimal hyperparameters is a key challenge in machine learning. An exciting recent result showed it is possible to learn high-performing hyperparameter schedules on the fly in a single training run through methods inspired by Evolutionary Algorithms. These approaches have been shown to increase performance across a wide variety of machine learning tasks, ranging from supervised (SL) to reinforcement learning (RL). However, since they remain primarily evolutionary, they act in a greedy fashion, thus require a combination of vast computational resources and carefully selected meta-parameters to effectively explore the hyperparameter space. To address these shortcomings we look to Bayesian Optimization (BO), where a Gaussian Process surrogate model is combined with an acquisition function to produce a principled mechanism to trade off exploration vs exploitation. Our approach, which we call Probabilistic Population-Based Training ($\mathrm{P2BT}$), is able to transfer sample efficiency of BO to the online setting, making it possible to achieve these traits in a single training run. We show that $\mathrm{P2BT}$ is able to achieve high performance with only a small population size, making it useful for all researchers regardless of their computational resources.
RAID: Randomized Adversarial-Input Detection for Neural Networks

In recent years, neural networks have become the default choice for image classification and many other learning tasks, even though they are vulnerable to so-called adversarial attacks. To increase their robustness against these attacks, there have emerged numerous detection mechanisms that aim to automatically determine if an input is adversarial. However, state-of-the-art detection mechanisms either rely on being tuned for each type of attack, or they do not generalize across different attack types. To alleviate these issues, we propose a novel technique for adversarial-image detection, RAID, that trains a secondary classifier to identify differences in neuron activation values between benign and adversarial inputs. Our technique is both more reliable and more effective than the state of the art when evaluated against six popular attacks. Moreover, a straightforward extension of RAID increases its robustness against detection-aware adversaries without affecting its effectiveness.
A Repeated Game Freeway Lane Changing Model

Lane changes are complex safety- and throughput-critical driver actions. Most lane-changing models deal with lane-changing maneuvers solely from the merging driver’s standpoint and thus ignore driver interaction. To overcome this shortcoming, we develop a game-theoretical decision-making model and validate the model using empirical merging maneuver data at a freeway on-ramp. Specifically, this paper advances our repeated game model by using updated payoff functions. Validation results using the Next Generation SIMulation (NGSIM) empirical data show that the developed game-theoretical model provides better prediction accuracy compared to previous work, giving correct predictions approximately 86% of the time. In addition, a sensitivity analysis demonstrates the rationality of the model and its sensitivity to variations in various factors. To provide evidence of the benefits of the repeated game approach, which takes into account previous decision-making results, a case study is conducted using an agent-based simulation model. The proposed repeated game model produces superior performance to a one-shot game model when simulating actual freeway merging behaviors. Finally, this lane change model, which captures the collective decision-making between human drivers, can be used to develop automated vehicle driving strategies.
The impact of AGN feedback on the 1D power spectra from the Ly α forest using the Horizon-AGN suite of simulations

The Lyman-α forest is a powerful probe for cosmology, but it is also strongly impacted by galaxy evolution and baryonic processes such as active galactic nucleus (AGN) feedback, which can redistribute mass and energy on large scales. We constrain the signatures of AGN feedback on the 1D power spectrum of the Lyman-α forest using a series of eight hydro-cosmological simulations performed with the adaptive mesh refinement code ramses. This series starts from the Horizon-AGN simulation and varies the subgrid parameters for AGN feeding, feedback, and stochasticity. These simulations cover the whole plausible range of feedback and feeding parameters according to the resulting galaxy properties. AGNs globally suppress the Lyman-α power at all scales. On large scales, the energy injection and ionization dominate over the supply of gas mass from AGN-driven galactic winds, thus suppressing power. On small scales, faster cooling of denser gas mitigates the suppression. This effect increases with decreasing redshift. We provide lower and upper limits of this signature at nine redshifts between z = 4.25 and 2.0, making it possible to account for it at post-processing stage in future work given that running simulations without AGN feedback can save considerable amounts of computing resources. Ignoring AGN feedback in cosmological inference analyses leads to strong biases with 2 per cent shift on σ8 and 1 per cent shift on ns, which represents twice the standards deviation of the current constraints on ns.
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data

Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to conventional time-series forecasting using Autoregressive Integrated Moving Average (ARIMA). A prediction horizon up to 90 min into the future is considered. In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed. We find that the population-based RNN model is the best performing model across the considered prediction horizons without the need of patient-specific data. This demonstrates the potential of RNNs for STBG prediction in diabetes patients towards detecting/mitigating severe events in the STBG, in particular hypoglycemic events. However, further studies are needed in regards to the robustness and practical use of the investigated STBG prediction models.
Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays

Most deep learning models in chest X-ray prediction utilize the posteroanterior (PA) view due to the lack of other views available. PadChest is a large-scale chest X-ray dataset that has almost 200 labels and multiple views available. In this work, we use PadChest to explore multiple approaches to merging the PA and lateral views for predicting the radiological labels associated with the X-ray image. We find that different methods of merging the model utilize the lateral view differently. We also find that including the lateral view increases performance for 32 labels in the dataset, while being neutral for the others. The increase in overall performance is comparable to the one obtained by using only the PA view with twice the amount of patients in the training set.
Convective boundary mixing in low- and intermediate-mass stars – I. Core properties from pressure-mode asteroseismology

Convective boundary mixing (CBM) is ubiquitous in stellar evolution. It is a necessary ingredient in the models in order to match observational constraints from clusters, binaries and single stars alike. We compute `effective overshoot' measures that reflect the extent of mixing and which can differ significantly from the input overshoot values set in the stellar evolution codes. We use constraints from pressure modes to infer the CBM properties of Kepler and CoRoT main-sequence and subgiant oscillators, as well as in two radial velocity targets (Procyon A and $\alpha$ Cen A). Collectively these targets allow us to identify how measurement precision, stellar spectral type, and overshoot implementation impact the asteroseismic solution. With these new measures we find that the `effective overshoot' for most stars is in line with physical expectations and calibrations from binaries and clusters. However, two F-stars in the CoRoT field (HD 49933 and HD 181906) still necessitate high overshoot in the models. Due to short mode lifetimes, mode identification can be difficult in these stars. We demonstrate that an incongruence between the radial and non-radial modes drives the asteroseismic solution to extreme structures with highly-efficient CBM as an inevitable outcome. Understanding the cause of seemingly anomalous physics for such stars is vital for inferring accurate stellar parameters from TESS data with comparable timeseries length.
Logistic Regression Regret: What's the Catch?

We address the problem of the achievable regret rates with online logistic regression. We derive lower bounds with logarithmic regret under $L_1$, $L_2$, and $L_\infty$ constraints on the parameter values. The bounds are dominated by $d/2 \log T$, where $T$ is the horizon and $d$ is the dimensionality of the parameter space. We show their achievability for $d=o(T^{1/3})$ in all these cases with Bayesian methods, that achieve them up to a $d/2 \log d$ term. Interesting different behaviors are shown for larger dimensionality. Specifically, on the negative side, if $d = \Omega(\sqrt{T})$, any algorithm is guaranteed regret of $\Omega(d \log T)$ (greater than $\Omega(\sqrt{T})$) under $L_\infty$ constraints on the parameters (and the example features). On the positive side, under $L_1$ constraints on the parameters, there exist algorithms that can achieve regret that is sub-linear in $d$ for the asymptotically larger values of $d$. For $L_2$ constraints, it is shown that for large enough $d$, the regret remains linear in $d$ but no longer logarithmic in $T$. Adapting the redundancy-capacity theorem from information theory, we demonstrate a principled methodology based on grids of parameters to derive lower bounds. Grids are also utilized to derive some upper bounds. Our results strengthen results by Kakade and Ng (2005) and Foster et al. (2018) for upper bounds for this problem, introduce novel lower bounds, and adapt a methodology that can be used to obtain such bounds for other related problems. They also give a novel characterization of the asymptotic behavior when the dimension of the parameter space is allowed to grow with $T$. They additionally establish connections to the information theory literature, demonstrating that the actual regret for logistic regression depends on the richness of the parameter class, where even within this problem, richer classes lead to greater regret.
On the Estimation of Information Measures of Continuous Distributions

The estimation of information measures of continuous distributions based on samples is a fundamental problem in statistics and machine learning. In this paper, we analyze estimates of differential entropy in $K$-dimensional Euclidean space, computed from a finite number of samples, when the probability density function belongs to a predetermined convex family $\mathcal{P}$. First, estimating differential entropy to any accuracy is shown to be infeasible if the differential entropy of densities in $\mathcal{P}$ is unbounded, clearly showing the necessity of additional assumptions. Subsequently, we investigate sufficient conditions that enable confidence bounds for the estimation of differential entropy. In particular, we provide confidence bounds for simple histogram based estimation of differential entropy from a fixed number of samples, assuming that the probability density function is Lipschitz continuous with known Lipschitz constant and known, bounded support. Our focus is on differential entropy, but we provide examples that show that similar results hold for mutual information and relative entropy as well.
Broadband X-ray analysis of 1E 1740.7−2942: constraints on spin, inclination, and a tentative black hole mass

1E 1740.7-2942 is one of the strongest hard X-ray emitters in the Galactic Centre region, believed to be a black hole in a high-mass X-ray binary system. Although extensively studied in X-rays, many aspects about the underlying nature of the system are still unknown. For example, X-ray data analyses of 1E 1740.7-2942 up to date have not yet unveiled the signature of a reflection component, whose modelling could be used to estimate parameters such as the spin of the black hole and inclination of the disc. We report here on the determination of these parameters from the analysis of the reflection component present in a public \textit{NuSTAR} observation which hasn't been subject to any previous study. We include \textit{XMM-Newton} and \textit{INTEGRAL} data to build a combined spectrum, enabling a joint analysis of both the disc and comptonisation components. Results point to a relatively high inclination disc $\gtrsim$ 50$^{\circ}$ (3 $\sigma$) and a near-maximum speed rotating black hole. The former is in agreement with a previous radio study and the latter is reported here for the first time. Lastly, we follow the methodology of recent efforts to weight black holes with only X-ray spectra and find results that suggest a black hole mass of about 5 M$_\odot$ for 1E 1740.7-2942.
Concrete method for recovering the Euler characteristic of quantum graphs

Trace formulas play a central role in the study of spectral geometry and in particular of quantum graphs. The basis of our work is the result by Kurasov which links the Euler characteristic χ of metric graphs to the spectrum of their standard Laplacian. These ideas were shown to be applicable even in an experimental context where only a finite number of eigenvalues from a physical realization of quantum graph can be measured. In the present work we analyse sufficient hypotheses which guarantee the successful recovery of χ. We also study how to improve the efficiency of the method and in particular how to minimise the number of eigenvalues required. Finally, we compare our findings with numerical examples—surprisingly, just a few dozens of eigenvalues can be enough.
A Geometric Proof of the Quasi-Linearity of the Water Waves System

In the first part of this paper we prove that the flow associated to the Burgers equation with a non local term of the form $H\langle D \rangle^\alpha u$ fails to be uniformly continuous from bounded sets of $H^s({\mathbb D})$ to $C^0([0,T],H^s({\mathbb D}))$ for $T>0$, $s>\frac{1}{2}+2$, $0\leq \alpha 0$. We generalize this result to a large class of nonlinear transport-dispersive equations in any dimension, that in particular contains the Whitham equation and the paralinearization of the water waves system with and without surface tension. The current result is optimal in the sense that for $\alpha=2$ and ${\mathbb D}={\mathbb T}$ the flow associated to the Benjamin-Ono equation is Lipschitz on function with $0$ mean value $H^s_0$. In the second part of this paper we apply this method to deduce the quasi-linearity of the water waves system, which is the main result of this paper.