Miracle-dz/UniFAR-FTUs huggingface dataset:
Bulges and discs in the local Universe. Linking the galaxy structure to star formation activity

We use a sample built on the SDSS DR7 catalogue and the bulge-disc decomposition of Simard et al. (2011, ApJS, 196, 11) to study how the bulge and disc components contribute to the parent galaxy’s star formation activity, by determining its position in the star formation rate (SFR) – stellar mass ( M ⋆ ) plane at 0.02 z α or D 4000 . We study the mean galaxy bulge-total mass ratio ( B / T ) as a function of the residual from the MS (Δ MS ) and find that the B / T -Δ MS relation exhibits a parabola-like shape with the peak of the MS corresponding to the lowest B / T s at any stellar mass. The lower and upper envelope of the MS are populated by galaxies with similar B / T , velocity dispersion and concentration ( R 90 / R 50 ) values. The mean values of such distributions indicate that the majority of the galaxies are characterised by classical bulges and not pseudo-bulges. Bulges above the MS are characterised by blue colours or, when red, by a high level of dust obscuration, thus indicating that in both cases they are actively star forming. When on the MS or below it, bulges are mostly red and dead. At stellar masses above 10 10.5 M ⊙ , bulges on the MS or in the green valley tend to be significantly redder than their counterparts in the quiescence region, despite similar levels of dust obscuration. This could be explained with different age or metallicity content, suggesting different evolutionary paths for bulges on the MS and green valley with respect to those in the quiescence region. The disc g − r colour anti-correlates at any mass with the distance from the MS, getting redder when approaching the MS lower envelope and the quiescence region. The anti-correlation flattens as a function of the stellar mass, likely due to a higher level of dust obscuration in massive SF galaxies. We conclude that the position of a galaxy in the Log SFR – Log M ⋆ plane depends on the star formation activity of its components: above the MS both bulge and disc are actively star forming. The nuclear activity is the first to be suppressed, moving the galaxies on the MS. Once the disc stops forming stars as well, the galaxy moves below the MS and eventually to the quiescence region. This is confirmed by a significant percentage (~45%) of passive galaxies with a secure two component morphology, coexisting with a population of pure spheroidals. Our findings are qualitatively in agreement with the compaction-depletion scenario, in which subsequent phases of gas inflow in the centre of a galaxy and depletion due to high star formation activity move the galaxy across the MS before the final quenching episode takes place.
Collapse of a Relativistic Self-Gravitating Star with Radial Heat Flux: Impact of Anisotropic Stresses

We develop a simple model for a self-gravitating spherically symmetric relativistic star which begins to collapse from an initially static configuration by dissipating energy in the form of radial heat flow. We utilize the model to show how local anisotropy affects the collapse rate and thermal behavior of gravitationally evolving systems.
THE BLACK HOLE CENTRAL ENGINE FOR ULTRA-LONG GAMMA-RAY BURST 111209A AND ITS ASSOCIATED SUPERNOVA 2011KL

Recently, the first association between an ultra-long gamma-ray burst (GRB) and a supernova was reported, i.e., GRB 111209A/SN 2011kl, enabling us to investigate the physics of central engines or even progenitors for ultra-long GRBs. In this paper, we inspect the broadband data of GRB 111209A/SN 2011kl. The late-time X-ray light curve exhibits a GRB 121027A-like fallback bump, suggesting a black hole (BH) central engine. We thus propose a collapsar model with fallback accretion for GRB 111209A/SN 2011kl. The required model parameters, such as the total mass and radius of the progenitor star, suggest that the progenitor of GRB 111209A is more likely a Wolf–Rayet star instead of a blue supergiant, and the central engine of this ultra-long burst is a BH. The implications of our results are discussed.
Exploration Strategy Improved DDPG for Lane Keeping Tasks in Autonomous Driving

We propose an Exploration Strategy Improved Deep Deterministic Policy Gradient algorithm called ESI-DDPG for lane keeping tasks in autonomous driving. The actor network in DDPG outputs a policy which is deterministic, so it is necessary to add noise to actions so that the autonomous vehicle can fully explore the environment and learn the optimal policy. However, the initial weight of exploration noise is large, which makes the autonomous vehicle carry out a lot of invalid exploration in the early training stage. Therefore, we combine the Stanley method to make a weighted correction to the exploration noise, so that the exploration of the vehicle tends to be in the right direction and the training efficiency can be improved. In addition, due to the good sample data obtained in the training process, the driving policy finally learned is better. We choose TORCS as the experiment platform and the results show that, compared with DDPG, TD3 and SAC, our proposed algorithm can learn the driving policy faster and the final policy has smaller trajectory error while driving.
A shift in Jupiter's equatorial haze distribution imaged with the Multi-Conjugate Adaptive Optics Demonstrator at the VLT.

Jupiter was imaged during the Science Demonstration of the MCAO Demonstrator (MAD) at the European Southern Observatory’s UT3 Very Large Telescope unit. Io and Europa were used as natural guide stars on either side of Jupiter, separated from each other by about 1.6 arcmin from 23:41 to 01:32 UT (2008 Aug 16/17). The corrected angular resolution was 0.090 arcsec across the entire field of view, as measured on background stars. The observations at 2.02, 2.14, and 2.16 µm were sensitive to portions of the Jovian spectrum with strong methane absorption. The data probe the upper troposphere, which is populated with a fine (~0.5 µm) haze. Two haze sources have been proposed: lofting of fine cloud particles into the stable upper troposphere, and condensation of hydrazine produced via ammonia photochemistry. The upper tropospheric haze is enhanced over Jupiter’s equatorial region. Dramatic changes in the underlying cloud cover—part of the 2006/2007 “global upheaval”—may be associated with changes in the equatorial haze distribution now evident in the 2008 MAD images (Fig. 1). Haze reflectivity peaked at 5° N in HST/NICMOS data from 2005, but it now peaks at the equator. The observations suggest that haze variation is controlled by particle size, cloud source variation, diffusion, and horizontal transport.
Microstructural characteristics of sandwiched T-shaped metamaterials for three-port splitting

This paper presents a polarization-insensitive sandwiched three-port beam splitting grating at normal incidence. The grating adopts a sandwiched structure. Based on the rigorous coupled-wave analysis (RCWA) and the simulated annealing algorithm, the grating parameters are optimized to obtain the diffraction efficiencies of 32.83% at 0th order and 32.87% at ±1st orders for transverse electric polarization, and 32.63% at 0th order and 32.39% at ±1st orders for transverse magnetic polarization. Meanwhile, the calculation results of the finite element method are in general agreement with those of RCWA. In addition, the analysis results of grating period, duty cycle, and wavelength bandwidth further demonstrate the good wavelength bandwidth of the grating and its feasibility for practical production. Therefore, the grating can be widely used in actual production.
Reconfigurable Stochastic neurons for Restricted Boltzmann Machine

Neuromorphic implementation of Boltzmann Machine using a network of stochastic neurons can allow non-deterministic polynomial-time (NP) hard combinatorial optimization problems to be efficiently solved. Efficient implementation of such Boltzmann Machine with simulated annealing desires the statistical parameters of the stochastic neurons to be dynamically tunable, however, there has been limited research on stochastic semiconductor devices with controllable statistical distributions. Here, we demonstrate a fully digitized stochastic neurons based on reconfigurable stochastic model that can realize tunable stochastic dynamics in its output sampling characteristics. We emulate the Restricted Boltzmann Machine based on reconfigurable stochastic neurons and leverage the handwritten digits from the MNIST dataset to validate its recognition capabilities and investigate the accuracy of several distinct network parameters.
Magnitude uncertainties impact seismic rate estimates, forecasts, and predictability experiments

[1] The Collaboratory for the Study of Earthquake Predictability (CSEP) aims to prospectively test time-dependent earthquake probability forecasts on their consistency with observations. To compete, time-dependent seismicity models are calibrated on earthquake catalog data. However, catalogs contain much observational uncertainty. We study the impact of magnitude uncertainties on rate estimates in clustering models, on their forecasts and on their evaluation by CSEP's consistency tests. First, we quantify magnitude uncertainties. We find that magnitude uncertainty is more heavy-tailed than a Gaussian, such as a double-sided exponential distribution, with scale parameter νc = 0.1 – 0.3. Second, we study the impact of such noise on the forecasts of a simple clustering model which captures the main ingredients of popular short term models. We prove that the deviations of noisy forecasts from an exact forecast are power law distributed in the tail with exponent α = (aνc)−1, where a is the exponent of the productivity law of aftershocks. We further prove that the typical scale of the fluctuations remains sensitively dependent on the specific catalog. Third, we study how noisy forecasts are evaluated in CSEP consistency tests. Noisy forecasts are rejected more frequently than expected for a given confidence limit because the Poisson assumption of the consistency tests is inadequate for short-term forecast evaluations. To capture the idiosyncrasies of each model together with any propagating uncertainties, the forecasts need to specify the entire likelihood distribution of seismic rates.
Hawking-Page phase transition, Page curve and islands in black holes

We study the eﬀect of the Hawking-Page phase transition on the Page Curve for the BTZ black hole coupled to two thermal baths and with the inclusion of island. The Page Curve is shown by us when the Hawking-Page phase transition happens. The results show that the Hawking-Page phase transition may happen before the Page time or after the Page time under certain conditions. We also study the holographic entanglement entropy when the Hawking-Page phase transition happens for the BTZ black hole in the gauge/gravity setup and derive a ﬁrst law of entanglement in the boundary and bulk. If a ﬁrst law of entanglement thermodynamics is universally true, then we can conjecture that all the information of the black hole will come out at the critical temperature when the Hawking-Page phase transition happens. Furthermore, We further speculate that the eﬀect of the ﬁrst-order phase transition of black holes on the black hole information is universal. These results mean that black holes information does not have to escape by the radiation, and the ﬁrst-order phase transition of black holes can also cause information to escape.
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth

A scalable and computationally efficient framework is designed to fingerprint real-world Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its generalization capability is analyzed in different settings and the effect of sample length and anti-aliasing decimation is demonstrated. The embedding module serves as a dimensionality reduction unit that maps the high dimensional 3D input tensor to a 1D feature vector for further processing by the ATN module. Furthermore, unlike the prior research in this field, we closely evaluate the complexity of the model and test its fingerprinting capability with real-world Bluetooth dataset collected under a different time frame and experimental setting while being trained on another. Our study reveals a <inline-formula> <tex-math notation="LaTeX">$9.17\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$65.2\times $ </tex-math></inline-formula> lesser memory usage at a sample length of 100 kS when compared to the benchmark - GRU and Oracle models respectively. Further, the proposed Mbed-ATN showcases a <inline-formula> <tex-math notation="LaTeX">$16.9\times $ </tex-math></inline-formula> fewer FLOPs and <inline-formula> <tex-math notation="LaTeX">$7.5\times $ </tex-math></inline-formula> lesser trainable parameters when compared to Oracle. Finally, we show that when subject to anti-aliasing decimation and at greater input sample lengths of 1 MS, the proposed Mbed-ATN framework results in a <inline-formula> <tex-math notation="LaTeX">$5.32\times $ </tex-math></inline-formula> higher TPR, 37.9% fewer false alarms, and <inline-formula> <tex-math notation="LaTeX">$6.74\times $ </tex-math></inline-formula> higher accuracy under the challenging real-world setting.
Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI

—Open-source is frequently described as a driver for unprecedented communication and collaboration, and the process works best when projects support teamwork. Yet, their cooperation processes in no way protect project contributors from considerations of trust, power, and inﬂuence. Indeed, achieving the level of trust necessary to contribute to a project and thus inﬂuence its direction is a constant process of change, and developers take many different routes over many communication channels to achieve it. We refer to this process of inﬂuence- seeking and trust-building, trust ascendancy . This paper describes a methodology for understanding the notion of trust ascendancy, and introduces the capabilities that are needed to localizing trust ascendancy operations happening over open-source projects. Much of the prior work in understanding trust in open-source software development has focused on a static view of the problem, and study it using different forms of quantity measures. However, trust ascendancy is not static but rather adapt to changes in the open-source ecosystem in response to developer role changes, new functionality, new technologies, and so on. This paper is the ﬁrst attempt to articulate and study these signals, from a dynamic view of the problem. In that respect, we identify related work that may help illuminate research challenges, implementation tradeoffs, and complementary solutions. Our preliminary results show the effectiveness of our method at capturing the trust ascendancy developed by individuals involved in a well-documented 2020 social engineering attack. Our future plans highlight research challenges, and encourage cross-disciplinary collaboration to create more automated, accurate, and efﬁcient ways to modeling and then tracking trust ascendancy in open-source projects.
Machine learning classification of CHIME fast radio bursts -- I. Supervised methods

Observationally, the mysterious fast radio bursts (FRBs) are classified as repeating ones and apparently non-repeating ones. While repeating FRBs cannot be classified into the non-repeating group, it is unknown whether the apparently non-repeating FRBs are actually repeating FRBs whose repetitions are yet to be discovered, or whether they belong to another physically distinct type from the repeating ones. In a series of two papers, we attempt to disentangle this mystery with machine learning methods. In this first paper, we focus on an array of supervised machine learning methods. We train the machine learning algorithms with a fraction of the observed FRBs in the first CHIME/FRB catalog, telling them which ones are apparently non-repeating and which ones are repeating. We then let the trained models predict the repetitiveness of the rest of the FRB data with the observed parameters, and we compare the predictions with the observed repetitiveness. We find that the models can predict most FRBs correctly, hinting towards distinct mechanisms behind repeating and non-repeating FRBs. We also find that the two most important distinguishing factors between non-repeating and repeating FRBs are brightness temperature and rest-frame frequency bandwidth. By applying the trained models back to the entire first CHIME catalog, we further identify some potentially repeating FRBs currently reported as non-repeating. We recommend a list of these bursts as targets for future observing campaigns to search for repeated bursts in a combination with the results presented in Paper II using unsupervised machine learning methods.
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data

Artificial Intelligence (AI) is one of the approaches that has been proposed to analyze the collected data (e.g., vibration signals) providing a diagnosis of the asset's operating condition. It is known that models trained with labeled data (supervised) achieve excellent results, but two main problems make their application in production processes difficult: (i) impossibility or long time to obtain a sample of all operational conditions (since faults seldom happen) and (ii) high cost of experts to label all acquired data. Another limitating factor for the applicability of AI approaches in this context is the lack of interpretability of the models (black-boxes), which reduces the confidence of the diagnosis and trust/adoption from users. To overcome these problems, a new generic and interpretable approach for classifying faults in rotating machinery based on transfer learning from augmented synthetic data to real rotating machinery is here proposed, namelly FaultD-XAI (Fault Diagnosis using eXplainable AI). To provide scalability using transfer learning, synthetic vibration signals are created mimicking the characteristic behavior of failures in operation. The application of Gradient-weighted Class Activation Mapping (Grad-CAM) with 1D Convolutional Neural Network (1D CNN) allows the interpretation of results, supporting the user in decision making and increasing diagnostic confidence. The proposed approach not only obtained promising diagnostic performance, but was also able to learn characteristics used by experts to identify conditions in a source domain and apply them in another target domain. The experimental results suggest a promising approach on exploiting transfer learning, synthetic data and explainable artificial intelligence for fault diagnosis. Lastly, to guarantee reproducibility and foster research in the field, the developed dataset is made publicly available.
Nuclear binding energies in artificial neural networks

The binding energy (BE) or mass is one of the most fundamental properties of an atomic nucleus. Precise binding energies are vital inputs for many nuclear physics and nuclear astrophysics studies. However, due to the complexity of atomic nuclei and of the non-perturbative strong interaction, up to now, no conventional physical model can describe nuclear binding energies with a precision below 0.1 MeV, the accuracy needed by nuclear astrophysical studies. In this work, artificial neural networks (ANNs), the so called ``universal approximators", are used to calculate nuclear binding energies. We show that the ANN can describe all the nuclei in AME2020 with a root-mean-square deviation (RMSD) around 0.2 MeV, which is better than the best macroscopic-microscopic models, such as FRDM and WS4. The success of the ANN is mainly due to the proper and essential input features we identify, which contain the most relevant physical information, i.e., shell, paring, and isospin-asymmetry effects. We show that the well-trained ANN has excellent extrapolation ability and can predict binding energies for those nuclei so far inaccessible experimentally. In particular, we highlight the important role played by ``feature engineering'' for physical systems where data are relatively scarce, such as nuclear binding energies.
Plasma zone plate for high-power lasers driven by a Laguerre–Gaussian beam

Plasma-based optics has emerged as an attractive alternative to traditional solid-state optics for high-power laser manipulation due to its higher damage threshold. In this work, we propose a plasma zone plate (PZP) driven by the ponderomotive force of a Laguerre–Gaussian beam when it irradiates an underdense plasma slice. We formulate the theory of the PZP and demonstrate its formation and functioning using particle-in-cell simulations. The proposed scheme may offer a new plasma-based method to manipulate high-power lasers.
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces

Data-driven machine learning models are being increasingly employed in several important inference problems in biology, chemistry, and physics which require learning over combinatorial spaces. Recent empirical evidence (see, e.g., [1], [2], [3]) suggests that regularizing the spectral representation of such models improves their generalization power when labeled data is scarce. However, despite these empirical studies, the theoretical underpinning of when and how spectral regularization enables improved generalization is poorly understood. In this paper, we focus on learning pseudo-Boolean functions and demonstrate that regularizing the empirical mean squared error by the L_1 norm of the spectral transform of the learned function reshapes the loss landscape and allows for data-frugal learning, under a restricted secant condition on the learner's empirical error measured against the ground truth function. Under a weaker quadratic growth condition, we show that stationary points which also approximately interpolate the training data points achieve statistically optimal generalization performance. Complementing our theory, we empirically demonstrate that running gradient descent on the regularized loss results in a better generalization performance compared to baseline algorithms in several data-scarce real-world problems.
See and Copy: Generation of complex compositional movements from modular and geometric RNN representations

A hallmark of biological intelligence and control is combinatorial generalization: animals are able to learn various things, then piece them together in new combinations to produce appropriate outputs for new tasks. Inspired by the ability of primates to readily imitate seen movement sequences, we present a model of motor control using a realistic model of arm dynamics, tasked with imitating a guide that makes arbitrary two-segment drawings. We hypothesize that modular organization is one of the keys to such flexible and generalizable control. We construct a modular control model consisting of separate encoding and motor RNNs and a scheduler, which we train end-to-end on the task. We show that the modular structure allows the model to generalize not only to unseen two-segment trajectories, but to new drawings consisting of many more segments than it was trained on, and also allows for rapid adaptation to perturbations. Finally, our model recapitulates experimental observations of the preparatory and execution-related processes unfolding during motor control, providing a normative explanation for functional segregation of preparatory and execution-related activity within the motor cortex.