Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-frequency radio spectra of submillimetre galaxies in the Lockman Hole

Aims. We investigate the radio properties of a sample of 850 μm-selected sources from the SCUBA-2 Cosmology Legacy Survey (S2CLS) using new deep, low-frequency radio imaging of the Lockman Hole field from the Low Frequency Array. This sample consists of 53 sources, 41 of which are detected at >5σ at 150 MHz. Methods. Combining these data with additional observations at 324 MHz, 610 MHz, and 1.4 GHz from the Giant Metrewave Radio Telescope and the Jansky Very Large Array, we find a variety of radio spectral shapes and luminosities (L1.4 GHz ranging from ~4 × 1023−1 × 1025) within our sample despite their similarly bright submillimetre flux densities (>4 mJy). We characterise their spectral shapes in terms of multi-band radio spectral indices. Finding strong spectral flattening at low frequencies in ~20% of sources, we investigate the differences between sources with extremely flat low-frequency spectra and those with ‘normal’ radio spectral indices (α > −0.25). Results. As there are no other statistically significant differences between the two subgroups of our sample as split by the radio spectral index, we suggest that any differences are undetectable in galaxy-averaged properties that we can observe with our unresolved images, and likely relate to galaxy properties that we cannot resolve, on scales ≲1 kpc. We attribute the observed spectral flattening in the radio to free–free absorption, proposing that those sources with significant low-frequency spectral flattening have a clumpy distribution of star-forming gas. We estimate an average spatial extent of absorbing material of at most several hundred parsecs to produce the levels of absorption observed in the radio spectra. This estimate is consistent with the highest-resolution observations of submillimetre galaxies in the literature, which find examples of non-uniform dust distributions on scales of ~100 pc, with evidence for clumps and knots in the interstellar medium. Additionally, we find two bright (>6 mJy) S2CLS sources undetected at all other wavelengths. We speculate that these objects may be very high redshift sources, likely residing at z > 4.
On the exponentially small corrections to ${\cal N} = 2$ superconformal correlators at large R-charge

In this note we consider Coulomb-branch chiral primary correlation functions in N = 2 superconformal QCD with gauge group SU(2), in the limit of large R-charge J = 2n for the chiral primary operators [O(x)] with th e gauge coupling τ held fixed. In previous work [5–8] these correlation functions were determined to all orders in n, up to unknown exponentially small corrections. In this paper we determine the first several orders of the asymptotic expansion of the exponentially small correction itself. To do this we use: the physical interpretation of the exponentially small correction as the virtual propagation of a massive BPS particle, to fix the leading term in the expansion; the supersymmetric recursion relations of [3, 14, 60, 61] to derive differential equations for the coupling-dependence of the subleading terms; and the double-scaling limit of [9,13], to fix undetermined coefficients in the solution of the differential equation. We calculate the expansion of the exponentially small term up to and including relative order n− 5 2 . We also use the recursion relations to calculate the subleading large-J corrections to the exponentially small correction in the double-scaling limit, up to and including relative order n−5 at fixed double-scaled coupling λ. We compare the expansion to exact results from supersymmetric localization [84] at the coupling τ = 25 π i, up to n = 150. At values n ∼ 100− 150, we find the fixed-coupling and double-scaled largeR-charge expansions are accurate to within one part in 10 and 10, respectively, of the size of the exponentially small correction itself. Relative to the full correlator including the dominant EFT contribution, these estimates give results accuracte to one part in 10 and 10, respectively. March 18, 2021 ar X iv :2 10 3. 09 31 2v 1 [ he pth ] 1 6 M ar 2 02 1
Implication of the Hubble tension for the primordial Universe in light of recent cosmological data

In prerecombination resolutions of the Hubble tension, such as early dark energy, new physics before recombination shifts the values of relevant cosmological parameters so that the models can fit with cosmic microwave background and baryon acoustic oscillations observations as well as $\Lambda$CDM does. In this paper, we clarify how the parameter shifts are related with $\delta H_0$, particularly we find the shift of primordial scalar spectral index scales as ${\delta n_s}\simeq 0.4{\delta H_0\over H_0}$ by performing the Monte Carlo Markov chain analysis with the Planck2018+BAO+Pantheon+R19+Keck Array/BICEP dataset. A novel point of our result is that if the current $H_0$ measured locally is correct, complete resolution of the Hubble tension seems to be pointing to a scale invariant Harrison-Zeldovich spectrum, i.e. $n_s= 1$ for $H_0\sim 73$km/s/Mpc.
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages

The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in autonomous vehicles. In this paper, we present a reinforcement learning based approach to autonomous vehicle longitudinal control, where the rule-based safety cages provide enhanced safety for the vehicle as well as weak supervision to the reinforcement learning agent. By guiding the agent to meaningful states and actions, this weak supervision improves the convergence during training and enhances the safety of the final trained policy. This rule-based supervisory controller has the further advantage of being fully interpretable, thereby enabling traditional validation and verification approaches to ensure the safety of the vehicle. We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance. Additionally, we show that when the model parameters are constrained or sub-optimal, the safety cages can enable a model to learn a safe driving policy even when the model could not be trained to drive through reinforcement learning alone.
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias

Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells. Genetic tests currently used to diagnose such anemias are expensive and unavailable in the majority of clinical labs. Here, we propose a method for identifying hereditary hemolytic anemias based on a standard biochemistry method, called Percoll gradient, obtained by centrifuging a patient’s blood. Our hybrid approach consists on using spatial data-driven features, extracted with a convolutional neural network and spectral handcrafted features obtained from fast Fourier transform. We compare late and early feature fusion with AlexNet and VGG16 architectures. AlexNet with late fusion of spectral features performs better compared to other approaches. We achieved an average F1-score of 88% on different classes suggesting the possibility of diagnosing of hereditary hemolytic anemias from Percoll gradients. Finally, we utilize Grad-CAM to explore the spatial features used for classification.
Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones

Targets search and detection encompasses a variety of decision problems such as coverage, surveillance, search, observing and pursuit-evasion along with others. In this paper we develop a multi-agent deep reinforcement learning (MADRL) method to coordinate a group of aerial vehicles (drones) for the purpose of locating a set of static targets in an unknown area. To that end, we have designed a realistic drone simulator that replicates the dynamics and perturbations of a real experiment, including statistical inferences taken from experimental data for its modeling. Our reinforcement learning method, which utilized this simulator for training, was able to find near-optimal policies for the drones. In contrast to other state-of-the-art MADRL methods, our method is fully decentralized during both learning and execution, can handle high-dimensional and continuous observation spaces, and does not require tuning of additional hyperparameters.
Electron probe microanalysis of irradiated nuclear fuel: an overview

This paper focuses on the features that set apart the electron probe microanalysis (EPMA) of highly radioactive material from standard EPMA. In addition, it describes the difficulties encountered in the analysis of irradiated nuclear fuel and explains how certain problems have been solved or at least overcome. The paper then presents specific examples showing how EPMA is currently utilized in nuclear fuel research. It is shown that, despite the use of incorrect standards for certain elements, problems arising from X-ray line interference and uncertainties in the matrix correction, EPMA is still able to deliver basic data on chemical composition which are of fundamental importance in understanding the in-pile behaviour of nuclear fuel.
Understanding the Kerala Floods of 2018: Role of Mixed Rossby-Gravity Waves

Kerala, the south-west coastal state of India, was ravaged by a series of floods during the South-West Monsoon of 2018, as a consequence of severely anomalous rainfall trends. Although, there were many studies about the hydrogeological factors which aggravated the floods in Kerala, no attempt was made to delve into the physics which actually resulted in anomalous precipitation during the year. This study intends to document the dynamical phenomenon which caused the Kerala Floods of 2018. The westward propagating convectively-coupled Mixed Rossby-Gravity (MRG) waves were excited by the synoptic disturbances of the tropical Pacific at the pressure level of 700 hPa, during the Indian Monsoon of 2018. They travelled across the Indian Ocean in two significant modes – a predominant slow moving wave of 20-40 days period (as Madden-Julian Oscillation) and a secondary faster wave of 5-8 days period. They are characterised by vertical phase propagation to the upper troposphere, a precursor to deep convection and intense precipitation. Further, the propagation of these waves through a medium enhances its relative vorticity and the gyres or circulations thus formed are symmetrical about the equator. Consequently, the meandering of wind field and widening of the Intertropical Convergence Zone were observed.
Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study

With the development of measurement technology, data on the movements of actual games in various sports can be obtained and used for planning and evaluating the tactics and strategy. Defense in team sports is generally difficult to be evaluated because of the lack of statistical data. Conventional evaluation methods based on predictions of scores are considered unreliable because they predict rare events throughout the game. Besides, it is difficult to evaluate various plays leading up to a score. In this study, we propose a method to evaluate team defense from a comprehensive perspective related to team performance by predicting ball recovery and being attacked, which occur more frequently than goals, using player actions and positional data of all players and the ball. Using data from 45 soccer matches, we examined the relationship between the proposed index and team performance in actual matches and throughout a season. Results show that the proposed classifiers predicted the true events (mean F1 score > 0.483) better than the existing classifiers which were based on rare events or goals (mean F1 score < 0.201). Also, the proposed index had a moderate correlation with the long-term outcomes of the season (r = 0.397). These results suggest that the proposed index might be a more reliable indicator rather than winning or losing with the inclusion of accidental factors.
Ab-initio calculations of quantum light-matter interactions in van der Waals heterostructures

Making quantitative predictions for quantum light-matter interactions in ultra thin solids involves several fundamental challenges. Any realistic theory must simultaneously account for the highly confined light modes and their quantization in the presence of optical losses, while simultaneously describing the electronic states from first principles. In this paper we develop a framework that combines density functional theory (DFT) with macroscopic quantum electrodynamics (MQED). We exemplify our methods by presenting ab-initio predictions for the Purcell effect in atomically thin van der 1 ar X iv :2 10 3. 09 50 1v 1 [ co nd -m at . m tr lsc i] 1 7 M ar 2 02 1 Waals heterostructures. Our ab-initio predictions for the spontaneous emission rate enhancement of intersubband transitions in few-layer transition metal dichalcogenides (TMDs) sandwiched between graphene and a perfect conductor can approach 107. Such enhancement corresponds to a lifetime reduction from microseconds to picoseconds due to the the extreme confinement of light modes associated with the graphene plasmons. The general validity of our methodology allows us to test the limitations of widely used approximations, including the dipole-approximation, 1D quantum well wave functions, and Fermi’s Golden rule for spontaneous emission. Our approach lays the foundation for ab-initio modeling of light–matter interactions in structures where the quantum nature of light influences the electron system.
Prediction-assistant Frame Super-Resolution for Video Streaming

Video frame transmission delay is critical in real-time applications such as online video gaming, live show, etc. The receiving deadline of a new frame must catch up with the frame rendering time. Otherwise, the system will buffer a while, and the user will encounter a frozen screen, resulting in unsatisfactory user experiences. An effective approach is to transmit frames in lower-quality under poor bandwidth conditions, such as using scalable video coding. In this paper, we propose to enhance video quality using lossy frames in two situations. First, when current frames are too late to receive before rendering deadline (i.e., lost), we propose to use previously received high-resolution images to predict the future frames. Second, when the quality of the currently received frames is low~(i.e., lossy), we propose to use previously received high-resolution frames to enhance the low-quality current ones. For the first case, we propose a small yet effective video frame prediction network. For the second case, we improve the video prediction network to a video enhancement network to associate current frames as well as previous frames to restore high-quality images. Extensive experimental results demonstrate that our method performs favorably against state-of-the-art algorithms in the lossy video streaming environment.
Set-to-Sequence Methods in Machine Learning: a Review

Machine learning on sets towards sequential output is an important and ubiquitous task, with applications ranging from language modelling and meta-learning to multi-agent strategy games and power grid optimization. Combining elements of representation learning and structured prediction, its two primary challenges include obtaining a meaningful, permutation invariant set representation and subsequently utilizing this representation to output a complex target permutation. This paper provides a comprehensive introduction to the _eld as well as an overview of important machine learning methods tackling both of these key challenges, with a detailed qualitative comparison of selected model architectures.
eduPIC: an introductory particle based code for radio-frequency plasma simulation

Particle based simulations are indispensable tools for numerical studies of charged particle swarms and low-temperature plasma sources. The main advantage of such approaches is that they do not require any assumptions regarding the shape of the particle velocity/energy distribution function (VDF/EDF), but provide these basic quantities of kinetic theory as a result of the computations. Additionally, they can provide, e.g. transport coefficients, under arbitrary time and space dependence of the electric/magnetic fields. For the self-consistent description of various plasma sources operated in the low-pressure (nonlocal, kinetic) regime, the particle-in-cell simulation approach, combined with the Monte Carlo treatment of collision processes (PIC/MCC), has become an important tool during the past decades. In particular, for radio-frequency (RF) capacitively coupled plasma (CCP) systems PIC/MCC is perhaps the primary simulation tool these days. This approach is able to describe discharges over a wide range of operating conditions, and has largely contributed to the understanding of the physics of CCPs operating in various gases and their mixtures, in chambers with simple and complicated geometries, driven by single- and multi-frequency (tailored) waveforms. PIC/MCC simulation codes have been developed and maintained by many research groups, some of these codes are available to the community as freeware resources. While this computational approach has already been present for a number of decades, the rapid evolution of the computing infrastructure makes it increasingly more popular and accessible, as simulations of simple systems can be executed now on personal computers or laptops. During the past few years we have experienced an increasing interest in lectures and courses dealing with the basics of particle simulations, including the PIC/MCC technique. In a response to this, the current paper (i) provides a tutorial on the physical basis and the algorithms of the PIC/MCC technique and (ii) presents a basic (spatially one-dimensional) electrostatic PIC/MCC simulation code, whose source is made freely available in various programming languages. We share the code in C/C++ versions, as well as in a version written in Rust, which is a rapidly emerging computational language. Our code intends to be a ‘starting tool’ for those who are interested in learning the details of the PIC/MCC technique and would like to develop the ‘skeleton’ code further, for their research purposes. Following the description of the physical basis and the algorithms used in the code, a few examples of results obtained with this code for single- and dual-frequency CCPs in argon are also given.
Materials Challenges for High Performance Magnetocaloric Refrigeration Devices

Magnetocaloric materials with a Curie temperature near room temperature have attracted significant interest for some time due to their possible application for high‐efficiency refrigeration devices. This review focuses on a number of key issues of relevance for the characterization, performance and implementation of such materials in actual devices. The phenomenology and fundamental thermodynamics of magnetocaloric materials is discussed, as well as the hysteresis behavior often found in first‐order materials. A number of theoretical and experimental approaches and their implications are reviewed. The question of how to evaluate the suitability of a given material for use in a magnetocaloric device is covered in some detail, including a critical assessment of a number of common performance metrics. Of particular interest is which non‐magnetocaloric properties need to be considered in this connection. An overview of several important materials classes is given before considering the performance of materials in actual devices. Finally, an outlook on further developments is presented.
Structure formation during the crystallization induction period of a short chain-molecule system: A molecular dynamics study

A molecular dynamics simulation has been performed for a short chain-molecule system to study structure formation during the crystallization induction period. A model simulating n-alkane having 20 methylene units was used for the short-chain molecule. A model system containing either 250 or 2000 chains was quenched from a high temperature, and the structure formation was examined at constant temperature and pressure. The model system containing 250 chains eventually forms an ordered phase after the induction period, during which macroscopic quantities, such as volume, remain almost constant. In spite of these small changes in the macroscopic quantities, development of local parallel order is significant during the induction period. We also found that the development of local parallel order causes density fluctuations which appear as a weak small-angle peak in the structure factor. A characteristic length scale corresponding to the density fluctuations becomes larger as time elapses in the induction period. These features are qualitatively in accord with recent experimental findings on a different polymer system [M. Imai et al., Phys. Rev. B 52, 12 696 (1995)].
Do Picardy Thirds Smile? Tonal Hierarchy and Tonal Valence

Western tonality provides a hierarchy among melodic scale-degrees, from the closural tonic triad notes to “out-of-key” chromatic notes. That hierarchy has been occasionally linked to emotion, with more closural degrees associated with more positive valence. However, systematic investigations of that association are lacking. Here, we examined the associations between tonality and emotion in three experiments, in musicians and in nonmusicians. We used an explicit task, in which participants matched probe tones following key-establishing sequences in major and minor keys to facial expressions ranging from sad to happy, and an implicit speeded task, adapting the Implicit Association Test. More closural scale-degrees were associated with more positive valence in all experiments, for both musicians and nonmusicians, with larger effects for major keys. The pattern of results significantly differed from that observed in a comparable goodness-of-fit task, suggesting that perceived scale-degree valence is not reducible to tonal fit. The comparison between the results from the explicit and implicit measures suggests that tonal valence may rely on two distinct mechanisms, one mediated by conceptual musical knowledge and conscious decisional processes, and the other largely modulated by nonconceptual, involuntary processes. The experimental paradigms introduced here may help mapping additional connotative meanings, both emotional and cross-modal, embedded in tonal structure, thus suggesting how “extra-musical” meanings are conveyed through tonal hierarchy.