Miracle-dz/UniFAR-FTUs huggingface dataset:
New constraints for nuclear parton distribution functions from hadron–nucleus collision processes

This work studies collinearly factorizable nuclear parton distribution functions (nPDFs) in perturbative Quantum Chromodynamics (QCD) at next-to-leading order in the light of hadron-nucleus collision data which have not been included in nPDF analyses previously. The aim is at setting new constraints on the nuclear modifications of the gluon distribution and on the flavour separation of quark nuclear modifications. The introductory part provides an outline of the theoretical framework of QCD collinear factorization and the used statistical methods and relates the work presented here to other similar contemporary analyses. As a result, a new set of nPDFs, EPPS16, is presented, including for the first time electroweak-boson and dijet production data from CERN-LHC proton-lead collisions and allowing a full flavour separation in the fit. The flavour separation is constrained with Drell-Yan dilepton-production data from fixed target pion-nucleus experiments and neutrino-nucleus deep-inelastic scattering data, which are shown to give evidence for the similarity of the u and d valence-quark nuclear modifications. For studying the gluon degrees of freedom, collider data are essential and in the EPPS16 analysis new constraints are derived from the dijet production at the LHC. Possible further constraints for the gluons are investigated in terms of the LHC data on nuclear modification ratios of dijet and D-meson production. Using a non-quadratically improved Hessian reweighting method, these measurements are found to put stringent constraints on the gluon modifications in the lead nucleus, reaching smaller values of the nucleon momentum fraction than previously accessible. A study on the future prospects of constraining nPDFs within a multi-observable approach with the BNL-RHIC is also given.
Stellar populations and physical properties of starbursts in the antennae galaxy from self-consistent modelling of MUSE spectra

We have modelled the stellar and nebular continua and emission-line intensity ratios of massive stellar populations in the Antennae galaxy using high resolution and self-consistent libraries of model H ii regions around central clusters of ageing stars. The model libraries are constructed using the stellar population synthesis code, starburst99, and photoionization model, and cloudy. The Geneva and PARSEC stellar evolutionary models are plugged into starburst99 to allow comparison between the two models. Using a spectrum-fitting methodology that allows the spectral features in the stellar and nebular continua [e.g. Wolf–Rayet (WR) features, Paschen jump], and emission-line diagnostics to constrain the models, we apply the libraries to the high-resolution Multi-Unit Spectroscopic Explorer spectra of the starbursting regions in the Antennae galaxy. Through this approach, we were able to model the continuum emission from WR stars and extract stellar and gas metallicities, ages, electron temperatures, and densities of starbursts by exploiting the full spectrum. From the application to the Antennae galaxy, we find that (1) the starbursts in the Antennae galaxy are characterized by stellar and gas metallicities of around solar, (2) the star-forming gas in starbursts in the Western loop of NGC 4038 appears to be more enriched, albeit slightly, than the rest of galaxy, (3) the youngest starbursts are found across the overlap region and over parts of the western-loop, though in comparison, the regions in the western-loop appear to be at a slightly later stage in star formation than the overlap region, and (4) the results obtained from fitting the Geneva and Parsec models are largely consistent.
Libri-Light: A Benchmark for ASR with Limited or No Supervision

We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books from the LibriVox project. It contains over 60K hours of audio, which is, to our knowledge, the largest freely-available corpus of speech. The audio has been segmented using voice activity detection and is tagged with SNR, speaker ID and genre descriptions. Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi- supervised setting (PER, CER) and (3) the distant supervision setting (WER). Settings (2) and (3) use limited textual resources (10 minutes to 10 hours) aligned with the speech. Setting (3) uses large amounts of unaligned text. They are evaluated on the standard LibriSpeech dev and test sets for comparison with the supervised state-of-the-art.
Modeling the Survival of Population III Stars to the Present Day

Recent numerical simulations have suggested the probability of a fraction of the primordial stars being ejected from the cluster of their origin. We explore the possibility that some of these can remain on the main sequence until the present epoch. We develop a semianalytical model guided by results of cosmological simulations to study the mass accretion by these protostars as a function of the original stellar mass and other parameters such as angular momentum and gravitational drag due to ambient gas. We also explore whether some of the protostars remain sufficiently low mass and long-lived to survive to the present day. This requires that the protostars are ejected from the star-forming region while their mass is less than . Assuming that the protostars gain mass via the spherical Bondi–Hoyle accretion from the ambient medium, we show that Population III protostars that initially form within a certain range of mass and are ejected with velocity larger than the escape velocity may survive to the present day on the main sequence. Thus, they may even be found in our Milky Way or its satellites. Our calculations also reveal that protostars that do not get ejected from the parent gas clump accrete a large amount of gas. We predict that these can become massive enough to be progenitors of black holes.
Learning mixtures of linear regressions in subexponential time via Fourier moments

We consider the problem of learning a mixture of linear regressions (MLRs). An MLR is specified by k nonnegative mixing weights p 1, …, p k summing to 1, and k unknown regressors w 1,. ,w k ∈ℝ d . A sample from the MLR is drawn by sampling i with probability p i , then outputting (x, y) where y = ⟨ x, w i ⟩ + η, where η∼(0,ς2) for noise rate ς. Mixtures of linear regressions are a popular generative model and have been studied extensively in machine learning and theoretical computer science. However, all previous algorithms for learning the parameters of an MLR require running time and sample complexity scaling exponentially with k. In this paper, we give the first algorithm for learning an MLR that runs in time which is sub-exponential in k. Specifically, we give an algorithm which runs in time O(d)·exp(O(√k)) and outputs the parameters of the MLR to high accuracy, even in the presence of nontrivial regression noise. We demonstrate a new method that we call “Fourier moment descent,” which uses univariate density estimation and low-degree moments of the Fourier transform of suitable univariate projections of the MLR to iteratively refine our estimate of the parameters. To the best of our knowledge, these techniques have never been used in the context of high dimensional distribution learning, and may be of independent interest. We also show that our techniques can be used to give a sub-exponential time algorithm for a natural hard instance of the subspace clustering problem, which we call learning mixtures of hyperplanes.
A Spatiotemporal Model for Peak AoI in Uplink IoT Networks: Time Versus Event-Triggered Traffic

Timely message delivery is a key enabler for Internet of Things (IoT) and cyber–physical systems to support a wide range of context-dependent applications. Conventional time-related metrics (e.g., delay and jitter) fail to characterize the timeliness of the system update. Age of Information (AoI) is a time-evolving metric that accounts for the packet interarrival and waiting times to assess the freshness of information. In the foreseen large-scale IoT networks, mutual interference imposes a delicate relation between traffic generation patterns and transmission delays. To this end, we provide a spatiotemporal framework that captures the peak AoI (PAoI) for the large-scale IoT uplink network under time-triggered (TT) and event-triggered (ET) traffic. Tools from the stochastic geometry and queueing theory are utilized to account for the macroscopic and microscopic network scales. Simulations are conducted to validate the proposed mathematical framework and assess the effect of traffic load on the PAoI. The results unveil a counter-intuitive superiority of the ET traffic over the TT in terms of PAoI, which is due to the involved temporal interference correlations. Insights regarding the network stability frontiers and the location-dependent performance are presented. Key design recommendations regarding the traffic load and decoding thresholds are highlighted.
Spectral convergence in geometric quantization --- the case of non-singular Langrangian fibrations

We develop a new approach to geometric quantization using the theory of convergence of metric measure spaces. Given a family of Kahler polarizations converging to a non-singular real polarization on a prequantized symplectic manifold, we show the spectral convergence result of $\bar{\partial}$-Laplacians, as well as the convergence result of quantum Hilbert spaces. We also consider the case of almost Kahler quantization for compatible almost complex structures, and show the analogous convergence results.
Scalable Bayesian Hamiltonian learning

As the size of quantum devices continues to grow, the development of scalable methods to characterise and diagnose noise is becoming an increasingly important problem. Recent methods have shown how to efficiently estimate Hamiltonians in principle, but they are poorly conditioned and can only characterize the system up to a scalar factor, making them difficult to use in practice. In this work we present a Bayesian methodology, called Bayesian Hamiltonian Learning (BHL), that addresses both of these issues by making use of any or all, of the following: well-characterised experimental control of Hamiltonian couplings, the preparation of multiple states, and the availability of any prior information for the Hamiltonian. Importantly, BHL can be used online as an adaptive measurement protocol, updating estimates and their corresponding uncertainties as experimental data become available. In addition, we show that multiple input states and control fields enable BHL to reconstruct Hamiltonians that are neither generic nor spatially local. We demonstrate the scalability and accuracy of our method with numerical simulations on up to 100 qubits. These practical results are complemented by several theoretical contributions. We prove that a $k$-body Hamiltonian $H$ whose correlation matrix has a spectral gap $\Delta$ can be estimated to precision $\varepsilon$ with only $\tilde{O}\bigl(n^{3k}/(\varepsilon \Delta)^{3/2}\bigr)$ measurements. We use two subroutines that may be of independent interest: First, an algorithm to approximate a steady state of $H$ starting from an arbitrary input that converges factorially in the number of samples; and second, an algorithm to estimate the expectation values of $m$ Pauli operators with weight $\le k$ to precision $\epsilon$ using only $O(\epsilon^{-2} 3^k \log m)$ measurements, which quadratically improves a recent result by Cotler and Wilczek.
Probing the Origin of Diffuse Radio Emission in the Cool Core of the Phoenix Galaxy Cluster

Cool core galaxy clusters are considered to be dynamically relaxed clusters with regular morphology and highly X-ray luminous central region. However, cool core clusters can also be sites for merging events that exhibit cold fronts in X-ray and minihalos in radio. We present recent radio/X-ray observations of the Phoenix Cluster or South Pole Telescope (SPT)-CL J2344-4243 at the redshift of z = 0.596. Using archival Chandra X-ray observations, we detect spiraling cool gas around the cluster core as well as discover two cold fronts near the core. This is perhaps the most distant galaxy cluster to date known to host cold fronts. Also, we present JVLA 1.52 GHz observations of the minihalo, previously discovered at 610 MHz with GMRT observations in the center of the Phoenix galaxy cluster. The minihalo flux density at 1.52 GHz is 9.65 ± 0.97 mJy with the spectral index between 610 MHz and 1.52 GHz being −0.98 ± 0.16. A possible origin of these radio sources is turbulence induced by sloshing of the gas in the cluster core.
Nonlinear dynamics of energetic-particle driven geodesic acoustic modes in ASDEX Upgrade

Turbulence in tokamaks generates radially sheared zonal flows (ZFs). Their oscillatory counterparts, geodesic acoustic modes (GAMs), appear due to the action of the magnetic field curvature. The GAMs can also be driven unstable by an anisotropic energetic particle (EP) population leading to the formation of global radial structures, called EGAMs. The EGAMs might play the role of an intermediate agent between the EPs and thermal plasma, by redistributing EP energy to the bulk plasma through collisionless wave-particle interaction. In such a way, the EGAMs might contribute to the plasma heating. Thus, investigation of EGAM properties, especially in the velocity space, is necessary for precise understanding of the transport phenomena in tokamak plasmas. In this work, the nonlinear dynamics of EGAMs is investigated with the help of a Mode-Particle-Resonance (MPR) diagnostic recently implemented in the global gyrokinetic (GK) particle-in-cell code ORB5. This enables to investigate the relative importance and the evolution of the resonances responsible for the ion and electron Landau damping, and of the EP drive. An ASDEX Upgrade discharge is chosen as a reference case for this investigation due to its rich EP nonlinear dynamics.
LTLf Synthesis with Fairness and Stability Assumptions

In synthesis, assumptions are constraints on the environment that rule out certain environment behaviors. A key observation here is that even if we consider systems with LTLf goals on finite traces, environment assumptions need to be expressed over infinite traces, since accomplishing the agent goals may require an unbounded number of environment action. To solve synthesis with respect to finite-trace LTLf goals under infinite-trace assumptions, we could reduce the problem to LTL synthesis. Unfortunately, while synthesis in LTLf and in LTL have the same worst-case complexity (both 2EXPTIME-complete), the algorithms available for LTL synthesis are much more difficult in practice than those for LTLf synthesis. In this work we show that in interesting cases we can avoid such a detour to LTL synthesis and keep the simplicity of LTLf synthesis. Specifically, we develop a BDD-based fixpoint-based technique for handling basic forms of fairness and of stability assumptions. We show, empirically, that this technique performs much better than standard LTL synthesis.
Global cosmic string networks as a function of tension

We investigate the properties of global cosmic string networks as a function of the ratio of string tension to Goldstone-field coupling, and as a function of the Hubble damping strength. Our results show unambiguously that the string density is sensitive to this ratio. We also find that existing semi-analytical (one-scale) models must be missing some important aspect of the network dynamics. Our results point the way towards improving such models.
Density scaling of generalized Lennard-Jones fluids in different dimensions

Liquids displaying strong virial-potential energy correlations conform to an approximate density scaling of their structural and dynamical observables. This scaling property does not extend to the entire phase diagram, in general. The validity of the scaling can be quantified by a correlation coefficient. In this work a simple scheme to predict the correlation coefficient and the density-scaling exponent is presented. Although this scheme is exact only in the dilute gas regime or in high dimension d, a comparison with results from molecular dynamics simulations in d=1 to 4 shows that it reproduces well the behavior of generalized Lennard-Jones systems in a large portion of the fluid phase.
Location of the last stable orbit in Kerr spacetime

Black hole spacetimes, like the Kerr spacetime, admit both stable and plunging orbits, separated in parameter space by the separatrix. Determining the location of the separatrix is of fundamental interest in understanding black holes, and is of crucial importance for modeling extreme-mass-ratio inspirals. Previous numerical approaches to locating the Kerr separatrix were not always efficient or stable across all of parameter space. In this paper we show that the Kerr separatrix is the zero set of a single polynomial in parameter space. This gives two main results. First, we thoroughly analyze special cases (extreme Kerr, polar orbits, etc.), finding strict bounds on the limits of roots, and unifying a number of results in the literature. Second, we pose a stable numerical method which is guaranteed to quickly and robustly converge to the separatrix. This new approach is implemented in the Black Hole Perturbation Toolkit, and results in a $\ensuremath{\sim}45\ifmmode\times\else\texttimes\fi{}$ speedup over the prior robust approach.
Deep SCNN-Based Real-Time Object Detection for Self-Driving Vehicles Using LiDAR Temporal Data

Real-time accurate detection of three-dimensional (3D) objects is a fundamental necessity for self-driving vehicles. Most existing computer vision approaches are based on convolutional neural networks (CNNs). Although the CNN-based approaches can achieve high detection accuracy, their high energy consumption is a severe drawback. To resolve this problem, novel energy efficient approaches should be explored. Spiking neural network (SNN) is a promising candidate because it has orders-of-magnitude lower energy consumption than CNN. Unfortunately, the studying of SNN has been limited in small networks only. The application of SNN for large 3D object detection networks has remain largely open. In this paper, we integrate spiking convolutional neural network (SCNN) with temporal coding into the YOLOv2 architecture for real-time object detection. To take the advantage of spiking signals, we develop a novel data preprocessing layer that translates 3D point-cloud data into spike time data. We propose an analog circuit to implement the non-leaky integrate and fire neuron used in our SCNN, from which the energy consumption of each spike is estimated. Moreover, we present a method to calculate the network sparsity and the energy consumption of the overall network. Extensive experiments have been conducted based on the KITTI dataset, which show that the proposed network can reach competitive detection accuracy as existing approaches, yet with much lower average energy consumption. If implemented in dedicated hardware, our network could have a mean sparsity of 56.24% and extremely low total energy consumption of 0.247mJ only. Implemented in NVIDIA GTX 1080i GPU, we can achieve 35.7 fps frame rate, high enough for real-time object detection.
SINR percolation for Cox point processes with random powers

Abstract Signal-to-interference-plus-noise ratio (SINR) percolation is an infinite-range dependent variant of continuum percolation modeling connections in a telecommunication network. Unlike in earlier works, in the present paper the transmitted signal powers of the devices of the network are assumed random, independent and identically distributed, and possibly unbounded. Additionally, we assume that the devices form a stationary Cox point process, i.e., a Poisson point process with stationary random intensity measure, in two or more dimensions. We present the following main results. First, under suitable moment conditions on the signal powers and the intensity measure, there is percolation in the SINR graph given that the device density is high and interferences are sufficiently reduced, but not vanishing. Second, if the interference cancellation factor $\gamma$ and the SINR threshold $\tau$ satisfy $\gamma \geq 1/(2\tau)$ , then there is no percolation for any intensity parameter. Third, in the case of a Poisson point process with constant powers, for any intensity parameter that is supercritical for the underlying Gilbert graph, the SINR graph also percolates with some small but positive interference cancellation factor.
Robust adaptive least squares polynomial chaos expansions in high‐frequency applications

We present an algorithm for computing sparse, least squares‐based polynomial chaos expansions, incorporating both adaptive polynomial bases and sequential experimental designs. The algorithm is employed to approximate stochastic high‐frequency electromagnetic models in a black‐box way, in particular, given only a dataset of random parameter realizations and the corresponding observations regarding a quantity of interest, typically a scattering parameter. The construction of the polynomial basis is based on a greedy, adaptive, sensitivity‐related method. The sequential expansion of the experimental design employs different optimality criteria, with respect to the algebraic form of the least squares problem. We investigate how different conditions affect the robustness of the derived surrogate models, that is, how much the approximation accuracy varies given different experimental designs. It is found that relatively optimistic criteria perform on average better than stricter ones, yielding superior approximation accuracies for equal dataset sizes. However, the results of strict criteria are significantly more robust, as reduced variations regarding the approximation accuracy are obtained, over a range of experimental designs. Two criteria are proposed for a good accuracy‐robustness trade‐off.
Turbulent Viscosity Acting on the Equilibrium Tidal Flow in Convective Stars

Convection is thought to act as a turbulent viscosity in damping tidal flows and in driving spin and orbital evolution in close convective binary systems. This turbulent viscosity should be reduced, compared to mixing-length predictions, when the forcing (tidal) frequency exceeds the turnover frequency ωcv of the dominant convective eddies. However, two contradictory scaling laws have been proposed and this issue remains highly disputed. To revisit this controversy, we conduct the first direct numerical simulations of convection interacting with the equilibrium tidal flow in an idealized global model of a low-mass star. We present direct computations of the turbulent effective viscosity, νE, acting on the equilibrium tidal flow. We unexpectedly report the coexistence of the two disputed scaling laws, which reconciles previous theoretical (and numerical) findings. We recover the universal quadratic scaling in the high-frequency regime . Our results also support the linear scaling in an intermediate regime with . Both regimes may be relevant to explain the observed properties of close binaries, including spin synchronization of solar-type stars and the circularization of low-mass stars. The robustness of these two regimes of tidal dissipation, and the transition between them, should be explored further in more realistic models. A better understanding of the interaction between convection and tidal flows is indeed essential to correctly interpret observations of close binary stars and short-period planetary orbits.