Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the quantum field theory of neutrino oscillations in vacuum

We consider neutrino oscillations in vacuum in the framework of quantum field theory in which neutrino production and detection processes are part of a single Feynman diagram and the corresponding cross section is computed in the standard way, i.e. with final states represented by plane waves. We use assumptions which are realized in actual experiments and concentrate on the detection process. Moreover, we also allow for a finite time interval of length $\tau$ during which the detector records neutrino events. In this context we are motivated by accelerator-neutrino oscillation experiments where data taking is synchronized in time with the proton spill time of the accelerator. Given the final momenta and the direction of neutrino propagation, we find that in the oscillation amplitude---for all practical purposes---the neutrino energy $Q$ is fixed, apart from an interval of order $2\pi\hbar/\tau$ around a mean energy $\bar Q$; this is an expression of energy non-conservation or the time-energy uncertainty relation in the detection process due to $\tau < \infty$. We derive in excellent approximation that in the amplitude the oscillation effect originates from massive neutrinos with the same energy $\bar Q$, i.e. oscillations take place in space without any decoherece effect, while the remaining integration over $Q$ in the interval of order $2\pi\hbar/\tau$ around $\bar Q$ results in a time-correlation function expressing the time delay between neutrino production and detection.
Small-GAN: Speeding Up GAN Training Using Core-sets

Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes. Unfortunately, using large batches is slow and expensive on conventional hardware. Thus, it would be nice if we could generate batches that were effectively large though actually small. In this work, we propose a method to do this, inspired by the use of Coreset-selection in active learning. When training a GAN, we draw a large batch of samples from the prior and then compress that batch using Coreset-selection. To create effectively large batches of 'real' images, we create a cached dataset of Inception activations of each training image, randomly project them down to a smaller dimension, and then use Coreset-selection on those projected activations at training time. We conduct experiments showing that this technique substantially reduces training time and memory usage for modern GAN variants, that it reduces the fraction of dropped modes in a synthetic dataset, and that it allows GANs to reach a new state of the art in anomaly detection.
An ASKAP Search for a Radio Counterpart to the First High-significance Neutron Star–Black Hole Merger LIGO/Virgo S190814bv

We present results from a search for a radio transient associated with the LIGO/Virgo source S190814bv, a likely neutron star–black hole (NSBH) merger, with the Australian Square Kilometre Array Pathfinder. We imaged a 30 deg2 field at ΔT = 2, 9, and 33 days post-merger at a frequency of 944 MHz, comparing them to reference images from the Rapid ASKAP Continuum Survey observed 110 days prior to the event. Each epoch of our observations covers 89% of the LIGO/Virgo localization region. We conducted an untargeted search for radio transients in this field, resulting in 21 candidates. For one of these, AT2019osy, we performed multiwavelength follow-up and ultimately ruled out the association with S190814bv. All other candidates are likely unrelated variables, but we cannot conclusively rule them out. We discuss our results in the context of model predictions for radio emission from NSBH mergers and place constrains on the circum-merger density and inclination angle of the merger. This survey is simultaneously the first large-scale radio follow-up of an NSBH merger, and the most sensitive widefield radio transients search to-date.
Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks with Backward Residual Connections, Stochastic Softmax and Hybridization

Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications. In recent years, there have been several proposals focused on supervised (conversion, spike-based gradient descent) and unsupervised (spike timing dependent plasticity) training methods to improve the accuracy of SNNs on large-scale tasks. However, each of these methods suffer from scalability, latency and accuracy limitations. In this paper, we propose novel algorithmic techniques of modifying the SNN configuration with backward residual connections, stochastic softmax and hybrid artificial-and-spiking neuronal activations to improve the learning ability of the training methodologies to yield competitive accuracy, while, yielding large efficiency gains over their artificial counterparts. Note, artificial counterparts refer to conventional deep learning/artificial neural networks. Our techniques apply to VGG/Residual architectures, and are compatible with all forms of training methodologies. Our analysis reveals that the proposed solutions yield near state-of-the-art accuracy with significant energy-efficiency and reduced parameter overhead translating to hardware improvements on complex visual recognition tasks, such as, CIFAR10, Imagenet datatsets.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance.
Risk bounds for reservoir computing

We analyze the practices of reservoir computing in the framework of statistical learning theory. In particular, we derive finite sample upper bounds for the generalization error committed by specific families of reservoir computing systems when processing discrete-time inputs under various hypotheses on their dependence structure. Non-asymptotic bounds are explicitly written down in terms of the multivariate Rademacher complexities of the reservoir systems and the weak dependence structure of the signals that are being handled. This allows, in particular, to determine the minimal number of observations needed in order to guarantee a prescribed estimation accuracy with high probability for a given reservoir family. At the same time, the asymptotic behavior of the devised bounds guarantees the consistency of the empirical risk minimization procedure for various hypothesis classes of reservoir functionals.
FeO Content of Earth’s Liquid Core

Monica Pozzo, Chris Davies, David Gubbins, and Dario Alfè 1,3,* Department of Earth Sciences, London Centre for Nanotechnology and Thomas Young Centre at University College London, University College London, Gower Street, London WC1E 6BT, United Kingdom School of Earth and Environment, University of Leeds, Leeds LS2 9JT, United Kingdom Dipartimento di Fisica Ettore Pancini, Università di Napoli Federico II, Monte Sant’Angelo, I-80126 Napoli, Italy
SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition

We present a multi-channel database of overlapping speech for training, evaluation, and detailed analysis of source separation and extraction algorithms: SMS-WSJ -- Spatialized Multi-Speaker Wall Street Journal. It consists of artificially mixed speech taken from the WSJ database, but unlike earlier databases we consider all WSJ0+1 utterances and take care of strictly separating the speaker sets present in the training, validation and test sets. When spatializing the data we ensure a high degree of randomness w.r. t. room size, array center and rotation, as well as speaker position. Furthermore, this paper offers a critical assessment of recently proposed measures of source separation performance. Alongside the code to generate the database we provide a source separation baseline and a Kaldi recipe with competitive word error rates to provide common ground for evaluation.
On the Gas Content, Star Formation Efficiency, and Environmental Quenching of Massive Galaxies in Protoclusters at z ≈ 2.0–2.5

We present ALMA Band 6 (ν = 233 GHz, λ = 1.3 mm) continuum observations toward 68 “normal” star-forming galaxies within two Coma-like progenitor structures at z = 2.10 and 2.47, from which ISM masses are derived, providing the largest census of molecular gas mass in overdense environments at these redshifts. Our sample comprises galaxies with a stellar mass range of 1 × 109 M⊙–4 × 1011 M⊙ with a mean M⋆ ≈ 6 × 1010 M⊙. Combining these measurements with multiwavelength observations and spectral energy distribution modeling, we characterize the gas mass fraction and the star formation efficiency, and infer the impact of the environment on galaxies’ evolution. Most of our detected galaxies (≳70%) have star formation efficiencies and gas fractions similar to those found for coeval field galaxies and in agreement with the field scaling relations. However, we do find that the protoclusters contain an increased fraction of massive, gas-poor galaxies, with low gas fractions (fgas ≲ 6%–10%) and red rest-frame ultraviolet/optical colors typical of post-starburst and passive galaxies. The relatively high abundance of passive galaxies suggests an accelerated evolution of massive galaxies in protocluster environments. The large fraction of quenched galaxies in these overdense structures also implies that environmental quenching takes place during the early phases of cluster assembly, even before virialization. From our data, we derive a quenching efficiency of ϵq ≈ 0.45 and an upper limit on the quenching timescale of τq < 1 Gyr.
Scoring over Invasive Candidiasis with Candida score

Introduction: Invasive Fungal Infection (IFI) is a critical event in clinical scenario, and Invasive Candidiasis is chief among them, accounting for 70-90% of all IFIs. In 2006, Candida score was developed to predict this life-threating infection . This study makes a similar attempt to identify invasive candidiasis and measure the candida score in the present scenario of a tertiary care hospital, as well as to detect the prevalence of Candida albicansand non-albicansspecies. Materials and methods: This study was undertaken on patients admitted in Intensive care unit of a tertiary medical care hospital. Specimens obtained from different sites were taken to laboratory to culture in Blood agar media If there was growth in Blood agar – Gram staining was done and if Candida was found, it was subcultured in Chromogenic agar plates to identify species. Result: Out of 129 samples, 11 were positive for Candida (8.49%). Out of 11 Candida isolates, only one was Candida albicans, others being non-albicans Candida. Most of them were Candida krusei. Discussion: None of the cases touched the Candida score of 2.5, a predictor of invasive candidiasis. This was a great finding in itself, and was in stark contrast with findings from many institutions. However, our findings also collaborated with them in that maximum clinical isolates were non-albicans type. But here maximum isolates were C. krusei, although most clinical isolates are reported to be C. tropicalis from Asian subcontinents including India, and C. glabrata from Western countries.
Green operators in low regularity spacetimes and quantum field theory

In this paper we develop the mathematics required in order to provide a description of the observables for quantum fields on low-regularity spacetimes. In particular we consider the case of a massless scalar field ϕ on a globally hyperbolic spacetime M with C1,1 metric g. This first entails showing that the (classical) Cauchy problem for the wave equation is well-posed for initial data and sources in Sobolev spaces and then constructing low-regularity advanced and retarded Green operators as maps between suitable function spaces. In specifying the relevant function spaces we need to control the norms of both ϕ and □gϕ in order to ensure that □g◦G± and G±◦□g are the identity maps on those spaces. The causal propagator G = G+ − G− is then used to define a symplectic form ω on a normed space V(M) which is shown to be isomorphic to ker(□g). This enables one to provide a locally covariant description of the quantum fields in terms of the elements of quasi-local C*-algebras.
HCN-to-HNC intensity ratio: a new chemical thermometer for the molecular ISM

Context. The gas kinetic temperature (TK) determines the physical and chemical evolution of the interstellar medium (ISM). However, obtaining reliable TK estimates usually requires expensive observations including the combination of multi-line analysis and dedicated radiative transfer calculations. Aims. This work explores the use of HCN and HNC observations, and particularly the I(HCN)-to-I(HNC) intensity ratio (I(HCN)/I(HNC)) of their J = 1–0 lines, as direct probe of the gas kinetic temperature in the molecular ISM. Methods. We obtained a new set of large-scale observations of the HCN and HNC (1–0) lines throughout the Integral Shape Filament (ISF) in Orion. In combination with ancillary gas and dust temperature measurements, we find a systematic temperature dependence of the observed I(HCN)-to-I(HNC) intensity ratio throughout our maps. Additional comparisons with chemical models demonstrate that these observed I(HCN)/I(HNC) variations are driven by the effective destruction and isomerization mechanisms of HNC under low-energy barriers. Results. The observed variations of I(HCN)/I(HNC) with TK can be described with a two-part linear function. This empirical calibration is then used to create a temperature map of the entire ISF. Comparisons with similar dust temperature measurements in this cloud, as well as in other regions and galactic surveys, validate this simple technique for obtaining direct estimates of the gas kinetic temperature in a wide range of physical conditions and scales with an optimal working range between 15 K ≲ TK ≤ 40 K. Conclusions. Both observations and models demonstrate the strong sensitivity of the I(HCN)/I(HNC) ratio to the gas kinetic temperature. Since these lines are easily obtained in observations of local and extragalactic sources, our results highlight the potential use of this observable as new chemical thermometer for the ISM.
MonSter: Awakening the Mono in Stereo

Passive depth estimation is among the most long-studied fields in computer vision. The most common methods for passive depth estimation are either a stereo or a monocular system. Using the former requires an accurate calibration process, and has a limited effective range. The latter, which does not require extrinsic calibration but generally achieves inferior depth accuracy, can be tuned to achieve better results in part of the depth range. In this work, we suggest combining the two frameworks. We propose a two-camera system, in which the cameras are used jointly to extract a stereo depth and individually to provide a monocular depth from each camera. The combination of these depth maps leads to more accurate depth estimation. Moreover, enforcing consistency between the extracted maps leads to a novel online self-calibration strategy. We present a prototype camera that demonstrates the benefits of the proposed combination, for both self-calibration and depth reconstruction in real-world scenes.
Increasing stability in a partial data inverse boundary value problem for biharmonic operators

We study the inverse boundary value problems of determining a potential in the Helmholtz type equation for the perturbed biharmonic operator from the knowledge of the partial Cauchy data set. Our geometric setting is that of a domain whose inaccessible portion of the boundary is contained in a hyperplane, and we are given the Cauchy data set on the complement. The uniqueness and logarithmic stability for this problem were established in [34] and [7], respectively. We show, under mild regularity assumptions on the potentials, that the logarithmic stability estimate can be improved to the one of Holder type, in the high frequency regime.
Balancing Gaussian vectors in high dimension

Motivated by problems in controlled experiments, we study the discrepancy of random matrices with continuous entries where the number of columns $n$ is much larger than the number of rows $m$. Our first result shows that if $\omega(1) \leq m \leq o(n)$, a matrix with i.i. d. standard Gaussian entries has discrepancy $\Theta(\sqrt{n} \, 2^{-n/m})$ with high probability. This provides sharp guarantees for Gaussian discrepancy in a regime that had not been considered before in the existing literature. Our results also apply to a more general family of random matrices with continuous i.i. d. entries, assuming that $m \leq O(n/\log{n})$. The proof is non-constructive and is an application of the second moment method. Our second result is algorithmic and applies to random matrices whose entries are i.i. d. and have a Lipschitz density. We present a randomized polynomial-time algorithm that achieves discrepancy $e^{-\Omega(\log^2(n)/m)}$ with high probability, provided that $m \leq O(\sqrt{\log{n}})$. In the one-dimensional case, this matches the best known algorithmic guarantees due to Karmarkar--Karp. For higher dimensions $2 \leq m \leq O(\sqrt{\log{n}})$, this establishes the first efficient algorithm achieving discrepancy smaller than $O( \sqrt{m} )$.
Exploiting Intelligent Reflecting Surfaces in Multi-Antenna Aided NOMA Systems.

This paper investigates a downlink multiple-input single-output intelligent reflecting surface (IRS) non-orthogonal multiple access (NOMA) system, where a base station (BS) serves multiple users with the aid of IRSs. Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints. In term of the characteristics of reflection amplitudes and phase shifts, we consider ideal and non-ideal IRS assumptions. To tackle the formulated non-convex problems, we propose efficient algorithms by invoking alternating optimization, which design the active beamforming and passive beamforming alternately. For the ideal IRS scenario, the two subproblems are solved by invoking the successive convex approximation technique. For the non-ideal IRS scenario, constant modulus IRS elements are further divided into continuous phase shifters and discrete phase shifters. To tackle the passive beamforming problem with continuous phase shifters, a novel algorithm is developed by utilizing the sequential rank-one constraint relaxation approach, which is guaranteed to find a locally optimal rank-one solution. Then, a quantization-based scheme is proposed for discrete phase shifters. Finally, numerical results illustrate that: i) the system sum rate can be significantly improved by deploying the IRS with our proposed algorithms; ii) 3-bit phase shifters are capable of achieving almost the same performance as the ideal IRS; iii) the proposed IRS-NOMA systems achieve higher system sum rate than the IRS-aided orthogonal multiple access system.
Deep convolutional neural network application on rooftop detection for aerial image

As one of the most destructive disasters in the world, earthquake causes death, injuries, destruction and enormous damage to the affected area. It is significant to detect buildings after an earthquake in response to reconstruction and damage evaluation. In this research, we proposed an automatic rooftop detection method based on the convolutional neural network (CNN) to extract buildings in the city of Christchurch and tuned hyperparameters to detect small detached houses from the aerial image. The experiment result shows that our approach can effectively and accurately detect and segment buildings and has competitive performance.
Probabilistic performance validation of deep learning‐based robust NMPC controllers

Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low‐cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system.
Energy-momentum tensor and helicity for gauge fields coupled to a pseudoscalar inflaton

We study the energy-momentum tensor and helicity of gauge fields coupled through $g \phi F \tilde{F}/4$ to a pseudo-scalar field $\phi$ driving inflation. Under the assumption of a constant time derivative of the background inflaton, we compute analitically divergent and finite terms of the energy density and helicity of gauge fields for any value of the coupling $g$. We introduce a suitable adiabatic expansion for mode functions of physical states of the gauge fields which correctly reproduces ultraviolet divergences in average quantities and identify corresponding counterterms. Our calculations shed light on the accuracy and the range of validity of approximated analytic estimates of the energy density and helicity terms previously existed in the literature in the strongly coupled regime only, i.e. for $g \dot \phi/(2H) \gg 1$. We discuss the implications of our analytic calculations for the backreaction of quantum fluctuations onto the inflaton evolution.
Title : Becoming a grandparent and its effect on well-being : the role of order of transitions , time , and gender . Corresponding

© The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Title: Becoming a grandparent and its effect on well-being: the role of order of transitions, time, and gender.