Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

Astrophysical implications of GW190412 as a remnant of a previous black-hole merger

Two of the dominant channels to produce merging stellar-mass black-hole binaries are believed to be the isolated evolution of binary stars in the field and dynamical formation in star clusters. The first reported black-hole binary event from the third LIGO/Virgo observing run (GW190412) is unusual in that it has unequal masses, nonzero effective spin, and nonzero primary spin at 90\% confidence interval. We show that this event should be exceedingly rare in the context of both the field and cluster formation scenarios. Interpreting GW190412 as a remnant of a previous black-hole merger provides a promising route to explain its features. If GW190412 indeed formed hierarchically, we show that the region of the parameter space that is best motivated from an astrophysical standpoint (low natal spins and light clusters) cannot accommodate the observation. We analyze public GW190412 LIGO/Virgo data with a Bayesian prior where the more massive black hole resulted from a previous merger, and find that this interpretation is equally supported by the data. If the heavier component of GW190412 is indeed a merger remnant, then its spin magnitude is $\chi_1=0.56_{-0.21}^{+0.19}$, which is higher than the value previously reported by the LIGO/Virgo collaboration.Comment: 7 pages, 3 figures, 1 table. Published in PR

Quantum geometric tensor away from Equilibrium

The manifold of ground states of a family of quantum Hamiltonians can be endowed with a quantum geometric tensor whose singularities signal quantum phase transitions and give a general way to define quantum phases. In this paper, we show that the same information-theoretic and geometrical approach can be used to describe the geometry of quantum states away from equilibrium. We construct the quantum geometric tensor $Q_{\mu\nu}$ for ensembles of states that evolve in time and study its phase diagram and equilibration properties. If the initial ensemble is the manifold of ground states, we show that the phase diagram is conserved, that the geometric tensor equilibrates after a quantum quench, and that its time behavior is governed by out-of-time-order commutators (OTOCs). We finally demonstrate our results in the exactly solvable Cluster-XY model

Guided patch-wise nonlocal SAR despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter

Impact of Bayesian prior on the characterization of binary black hole coalescences

In a regime where data are only mildly informative, prior choices can play a significant role in Bayesian statistical inference, potentially affecting the inferred physics. We show this is indeed the case for some of the parameters inferred from current gravitational-wave measurements of binary black hole coalescences. We reanalyze the first detections performed by the twin LIGO interferometers using alternative (and astrophysically motivated) prior assumptions. We find different prior distributions can introduce deviations in the resulting posteriors that impact the physical interpretation of these systems. For instance, (i) limits on the $90\%$ credible interval on the effective black hole spin $\chi_{\rm eff}$ are subject to variations of $\sim 10\%$ if a prior with black hole spins mostly aligned to the binary's angular momentum is considered instead of the standard choice of isotropic spin directions, and (ii) under priors motivated by the initial stellar mass function, we infer tighter constraints on the black hole masses, and in particular, we find no support for any of the inferred masses within the putative mass gap $M \lesssim 5 M_\odot$.Comment: 6 Pages, 2 Figures; see also 1712.06635 Data release at https://github.com/vitale82/GWprior

Reanalysis of LIGO black-hole coalescences with alternative prior assumptions

We present a critical reanalysis of the black-hole binary coalescences detected during LIGO's first observing run under different Bayesian prior assumptions. We summarize the main findings of Vitale et al. (2017) and show additional marginalized posterior distributions for some of the binaries' intrinsic parameters.Comment: Proceedings of IAU Symposium 338: Gravitational Wave Astrophysics (Baton Rouge, LA, October 2017

Influence of the listening context on the perceived realism of binaural recordings

Binaural recordings and audio are becoming an interesting resource for composers, live performances and augmented reality. This paper focuses on the acceptance and the perceived quality by the audience of such spatial recordings. We present the results of a preliminary study of psychoacoustic perception where N=26 listeners had to report on the realism and the quality of different couples of sounds taken from two different rooms with peculiar reverb. Sounds are recorded with a self-made dummy head. The stimuli are grouped into classes with respects to some characteristics highlighted as potentially important for the task. Listening condition is fixed with headphones. Participants are divided into musically trained and naive subjects. Results show that there exists differences between the two groups of participants and that the “semantic relevance” of a sound plays a central role

Gravitational-wave astrophysics with effective-spin measurements: asymmetries and selection biases

Gravitational waves emitted by coalescing compact objects carry information about the spin of the individual bodies. However, with present detectors only the mass-weighted combination of the components of the spin along the orbital angular momentum can be measured accurately. This quantity, the effective spin $\chi_{\mathrm{eff}}$, is conserved up to at least the second post-Newtonian order. The measured distribution of $\chi_{\mathrm{eff}}$ values from a population of detected binaries, and in particular whether this distribution is symmetric about zero, encodes valuable information about the underlying compact-binary formation channels. In this paper we focus on two important complications of using the effective spin to study astrophysical population properties: (i) an astrophysical distribution for $\chi_{\mathrm{eff}}$ values which is symmetric does not necessarily lead to a symmetric distribution for the detected effective spin values, leading to a \emph{selection bias}; and (ii) the posterior distribution of $\chi_{\mathrm{eff}}$ for individual events is \emph{asymmetric} and it cannot usually be treated as a Gaussian. We find that the posterior distributions for $\chi_{\mathrm{eff}}$ systematically show fatter tails toward larger positive values, unless the total mass is large or the mass ratio $m_2/m_1$ is smaller than $\sim 1/2$. Finally we show that uncertainties in the measurement of $\chi_{\mathrm{eff}}$ are systematically larger when the true value is negative than when it is positive. All these factors can bias astrophysical inference about the population when we have more than $\sim 100$ events and should be taken into account when using gravitational-wave measurements to characterize astrophysical populations.Comment: An online generator for synthetic $\chi_{\mathrm{eff}}$ posteriors can be found at: http://superstring.mit.edu/welcome.html Comments are welcom

Event-driven Vision and Control for UAVs on a Neuromorphic Chip

Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enabling ultra-fast vision-driven control. Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller. We show how seamless integration of event-based perception on chip leads to even faster control rates and lower latency. In addition, we demonstrate how online adaptation of the SNN controller can be realised using on-chip learning. Our spiking neuronal network on chip is the first example of a neuromorphic vision-based controller on chip solving a high-speed UAV control task. The excellent scalability of processing in neuromorphic hardware opens the possibility to solve more challenging visual tasks in the future and integrate visual perception in fast control loops

Inferring the properties of a population of compact binaries in presence of selection effects

Shortly after a new class of objects is discovered, the attention shifts from the properties of the individual sources to the question of their origin: do all sources come from the same underlying population, or several populations are required? What are the properties of these populations? As the detection of gravitational waves is becoming routine and the size of the event catalog increases, finer and finer details of the astrophysical distribution of compact binaries are now within our grasp. This Chapter presents a pedagogical introduction to the main statistical tool required for these analyses: hierarchical Bayesian inference in the presence of selection effects. All key equations are obtained from first principles, followed by two examples of increasing complexity. Although many remarks made in this Chapter refer to gravitational-wave astronomy, the write-up is generic enough to be useful to researchers and graduate students from other fields.Comment: 57 pages. Chapter of "Handbook of Gravitational Wave Astronomy" (Eds. C. Bambi, S. Katsanevas and K. Kokkotas; Springer Singapore, 2021). Updated and revised w.r.t. v1. Includes new section (5.2). v2. adds back the glossary, that was lost in previous versio