Search

Studying the magnetic fields of exoplanets will provide valuable information about their interior structures, atmospheric properties (escape and dynamics), and potential habitability. One of the most promising methods to detect exoplanetary magnetic fields is to study their auroral radio emission. However, there are no confirmed detections of an exoplanet in the radio despite decades of searching. Recently, Turner et al. 2021 reported a tentative detection of circularly polarized bursty emission from the $\tau$ Boo exoplanetary system using LOFAR low-frequency beamformed observations. The likely source of this emission was presumed to be from the $\tau$ Boo planetary system and a possible explanation is radio emission from the exoplanet $\tau$ Boo b, produced via the cyclotron maser mechanism. Assuming the emission is from the planet, Turner et al. 2021 found that the derived planetary magnetic field is compatible with theoretical predictions. The need to confirm this tentative detection is critical as a conclusive detection would have broad implications for exoplanetary science. In this study, we performed a follow-up campaign on the $\tau$ Boo system using the newly commissioned NenuFAR telescope in 2020. We do not detect any bursty emission in the NenuFAR observations. There are many different degenerate explanations for our non-detection. For example, the original bursty signal may have been caused by an unknown instrumental systematic. Alternatively, the planetary emission from $\tau$ Boo b is variable. As planetary radio emission is triggered by the interaction of the planetary magnetosphere with the magnetized stellar wind, the expected intensity of the planetary radio emission varies greatly with stellar rotation and along the stellar magnetic cycle. More observations are needed to fully understand the mystery of the possible variability of the $\tau$ Boo b radio emission., Comment: 12 pages, 4 figures, 3 tables. Accepted for publication (Sept 4, 2023) for the Planetary, Solar, and Heliospheric Radio Emissions IX Proceedings. Refereed by 2 referees

Radio astronomy is currently thriving with new large ground-based radio telescopes coming online in preparation for the upcoming Square Kilometre Array (SKA). Facilities like LOFAR, MeerKAT/SKA, ASKAP/SKA, and the future SKA-LOW bring tremendous sensitivity in time and frequency, improved angular resolution, and also high-rate data streams that need to be processed. They enable advanced studies of radio transients, volatile by nature, that can be detected or missed in the data. These transients are markers of high-energy accelerations of electrons and manifest in a wide range of temporal scales. Usually studied with dynamic spectroscopy of time series analysis, there is a motivation to search for such sources in large interferometric datasets. This requires efficient and robust signal reconstruction algorithms. To correctly account for the temporal dependency of the data, we improve the classical image deconvolution inverse problem by adding the temporal dependency in the reconstruction problem. Then, we introduce two novel neural network architectures that can do both spatial and temporal modeling of the data and the instrumental response. Then, we simulate representative time-dependent image cubes of point source distributions and realistic telescope pointings of MeerKAT to generate toy models to build the training, validation, and test datasets. Finally, based on the test data, we evaluate the source profile reconstruction performance of the proposed methods and classical image deconvolution algorithm CLEAN applied frame-by-frame. In the presence of increasing noise level in data frame, the proposed methods display a high level of robustness compared to frame-by-frame imaging with CLEAN. The deconvolved image cubes bring a factor of 3 improvement in fidelity of the recovered temporal profiles and a factor of 2 improvement in background denoising., Comment: 15 pages, 19 figures. Published in A&A, Section Astronomical instrumentation

Computationally removing the motion blur introduced by camera shake or object motion in a captured image remains a challenging task in computational photography. Deblurring methods are often limited by the fixed global exposure time of the image capture process. The post-processing algorithm either must deblur a longer exposure that contains relatively little noise or denoise a short exposure that intentionally removes the opportunity for blur at the cost of increased noise. We present a novel approach of leveraging spatially varying pixel exposures for motion deblurring using next-generation focal-plane sensor--processors along with an end-to-end design of these exposures and a machine learning--based motion-deblurring framework. We demonstrate in simulation and a physical prototype that learned spatially varying pixel exposures (L-SVPE) can successfully deblur scenes while recovering high frequency detail. Our work illustrates the promising role that focal-plane sensor--processors can play in the future of computational imaging., Comment: Project page with code: https://ccnguyen.github.io/lsvpe/

The ability to image high-dynamic-range (HDR) scenes is crucial in many computer vision applications. The dynamic range of conventional sensors, however, is fundamentally limited by their well capacity, resulting in saturation of bright scene parts. To overcome this limitation, emerging sensors offer in-pixel processing capabilities to encode the incident irradiance. Among the most promising encoding schemes is modulo wrapping, which results in a computational photography problem where the HDR scene is computed by an irradiance unwrapping algorithm from the wrapped low-dynamic-range (LDR) sensor image. Here, we design a neural network--based algorithm that outperforms previous irradiance unwrapping methods and we design a perceptually inspired "mantissa" encoding scheme that more efficiently wraps an HDR scene into an LDR sensor. Combined with our reconstruction framework, MantissaCam achieves state-of-the-art results among modulo-type snapshot HDR imaging approaches. We demonstrate the efficacy of our method in simulation and show benefits of our algorithm on modulo images captured with a prototype implemented with a programmable sensor.

Neural representations have emerged as a new paradigm for applications in rendering, imaging, geometric modeling, and simulation. Compared to traditional representations such as meshes, point clouds, or volumes they can be flexibly incorporated into differentiable learning-based pipelines. While recent improvements to neural representations now make it possible to represent signals with fine details at moderate resolutions (e.g., for images and 3D shapes), adequately representing large-scale or complex scenes has proven a challenge. Current neural representations fail to accurately represent images at resolutions greater than a megapixel or 3D scenes with more than a few hundred thousand polygons. Here, we introduce a new hybrid implicit-explicit network architecture and training strategy that adaptively allocates resources during training and inference based on the local complexity of a signal of interest. Our approach uses a multiscale block-coordinate decomposition, similar to a quadtree or octree, that is optimized during training. The network architecture operates in two stages: using the bulk of the network parameters, a coordinate encoder generates a feature grid in a single forward pass. Then, hundreds or thousands of samples within each block can be efficiently evaluated using a lightweight feature decoder. With this hybrid implicit-explicit network architecture, we demonstrate the first experiments that fit gigapixel images to nearly 40 dB peak signal-to-noise ratio. Notably this represents an increase in scale of over 1000x compared to the resolution of previously demonstrated image-fitting experiments. Moreover, our approach is able to represent 3D shapes significantly faster and better than previous techniques; it reduces training times from days to hours or minutes and memory requirements by over an order of magnitude., Comment: J. N. P. Martel and D. B. Lindell equally contributed to this work

Compressive imaging using coded apertures (CA) is a powerful technique that can be used to recover depth, light fields, hyperspectral images and other quantities from a single snapshot. The performance of compressive imaging systems based on CAs mostly depends on two factors: the properties of the mask's attenuation pattern, that we refer to as "codification" and the computational techniques used to recover the quantity of interest from the coded snapshot. In this work, we introduce the idea of using time-varying CAs synchronized with spatially varying pixel shutters. We divide the exposure of a sensor into sub-exposures at the beginning of which the CA mask changes and at which the sensor's pixels are simultaneously and individually switched "on" or "off". This is a practically appealing codification as it does not introduce additional optical components other than the already present CA but uses a change in the pixel shutter that can be easily realized electronically. We show that our proposed time multiplexed coded aperture (TMCA) can be optimized end-to-end and induces better coded snapshots enabling superior reconstructions in two different applications: compressive light field imaging and hyperspectral imaging. We demonstrate both in simulation and on real captures (taken with prototypes we built) that this codification outperforms the state-of-the-art compressive imaging systems by more than 4dB in those applications.

Images acquired with a telescope are blurred and corrupted by noise. The blurring is usually modeled by a convolution with the Point Spread Function and the noise by Additive Gaussian Noise. Recovering the observed image is an ill-posed inverse problem. Sparse deconvolution is well known to be an efficient deconvolution technique, leading to optimized pixel Mean Square Errors, but without any guarantee that the shapes of objects (e.g. galaxy images) contained in the data will be preserved. In this paper, we introduce a new shape constraint and exhibit its properties. By combining it with a standard sparse regularization in the wavelet domain, we introduce the Shape COnstraint REstoration algorithm (SCORE), which performs a standard sparse deconvolution, while preserving galaxy shapes. We show through numerical experiments that this new approach leads to a reduction of galaxy ellipticity measurement errors by at least 44%., Comment: 22 pages, 6 figures, 1 table, accepted in Journal of Fourier Analysis and Applications

Observing planetary auroral radio emission is the most promising method to detect exoplanetary magnetic fields, the knowledge of which will provide valuable insights into the planet's interior structure, atmospheric escape, and habitability. We present LOFAR-LBA circularly polarized beamformed observations of the exoplanetary systems 55 Cancri, $\upsilon$ Andromedae, and $\tau$ Bo\"{o}tis. We tentatively detect circularly polarized bursty emission from the $\tau$ Bo\"{o}tis system in the range 14-21 MHz with a flux density of $\sim$890 mJy and with a significance of $\sim$3$\sigma$. For this detection, no signal is seen in the OFF-beams, and we do not find any potential causes which might cause false positives. We also tentatively detect slowly variable circularly polarized emission from $\tau$ Bo\"{o}tis in the range 21-30 MHz with a flux density of $\sim$400 mJy and with a statistical significance of $>$8$\sigma$. The slow emission is structured in the time-frequency plane and shows an excess in the ON-beam with respect to the two simultaneous OFF-beams. Close examination casts some doubts on the reality of the slowly varying signal. We discuss in detail all the arguments for and against an actual detection. Furthermore, a $\sim$2$\sigma$ marginal signal is found from the $\upsilon$ Andromedae system and no signal is detected from the 55 Cancri system. Assuming the detected signals are real, we discuss their potential origin. Their source probably is the $\tau$ Bootis planetary system, and a possible explanation is radio emission from the exoplanet $\tau$ Bootis b via the cyclotron maser mechanism. Assuming a planetary origin, we derived limits for the planetary polar surface magnetic field strength, finding values compatible with theoretical predictions. Further low-frequency observations are required to confirm this possible first detection of an exoplanetary radio signal. [Abridged], Comment: Accepted for publication in A&A (Oct. 22, 2020). 29 pages (15 pgs main text), 17 figures, 8 tables, 10 appendices

Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed to approximate those integrations with Monte Carlo sampling. Here, we propose automatic integration, a new framework for learning efficient, closed-form solutions to integrals using coordinate-based neural networks. For training, we instantiate the computational graph corresponding to the derivative of the network. The graph is fitted to the signal to integrate. After optimization, we reassemble the graph to obtain a network that represents the antiderivative. By the fundamental theorem of calculus, this enables the calculation of any definite integral in two evaluations of the network. Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10 times with a tradeoff of slightly reduced image quality.

Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations. We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks, dubbed sinusoidal representation networks or Sirens, are ideally suited for representing complex natural signals and their derivatives. We analyze Siren activation statistics to propose a principled initialization scheme and demonstrate the representation of images, wavefields, video, sound, and their derivatives. Further, we show how Sirens can be leveraged to solve challenging boundary value problems, such as particular Eikonal equations (yielding signed distance functions), the Poisson equation, and the Helmholtz and wave equations. Lastly, we combine Sirens with hypernetworks to learn priors over the space of Siren functions., Comment: Project website: https://vsitzmann.github.io/siren/ Project video: https://youtu.be/Q2fLWGBeaiI

The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically. This obstructs the use of mobile eye trackers to perform, e.g., low latency predictive rendering, or to study quick and subtle eye motions like microsaccades using head-mounted devices in the wild. Here, we propose a hybrid frame-event-based near-eye gaze tracking system offering update rates beyond 10,000 Hz with an accuracy that matches that of high-end desktop-mounted commercial trackers when evaluated in the same conditions. Our system builds on emerging event cameras that simultaneously acquire regularly sampled frames and adaptively sampled events. We develop an online 2D pupil fitting method that updates a parametric model every one or few events. Moreover, we propose a polynomial regressor for estimating the point of gaze from the parametric pupil model in real time. Using the first event-based gaze dataset, available at https://github.com/aangelopoulos/event_based_gaze_tracking , we demonstrate that our system achieves accuracies of 0.45 degrees--1.75 degrees for fields of view from 45 degrees to 98 degrees. With this technology, we hope to enable a new generation of ultra-low-latency gaze-contingent rendering and display techniques for virtual and augmented reality., Comment: IEEEVR oral/TVCG paper Dataset at https://github.com/aangelopoulos/event_based_gaze_tracking Some typo fixes in the new version

Serial section electron microscopy (ssEM) is a widely used technique for obtaining volumetric information of biological tissues at nanometer scale. However, accurate 3D reconstructions of identified cellular structures and volumetric quantifications require precise estimates of section thickness and anisotropy (or stretching) along the XY imaging plane. In fact, many image processing algorithms simply assume isotropy within the imaging plane. To ameliorate this problem, we present a method for estimating thickness and stretching of electron microscopy sections using non-parametric Bayesian regression of image statistics. We verify our thickness and stretching estimates using direct measurements obtained by atomic force microscopy (AFM) and show that our method has a lower estimation error compared to a recent indirect thickness estimation method as well as a relative Z coordinate estimation method. Furthermore, we have made the first dataset of ssSEM images with directly measured section thickness values publicly available for the evaluation of indirect thickness estimation methods.

We briefly review the various proposed scenarios that may lead to nonthermal radio emissions from exoplanetary systems (planetary magnetospheres, magnetosphere-ionosphere and magnetosphere-satellite coupling, and star-planet interactions), and the physical information that can be drawn from their detection. The latter scenario is especially favorable to the production of radio emission above 70\,MHz. We summarize the results of past and recent radio searches, and then discuss FAST characteristics and observation strategy, including synergies. We emphasize the importance of polarization measurements and a high duty-cycle for the very weak targets that radio-exoplanets prove to be., Comment: 6 pages, 3 figures, published in the mini-volume on FAST science potentials in the journal Research in Astronomy and Astrophysics

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available., Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conferences

We present an auxiliary task to Mask R-CNN, an instance segmentation network, which leads to faster training of the mask head. Our addition to Mask R-CNN is a new prediction head, the Edge Agreement Head, which is inspired by the way human annotators perform instance segmentation. Human annotators copy the contour of an object instance and only indirectly the occupied instance area. Hence, the edges of instance masks are particularly useful as they characterize the instance well. The Edge Agreement Head therefore encourages predicted masks to have similar image gradients to the ground-truth mask using edge detection filters. We provide a detailed survey of loss combinations and show improvements on the MS COCO Mask metrics compared to using no additional loss. Our approach marginally increases the model size and adds no additional trainable model variables. While the computational costs are increased slightly, the increment is negligible considering the high computational cost of the Mask R-CNN architecture. As the additional network head is only relevant during training, inference speed remains unchanged compared to Mask R-CNN. In a default Mask R-CNN setup, we achieve a training speed-up and a relative overall improvement of 8.1% on the MS COCO metrics compared to the baseline., Comment: 9 pages, 7 figures, 5 tables

Kliko is a Docker-based container specification for running one or multiple related compute jobs. The key concepts of Kliko are the encapsulation of data processing software into a container and the formalization of the input, output and task parameters. By formalizing the parameters, the software is represented as abstract building blocks with a uniform and consistent interface. The main advantage is enhanced scriptability and empowering pipeline composition. Formalization is realized by bundling a container with a Kliko file, which describes the IO and task parameters. This Kliko container can then be opened and run by a Kliko runner. The Kliko runner will parse the Kliko definition and gather the values for these parameters, for example by requesting user input or retrieving pre-defined values from disk. Parameters can be various primitive types, for example: float, int or the path to a file. This paper will also discuss the implementation of a support library named Kliko which can be used to create Kliko containers, parse Kliko definitions, chain Kliko containers in workflows using a workflow manager library such as Luigi. The Kliko library can be used inside the container to interact with the Kliko runner. Finally, to illustrate the applicability of the Kliko definition, this paper will discuss two reference implementations based on the Kliko library: RODRIGUES, a web-based Kliko container scheduler, and output visualizer specifically for astronomical data, and VerMeerKAT, a multi-container workflow data reduction pipeline which is being used as a prototype pipeline for the commissioning of the MeerKAT radio telescope., Comment: 10 pages, 4 figues

Ground based low frequency radio interferometers have been developed in the last decade and are providing the scientific community with high quality observations. Conversely, current radioastronomy instruments in space have a poor angular resolution with single point observation systems. Improving the observation capabilities of the low frequency range (a few kHz to 100 MHz) requires to go to space and to set up a space based network of antenna that can be used as an interferometer. We present the outcome of the NOIRE (Nanosatellites pour un Observatoire Interf\'erom\'etrique Radio dans l'Espace / Nanosatellites for a Radio Interferometer Observatory in Space) study which assessed, with help of CNES PASO (Architecture Platform for Orbital Systems is CNES' cross-disciplinary team in charge of early mission and concept studies), the feasibility of a swarm of nanosatellites dedicated to a low frequency radio observatory. With such a platform, space system engineering and instrument development must be studied as a whole: each node is a sensor and all sensors must be used together to obtain a measurement. The study was conducted on the following topics: system principle and concept (swarm, node homogeneity); Space and time management (ranging, clock synchronization); Orbitography (Moon orbit, Lagrange point options); Telecommunication (between nodes and with ground) and networking; Measurements and processing; Propulsion; Power; Electromagnetic compatibility. No strong show-stopper was identified during the preliminary study, although the concept is not yet ready. Several further studies and milestones are identified. The NOIRE team will collaborate with international teams to try and build this next generation of space systems., Comment: Submitted to IEEEAeroconf 2018 proceedings

Compressed sensing theory is slowly making its way to solve more and more astronomical inverse problems. We address here the application of sparse representations, convex optimization and proximal theory to radio interferometric imaging. First, we expose the theory behind interferometric imaging, sparse representations and convex optimization, and second, we illustrate their application with numerical tests with SASIR, an implementation of the FISTA, a Forward-Backward splitting algorithm hosted in a LOFAR imager. Various tests have been conducted in Garsden et al., 2015. The main results are: i) an improved angular resolution (super resolution of a factor ~2) with point sources as compared to CLEAN on the same data, ii) correct photometry measurements on a field of point sources at high dynamic range and iii) the imaging of extended sources with improved fidelity. SASIR provides better reconstructions (five time less residuals) of the extended emission as compared to CLEAN. With the advent of large radiotelescopes, there is scope for improving classical imaging methods with convex optimization methods combined with sparse representations., Comment: 18 pages, 3 figures, INFIERI 2014 Published