Data compression, field of interest shaping and fast algorithms for direction-dependent deconvolution in radio interferometry

In radio interferometry, observed visibilities are intrinsically sampled at some interval in time and frequency. Modern interferometers are capable of producing data at very high time and frequency resolution; practical limits on storage and computation costs require that some form of data compression be imposed. The traditional form of compression is simple averaging of the visibilities over coarser time and frequency bins. This has an undesired side effect: the resulting averaged visibilities “decorrelate”, and do so differently depending on the baseline length and averaging interval. This translates into a non-trivial signature in the image domain known as “smearing”, which manifests itself as an attenuation in amplitude towards off-centre sources. With the increasing fields of view and/or longer baselines employed in modern and future instruments, the trade-off between data rate and smearing becomes increasingly unfavourable. Averaging also results in baseline length and a position-dependent point spread function (PSF). In this work, we investigate alternative approaches to low-loss data compression. We show that averaging of the visibility data can be understood as a form of convolution by a boxcar-like window function, and that by employing alternative baseline-dependent window functions a more optimal interferometer smearing response may be induced. Specifically, we can improve amplitude response over a chosen field of interest and attenuate sources outside the field of interest. The main cost of this technique is a reduction in nominal sensitivity; we investigate the smearing vs. sensitivity trade-off and show that in certain regimes a favourable compromise can be achieved. We show the application of this technique to simulated data from the Jansky Very Large Array and the European Very Long Baseline Interferometry Network. Furthermore, we show that the position-dependent PSF shape induced by averaging can be approximated using linear algebraic properties to effectively reduce the computational complexity for evaluating the PSF at each sky position. We conclude by implementing a position-dependent PSF deconvolution in an imaging and deconvolution framework. Using the Low-Frequency Array radio interferometer, we show that deconvolution with position-dependent PSFs results in higher image fidelity compared to a simple CLEAN algorithm and its derivatives

What do Deep Neural Networks Learn in Medical Images?

Deep learning is increasingly gaining rapid adoption in healthcare to help improve patient outcomes. This is more so in medical image analysis which requires extensive training to gain the requisite expertise to become a trusted practitioner. However, while deep learning techniques have continued to provide state-of-the-art predictive performance, one of the primary challenges that stands to hinder this progress in healthcare is the opaque nature of the inference mechanism of these models. So, attribution has a vital role in building confidence in stakeholders for the predictions made by deep learning models to inform clinical decisions. This work seeks to answer the question: what do deep neural network models learn in medical images? In that light, we present a novel attribution framework using adaptive path-based gradient integration techniques. Results show a promising direction of building trust in domain experts to improve healthcare outcomes by allowing them to understand the input-prediction correlative structures, discover new bio-markers, and reveal potential model biases

The extended H i halo of NGC 4945 as seen by MeerKAT

The State Agency for Research of the Spanish Ministry of Science, Innovation and Universities through the ‘Center of Excellence Severo Ochoa’ awarded to the Instituto de Astrofísica de Andalucía; the Economic Transformation, Industry, Knowledge and Universities Council of the Regional Government of Andalusia and the European Regional Development Fund from the European Union; the South African Radio Astronomy Observatory (SARAO); BMBF Verbundforschung; DFG Sonderforschungsbereich and the European Research Council (ERC).http://mnras.oxfordjournals.orghj2022Physic

The extended H I halo of NGC 4945 as seen by MeerKAT

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Observations of the neutral atomic hydrogen (H I) in the nuclear starburst galaxy NGC 4945 with MeerKAT are presented. We find a large amount of halo gas, previously missed by H I observations, accounting for 6.8 per cent of the total H I mass. This is most likely gas blown into the halo by star formation. Our maps go down to a 3σ column density level of 5 × 1018 cm−2. We model the H I distribution using tilted-ring fitting techniques and find a warp on the galaxy’s approaching and receding sides. The H I in the northern side of the galaxy appears to be suppressed. This may be the result of ionization by the starburst activity in the galaxy, as suggested by a previous study. The origin of the warp is unclear but could be due to past interactions or ram pressure stripping. Broad, asymmetric H I absorption lines extending throughout the H I emission velocity channels are present towards the nuclear region of NGC 4945. Such broad lines suggest the existence of a nuclear ring moving at a high circular velocity. This is supported by the clear rotation patterns in the H I absorption velocity field. The asymmetry of the absorption spectra can be caused by outflows or inflows of gas in the nuclear region of NGC 4945. The continuum map shows small extensions on both sides of the galaxy’s major axis that might be signs of outflows resulting from the starburst activity. © The Author(s) 2022. Published by Oxford University Press on behalf of Royal Astronomical Society.RI acknowledges financial support from grant RTI2018-096228-B-C31 (MCIU/AEI/FEDER,UE) and from the State Agency for Research of the Spanish Ministry of Science, Innovation and Universities through the ‘Center of Excellence Severo Ochoa’ awarded to the Instituto de Astrofísica de Andalucía (SEV-2017-0709), from the grant IAA4SKA (Ref. R18-RT-3082) from the Economic Transformation, Industry, Knowledge and Universities Council of the Regional Government of Andalusia and the European Regional Development Fund from the European Union. The MeerKAT telescope is operated by the South African Radio Astronomy Observatory, which is a facility of the National Research Foundation, an agency of the Department of Science and Innovation. This work is based upon research supported by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation. The financial assistance of the South African Radio Astronomy Observatory (SARAO) towards this research is hereby acknowledged (www.sarao.ac.za). At Ruhr University Bochum, this research is supported by BMBF Verbundforschung grant 05A20PC4 and by DFG Sonderforschungsbereich SFB1491. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme grant agreement no. 882793, project name MeerGas. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no.679627; project name FORNAX).Peer reviewe

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Worldwide, the persistent trend of human and animal life losses, as well as damage to properties due to wildlife–vehicle collisions (WVCs) remains a significant source of concerns for a broad range of stakeholders. To mitigate their occurrences and impact, many approaches are being adopted, with varying successes. Because of their increased versatility and increasing efficiency, Artificial Intelligence-based methods have been experiencing a significant level of adoption. The present work extensively reviews the literature on intelligent systems incorporating sensor technologies and/or machine learning methods to mitigate WVCs. Included in our review is an investigation of key factors contributing to human–wildlife conflicts, as well as a discussion of dominant state-of-the-art datasets used in the mitigation of WVCs. Our study combines a systematic review with bibliometric analysis. We find that most animal detection systems (excluding autonomous vehicles) are relying neither on state-of-the-art datasets nor on recent breakthrough machine learning approaches. We, therefore, argue that the use of the latest datasets and machine learning techniques will minimize false detection and improve model performance. In addition, the present work covers a comprehensive list of associated challenges ranging from failure to detect hotspot areas to limitations in training datasets. Future research directions identified include the design and development of algorithms for real-time animal detection systems. The latter provides a rationale for the applicability of our proposed solutions, for which we designed a continuous product development lifecycle to determine their feasibility

Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images

The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making mechanisms, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging ten state-of-the-art DL frameworks with transfer learning, we conducted a comprehensive evaluation encompassing both classification accuracy and interpretability. These models underwent thorough training, testing, and fine-tuning, resulting in EfficientNetB0, DenseNet121, and Xception outperforming the other models. These top-performing models were examined using adaptive path-based techniques to understand the underlying decision-making mechanisms. Grad-CAM and Grad-CAM++ highlighted critical image regions where the models identified patterns and features associated with each class of the brain tumor. The regions where the models identified patterns and features correspond visually to the regions where the tumors are located in the images. This result shows that DL models learn important features and patterns in the regions where tumors are located for decision-making

A Reliable Weighting Scheme for the Aggregation of Crowd Intelligence to Detect Fake News

Social networks play an important role in today’s society and in our relationships with others. They give the Internet user the opportunity to play an active role, e.g., one can relay certain information via a blog, a comment, or even a vote. The Internet user has the possibility to share any content at any time. However, some malicious Internet users take advantage of this freedom to share fake news to manipulate or mislead an audience, to invade the privacy of others, and also to harm certain institutions. Fake news seeks to resemble traditional media to establish its credibility with the public. Its seriousness pushes the public to share them. As a result, fake news can spread quickly. This fake news can cause enormous difficulties for users and institutions. Several authors have proposed systems to detect fake news in social networks using crowd signals through the process of crowdsourcing. Unfortunately, these authors do not use the expertise of the crowd and the expertise of a third party in an associative way to make decisions. Crowds are useful in indicating whether or not a story should be fact-checked. This work proposes a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party side. An experimentation has been conducted on 25 posts and 50 voters. A quantitative comparison with the majority vote model reveals that our aggregation model provides slightly better results due to weights assigned to accredited users. A qualitative investigation against existing aggregation models shows that the proposed approach meets the requirements or properties expected of a crowdsourcing system and a voting system

Radio Astronomical Antennas in the Central African Region to Improve the Sampling Function of the VLBI Network in the SKA Era?

On the African continent, South Africa has world-class astronomical facilities for advanced radio astronomy research. With the advent of the Square Kilometre Array project in South Africa (SA SKA), six countries in Africa (SA SKA partner countries) have joined South Africa to contribute towards the African Very Long Baseline Interferometry (VLBI) Network (AVN). Each of the AVN countries aims to construct a single-dish radio telescope that will be part of the AVN, the European VLBI Network, and the global VLBI network. The SKA and the AVN will enable very high sensitivity VLBI in the southern hemisphere. In the current AVN, there is a gap in the coverage in the central African region. This work analyses the increased scientific impact of having additional antennas in each of the six countries in central Africa, i.e., Cameroon, Gabon, Congo, Equatorial Guinea, Chad, and the Central African Republic. A number of economic human capital impacts of having a radio interferometer in central Africa are also discussed. This work also discusses the recent progress on the AVN project and shares a few lessons from some past successes in ground stations retrofitting