This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a self-organizing map (SOM) and a convolutional autoencoder. The rapidly increasing volume of ...radio-astronomical data has increased demand for machine-learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labeled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighborhood similarity and K-means clustering of radio-astronomical features. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) data set image features which can be applied to new radio survey data.
Radio observations of supernova remnant G1.9+0.3 Luken, Kieran J; Filipović, Miroslav D; Maxted, Nigel I ...
Monthly notices of the Royal Astronomical Society,
02/2020, Letnik:
492, Številka:
2
Journal Article
Recenzirano
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ABSTRACT
We present 1–10 GHz radio continuum flux density, spectral index, polarization, and rotation measure (RM) images of the youngest known Galactic supernova remnant (SNR) G1.9+0.3, using ...observations from the Australia Telescope Compact Array. We have conducted an expansion study spanning eight epochs between 1984 and 2017, yielding results consistent with previous expansion studies of G1.9+0.3. We find a mean radio continuum expansion rate of (0.78 ± 0.09) per cent yr−1 (or ∼8900 km s−1 at an assumed distance of 8.5 kpc), although the expansion rate varies across the SNR perimetre. In the case of the most recent epoch between 2016 and 2017, we observe faster-than-expected expansion of the northern region. We find a global spectral index for G1.9+0.3 of −0.81 ± 0.02 (76 MHz–10 GHz). Towards the northern region, however, the radio spectrum is observed to steepen significantly (∼−1). Towards the two so-called (east and west) ‘ears’ of G1.9+0.3, we find very different RM values of 400–600 and 100–200 rad m2, respectively. The fractional polarization of the radio continuum emission reaches (19 ± 2) per cent, consistent with other, slightly older, SNRs such as Cas A.
Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms-from algae to primates-excel in sensing their ...environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal-taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future.
Disease progression during or after anti-PD-1-based treatment is common in advanced melanoma. Sotigalimab is a CD40 agonist antibody with a unique epitope specificity and Fc receptor binding profile ...optimized for activation of CD40-expressing antigen-presenting cells. Preclinical data indicated that CD40 agonists combined with anti-PD1 could overcome resistance to anti-PD-1.
We conducted a multicenter, open-label, phase II trial to evaluate the combination of sotigalimab 0.3 mg/kg and nivolumab 360 mg every 3 weeks in patients with advanced melanoma following confirmed disease progression on a PD-1 inhibitor. The primary objective was to determine the objective response rate (ORR).
Thirty-eight subjects were enrolled and evaluable for safety. Thirty-three were evaluable for activity. Five confirmed partial responses (PR) were observed for an ORR of 15%. Two PRs are ongoing at 45.9+ and 26+ months, whereas the other three responders relapsed at 41.1, 18.7, and 18.4 months. The median duration of response was at least 26 months. Two additional patients had stable disease for >6 months. Thirty-four patients (89%) experienced at least one adverse event (AE), and 13% experienced a grade 3 AE related to sotigalimab. The most common AEs were pyrexia, chills, nausea, fatigue, pruritus, elevated liver function, rash, vomiting, headache, arthralgia, asthenia, myalgia, and diarrhea. There were no treatment-related SAEs, deaths, or discontinuation of sotigalimab due to AEs.
Sotigalimab plus nivolumab had a favorable safety profile consistent with the toxicity profiles of each agent. The combination resulted in durable and prolonged responses in a subset of patients with anti-PD-1-resistant melanoma, warranting further evaluation in this setting. See related commentary by Wu and Luke, p. 9.
Positively charged gold nanoparticles featuring photocleavable units within their surrounding monolayer are switched from non-interacting species to inhibitors of chymotrypsin through UV irradiation.
Radio Galaxy Zoo Ralph, Nicholas O.; Norris, Ray P.; Fang, Gu ...
Publications of the Astronomical Society of the Pacific,
11/2019, Letnik:
131, Številka:
1004
Journal Article
Recenzirano
This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a selforganizing map (SOM) and a convolutional autoencoder. The rapidly increasing volume of ...radio-astronomical data has increased demand for machine-learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labeled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighborhood similarity and K-means clustering of radio-astronomical features. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) data set image features which can be applied to new radio survey data.
TexTonic is a visual analytic system for interactive exploration of very large unstructured text collections. TexTonic visualizes hierarchical clusters of representative terms, snippets, and ...documents in a single, multi-scale spatial layout. Exploration is supported by interacting with the visualization and directly manipulating the terms in the visualization using semantic interactions. These semantic interactions steer the underlying analytic model by translating user interactions within the visualization to contextual updates to the supporting data model. The combination of semantic interactions and information visualization at multiple levels of the data hierarchy helps users manage information overload so that they can more effectively explore very large text collections. In this article, we describe TexTonic’s data processing and analytic pipeline, user interface and interaction design principles, and results of a user study conducted mid-development with experienced data analysts. We also discuss the implications TexTonic could have on visual exploration and discovery tasks.
In this thesis, I demonstrate a novel and efficient unsupervised clustering and data exploration method with the combination of a Self-Organising Map (SOM) and a Convolutional Autoencoder, applied to ...radio-astronomical images from the Radio Galaxy Zoo (RGZ) dataset.The rapidly increasing volume and complexity of radio-astronomical data have ushered in a new era of big-data astronomy which has increased the demand for Machine Learning (ML) solutions. In this era, the sheer amount of image data produced with modern instruments and has resulted in a significant data deluge. Furthermore, the morphologies of objects captured in these radio-astronomical images are highly complex and challenging to classify conclusively due to their intricate and indiscrete nature. Additionally, major radio-astronomical discoveries are unplanned and found in the unexpected, making unsupervised ML highly desirable by operating with few assumptions and without labelled training data.In this thesis, I developed a novel unsupervised ML approach as a practical solution to these astronomy challenges. Using this system, I demonstrated the use of convolutional autoencoders and SOM’s as a dimensionality reduction method to delineate the complexity and volume of astronomical data. My optimised system shows that the coupling of these methods is a powerful method of data exploration and unsupervised clustering of radio-astronomical images.The results of this thesis show this approach is capable of accurately separating features by complexity on a SOM manifold and unified distance matrix with neighbourhood similarity and hierarchical clustering of the mapped astronomical features. This method provides an effective means to explore the high-level topological relationships of image features and morphology in large datasets automatically with minimal processing time and computational resources. I achieved these capabilities with a new and innovative method of SOM training using the autoencoder compressed latent feature vector representations of radio-astronomical data, rather than raw images. Using this system, I successfully investigated SOM affine transformation invariance and analysed the true nature of rotational effects on this manifold using autoencoder random rotation training augmentations.Throughout this thesis, I present my method as a powerful new approach to data exploration technique and contribution to the field. The speed and effectiveness of this method indicates excellent scalability and holds implications for use on large future surveys, large-scale instruments such as the Square Kilometre Array and in other big-data and complexity analysis applications.
This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a Self-Organising Map (SOM) and a convolutional autoencoder. The rapidly increasing volume of ...radio-astronomical data has increased demand for machine learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labelled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighbourhood similarity and K-means clustering of radio-astronomical features complexity. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) dataset image features which can be applied to new radio survey data.
We present 1 to 10GHz radio continuum flux density, spectral index, polarisation and Rotation Measure (RM) images of the youngest known Galactic Supernova Remnant (SNR) G1.9+0.3, using observations ...from the Australia Telescope Compact Array (ATCA). We have conducted an expansion study spanning 8 epochs between 1984 and 2017, yielding results consistent with previous expansion studies of G1.9+0.3. We find a mean radio continuum expansion rate of (\(0.78 \pm 0.09\)) per cent year\(^{-1}\) (or \(\sim8900\) km s\(^{-1}\) at an assumed distance of 8.5 kpc), although the expansion rate varies across the SNR perimeter. In the case of the most recent epoch between 2016 and 2017, we observe faster-than-expected expansion of the northern region. We find a global spectral index for G1.9+0.3 of \(-0.81\pm0.02\) (76 MHz\(-\)10 GHz). Towards the northern region, however, the radio spectrum is observed to steepen significantly (\(\sim -\)1). Towards the two so called (east & west) "ears" of G1.9+0.3, we find very different RM values of 400-600 rad m\(^{2}\) and 100-200 rad m\(^{2}\) respectively. The fractional polarisation of the radio continuum emission reaches (19 \(\pm\) 2)~per~cent, consistent with other, slightly older, SNRs such as Cas~A.
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