Extracting cosmological information from small scales in weak gravitational lensing data

This work is concerned with how to extract information encoded in small scales of non-Gaussian fields, with the purpose of learning about cosmology using weak gravitational lensing. We do so by comparing different methods on simulated data sets. The topic is relevant, for upcoming galaxy surveys will map the late evolution of the matter density field, which is non-Gaussian, with an unprecedented level of detail, and any improvement on the analysis techniques will increase the experiments' scientific return. First, we investigate some non-Gaussian observables used in the weak lensing community. We analyze to what extent they are sensitive to the background expansion of the universe, and to what extent to the evolution of the structures responsible for the lensing. We then focus our attention on one such statistic, lensing peaks, and assess the performance of a simple halo-based model that has been proposed to forecast their abundance. We find some shortcomings of that semi-analytic approach, and proceed to review some minimal requirements for numerical simulations used to forecast non-Gaussian statistics, to reduce their computational cost while fulfilling the accuracy and precision required by future experiments. Second, we propose a novel measurement, that of the temperature dipole induced on the cosmic microwave background induced by the rotation of ionized gas around galaxies, as an additional observation to help constrain the distribution of baryonic matter on the smallest scales probed by WL experiments. The uncertainty in this distribution is a major theoretical systematic for future surveys. Third, we show how deep neural networks can be used to map pixel-level data into the cosmological parameters of interest, by-passing the previous compression step of measuring pre-designed statistics. We provide the first (simulation-based) credible contours based on neural networks applied to weak lensing data, and discuss how to interpret these models

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security

Signal processing and machine learning techniques for Doppler ultrasound haemodynamic measurements

Haemodynamic monitoring is an invaluable tool for evaluating, diagnosing and treating the cardiovascular system, and is an integral component of intensive care units, obstetrics wards and other medical units. Doppler ultrasound provides a non-invasive, cost-effective and fast means of haemodynamic monitoring, which traditionally necessitates highly invasive methods such as Pulmonary artery catheter or transoesophageal echocardiography. However, Doppler ultrasound scan acquisition requires a highly experienced operator and can be very challenging. Machine learning solutions that quantify and guide the scanning process in an automatic and intelligent manner could overcome these limitations and lead to routine monitoring. Development of such methods is the primary goal of the presented work. In response to this goal, this thesis proposes a suite of signal processing and machine learning techniques. Among these is a new and real-time method of maximum frequency envelope estimation. This method, which is based on image-processing techniques and is highly adaptive to varying signal quality, was developed to facilitate automatic and consistent extraction of features from Doppler ultrasound measurements. Through a thorough evaluation, this method was demonstrated to be accurate and more stable than alternative state-of-art methods. Two novel real-time methods of beat segmentation, which operate using the maximum frequency envelope, were developed to enable systematic feature extraction from individual cardiac cycles. These methods do not require any additional hardware, such as an electrocardiogram machine, and are fully automatic, real-time and highly resilient to noise. These qualities are not available in existing methods. Extensive evaluation demonstrated the methods to be highly successful. A host of machine learning solutions were analysed, designed and evaluated. This led to a set of novel features being proposed for Doppler ultrasound analysis. In addition, a state of- the-art image recognition classification method, hitherto undocumented for Doppler ultrasound analysis, was shown to be superior to more traditional modelling approaches. These contributions facilitated the design of two innovative types of feedback. To reflect beneficial probe movements, which are otherwise difficult to distinguish, a regression model to quantitatively score ultrasound measurements was proposed. This feedback was shown to be highly correlated with an ideal response. The second type of feedback explicitly predicted beneficial probe movements. This was achieved using classification models with up to five categories, giving a more challenging scenario than those addressed in prior disease classification work. Evaluation of these, for the first time, demonstrated that Doppler scan information can be used to automatically indicate probe position. Overall, the presented work includes significant contributions for Doppler ultrasound analysis, it proposes valuable new machine learning techniques, and with continued work, could lead to solutions that unlock the full potential of Doppler ultrasound haemodynamic monitoring

Spanish pavilion : 17th International Architecture Exhibition

Catálogo publicado con motivo de la celebración de la Bienal de Arquitectura celebrada en Venecia del 22 de mayo al 21 de noviembre de 202

Erasure and epoche: phenomenological strategies for thinking in and with devastation

In this essay, I present a phenomenological approach to knowing, learning from, and teaching with what I call ‘orphaned matter’ – that is, images, objects or artefacts that are commonly regarded as ‘mute’, deactivated, or redundant because the meanings that accompanied their creation and journey into the present have been erased. Here, research is not directed towards the reconstruction of those lost contexts. Instead, counter-intuitively, researchers honour the gaps and losses that have occurred, however catastrophic, and work with what remains so that alternate insights, situated in the present for the sake of a different future, might begin to reveal themselves. Phenomenology is particularly well-suited in this regard because, with its embrace of epoché, a profound openness to erasure is methodologically central to it. Epoché (or phenomenological reduction, and more broadly the suspension of judgement) sets in motion an investigative attitude in which researchers seek to have their inherited habits of thought - their presumptions - illuminated and where necessary disposed of. Notably, this occurs through the agencies of the phenomenon under investigation as it progressively reveals itself, on its own terms, as far as this is possible

UMSL Bulletin 2020-2021

The 2020-2021 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1084/thumbnail.jp

COVID-2019 Impacts on Education Systems and Future of Higher Education

The rapid outbreak of the COVID-19 has presented unprecedented challenges on education systems. Closing schools and universities and cancelling face-to-face activities have become a COVID-19 inevitable reality in most parts of the world. To be business-as-usual, many higher education providers have taken steps toward digital transformation, and implementing a range of remote teaching, learning and assessment approaches. This book provides timely research on COVID-19 impacts on education systems and seeks to bring together scholars, educators, policymakers and practitioners to collectively and critically identify, investigate and share best practices that lead to rethinking and reframing the way we deliver education in future