Shape: A 3D Modeling Tool for Astrophysics

We present a flexible interactive 3D morpho-kinematical modeling application for astrophysics. Compared to other systems, our application reduces the restrictions on the physical assumptions, data type and amount that is required for a reconstruction of an object's morphology. It is one of the first publicly available tools to apply interactive graphics to astrophysical modeling. The tool allows astrophysicists to provide a-priori knowledge about the object by interactively defining 3D structural elements. By direct comparison of model prediction with observational data, model parameters can then be automatically optimized to fit the observation. The tool has already been successfully used in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE Transactions on Visualization and Computer Graphics

Double elevation: Autonomous weapons and the search for an irreducible law of war

What should be the role of law in response to the spread of artificial intelligence in war? Fuelled by both public and private investment, military technology is accelerating towards increasingly autonomous weapons, as well as the merging of humans and machines. Contrary to much of the contemporary debate, this is not a paradigm change; it is the intensification of a central feature in the relationship between technology and war: Double elevation, above one's enemy and above oneself. Elevation above one's enemy aspires to spatial, moral, and civilizational distance. Elevation above oneself reflects a belief in rational improvement that sees humanity as the cause of inhumanity and de-humanization as our best chance for humanization. The distance of double elevation is served by the mechanization of judgement. To the extent that judgement is seen as reducible to algorithm, law becomes the handmaiden of mechanization. In response, neither a focus on questions of compatibility nor a call for a 'ban on killer robots' help in articulating a meaningful role for law. Instead, I argue that we should turn to a long-standing philosophical critique of artificial intelligence, which highlights not the threat of omniscience, but that of impoverished intelligence. Therefore, if there is to be a meaningful role for law in resisting double elevation, it should be law encompassing subjectivity, emotion and imagination, law irreducible to algorithm, a law of war that appreciates situated judgement in the wielding of violence for the collective

From creation to consolidation: a novel framework for memory processing

Long after playing squash, your brain continues to process the events that occurred during the game, thereby improving your game, and more generally, enhancing adaptive behavior. Understanding these mysterious processes may require novel theories

Potential bias in ophthalmic pharmaceutical clinical trials

To make clinicians aware of potential sources of error in ophthalmic pharmaceutical clinical trials that can lead to erroneous interpretation of results, a critical review of the study design of various pharmaceutical ophthalmic clinical trials was completed. Discrepancies as a result of study shortcomings may explain observed differences between reported ophthalmic trial data and observed clinical results

A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy.

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Demystifying Quantum Blockchain for Healthcare

The application of blockchain technology can be beneficial in the field of healthcare as well as in the fight against the COVID-19 epidemic. In this work, the importance of blockchain is analyzed and it is observed that blockchain technology and the processes associated with it will be utilised in the healthcare systems of the future for data acquisition from sensors, automatic patient monitoring, and secure data storage. This technology substantially simplifies the process of carrying out operations because it can store a substantial quantity of data in a dispersed and secure manner, as well as enable access whenever and wherever it is required to do so. With the assistance of quantum blockchain, the benefits of quantum computing, such as the capability to acquire thermal imaging based on quantum computing and the speed with which patients may be located and monitored, can all be exploited to their full potential. Quantum blockchain is another tool that can be utilised to maintain the confidentiality, authenticity, and accessibility of data records. The processing of medical records could potentially benefit from greater speed and privacy if it combines quantum computing and blockchain technology. The authors of this paper investigate the possible benefits and applications of blockchain and quantum technologies in the field of medicine, pharmacy and healthcare systems. In this context, this work explored and compared quantum technologies and blockchain-based technologies in conjunction with other cutting-edge information and communications technologies such as ratification intelligence, machine learning, drones, and so on

Explainable Retinal Screening with Self-Management Support to Improve Eye-Health of Diabetic Population via Telemedicine

Diabetic Retinopathy (DR) is one major complication of diabetes and is the leading cause of blindness worldwide. Progression of DR and complete vision loss can be prevented by keeping diabetes in control and by early diagnosis through annual eye screenings. However, cost, healthcare disparities, cultural limitations, lack of motivation, etc., are the main barriers against regular screening, especially for a few ethnically and racially minority communities. On the other hand, to well-manage and control diabetes, the diabetic population needs to be physically active and keep their weight healthy. From the perspective of Behavioral Science, Some self-management techniques based on motivational interviewing can be utilized to motivate people to take preventive and mandatory measures to control diabetes. However, technical solutions based on `Motivational Interviewing\u27 are still not sufficiently available to healthcare providers who work with the diabetic population. Thus, collaborative teamwork of Computer Science and Behavioral Science is contemporary to improve eye health and the overall health of the diabetic population. In this dissertation, a community telemedicine framework has been proposed and designed which can connect clinicians with community partners to organize retinal screenings in community settings rather than traditional clinical settings. Secondly, automating the initial retinal screenings utilizing Deep Learning models, particularly Convolutional Neural Network (CNN), can reduce ophthalmologists\u27 workload and cost of screening. However, such Machine Learning models lack transparency and cannot explain how these models make particular decisions. Thus, an explainable retinal screening model has been developed to facilitate the recommended annual screening to overcome this limitation. Finally, a Computer-guided Action Planning (CAP) tool has been designed and developed to motivate the diabetic population to adopt healthier behaviors through Brief Action Planning, a self-management support technique. Through several feasibility studies, it is evident that the contribution of this dissertation could be combined to help prevent vision loss from diabetes

Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution

Due to the limitations of hyperspectral imaging systems, hyperspectral imagery (HSI) often suffers from poor spatial resolution, thus hampering many applications of the imagery. Hyperspectral super-resolution refers to fusing HSI and MSI to generate an image with both high spatial and high spectral resolutions. Recently, several new methods have been proposed to solve this fusion problem, and most of these methods assume that the prior information of the Point Spread Function (PSF) and Spectral Response Function (SRF) are known. However, in practice, this information is often limited or unavailable. In this work, an unsupervised deep learning-based fusion method - HyCoNet - that can solve the problems in HSI-MSI fusion without the prior PSF and SRF information is proposed. HyCoNet consists of three coupled autoencoder nets in which the HSI and MSI are unmixed into endmembers and abundances based on the linear unmixing model. Two special convolutional layers are designed to act as a bridge that coordinates with the three autoencoder nets, and the PSF and SRF parameters are learned adaptively in the two convolution layers during the training process. Furthermore, driven by the joint loss function, the proposed method is straightforward and easily implemented in an end-to-end training manner. The experiments performed in the study demonstrate that the proposed method performs well and produces robust results for different datasets and arbitrary PSFs and SRFs