High-Energy Astrophysics in the 2020s and Beyond

With each passing decade, we gain new appreciation for the dynamic, connected, and often violent nature of the Universe. This reality necessarily places the study of high-energy processes at the very heart of modern astrophysics. This White Paper illustrates the central role of high-energy astrophysics to some of the most pressing astrophysical problems of our time, the formation/evolution of galaxies, the origin of the heavy elements, star and planet formation, the emergence of life on exoplanets, and the search for new physics. We also highlight the new connections that are growing between astrophysicists and plasma physicists. We end with a discussion of the challenges that must be addressed to realize the potential of these connections, including the need for integrated planning across physics and astronomy programs in multiple agencies, and the need to foster the creativity and career aspirations of individual scientists in this era of large projects.Comment: Astro2020 White Paper submissio

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Monte Carlo simulation of the transmission of measles: Beyond the mass action principle

We present a Monte Carlo simulation of the transmission of measles within a population sample during its growing and equilibrium states by introducing two different vaccination schedules of one and two doses. We study the effects of the contact rate per unit time $\xi$ as well as the initial conditions on the persistence of the disease. We found a weak effect of the initial conditions while the disease persists when $\xi$ lies in the range 1/L-10/L ($L$ being the latent period). Further comparison with existing data, prediction of future epidemics and other estimations of the vaccination efficiency are provided. Finally, we compare our approach to the models using the mass action principle in the first and another epidemic region and found the incidence independent of the number of susceptibles after the epidemic peak while it strongly fluctuates in its growing region. This method can be easily applied to other human, animals and vegetable diseases and includes more complicated parameters.Comment: 15 pages, 4 figures, 1 table, Submitted to Phys.Rev.

Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex

Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple patterns. In this paper we extend this idea by showing that a neuron with several thousand synapses arranged along active dendrites can learn to accurately and robustly recognize hundreds of unique patterns of cellular activity, even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where some of the patterns recognized by a neuron lead to action potentials and define the classic receptive field of the neuron, whereas the majority of the patterns recognized by a neuron act as predictions by slightly depolarizing the neuron without immediately generating an action potential. We then present a network model based on neurons with these properties and show that the network learns a robust model of time-based sequences. Given the similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory is a universal property of neocortical tissue. We further propose that cellular layers in the neocortex implement variations of the same sequence memory algorithm to achieve different aspects of inference and behavior. The neuron and network models we introduce are robust over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. The sequence capacity of the network scales linearly with the number of synapses on each neuron. Thus neurons need thousands of synapses to learn the many temporal patterns in sensory stimuli and motor sequences.Comment: Submitted for publicatio

Total anomalous pulmonary vein drainage in a 60-year-old woman diagnosed in an ECG-gated multidetector computed tomography : a case report and review of literature

Purpose: Total anomalous pulmonary vein drainage (TAPVD) is a congenital cardiac defect in which there is no connection between pulmonary veins and the left atrium. Pulmonary veins form a confluence independent of the left atrium and drain to a systemic vein. TAPVD types are: supracardiac, cardiac, infracardiac, and mixed. TAPVD accounts for approximately 1.5-2.2% of all congenital heart diseases. This anomaly is usually diagnosed in the neonatal period, and it coexists with atrial septal defect. Adult cases of TAPVD are rarely reported. Case report: We report a rare case of a 60-year-old woman with incidentally found, uncorrected TAPVD in ECG-gated multidetector computed tomography. In previous echocardiographic examinations partial anomalous pulmonary venous return and atrial septal defect were diagnosed. Conclusions: ECG-gated multidetector computed tomography is a valuable diagnostic method for adults with congenital heart disease. It enables evaluation of coronary arteries and simultaneously provides detailed anatomy of great vessels

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

Performance of the Birmingham Solar-Oscillations Network (BiSON)

The Birmingham Solar-Oscillations Network (BiSON) has been operating with a full complement of six stations since 1992. Over 20 years later, we look back on the network history. The meta-data from the sites have been analysed to assess performance in terms of site insolation, with a brief look at the challenges that have been encountered over the years. We explain how the international community can gain easy access to the ever-growing dataset produced by the network, and finally look to the future of the network and the potential impact of nearly 25 years of technology miniaturisation.Comment: 31 pages, 19 figures. Accepted by Solar Physics: 2015 October 20. First online: 2015 December 7. Open Acces