Extragalactic gamma-ray signal from dark matter annihilation: an appraisal

We re-evaluate the extragalactic gamma-ray flux prediction from dark matter annihilation in the approach of integrating over the nonlinear matter power spectrum, extrapolated to the free-streaming scale. We provide an estimate of the uncertainty based entirely on available N-body simulation results and minimal theoretical assumptions. We illustrate how an improvement in the simulation resolution, exemplified by the comparison between the Millennium and Millennium II simulations, affects our estimate of the flux uncertainty and we provide a "best guess" value for the flux multiplier, based on the assumption of stable clustering for the dark matter perturbations described as a collision-less fluid. We achieve results comparable to traditional Halo Model calculations, but with a much simpler procedure and a more general approach, as it relies only on one, directly measurable quantity. In addition we discuss the extension of our calculation to include baryonic effects as modeled in hydrodynamical cosmological simulations and other possible sources of uncertainty that would in turn affect indirect dark matter signals. Upper limit on the integrated power spectrum from supernovae lensing magnification are also derived and compared with theoretical expectations.Comment: 20 pages, 9 figures, 1 table. Updated to match the published version. New material and figures added, conclusions unchange

A multi-sensory interactive reading experience for visually impaired children; a user evaluation

© 2018 Springer-Verlag London Ltd., part of Springer Nature The children’s experience of reading is enhanced by visual displays, and through picture book experiences, young children expose themselves to develop socially, personally, intellectually, and culturally. While a sighted person’s mental imagining is constructed mostly through visual experiences, a visually impaired person’s mental images are a product of haptic, taste, smell, and sounds. In this paper, we are introducing a picture book with multi-sensory interactions for the visually impaired children. The key novelty in our concept is the integration of multi-sensory interactions (touch, sound, and smell) to create a new reading experience for visually impaired. Also, this concept is highlighting the lack of appropriately designed sensory reading experiences for visually impaired children. We have conducted a user study with 10 educators, and 25 children from a special school for visually impaired in Malaysia, and our evaluation revealed that this book is engaging and a novel experience of multi-sensory interactions to both children and educators

A Closer Look at Classical Measurement, an Algorithm for Deliberation in Rodents, and a Conjecture on Intertemporal Choice

In this three-part thesis, Part I is an examination of the measurement process in classical Hamiltonian mechanics. This part is concerned with the tradeoff that exists, when measuring any observable of a system, between the disturbance inflicted upon the system and the information that can be extracted. The main result takes the form of a Heisenberg-like precision-disturbance relation: measuring an observable leaves all compatible observables undisturbed but inevitably disturbs all incompatible observables. The magnitude of the disturbance (the analogue of Ò) is found to be proportional, in a sense that is made precise, to one’s initial uncertainty in the ready-state of the apparatus—a quantity that relates to the temperature of the apparatus. Part II of this thesis develops a model of the computations taking place in the deliberative decision-making system of rodents, during wakefulness and sleep, with focus on the role of hippocampus (HPC). In this model, medial prefrontal cortex performs high-level planning, and then tasks HPC with fleshing out the details of the plan, as needed. We describe this planning task of HPC as an optimal control problem, which allows us to draw insights from the powerful mathematics of optimal control theory. The model makes novel testable predictions, provides insights into memory consolidation during sleep, and offers a paradigm capable of accommodating a wide range of observed phenomena, such as the theta rhythm, the slow oscillation, spindle oscillations, sharp wave-ripples, θ-sequences, for-ward and reverse SWR-sequences, the formation and strengthening of episodic memories, and a need for two modes of operation—online and offline. The two parts described above are the main content of this thesis. Part I falls within the purview of classical theoretical physics, while Part II falls in that of computational neuroscience. The two may seem unrelated; however, while each part is self-contained, I see the two as connected. Part III of this thesis is my attempt to provide an outline of a bigger picture, which sees the foregoing as lines of inquiry towards the same far-reaching conjecture—one which has had a strong pull on my imagination during my PhD, and which I hope to be able to address in the future. This conjecture is that the probability calculus of quantum mechanics holds a kind of normative status for a class of decision problems involving intertemporal choice under uncertainty—a class of problems of great importance to artificial intelligence, brain sciences, economics, and, I argue, to physics too.Ph.D

Vector-based Pedestrian Navigation in Cities

How do pedestrians choose their paths within city street networks? Human path planning has been extensively studied at the aggregate level of mobility flows, and at the individual level with strictly designed behavioural experiments. However, a comprehensive, individual-level model of how humans select pedestrian paths in real urban environments is still lacking. Here, we analyze human path planning behaviour in a large dataset of individual pedestrians, whose GPS traces were continuously recorded as they pursued their daily goals. Through statistical analysis we reveal two robust empirical discoveries, namely that (1) people increasingly deviate from the shortest path as the distance between origin and destination increases, and (2) individual choices exhibit direction-dependent asymmetries when origin and destination are swapped. In order to address the above findings, which cannot be explained by existing models, we develop a vector-based navigation framework motivated by the neural evidence of direction-encoding cells in hippocampal brain networks, and by behavioural evidence of vector navigation in animals. Modelling pedestrian path preferences by vector-based navigation increases the model's predictive power by 35%, compared to a model based on minimizing distance with stochastic effects. We show that these empirical findings and modelling results generalise across two major US cities with drastically different street networks, suggesting that vector-based navigation is a universal property of human path planning, independent of specific city environments. Our results offer a simple, unified explanation of numerous findings about human navigation, and posit a computational mechanism that may underlie the human capacity to efficiently navigate in environments at various scales