Subalgebra-subregion duality: emergence of space and time in holography

In holographic duality, a higher dimensional quantum gravity system emerges from a lower dimensional conformal field theory (CFT) with a large number of degrees of freedom. We propose a formulation of duality for a general causally complete bulk spacetime region, called subalgebra-subregion duality, which provides a framework to describe how geometric notions in the gravity system, such as spacetime subregions, different notions of times, and causal structure, emerge from the dual CFT. Subalgebra-subregion duality generalizes and brings new insights into subregion-subregion duality (or equivalently entanglement wedge reconstruction). It provides a mathematically precise definition of subregion-subregion duality and gives an independent definition of entanglement wedges without using entropy. Geometric properties of entanglement wedges, including those that play a crucial role in interpreting the bulk as a quantum error correcting code, can be understood from the duality as the geometrization of the additivity anomaly of certain algebras. Using general boundary subalgebras rather than those associated with geometric subregions makes it possible to find duals for general bulk spacetime regions, including those not touching the boundary. Applying subalgebra-subregion duality to a boundary state describing a single-sided black hole also provides a precise way to define mirror operators.Comment: 104 pages, 29 figure

Developing interest to share and craft based on the Technology Acceptance Model

The Malaysian Ministry of Education aims to increase interest in learning Science, Technology, Engineering and Mathematics, through Science2Action. Among these initiatives in Science2Action, is the use of Art(s). By combining the Internet, technology and crafts, e-crafting is formed. This e-crafting project aims to increase awareness about what interests the audience through sharing of and development of craft, hopefully towards possibilities of ideation and mixing crafts, extending from the original craft such as origami. Designed based on the Technology Acceptance Model, findings are positive

Development of the MICROMEGAS Detector for Measuring the Energy Spectrum of Alpha Particles by using a 241-Am Source

We have developed MICROMEGAS (MICRO MEsh GASeous) detectors for detecting {\alpha} particles emitted from an 241-Am standard source. The voltage applied to the ionization region of the detector is optimized for stable operation at room temperature and atmospheric pressure. The energy of {\alpha} particles from the 241-Am source can be varied by changing the flight path of the {\alpha} particle from the 241 Am source. The channel numbers of the experimentally-measured pulse peak positions for different energies of the {\alpha} particles are associated with the energies deposited by the alpha particles in the ionization region of the detector as calculated by using GEANT4 simulations; thus, the energy calibration of the MICROMEGAS detector for {\alpha} particles is done. For the energy calibration, the thickness of the ionization region is adjusted so that {\alpha} particles may completely stop in the ionization region and their kinetic energies are fully deposited in the region. The efficiency of our MICROMEGAS detector for {\alpha} particles under the present conditions is found to be ~ 97.3 %

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier-Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods