Jonathan Lam | Searcharxiv

arXiv2025

S-BLE: A Participatory BLE Sensory Data Set Recorded from Real-World Bus Travel Events

This contribution describes S-BLE, a data set created for supporting the design of robust and reliable Be-In-Be-Out systems in public transit. S-BLE was recorded by the smartphones of 28 participants during their daily transit routines in a university campus setting. 20 shuttle bus vehicles in the campus fleet were equipped with two Bluetooth low energy (BLE) beacons each. RSSI data from these beacons was recorded during regular rides, along with odometry information (from GPS) and data from the smartphone's inertial sensors. The article describes the system used for data collection and presents some statistics of interest for the recorded data.

cs.OH↗

arXiv2021

Machine Learning Statistical Gravity from Multi-Region Entanglement Entropy

The Ryu-Takayanagi formula directly connects quantum entanglement and geometry. Yet the assumption of static geometry lead to an exponentially small mutual information between far-separated disjoint regions, which does not hold in many systems such as free fermion conformal field theories. In this work, we proposed a microscopic model by superimposing entanglement features of an ensemble of random tensor networks of different bond dimensions, which can be mapped to a statistical gravity model consisting of a massive scalar field on a fluctuating background geometry. We propose a machine-learning algorithm that recovers the underlying geometry fluctuation from multi-region entanglement entropy data by modeling the bulk geometry distribution via a generative neural network. To demonstrate its effectiveness, we tested the model on a free fermion system and showed mutual information can be mediated effectively by geometric fluctuation. Remarkably, locality emerged from the learned distribution of bulk geometries, pointing to a local statistical gravity theory in the holographic bulk.

hep-th↗