622 research outputs found
Random Delayed-Choice Quantum Eraser via Two-Photon Imaging
We report on a delayed-choice quantum eraser experiment based on a two-photon
imaging scheme using entangled photon pairs. After the detection of a photon
which passed through a double-slit, a random delayed choice is made to erase or
not erase the which-path information by the measurement of its distant
entangled twin; the particle-like and wave-like behavior of the photon are then
recorded simultaneously and respectively by only one set of joint detection
devices. The present eraser takes advantage of two-photon imaging. The complete
which-path information of a photon is transferred to its distant entangled twin
through a "ghost" image. The choice is made on the Fourier transform plane of
the ghost image between reading "complete information" or "partial information"
of the double-path.Comment: European Physical Journal D (in press
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
Urban dispersal events are processes where an unusually large number of
people leave the same area in a short period. Early prediction of dispersal
events is important in mitigating congestion and safety risks and making better
dispatching decisions for taxi and ride-sharing fleets. Existing work mostly
focuses on predicting taxi demand in the near future by learning patterns from
historical data. However, they fail in case of abnormality because dispersal
events with abnormally high demand are non-repetitive and violate common
assumptions such as smoothness in demand change over time. Instead, in this
paper we argue that dispersal events follow a complex pattern of trips and
other related features in the past, which can be used to predict such events.
Therefore, we formulate the dispersal event prediction problem as a survival
analysis problem. We propose a two-stage framework (DILSA), where a deep
learning model combined with survival analysis is developed to predict the
probability of a dispersal event and its demand volume. We conduct extensive
case studies and experiments on the NYC Yellow taxi dataset from 2014-2016.
Results show that DILSA can predict events in the next 5 hours with F1-score of
0.7 and with average time error of 18 minutes. It is orders of magnitude better
than the state-ofthe-art deep learning approaches for taxi demand prediction.Comment: To appear in AAAI-19 proceedings. The reason for the replacement was
the misspelled author name in the meta-data field. Author name was corrected
from "Ynahua Li" to "Yanhua Li". The author list in the paper was correct and
remained unchange
Autoencoder with Group-based Decoder and Multi-task Optimization for Anomalous Sound Detection
In industry, machine anomalous sound detection (ASD) is in great demand.
However, collecting enough abnormal samples is difficult due to the high cost,
which boosts the rapid development of unsupervised ASD algorithms. Autoencoder
(AE) based methods have been widely used for unsupervised ASD, but suffer from
problems including 'shortcut', poor anti-noise ability and sub-optimal quality
of features. To address these challenges, we propose a new AE-based framework
termed AEGM. Specifically, we first insert an auxiliary classifier into AE to
enhance ASD in a multi-task learning manner. Then, we design a group-based
decoder structure, accompanied by an adaptive loss function, to endow the model
with domain-specific knowledge. Results on the DCASE 2021 Task 2 development
set show that our methods achieve a relative improvement of 13.11% and 15.20%
respectively in average AUC over the official AE and MobileNetV2 across test
sets of seven machines.Comment: Submitted to the 2024 IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2024
Coherent manipulation of quantum states in a coupled cavity-atom system
We study atomic coherence and interference in four-level atoms confined in an optical cavity and explores the interplay between cavity QED and electromagnetically induced transparency (EIT). The destructive interference can be induced in the coupled cavityatom system with a free-space control laser tuned to the normal mode resonance and leads to suppression of the normal mode excitation. Then by adding a pump laser coupled to the four-level atoms from free space, the control-laser induced destructive interference can be reversed and the normal mode excitation is restored. When the free-space control laser is tuned to the atomic resonance and forms a Λ-type EIT configuration with the cavity-atom system, EIT is manifested as a narrow transmission peak of a weak probe laser coupled into the cavity mode. With the free-space pump laser driving the cavity-confined atoms in a four-level configuration, the narrow transmission peak of the cavity EIT can be split into two peaks and the dressed intra-cavity dark states are created analogous to the dressed states in free space. We report experimental studies of such coherently coupled cavityatom system realized with cold Rb atoms confined in an optical cavity and discuss possible applications in quantum nonlinear optics and quantum information science
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
Human mobility estimation is crucial during the COVID-19 pandemic due to its
significant guidance for policymakers to make non-pharmaceutical interventions.
While deep learning approaches outperform conventional estimation techniques on
tasks with abundant training data, the continuously evolving pandemic poses a
significant challenge to solving this problem due to data nonstationarity,
limited observations, and complex social contexts. Prior works on mobility
estimation either focus on a single city or lack the ability to model the
spatio-temporal dependencies across cities and time periods. To address these
issues, we make the first attempt to tackle the cross-city human mobility
estimation problem through a deep meta-generative framework. We propose a
Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that
estimates dynamic human mobility responses under a set of social and policy
conditions related to COVID-19. Facilitated by a novel spatio-temporal
task-based graph (STTG) embedding, STORM-GAN is capable of learning shared
knowledge from a spatio-temporal distribution of estimation tasks and quickly
adapting to new cities and time periods with limited training samples. The STTG
embedding component is designed to capture the similarities among cities to
mitigate cross-task heterogeneity. Experimental results on real-world data show
that the proposed approach can greatly improve estimation performance and
out-perform baselines.Comment: Accepted at the 22nd IEEE International Conference on Data Mining
(ICDM 2022) Full Pape
- …