The TREC-2002 video track report

TREC-2002 saw the second running of the Video Track, the goal of which was to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. The track used 73.3 hours of publicly available digital video (in MPEG-1/VCD format) downloaded by the participants directly from the Internet Archive (Prelinger Archives) (internetarchive, 2002) and some from the Open Video Project (Marchionini, 2001). The material comprised advertising, educational, industrial, and amateur films produced between the 1930's and the 1970's by corporations, nonprofit organizations, trade associations, community and interest groups, educational institutions, and individuals. 17 teams representing 5 companies and 12 universities - 4 from Asia, 9 from Europe, and 4 from the US - participated in one or more of three tasks in the 2001 video track: shot boundary determination, feature extraction, and search (manual or interactive). Results were scored by NIST using manually created truth data for shot boundary determination and manual assessment of feature extraction and search results. This paper is an introduction to, and an overview of, the track framework - the tasks, data, and measures - the approaches taken by the participating groups, the results, and issues regrading the evaluation. For detailed information about the approaches and results, the reader should see the various site reports in the final workshop proceedings

Two-mode squeezing over deployed fiber coexisting with conventional communications

Squeezed light is a crucial resource for continuous-variable (CV) quantum information science. Distributed multi-mode squeezing is critical for enabling CV quantum networks and distributed quantum sensing. To date, multi-mode squeezing measured by homodyne detection has been limited to single-room experiments without coexisting classical signals, i.e., on ``dark'' fiber. Here, after distribution through separate fiber spools (5~km), $-0.9\pm0.1$-dB coexistent two-mode squeezing is measured. Moreover, after distribution through separate deployed campus fibers (about 250~m and 1.2~km), $-0.5\pm0.1$-dB coexistent two-mode squeezing is measured. Prior to distribution, the squeezed modes are each frequency multiplexed with several classical signals -- including the local oscillator and conventional network signals -- demonstrating that the squeezed modes do not need dedicated dark fiber. After distribution, joint two-mode squeezing is measured and recorded for post-processing using triggered homodyne detection in separate locations. This demonstration enables future applications in quantum networks and quantum sensing that rely on distributed multi-mode squeezing.Comment: 23 pages, 13 figures, 2 table

Normalizing Flows for Human Pose Anomaly Detection

Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more. We distill the problem to anomaly detection of human pose, thus reducing the risk of nuisance parameters such as appearance affecting the result. Focusing on pose alone also has the side benefit of reducing bias against distinct minority groups. Our model works directly on human pose graph sequences and is exceptionally lightweight ($\sim1K$ parameters), capable of running on any machine able to run the pose estimation with negligible additional resources. We leverage the highly compact pose representation in a normalizing flows framework, which we extend to tackle the unique characteristics of spatio-temporal pose data and show its advantages in this use case. Our algorithm uses normalizing flows to learn a bijective mapping between the pose data distribution and a Gaussian distribution, using spatio-temporal graph convolution blocks. The algorithm is quite general and can handle training data of only normal examples, as well as a supervised dataset that consists of labeled normal and abnormal examples. We report state-of-the-art results on two anomaly detection benchmarks - the unsupervised ShanghaiTech dataset and the recent supervised UBnormal dataset

AI in Production: Video Analysis and Machine Learning for Expanded Live Events Coverage

In common with many industries, TV and video production is likely to be transformed by Artificial Intelligence (AI) and Machine Learning (ML), with software and algorithms assisting production tasks that, conventionally, could only be carried out by people. Expanded coverage of a diverse range of live events is particularly constrained by the relative scarcity of skilled people, and is a strong use case for AI-based automation. This paper describes recent BBC research into potential production benefits of AI algorithms, using visual analysis and other techniques. Rigging small, static UHD cameras, we have enabled a one-person crew to crop UHD footage in multiple ways and cut between the resulting shots, effectively creating multi-camera HD coverage of events that cannot accommodate a camera crew. By working with programme makers to develop simple deterministic rules and, increasingly, training systems using advanced video analysis, we are developing a system of algorithms to automatically frame, sequence and select shots, and construct acceptable multicamera coverage of previously untelevised types of event

Controlling the Cycloreversion Reaction of a Diarylethene Derivative Using Sequential Two-Photon Excitation

Diarylethenes (DAE) are a class of photochromic molecular switches that convert between two structural isomers upon excitation with light. A great deal of research has been dedicated to elucidating the mechanisms of the reversible electrocyclic reactions to make optical memory devices with DAE compounds, but details of the fundamental reaction mechanism after one- or two-photons of light is still lacking. The primary DAE discussed in this dissertation is 1,2-bis(2,4-dimethyl-5-phenyl-3-thienyl)perfluorocyclopentene (DMPT-PFCP), which is a model compound for studying the fundamental reaction dynamics using one- and two-photon excitation experiments. Pump-probe spectroscopy was used to study the low one-photon quantum yield cycloreversion reaction of DMPT-PFCP by changing the excitation wavelength, solvent, and temperature to describe the dynamics on the ground- and excited states. However, the primary goal of this work was to use sequential two-photon excitation with fs laser pulses to map out the cycloreversion reaction dynamics for DMPT-PFCP compound on the first and higher excited states. The cycloreversion quantum yield was selectively increased using sequential two-photon excitation, where after promotion to the S1 state, a second excitation pulse promotes the molecules to an even higher excited state. The mechanism of increasing the yield by promoting the molecules to a higher excited state was explored using pump-repump-probe (PReP) spectroscopy. The PReP experiments follow the excited-state dynamics as the molecules sample different regions of the S1 potential energy surface. The projection of the S1 dynamics onto the higher excited states showed that by changing the secondary excitation wavelength and the delay between excitation pulses, the cycloreversion quantum yield was selectively controlled. Future studies to obtain the specific modes involved in the ring-opening reaction coordinate on the excited-state would further improve our knowledge of the cycloreversion reaction and therefore improve the efficiency of the sequential two-photon excitation process to make very efficient optical memory devices using DAE compounds

One-second coherence for a single electron spin coupled to a multi-qubit nuclear-spin environment

Single electron spins coupled to multiple nuclear spins provide promising multi-qubit registers for quantum sensing and quantum networks. The obtainable level of control is determined by how well the electron spin can be selectively coupled to, and decoupled from, the surrounding nuclear spins. Here we realize a coherence time exceeding a second for a single electron spin through decoupling sequences tailored to its microscopic nuclear-spin environment. We first use the electron spin to probe the environment, which is accurately described by seven individual and six pairs of coupled carbon-13 spins. We develop initialization, control and readout of the carbon-13 pairs in order to directly reveal their atomic structure. We then exploit this knowledge to store quantum states for over a second by carefully avoiding unwanted interactions. These results provide a proof-of-principle for quantum sensing of complex multi-spin systems and an opportunity for multi-qubit quantum registers with long coherence times