Application of artificial intelligence to eyewitness identification.

Artificial intelligence is already all around us, and its usage will only increase. Knowing its capabilities is critical. A facial recognition system (FRS) is a tool for law enforcement during suspect searches and when presenting photos to eyewitnesses for identification. However, there are no comparisons between eyewitness and FRS accuracy using video, so it is unknown whether FRS face matches are more accurate than eyewitness memory when identifying a perpetrator. Ours is the first application of artificial intelligence to an eyewitness experience, using a comparative psychology approach. As a first step to test system accuracy relative to eyewitness accuracy, participants and an open-source FRS (FaceNet) attempted perpetrator identification/match from lineup photos (target-present, target-absent) after exposure to real crime videos with varied clarity and perpetrator race. FRS used video probe images of each perpetrator to achieve similarity ratings for each corresponding lineup member. Using receiver operating characteristic analysis to measure discriminability, FRS performance was superior to eyewitness performance, regardless of video clarity or perpetrator race. Video clarity impacted participant performance, with the unclear videos yielding lower performance than the clear videos. Using confidence-accuracy characteristic analysis to measure reliability (i.e., the likelihood the identified suspect is the actual perpetrator), when the FRS identified faces with the highest similarity values, they were accurate. The results suggest FaceNet, or similarly performing systems, may supplement eyewitness memory for suspect searches and subsequent lineup construction and knowing the system's strengths and weaknesses is critical.

pyWitness 1.0: A python eyewitness identification analysis toolkit.

pyWitness is a python toolkit for recognition memory experiments, with a focus on eyewitness identification (ID) data analysis and model fitting. The current practice is for researchers to use different statistical packages to analyze a single dataset. pyWitness streamlines the process. In addition to conducting key data analyses (e.g., receiver operating characteristic analysis, confidence accuracy characteristic analysis), statistical comparisons, signal-detection-based model fits, simulated data generation, and power analyses are also possible. We describe the package implementation and provide detailed instructions and tutorials with datasets so that users can follow. There is also an online manual that is regularly updated. We developed pyWitness to be user-friendly, reduce human interaction with pre-processing and processing of data and model fits, and produce publication-ready plots. All pyWitness features align with open science practices, such that the algorithms, fits, and methods are reproducible and documented. While pyWitness is a python toolkit, it can also be used from R for users more accustomed to this environment.

Illustrations of interactions needed when investigating sleep using a type of AM-PM PM-AM design.

Sleep has long been thought of and promoted to be beneficial for memory. Some claims that sleep aids memory have been made in the absence of a critical interaction. This condition is necessary when using a commonly-used experimental design (a type of AM-PM PM-AM design). We propose that a sleep effect exists only if there is an interaction between groups (experimental and time-of-day controls) and the time of test or study (morning and evening). We show different patterns of results that would and would not support a sleep effect with empirical and model-generated data from recognition memory experiments and hypothetical data. While we use these data to make our point, our suggestions apply to any memory and non-memory-related investigation (e.g., emotional memory, false memory susceptibility, language learning, problem-solving). Testing for and finding the proper interaction will add to the evidence that sleep boosts performance.

Lineup identification in young and older witnesses: does describing the criminal help or hinder?

The world population is getting older and, as a result, the number of older victims of crime is expected to increase. It is therefore essential to understand how ageing affects eyewitness identification, so procedures can be developed that enable victims of crime of all ages to provide evidence as accurately and reliably as possible. In criminal investigations, witnesses often provide a description of the perpetrator of the crime before later making an identification. While describing the perpetrator prior to making a lineup identification can have a detrimental effect on identification in younger adults, referred to as verbal overshadowing, it is unclear whether older adults are affected in the same way. Our study compared lineup identification of a group of young adults and a group of older adults using the procedure that has consistently revealed verbal overshadowing in young adults. Participants watched a video of a mock crime. Following a 20-min filled delay, they either described the perpetrator or completed a control task. Immediately afterwards, they identified the perpetrator from a lineup, or indicated that the perpetrator was not present, and rated their confidence. We found that describing the perpetrator decreased subsequent correct identification of the perpetrator in both young and older adults. This effect of verbal overshadowing was not explained by a change in discrimination but was consistent with participants adopting a more conservative criterion. Confidence and response time were both found to predict identification accuracy for young and older groups, particularly in the control condition.

Beam characterisation studies of the 62 MeV proton therapy beamline at the Clatterbridge Cancer Centre.

The Clatterbridge Cancer Centre (CCC) in the United Kingdom is the world's first hospital proton beam therapy facility, providing treatment for ocular cancers since 1989. A 62 MeV beam of protons is produced by a Scanditronix cyclotron and transported through a passive delivery system. In addition to the long history of clinical use, the facility supports a wide programme of experimental work and as such, an accurate and reliable simulation model of the treatment beamline is highly valuable. However, as the facility has seen several changes to the accelerator and beamline over the years, a comprehensive study of the CCC beam dynamics is needed to firstly examine the beam optics. An extensive analysis was required to overcome facility related constraints to determine fundamental beamline parameters and define an optical lattice written with the Methodical Accelerator Design (MAD-X) and the particle tracking Beam Delivery Simulation (BDSIM) code. An optimised case is presented and simulated results of the optical functions, beam distribution, losses and the transverse rms beam sizes along the beamline are discussed. Corresponding optical and beam information was used in TOPAS to simulate transverse beam profiles and compared to EBT3 film measurements. We provide an overview of the magnetic components, beam transport, cyclotron, beam and treatment related parameters necessary for the development of a present day optical model of the facility. This work represents the first comprehensive study of the CCC facility to date, as a basis to determine input beam parameters to accurately simulate and completely characterise the beamline.

First observation of the point spread function of optical transition radiation.

We represent the first experimental observation of the point spread function (PSF) of optical transition radiation (OTR) performed at KEK-Accelerator Test Facility extraction line. We have demonstrated that the PSF vertical polarization component has a central minimum with a two lobe distribution. However, the distribution width varied significantly with wavelength. We assume that we observed a severe effect from spherical or chromatic aberrations which are not taken into account in any existing theoretical model. We believe that the result of this work will encourage theoreticians to continue developing the theory as it is important for various transition radiation applications. Nonuniform distribution of the OTR PSF creates an opportunity for developing a submicrometer transverse beam size monitor.