Playing “guess who?”: when an episodic specificity induction increases trace distinctiveness and reduces memory errors during event reconstruction

Automatic Encoding of Gender by Three-Year-Old Children

10.31234/osf.io/we8s5 ◽

2017 ◽

Cited By ~ 1

Author(s):

Gina T Bednarek ◽

Kristin Shutts

Keyword(s):

Test Phase ◽

Older Children ◽

Memory Errors ◽

Automatic Encoding ◽

Target Children

The present research tested whether three-year-old children – like older children and adults – automatically encode other people’s gender. Three-year-old participants (N = 24) learned facts about unfamiliar target children who varied in gender and were asked to remember facts about the targets during a test phase. At test, children made more within-category memory errors (e.g., misattributing a fact associated with one girl to another girl) than between-category errors (e.g., misattributing a fact associated with a girl to a boy). The findings suggest that at least as early as three years of age, children automatically encode whether someone is a boy or a girl upon first meeting them. The results have implications for our understanding of the automaticity and emergence of stereotyping processes.

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Real-Time Event Reconstruction and Analysis in CBM and STAR Experiments

Journal of Physics Conference Series ◽

10.1088/1742-6596/1602/1/012006 ◽

2020 ◽

Vol 1602 ◽

pp. 012006

Author(s):

Ivan Kisel ◽

Keyword(s):

Real Time ◽

Event Reconstruction

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Medico-Legal Opinion Based on Autopsy Findings of a Victim of an Explosion Involving Mass Fatality

Academic Forensic Pathology ◽

10.1177/1925362120983669 ◽

2020 ◽

Vol 10 (3-4) ◽

pp. 158-165

Author(s):

Yalini Thivaharan ◽

Indira Deepthi Gamage Kitulwatte

Keyword(s):

Legal Issues ◽

Temporal Region ◽

Skull Fractures ◽

Autopsy Findings ◽

Parietal Lobes ◽

Event Reconstruction ◽

Police Personnel ◽

Autopsy Examination ◽

Mass Fatality ◽

The Brain

Introduction: Investigation into explosions is one of the major areas in forensic medicine and pathology. Medico legal issues associated with these deaths are diverse and forensic experts are often expected to make clarifications. Assistance of a methodical scientific investigation of such a death in evaluation of unanswered medico legal issues, of an autopsy of one of the victims of Easter Sunday explosions is discussed. Case history: The deceased was a 15-year-old girl who was participating in the Easter mass at St. Sebastian’s Church - Kattuwapaitya, Negombo, Sri Lanka when a suicide bomber blew himself up. The mother of the deceased noticed the deceased being rushed to the hospital. However, she was pronounced dead on admission. Pre-autopsy radiology revealed spherical shrapnel in the temporal region. At autopsy, the fatal injury was found on the head and a detailed study revealed skull fractures associated with penetration by 3 shrapnels. There was a keyhole lesion among the penetrations. Internal examination revealed an extensive dural tear underlying the compound fracture. The brain was grossly edematous with lacerations on the frontal and parietal lobes associated with localized subarachnoid hemorrhage. There were multiple underlying contusions on bilateral frontal white matter. Small subarachnoid haemorrhage was noted on the basal aspect of the brain. Discussion: Careful evaluation of the autopsy findings assisted in formulating the opinion scientifically on event reconstruction including the proximity of the victim to the epicenter of explosion and nature of explosive device, period of survival, mechanism of causation of skull fractures and the mechanism of death in addition to the cause of death. Conclusion: A forensic pathologist following a meticulous autopsy examination, along with a team of ballistic experts and specially trained police personnel play a pivotal task in analyzing a scene of explosion and an autopsy of a victim, in concluding the case and in bringing justice to all the victims and survivors of the catastrophe.

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Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

Journal of High Energy Physics ◽

10.1007/jhep04(2021)296 ◽

2021 ◽

Vol 2021 (4) ◽

Author(s):

Jack Y. Araz ◽

Michael Spannowsky

Keyword(s):

Neural Network ◽

Neural Networks ◽

Top Quark ◽

Feature Space ◽

Training Sample ◽

Bayesian Techniques ◽

Training Sample Size ◽

Event Reconstruction ◽

Neural Network Classifiers ◽

Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

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