A model for removing the increased recall of recent events from the temporal distribution of autobiographical memory

The reminiscence bump is the tendency to recall relatively many personal events from the period in which the individual was between 10 and 30 years old. This effect has only been found in autobiographical memory studies that used participants who were older than 40 years of age. The increased recall of recent events possibly obscures the reminiscence bump in the results of younger participants. In this study, a model was proposed that removes the increase for recent events from the temporal distribution. The model basically estimates a retention function based on the 10 most recent years from the observed distributions and divides the observed distributions by predictions derived from the estimated retention function. The model was examined with three simulated data sets and one experimental data set. The results of the experiment offered two practical examples of how the model could be used to investigate the temporal distribution of autobiographical memories

Adaptive Forgetting Curves for Spaced Repetition Language Learning

The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model.Cambridge Assessmen

Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of disorder-specific dynamics is crucial for early diagnosis and understanding the disorder mechanism. A common way of learning discriminatory features relies on training a classifier and evaluating feature importance. Classical classifiers, based on handcrafted features are quite powerful, but suffer the curse of dimensionality when applied to large input dimensions of spatio-temporal data. Deep learning algorithms could handle the problem and a model introspection could highlight discriminatory spatio-temporal regions but need way more samples to train. In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC). We pre-train the whole MILC model on unlabeled and unrelated healthy control data. We test our model on three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers and four different studies. Our algorithm outperforms existing self-supervised pre-training methods and provides competitive classification results to classical machine learning algorithms. Importantly, whole MILC enables attribution of subject diagnosis to specific spatio-temporal regions in the fMRI signal.Comment: Accepted at MICCAI 2020. arXiv admin note: substantial text overlap with arXiv:1912.0313

Universality in Systems with Power-Law Memory and Fractional Dynamics

There are a few different ways to extend regular nonlinear dynamical systems by introducing power-law memory or considering fractional differential/difference equations instead of integer ones. This extension allows the introduction of families of nonlinear dynamical systems converging to regular systems in the case of an integer power-law memory or an integer order of derivatives/differences. The examples considered in this review include the logistic family of maps (converging in the case of the first order difference to the regular logistic map), the universal family of maps, and the standard family of maps (the latter two converging, in the case of the second difference, to the regular universal and standard maps). Correspondingly, the phenomenon of transition to chaos through a period doubling cascade of bifurcations in regular nonlinear systems, known as "universality", can be extended to fractional maps, which are maps with power-/asymptotically power-law memory. The new features of universality, including cascades of bifurcations on single trajectories, which appear in fractional (with memory) nonlinear dynamical systems are the main subject of this review.Comment: 23 pages 7 Figures, to appear Oct 28 201

Mycobacterium avium-intracellulare cellulitis occurring with septic arthritis after joint injection: a case report

BACKGROUND: Cellulitis caused by Mycobacterium avium-intracellulare has rarely been described. Mycobacterium avium-intracellulare is a rare cause of septic arthritis after intra-articular injection, though the causative role of injection is difficult to ascertain in such cases. CASE PRESENTATION: A 57-year-old with rheumatoid arthritis treated with prednisone and azathioprine developed bilateral painful degenerative shoulder arthritis. After corticosteroid injections into both acromioclavicular joints, he developed bilateral cellulitis centered over the injection sites. Skin biopsy showed non-caseating granulomas, and culture grew Mycobacterium avium-intracellulare. Joint aspiration also revealed Mycobacterium avium-intracellulare infection. CONCLUSION: Although rare, skin and joint infections caused by Mycobacterium avium-intracellulare should be considered in any immunocompromised host, particularly after intra-articular injection. Stains for acid-fast bacilli may be negative in pathologic samples even in the presence of infection; cultures of tissue specimens should always be obtained

People’s understanding of verbal risk descriptors in patient information leaflets : a cross-sectional national survey of 18- to 65-year-olds in England

Introduction Evidence suggests the current verbal risk descriptors used to communicate side effect risk in patient information leaflets (PILs) are overestimated. Objectives The aim was to establish how people understand the verbal risk descriptors recommended for use in PILs by the European Commission (EC), and alternative verbal risk descriptors, in the context of mild and severe side effects. Methods A cross-sectional online survey was carried out by a market research company recruiting participants aged between 18 and 65 years living in England. Data were collected between 18 March and 1 April 2016. Participants were given a hypothetical scenario regarding the risk of mild or severe medication side effects and asked to estimate how many out of 10,000 people would be affected for each of the verbal risk descriptors being tested. Results A total of 1003 participants were included in the final sample. The risks conveyed by the EC recommended verbal risk descriptors were greatly overestimated by participants. Two distinct distributions were apparent for participant estimates of side effect risks: those for ‘high risk’ verbal descriptors (e.g. ‘common’, ‘likely’, ‘high chance’) and those for ‘low risk’ verbal descriptors (e.g. ‘uncommon’, ‘unlikely’, ‘low chance’). Within these two groups, the distributions were near to identical regardless of what adverb (e.g. very, high, fair) or adjective (e.g. common, likely, chance) was used. The EC recommended verbal risk descriptors were more likely to be understood in accordance with their intended meanings when describing severe side effects. Very few demographic or psychological factors were consistently associated with how well participants understood the EC recommended verbal risk descriptors. Discussion The current verbal risk descriptors used in PILs are ineffective at best and misleading at worst. Discontinuing the use of verbal risk descriptors would limit the likelihood of people overestimating the risk of side effects

Protecting eyewitness evidence: Examining the efficacy of a self-administered interview tool

Given the crucial role of eyewitness evidence, statements should be obtained as soon as possible after an incident. This is not always achieved due to demands on police resources. Two studies trace the development of a new tool, the Self-Administered Interview (SAI), designed to elicit a comprehensive initial statement. In Study 1, SAI participants reported more correct details than participants who provided a free recall account, and performed at the same level as participants given a Cognitive Interview. In Study 2, participants viewed a simulated crime and half recorded their statement using the SAI. After a delay of 1 week, all participants completed a free recall test. SAI participants recalled more correct details in the delayed recall task than control participants

Spin polarization versus lifetime effects at point contacts between superconducting niobium and normal metals

Point-contact Andreev reflection spectroscopy is used to measure the spin polarization of metals but analysis of the spectra has encountered a number of serious challenges, one of which is the difficulty to distinguish the effects of spin polarization from those of the finite lifetime of Cooper pairs. We have recently confirmed the polarization-lifetime ambiguity for Nb-Co and Nb-Cu contacts and suggested to use Fermi surface mismatch, the normal reflection due to the difference of Fermi wave vectors of the two electrodes, to solve this dilemma. Here we present further experiments on contacts between superconducting Nb and the ferromagnets Fe and Ni as well as the noble metals Ag and Pt that support our previous results. Our data indicate that the Nb - normal metal interfaces have a transparency of up to about 80 per cent and a small, if not negligible, spin polarization.Comment: 7 pages, 2 figures, submitted to Proceedings of the 26th Conference on Low Temperature Physic