Neurons in the medial temporal lobe (MTL), a critical area for declarative memory, have been shown to change their tuning in associative learning tasks. Yet, it is unclear how durable these neuronal representations are and if they outlast the execution of the task. To address this issue, we studied the responses of MTL neurons in neurosurgical patients to known concepts (people and places). Usi...

Associative memory is defined as memory for the relationship between two initially unrelated items, like a name and an unfamiliar face. Associative memory is not only one of the most common forms of memory used in everyday situations, but is highly dependent on the structures of the medial temporal lobe (MTL). The goal of this chapter is to review the patterns of neural activity shown to underl...

Enhanced memory for emotional faces is a significant component of adaptive social interactions, but little is known on its neural developmental correlates. We explored the role of amygdaloid complex (AC) and medial temporal lobe (MTL) in emotional memory recognition across development, by comparing fMRI activations of successful memory encoding of fearful and neutral faces in children (n = 12; ...

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems lack generalisation across different conditions. A key underlying reason for poor is scarcity of datasets, which a significant roadblock to designing robust machine learning (ML) models. Recent works SER focus on utilising multitask (MTL) methods improve by shared representations. However, most these studi...

The simplest expression of episodic memory is the experience of familiarity, the isolated recognition that something has been encountered previously. Brain structures of the medial temporal lobe (MTL) make essential contributions to episodic memory, but the distinct contributions from each MTL structure to familiarity are debatable. Here we used specialized tests to assess recognition impairmen...

INTRODUCTION Governments throughout the world are using or considering various front-of-package (FOP) food labeling systems to provide nutrition information to consumers. Our web-based study tested consumer understanding of different FOP labeling systems. METHODS Adult participants (N = 480) were randomized to 1 of 5 groups to evaluate FOP labels: 1) no label; 2) multiple traffic light (MTL);...

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as secondary tasks in an MTL model. We explore next word prediction, next character pr...

We compared value recovery of a modified tree-length (MTL) logging system that measures product diameter and length using a Waratah 626 harvester head to that of a tree-length (TL) system that estimates dimensions. A field test compared the actual value cut to the maximum potential value suggested by the log bucking optimization program Assessment of Value by Individual Stems (AVIS) for 25 fell...

Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features ...

Multi-task learning (MTL) is considered for logistic-regression classifiers, based on a Dirichlet process (DP) formulation. A symmetric MTL (SMTL) formulation is considered in which classifiers for multiple tasks are learned jointly, with a variational Bayesian (VB) solution. We also consider an asymmetric MTL (AMTL) formulation in which the posterior density function from the SMTL model parame...