Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, \textbf{edge2vec}\ significantly outperforms state-of-the-art models on all three tasks. We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.Comment: 10 page

Many Task Learning with Task Routing

Typical multi-task learning (MTL) methods rely on architectural adjustments and a large trainable parameter set to jointly optimize over several tasks. However, when the number of tasks increases so do the complexity of the architectural adjustments and resource requirements. In this paper, we introduce a method which applies a conditional feature-wise transformation over the convolutional activations that enables a model to successfully perform a large number of tasks. To distinguish from regular MTL, we introduce Many Task Learning (MaTL) as a special case of MTL where more than 20 tasks are performed by a single model. Our method dubbed Task Routing (TR) is encapsulated in a layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario successfully fits hundreds of classification tasks in one model. We evaluate our method on 5 datasets against strong baselines and state-of-the-art approaches.Comment: 8 Pages, 5 Figures, 2 Table

Disrupted working memory circuitry and psychotic symptoms in 22q11.2 deletion syndrome.

22q11.2 deletion syndrome (22q11DS) is a recurrent genetic mutation that is highly penetrant for psychosis. Behavioral research suggests that 22q11DS patients exhibit a characteristic neurocognitive phenotype that includes differential impairment in spatial working memory (WM). Notably, spatial WM has also been proposed as an endophenotype for idiopathic psychotic disorder, yet little is known about the neurobiological substrates of WM in 22q11DS. In order to investigate the neural systems engaged during spatial WM in 22q11DS patients, we collected functional magnetic resonance imaging (fMRI) data while 41 participants (16 22q11DS patients, 25 demographically matched controls) performed a spatial capacity WM task that included manipulations of delay length and load level. Relative to controls, 22q11DS patients showed reduced neural activation during task performance in the intraparietal sulcus (IPS) and superior frontal sulcus (SFS). In addition, the typical increases in neural activity within spatial WM-relevant regions with greater memory load were not observed in 22q11DS. We further investigated whether neural dysfunction during WM was associated with behavioral WM performance, assessed via the University of Maryland letter-number sequencing (LNS) task, and positive psychotic symptoms, assessed via the Structured Interview for Prodromal Syndromes (SIPS), in 22q11DS patients. WM load activity within IPS and SFS was positively correlated with LNS task performance; moreover, WM load activity within IPS was inversely correlated with the severity of unusual thought content and delusional ideas, indicating that decreased recruitment of working memory-associated neural circuitry is associated with more severe positive symptoms. These results suggest that 22q11DS patients show reduced neural recruitment of brain regions critical for spatial WM function, which may be related to characteristic behavioral manifestations of the disorder

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Is social decision making for close others consistent across domains and within individuals?

Humans make decisions across a variety of social contexts. Though social decision-making research has blossomed in recent decades, surprisingly little is known about whether social decision-making preferences are consistent across different domains. We conducted an exploratory study in which participants made choices about 2 types of close others: parents and friends. To elicit decision making preferences, we pit the interests in parents and friends against one another. To assess the consistency of preferences for close others, decision making was assessed in three domains-risk taking, probabilistic learning, and self-other similarity judgments. We reasoned that if social decision-making preferences are consistent across domains, participants ought to exhibit the same preference in all three domains (i.e., a parent preference, based on prior work), and individual differences in preference magnitude ought to be conserved across domains within individuals. A combination of computational modeling, random coefficient regression, and traditional statistical tests revealed a robust parent-over-friend preference in the risk taking and probabilistic learning domains but not the self-other similarity domain. Preferences for parent-over-friend in the risk-taking domain were strongly associated with similar preferences in the probabilistic learning domain but not the self-other similarity domain. These results suggest that distinct and dissociable value-based and social-cognitive computations underlie social decision making. (PsycInfo Database Record (c) 2020 APA, all rights reserved)