Do children use different forms of verbal rehearsal in serial picture recall tasks? A multi-method study

Use of verbal rehearsal is a key issue in memory development. However, we still lack detailed and triangulated information about the early development and the circumstances in which different forms of rehearsal are used. To further understand significant factors that affect children’s use of various forms of rehearsal, the present study involving 108 primary school children adopted a multi-method approach. It combined a carefully chosen word length effect method with a self-paced presentation time method to obtain behavioural indicators of verbal rehearsal. In addition, subsequent trial-by-trial self-reports were gathered. Word length effects in recall suggested that phonological recoding (converting images to names - a necessary precursor for rehearsal) took place, with evidence of more rehearsal among children with higher performance levels. According to self-paced presentation times, cumulative rehearsal was the dominant form of rehearsal only for children with higher spans on difficult trials. The combined results of self-paced times and word length effects in recall suggest that ‘naming’ as simple form of rehearsal was dominant for most children. Self-reports were in line with these conclusions. Additionally, children used a mixture of strategies with considerable intra-individual variability, yet strategy use was nevertheless linked to age as well as performance levels

Graph Distillation for Action Detection with Privileged Modalities

We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available. Common methods in transfer learning do not take advantage of the extra modalities potentially available in the source domain. On the other hand, previous work on multimodal learning only focuses on a single domain or task and does not handle the modality discrepancy between training and testing. In this work, we propose a method termed graph distillation that incorporates rich privileged information from a large-scale multimodal dataset in the source domain, and improves the learning in the target domain where training data and modalities are scarce. We evaluate our approach on action classification and detection tasks in multimodal videos, and show that our model outperforms the state-of-the-art by a large margin on the NTU RGB+D and PKU-MMD benchmarks. The code is released at http://alan.vision/eccv18_graph/.Comment: ECCV 201

Inadvertent Disclosure, the Attorney-Client Privilege, and Legal Ethics: An Examination and Suggestion for Alaska

Lantmäteriets stödenhet Utveckling och IT (UIT) ska inför ett mer agilt arbetssätt. Den agila arbetsmetoden ifråga är Lean Thinking, den västerländska adaptionen av Toyota Production System. För att underlätta vid en övergång till det nya arbetssättet, kartläggs de inledande faserna i projektarbetet, då dessa saknar specifika anvisningar i den nuvarande projektstyrningsmodellen, Praktisk projektstyrning (PPS). Utifrån kartläggningen utfördes så en tvåstegsanalys, den första utifrån ett distribuerat kognitivt perspektiv och sedan utifrån de fem huvudprinciper från Lean Thinking. Resultatet från analysen resulterade sedan i ett antal förändringspunkter som UIT kan använda sig av vid bytet av arbetssätt. Dessa punkter inkluderar förslag att utnyttja digitala systems potential, skapa ett kontinuerligt flöde i arbetsprocessen genom att minska antalet pågående arbetsuppgifter på en kanbantavla samt att projektgruppen har en medvetenhet om de olika system som används förmåga att hålla information aktuell

Network constraints on learnability of probabilistic motor sequences

Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.Comment: 29 pages, 4 figure

Guest Editorial Special Issue on Recent Advances in Theory, Methodology, and Applications of Imbalanced Learning

Imbalanced learning is a challenging task in machine learning, faced by practitioners, and intensively investigated by researchers from a wide range of communities. However, as pointed out in the book titled “ Imbalanced Learning: Foundations, Algorithms, and Applications ” and collectively authored by experts in the field, many if not most of the approaches to imbalanced learning are heuristic and ad hoc in nature, hence leaving many questions unanswered. To fill this gap, the aim of this Special Issue is to collect recent research works that focus on the theory, methodology, and applications of imbalanced learning. After carefully reviewing a large number of submissions, we selected 15 works to be included in this Special Issue. These works can be roughly categorized into three types: deep-learning-based methods (6), methods based on other machine-learning paradigms (7), and empirical comparative studies (2)