Adaptive Mesh Refinement for Hyperbolic Systems based on Third-Order Compact WENO Reconstruction

In this paper we generalize to non-uniform grids of quad-tree type the Compact WENO reconstruction of Levy, Puppo and Russo (SIAM J. Sci. Comput., 2001), thus obtaining a truly two-dimensional non-oscillatory third order reconstruction with a very compact stencil and that does not involve mesh-dependent coefficients. This latter characteristic is quite valuable for its use in h-adaptive numerical schemes, since in such schemes the coefficients that depend on the disposition and sizes of the neighboring cells (and that are present in many existing WENO-like reconstructions) would need to be recomputed after every mesh adaption. In the second part of the paper we propose a third order h-adaptive scheme with the above-mentioned reconstruction, an explicit third order TVD Runge-Kutta scheme and the entropy production error indicator proposed by Puppo and Semplice (Commun. Comput. Phys., 2011). After devising some heuristics on the choice of the parameters controlling the mesh adaption, we demonstrate with many numerical tests that the scheme can compute numerical solution whose error decays as $\langle N\rangle^{-3}$, where $\langle N\rangle$ is the average number of cells used during the computation, even in the presence of shock waves, by making a very effective use of h-adaptivity and the proposed third order reconstruction.Comment: many updates to text and figure

A Multi-Objective Affinity-Based Savings Algorithm for Improving Processes in Centralized Warehousing Operations

Traditional approaches to improving material management processes in warehousing operations tend to focus on one of three major areas: facility design, order picking and sorting, and order batching. In an effort to improve total system savings, a new affinity function is developed and applied to batching logic to create a multi-objective problem. The proposed multi-objective function incorporates user input to increase adaptability to changing demand and flexibility to changing requirements. Computational experience shows the new function leads to solutions that deviate no more than 25% from the most efficient distance based picking route by the same batching logic, while creating savings in the sorting process at the centralized warehouse. The new function reduces savings loss from noncompliance of order pickers through its multi-objective design and is quick to respond to a rapidly changing climate by effective user input. The promising results of the proposed function open the door for additional objectives to be applied to the same logic to expand the objective to include goals like on-time performance

Attentional coordination in demonstrator-observer dyads facilitates learning and predicts performance in a novel manual task

Observational learning is a form of social learning in which a demonstrator performs a target task in the company of an observer, who may as a consequence learn something about it. In this study, we approach social learning in terms of the dynamics of coordination rather than the more common perspective of transmission of information. We hypothesised that observers must continuously adjust their visual attention relative to the demonstrator's time-evolving behaviour to benefit from it. We eye-tracked observers repeatedly watching videos showing a demonstrator solving one of three manipulative puzzles before attempting at the task. The presence of the demonstrator's face and the availability of his verbal instruction in the videos were manipulated. We then used recurrence quantification analysis to measure the dynamics of coordination between the overt attention of the observers and the demonstrator's manipulative actions. Bayesian hierarchical logistic regression was applied to examine (1) whether the observers' performance was predicted by such indexes of coordination, (2) how performance changed as they accumulated experience, and (3) if the availability of speech and intentional gaze of the demonstrator mediated it. Results showed that learners better able to coordinate their eye movements with the manipulative actions of the demonstrator had an increasingly higher probability of success in solving the task. The availability of speech was beneficial to learning, whereas the presence of the demonstrator's face was not. We argue that focusing on the dynamics of coordination between individuals may greatly improve understanding of the cognitive processes underlying social learning

Age-related differences during visual search: the role of contextual expectations and cognitive control mechanisms

We explored the efficacy of drawing pictures as an encoding strategy to enhance memory performance in healthy older adults and individuals with probable dementia. In an incidental encoding phase, participants were asked to either draw a picture or write out each word from a set of 30 common nouns for 40 seconds each. Episodic memory for the target words was compared in a group of healthy older adults to individuals with probable dementia (MMSE/MOCA range 4 to 25). In two experiments we showed that recall and recognition performance was higher for words that were drawn than written out during encoding, for both participant groups. We suggest that incorporating visuo-perceptual information into memory enhanced performance by increasing reliance on visual-sensory brain regions, which are relatively intact in these populations. Our findings demonstrate that drawing is a valuable technique leading to measurable gains in memory performance for individuals with probable dementia

Ruptures and repairs of group therapy alliance. an untold story in psychotherapy research

Although previous studies investigated the characteristics of therapeutic alliance in group treatments, there is still a dearth of research on group alliance ruptures and repairs. The model by Safran and Muran was originally developed to address therapeutic alliance in individual therapies, and the usefulness of this approach to group intervention needs to be demonstrated. Alliance ruptures are possible at member to therapist, member to member, member to group levels. Moreover, repairs of ruptures in group are quite complex, i.e., because other group members have to process the rupture even if not directly involved. The aim of the current study is to review the empirical research on group alliance, and to examine whether the rupture repair model can be a suitable framework for clinical understanding and research of the complexity of therapeutic alliance in group treatments. We provide clinical vignettes and commentary to illustrate theoretical and research aspects of therapeutic alliance rupture and repair in groups. Our colleague Jeremy Safran made a substantial contribution to research on therapeutic alliance, and the current paper illustrates the enduring legacy of this work and its potential application to the group therapy context

Eye-movements reveal semantic interference effects during the encoding of naturalistic scenes in long-term memory

Similarity-based semantic interference (SI) hinders memory recognition. Within long-term visual memory paradigms, the more scenes (or objects) from the same semantic category are viewed, the harder it is to recognize each individual instance. A growing body of evidence shows that overt attention is intimately linked to memory. However, it is yet to be understood whether SI mediates overt attention during scene encoding, and so explain its detrimental impact on recognition memory. In the current experiment, participants watched 372 photographs belonging to different semantic categories (e.g., a kitchen) with different frequency (4, 20, 40 or 60 images), while being eye-tracked. After 10 minutes, they were presented with the same 372 photographs plus 372 new photographs and asked whether they recognized (or not) each photo (i.e., old/new paradigm). We found that the more the SI, the poorer the recognition performance, especially for old scenes of which memory representations existed. Scenes more widely explored were better recognized, but for increasing SI, participants focused on more local regions of the scene in search for its potentially distinctive details. Attending to the centre of the display, or to scene regions rich in low-level saliency was detrimental to recognition accuracy, and as SI increased participants were more likely to rely on visual saliency. The complexity of maintaining faithful memory representations for increasing SI also manifested in longer fixation durations; in fact, a more successful encoding was also associated with shorter fixations. Our study highlights the interdependence between attention and memory during high-level processing of semantic information

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence based analyses to quantify the dynamical structure of single and multivariate time series and capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version of our crqa 2.0 package. This package includes implementations of several recent advances in recurrence based analysis, among them applications to multivariate data and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage