Graph edit distance from spectral seriation

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that they lack some of the formality and rigor of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that string matching techniques can be used. To do this, we use a graph spectral seriation method to convert the adjacency matrix into a string or sequence order. We show how the serial ordering can be established using the leading eigenvector of the graph adjacency matrix. We pose the problem of graph-matching as a maximum a posteriori probability (MAP) alignment of the seriation sequences for pairs of graphs. This treatment leads to an expression in which the edit cost is the negative logarithm of the a posteriori sequence alignment probability. We compute the edit distance by finding the sequence of string edit operations which minimizes the cost of the path traversing the edit lattice. The edit costs are determined by the components of the leading eigenvectors of the adjacency matrix and by the edge densities of the graphs being matched. We demonstrate the utility of the edit distance on a number of graph clustering problems

A graph-spectral approach to shape-from-shading

In this paper, we explore how graph-spectral methods can be used to develop a new shape-from-shading algorithm. We characterize the field of surface normals using a weight matrix whose elements are computed from the sectional curvature between different image locations and penalize large changes in surface normal direction. Modeling the blocks of the weight matrix as distinct surface patches, we use a graph seriation method to find a surface integration path that maximizes the sum of curvature-dependent weights and that can be used for the purposes of height reconstruction. To smooth the reconstructed surface, we fit quadrics to the height data for each patch. The smoothed surface normal directions are updated ensuring compliance with Lambert's law. The processes of height recovery and surface normal adjustment are interleaved and iterated until a stable surface is obtained. We provide results on synthetic and real-world imagery

Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering

This paper describes work aimed at the unsupervised learning of shape-classes from shock trees. We commence by considering how to compute the edit distance between weighted trees. We show how to transform the tree edit distance problem into a series of maximum weight clique problems, and show how to use relaxation labeling to find an approximate solution. This allows us to compute a set of pairwise distances between graph-structures. We show how the edit distances can be used to compute a matrix of pairwise affinities using χ² statistics. We present a maximum likelihood method for clustering the graphs by iteratively updating the elements of the affinity matrix. This involves interleaved steps for updating the affinity matrix using an eigendecomposition method and updating the cluster membership indicators. We illustrate the new tree clustering framework on shock-graphs extracted from the silhouettes of 2D shapes

A shared fractal aesthetic across development

8 pagesFractal patterns that repeat at varying size scales comprise natural environments and are also present in artistic works deemed to be highly aesthetic. Observers’ aesthetic preferences vary in relation to fractal complexity. Previous work demonstrated that fractal preference consistently peaks at low-to-moderate complexity for patterns that repeat in a statistical manner across scale, whereas preference for exact repetition fractals peaks at a higher complexity due to the presence of order introduced by symmetry and exact recursion of features. However, these highly consistent preference trends have been demonstrated only in adult populations, and the extent to which exposure, development, or individual differences in perceptual strategies may impact preference has not yet been established. Here, we show differences in preference between fractal-type, but no differences between child and adult preferences, and no relationship between systemizing tendencies (demonstrated by the Systemizing Quotient and Ponzo task) and complexity preferences, further supporting the universality of fractal preference. Consistent preferences across development point toward shared general aesthetic experience of these complexities arising from a fluency of fractal processing established relatively early in development. This in part determines how humans experience natural patterns and interact with natural and built environments

Perceived Stress, Resilience, and Wellbeing in Seasoned Isha Yoga Practitioners Compared to Matched Controls During the COVID-19 Pandemic

Background: Yoga practices, including breathing, meditation, and posture protocols (asanas), have been shown to facilitate physical and mental wellbeing. Methods: Seasoned yoga practitioners were recruited from the Isha Foundation. Recruitment of the comparison group was achieved using snowball sampling and were not yoga practitioners. Participants in the non-yoga group were randomized to a 3-min Isha practice or a comparator group asked to perform 15-min of daily reading. Participants completed a series of web-based surveys (REDCap) at baseline, 6, and 12 weeks. These surveys include validated scales and objective questions on COVID-19 infection and medical history. The validated questionnaires assess for: perceived stress (PSS), mood states [anxiety and depression (PHQ-4), joy (DPES-Joy subscale)], mindfulness attention and awareness (MAAS), resilience (BRS), mental wellbeing (WEMWBS) and recovery from traumatic event (PTGI). Weekly activity diaries were employed as a tool for collecting compliance information from study participants. Perceived stress scale scores were identified as primary outcome for this study. Findings: The median Perceived Stress Scale (PSS) score for the yoga practitioners compared to the active and placebo comparators was significantly lower at all time-points: baseline: 11 [IQR 7–15] vs. 16 [IQR 12–21] in both the active and placebo comparators (p \u3c 0.0001); 6 weeks: 9 [IQR 6–13] vs. 12 [IQR 8–17] in the active comparator and 14 [IQR 9–18] in the placebo comparator (p \u3c 0.0001); and 12 weeks: 9 [IQR 5–13] vs. 11.5 [IQR 8–16] in the active comparators and 13 [IQR 8–17] in the placebo comparator (p \u3c 0.0001). Among the randomized participants that were compliant for the full 12 weeks, the active comparators had significantly lower median PSS scores than the placebo comparators 12 weeks [10 (IQR 5–14) vs. 13 (IQR 8–17), p = 0.017]. Further, yoga practitioners had significantly lower anxiety at all three-time points (p \u3c 0.0001), lower depression at baseline and 6 weeks (p \u3c 0.0003), and significantly higher wellbeing (p \u3c 0.0001) and joy (p \u3c 0.0001) at all three-time points, compared to the active and placebo comparator groups. Interpretation: The lower levels of stress, anxiety, depression, and higher level of wellbeing and joy seen in the yoga practitioners compared to the active and placebo comparators illustrate the impact of regular yoga practices on mental health even during the pandemic

Photometric stereo for 3D face reconstruction using non-linear illumination models

Face recognition in presence of illumination changes, variant pose and different facial expressions is a challenging problem. In this paper, a method for 3D face reconstruction using photometric stereo and without knowing the illumination directions and facial expression is proposed in order to achieve improvement in face recognition. A dimensionality reduction method was introduced to represent the face deformations due to illumination variations and self shadows in a lower space. The obtained mapping function was used to determine the illumination direction of each input image and that direction was used to apply photometric stereo. Experiments with faces were performed in order to evaluate the performance of the proposed scheme. From the experiments it was shown that the proposed approach results very accurate 3D surfaces without knowing the light directions and with a very small differences compared to the case of known directions. As a result the proposed approach is more general and requires less restrictions enabling 3D face recognition methods to operate with less data

JIP1-Mediated JNK Activation Negatively Regulates Synaptic Plasticity and Spatial Memory

The c-Jun N-terminal kinase (JNK) signal transduction pathway is implicated in learning and memory. Here, we examined the role of JNK activation mediated by the JIP1 scaffold protein. We compared male wild-type mice with a mouse model harboring a point mutation in the Jip1 gene that selectively blocks JIP1-mediated JNK activation. These male mutant mice exhibited increased NMDA receptor currents, increased NMDA receptor-mediated gene expression, and a lower threshold for induction of hippocampal long-term potentiation. The JIP1 mutant mice also displayed improved hippocampus-dependent spatial memory and enhanced associative fear conditioning. These results were confirmed using a second JIP1 mutant mouse model that suppresses JNK activity. Together, these observations establish that JIP1-mediated JNK activation contributes to the regulation of hippocampus-dependent, NMDA receptor-mediated synaptic plasticity and learning. SIGNIFICANCE STATEMENT: The results of this study demonstrate that JNK activation induced by the JIP1 scaffold protein negatively regulates the threshold for induction of long-term synaptic plasticity through the NMDA-type glutamate receptor. This change in plasticity threshold influences learning. Indeed, mice with defects in JIP1-mediated JNK activation display enhanced memory in hippocampus-dependent tasks, such as contextual fear conditioning and Morris water maze, indicating that JIP1-JNK constrains spatial memory. This study reports the identification of JIP1-mediated JNK activation as a novel molecular pathway that negatively regulates NMDA receptor-dependent synaptic plasticity and memory