aZIBO Shape Descriptor for Monitoring Tool Wear in Milling

El objetivo de este trabajo es estimar eficientemente el desgaste del mecanizado de metales y mejorar las operaciones de sustitución de la herramienta. El procesamiento de imágenes y la clasificación se utilizan para automatizar la toma de decisiones sobre el tiempo adecuado para el reemplazo dela herramienta. Específicamente, el descriptor de forma aZIBO (momentos absolutos de Zernike con orientación de contorno invariable) se ha utilizado para caracterizar el desgaste de la plaquita y garantizar su uso óptimo. Se ha creado un conjunto de datos compuesto por 577 regiones con diferentes niveles de desgaste. Se han llevado a cabo dos procesos de clasificación diferentes: el primero con tres clases diferentes (desgaste bajo, medio y alto -L, M y H, respectivamente) y el segundo con sólo dos clases: Low (L) y High (H). La clasificación se llevó a cabo utilizando por un lado kNN con cinco distancias diferentes y cinco valores de k y, por otra parte, una máquina de vectores de soporte (SVM). El rendimiento de aZIBO se ha comparado con descriptores de forma clásicos como los momentos de Hu y Flusser. Los supera, obteniendo tasas de éxito de hasta el 91,33% para la clasificación L-H y 90,12% para la clasificación L-M-H

Distinct patterns of functional and effective connectivity between perirhinal cortex and other cortical regions in recognition memory and perceptual discrimination.

Traditionally, the medial temporal lobe (MTL) is thought to be dedicated to declarative memory. Recent evidence challenges this view, suggesting that perirhinal cortex (PrC), which interfaces the MTL with the ventral visual pathway, supports highly integrated object representations in recognition memory and perceptual discrimination. Even with comparable representational demands, perceptual and memory tasks differ in numerous task demands and the subjective experience they evoke. Here, we tested whether such differences are reflected in distinct patterns of connectivity between PrC and other cortical regions, including differential involvement of prefrontal control processes. We examined functional magnetic resonance imaging data for closely matched perceptual and recognition memory tasks for faces that engaged right PrC equivalently. Multivariate seed analyses revealed distinct patterns of interactions: Right ventrolateral prefrontal and posterior cingulate cortices exhibited stronger functional connectivity with PrC in recognition memory; fusiform regions were part of the pattern that displayed stronger functional connectivity with PrC in perceptual discrimination. Structural equation modeling revealed distinct patterns of effective connectivity that allowed us to constrain interpretation of these findings. Overall, they demonstrate that, even when MTL structures show similar involvement in recognition memory and perceptual discrimination, differential neural mechanisms are reflected in the interplay between the MTL and other cortical regions

An fMRI investigation of the relationship between future imagination and cognitive flexibility

While future imagination is largely considered to be a cognitive process grounded in default mode network activity, studies have shown that future imagination recruits regions in both default mode and frontoparietal control networks. In addition, it has recently been shown that the ability to imagine the future is associated with cognitive flexibility, and that tasks requiring cognitive flexibility result in increased coupling of the default mode network with frontoparietal control and salience networks. In the current study, we investigated the neural correlates underlying the association between cognitive flexibility and future imagination in two ways. First, we experimentally varied the degree of cognitive flexibility required during future imagination by manipulating the disparateness of episodic details contributing to imagined events. To this end, participants generated episodic details (persons, locations, objects) within three social spheres; during fMRI scanning they were presented with sets of three episodic details all taken from the same social sphere (Congruent condition) or different social spheres (Incongruent condition) and required to imagine a future event involving the three details. We predicted that, relative to the Congruent condition, future simulation in the Incongruent condition would be associated with increased activity in regions of the default mode, frontoparietal and salience networks. Second, we hypothesized that individual differences in cognitive flexibility, as measured by performance on the Alternate Uses Task, would correspond to individual differences in the brain regions recruited during future imagination. A task partial least squares (PLS) analysis showed that the Incongruent condition resulted in an increase in activity in regions in salience networks (e.g. the insula) but, contrary to our prediction, reduced activity in many regions of the default mode network (including the hippocampus). A subsequent functional connectivity (within-subject seed PLS) analysis showed that the insula exhibited increased coupling with default mode regions during the Incongruent condition. Finally, a behavioral PLS analysis showed that individual differences in cognitive flexibility were associated with differences in activity in a number of regions from frontoparietal, salience and default-mode networks during both future imagination conditions, further highlighting that the cognitive flexibility underlying future imagination is grounded in the complex interaction of regions in these networks

Representation of faces in perirhinal cortex

The prevailing view of medial temporal lobe (MTL) functioning holds that its structures are dedicated to long-term declarative memory. Recent evidence challenges this view, suggesting that perirhinal cortex (PrC), which interfaces the MTL with the ventral visual pathway, supports highly integrated object representations that contribute to both recognition memory and perceptual discrimination. Here, I used functional magnetic resonance imaging to examine PrC activity, as well as its broader functional connectivity, during perceptual and mnemonic tasks involving faces, a stimulus class proposed to rely on integrated representations for discrimination. In Chapter 2, I revealed that PrC involvement was related to task demands that emphasized face individuation. Discrimination under these conditions is proposed to benefit from the uniqueness afforded by highly-integrated stimulus representations. Multivariate partial least squares analyses revealed that PrC, the fusiform face area (FFA), and the amygdala were part of a pattern of regions exhibiting preferential activity for tasks emphasizing stimulus individuation. In Chapter 3, I provided evidence of resting-state connectivity between face-selective aspects of PrC, the FFA, and amygdala. These findings point to a privileged functional relationship between these regions, consistent with task-related co- recruitment revealed in Chapter 2. In addition, the strength of resting-state connectivity was related to behavioral performance on a face discrimination task. These results suggest a mechanism by which PrC may participate in the representation of faces. In Chapter 4, I examined PrC connectivity during task contexts. I provided evidence that distinctions between tasks emphasizing recognition memory and perceptual discrimination demands are better reflected in the connectivity of PrC with other regions in the brain, rather than in the presence or absence of PrC activity. Further, this functional connectivity was related to behavioral performance for the memory task. Together, these findings indicate that mnemonic demands are not the sole arbiter of PrC involvement, counter to the prevailing view of MTL functioning. Instead, they highlight the importance of connectivity-based approaches in elucidating the contributions of PrC, and point to a role of PrC in the representation of faces in a manner that can support memory and perception, and that may apply to other object categories more broadly

Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions

Tool wear monitoring in milling using aZIBO shape descriptor

En este documento, se lleva a cabo un proceso de monitoreo del desgaste de la herramienta para determinar la condición de desgaste y para asegurar el uso óptimo de las herramientas antes de su reemplazo durante las operaciones de mecanizado de metales. El conjunto de datos se compone de 53 inserciones de corte. Todos ellos fueron preprocesados y el desgaste de los bordes fue segmentado, resultando 212 bordes establecidos. Para describir la forma de desgaste, se usó un descriptor de forma aZIBO y sus resultados se compararon con dos descriptores clásicos, Hu y Flusser. La clasificación se llevó a cabo utilizando kNN con 1, 3, 5, 7, 9 y 11 vecinos y seis distancias: Cosine, Euclidean, IntersetcDist, ChiSquare, SqDist y Cityblock. Se han llevado a cabo dos clasificaciones: una de ellas con tres clases diferentes (baja, media y alta wexar -L, M y H, respectivamente) y la otra con solo dos clases: baja (L) y alta (H). El descriptor aZIBO ofrece mejores resultados que los clásicos, con una tasa de aciertos del 60,84% y un 81,13% utilizando las etiquetas L-M-H y L-H, respectivamente

SIFT applied to CBIR

Content-Based Image Retrieval (CBIR) is a challenging task. Common approaches use only low-level features. Notwithstanding, such CBIR solutions fail on capturing some local features representing the details and nuances of scenes. Many techniques in image processing and computer vision can capture these scene semantics. Among them, the Scale Invariant Features Transform~(SIFT) has been widely used in a lot of applications. This approach relies on the choice of several parameters which directly impact its effectiveness when applied to retrieve images. In this paper, we discuss the results obtained in several experiments proposed to evaluate the application of the SIFT in CBIR tasks

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semi-supervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) i2015FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP [2013/08645-0, 2013/50169-1]CNPq [306580/2012-8, 484254/2012-0]2013/08645-0; 2013/50169-1306580/2012-8;484254/2012-0SEM INFORMAÇÃ

Adaptive Motor Imagery: A Multimodal Study of Immobilization-Induced Brain Plasticity.

The consequences of losing the ability to move a limb are traumatic. One approach that examines the impact of pathological limb nonuse on the brain involves temporary immobilization of a healthy limb. Here, we investigated immobilization-induced plasticity in the motor imagery (MI) circuitry during hand immobilization. We assessed these changes with a multimodal paradigm, using functional magnetic resonance imaging (fMRI) to measure neural activation, magnetoencephalography (MEG) to track neuronal oscillatory dynamics, and transcranial magnetic stimulation (TMS) to assess corticospinal excitability. fMRI results show a significant decrease in neural activation for MI of the constrained hand, localized to sensorimotor areas contralateral to the immobilized hand. MEG results show a significant decrease in beta desynchronization and faster resynchronization in sensorimotor areas contralateral to the immobilized hand. TMS results show a significant increase in resting motor threshold in motor cortex contralateral to the constrained hand, suggesting a decrease in corticospinal excitability in the projections to the constrained hand. These results demonstrate a direct and rapid effect of immobilization on MI processes of the constrained hand, suggesting that limb nonuse may not only affect motor execution, as evidenced by previous studies, but also MI. These findings have important implications for the effectiveness of therapeutic approaches that use MI as a rehabilitation tool to ameliorate the negative effects of limb nonuse

Parallel Regulation of Memory and Emotion Supports the Suppression of Intrusive Memories.

Intrusive memories often take the form of distressing images that emerge into a person's awareness, unbidden. A fundamental goal of clinical neuroscience is to understand the mechanisms allowing people to control these memory intrusions and reduce their emotional impact. Mnemonic control engages a right frontoparietal network that interrupts episodic retrieval by modulating hippocampal activity; less is known, however, about how this mechanism contributes to affect regulation. Here we report evidence in humans (males and females) that stopping episodic retrieval to suppress an unpleasant image triggers parallel inhibition of mnemonic and emotional content. Using fMRI, we found that regulation of both mnemonic and emotional content was driven by a shared frontoparietal inhibitory network and was predicted by a common profile of medial temporal lobe downregulation involving the anterior hippocampus and the amygdala. Critically, effective connectivity analysis confirmed that reduced amygdala activity was not merely an indirect consequence of hippocampal suppression; rather, both the hippocampus and the amygdala were targeted by a top-down inhibitory control signal originating from the dorsolateral prefrontal cortex. This negative coupling was greater when unwanted memories intruded into awareness and needed to be purged. Together, these findings support the broad principle that retrieval suppression is achieved by regulating hippocampal processes in tandem with domain-specific brain regions involved in reinstating specific content, in an activity-dependent fashion.SIGNIFICANCE STATEMENT Upsetting events sometimes trigger intrusive images that cause distress and that may contribute to psychiatric disorders. People often respond to intrusions by suppressing their retrieval, excluding them from awareness. Here we examined whether suppressing aversive images might also alter emotional responses to them, and the mechanisms underlying such changes. We found that the better people were at suppressing intrusions, the more it reduced their emotional responses to suppressed images. These dual effects on memory and emotion originated from a common right prefrontal cortical mechanism that downregulated the hippocampus and amygdala in parallel. Thus, suppressing intrusions affected emotional content. Importantly, participants who did not suppress intrusions well showed increased negative affect, suggesting that suppression deficits render people vulnerable to psychiatric disorders