9,519 research outputs found

    The Influence of Neuroendocrine and Genetic Markers of Stress on Cognitive Processing and Intrusive Symptoms

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    This body of research investigated the influence of neuroendocrine and genetic elements of arousal on cognitive processes in the development of intrusive memories and flash-forward intrusions as related to Post-Traumatic Stress Disorder. Specifically, this thesis investigated various mechanisms that may underlie intrusive symptoms as postulated by prevalent theories of PTSD. Study 1 examined the distinctive relationship between peritraumatic dissociation and subsequent re-experiencing symptoms. Network analyses revealed strong positive edges between peritraumatic dissociation and subsequent amnesia, as well as the re-experiencing symptoms of physical reactivity to reminders, flashbacks, intrusions, and dreams, and to a lesser extent emotional numbness and hypervigilance. The finding that peritraumatic dissociation is related to subsequent re-experiencing symptoms is consistent with cognitive models that emphasize the role of dissociative experiences during a traumatic event in the etiology of PTSD re-experiencing symptoms. Study 2 aimed to determine whether peri-traumatic stress, as measured via salivary cortisol and salivary alpha-amylase, as well as pre-existing genetic polymorphisms on the FKBP5 gene increased dissociation and data-driven processing, and subsequently impacted intrusive memories related to a trauma film. The findings revealed that greater noradrenergic arousal predicted less intrusive memory distress in individuals who scored higher on data-driven processing and trait dissociation, and in FKBP5 low-risk carriers. For individuals who reported less data-driven processing and trait dissociation, and in FKBP5 high-risk carriers, as noradrenergic arousal increased, intrusive memory distress increased. This study also showed no association between data-driven processing with memory fragmentation, and fragmentation with intrusive memories. Whilst these findings support some aspect of cognitive models of PTSD as they indicate a role for data-driven processing and dissociation in intrusive symptoms, they highlight a threshold at which these variables stop moderating the relationship between arousal and intrusive memories and suggest that memory fragmentation is not related to intrusive memories. Study 3 examined the role of cognitive control in flash-forward intrusions in the context of an enduring stressor, the COVID-19 pandemic. In line with expectations, results showed that as cognitive control worsened, FKBP5 high-risk carriers reported more flash-forward distress, and low-risk carriers reported less distress. These findings are considered in the context of hippocampal changes and are consistent with emerging theories of PTSD. Lastly, study 4 sought to investigate the role of two neurological processes, pattern separation and pattern completion in intrusive memories in individuals with PTSD compared to trauma exposed controls. Consistent with existing literature, the data indicate that individuals with PTSD reported more data-driven processing, more intrusive symptoms, and demonstrated better behavioural pattern completion than trauma-exposed controls. These findings are in line with current cognitive models of PTSD, as they again indicate a role for data-driven processing in PTSD. However, study 4 found no support for the postulate that deficient pattern separation is a feature of PTSD and found an opposite effect for the role of pattern completion. Whilst these findings are inconsistent with theory, they are in line with existing experimental studies. Overall, the findings from this thesis provide insight into cognitive and biological models of PTSD and shed light on the mechanisms underlying the nature and development of intrusive symptoms

    Compact Twice Fusion Network for Edge Detection

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    The significance of multi-scale features has been gradually recognized by the edge detection community. However, the fusion of multi-scale features increases the complexity of the model, which is not friendly to practical application. In this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate multi-scale features while maintaining the compactness of the model. CTFN includes two lightweight multi-scale feature fusion modules: a Semantic Enhancement Module (SEM) that can utilize the semantic information contained in coarse-scale features to guide the learning of fine-scale features, and a Pseudo Pixel-level Weighting (PPW) module that aggregate the complementary merits of multi-scale features by assigning weights to all features. Notwithstanding all this, the interference of texture noise makes the correct classification of some pixels still a challenge. For these hard samples, we propose a novel loss function, coined Dynamic Focal Loss, which reshapes the standard cross-entropy loss and dynamically adjusts the weights to correct the distribution of hard samples. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and BIPEDv2. Compared with state-of-the-art methods, CTFN achieves competitive accuracy with less parameters and computational cost. Apart from the backbone, CTFN requires only 0.1M additional parameters, which reduces its computation cost to just 60% of other state-of-the-art methods. The codes are available at https://github.com/Li-yachuan/CTFN-pytorch-master.Comment: Manuscript submitted to a Springer journa

    The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures

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    Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation. Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents. In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI

    Exploring cognitive mechanisms involved in self-face recognition

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    Due to the own face being a significant stimulus that is critical to one’s identity, the own face is suggested to be processed in a quantitatively different (i.e., faster and better recognition) and qualitatively different (i.e., processed in a more featural manner) manner compared to other faces. This thesis further explored the cognitive mechanisms (perceptual and attentional systems) involved in the processing of the own face. Chapter 2 explored the role of holistic and featural processing involved in the processing of self-face (and other faces) with eye-tracking measures in a passive-viewing paradigm and a face identification task. In the passive-viewing paradigm, the own face was sampled in a more featural manner compared to other faces whereas when asked to identify faces, all faces were sampled in a more holistic manner. Chapter 3 further explored the role of holistic and featural processing in the identification of the own face using the three standard measures of holistic face processing: The face inversion task, the composite face task, and the part-whole task. Compared to other faces, individuals showed a smaller “holistic interference” by a task irrelevant bottom half for the own face in the composite face task and a stronger feature advantage for the own face, but inversion impaired the identification of all faces. These findings suggest that self-face is processed in a more featural manner, but the findings do not deny the role of holistic processing. The final experimental chapter, Chapter 4, explored the modulation effects of cultural differences in one’s self-concept (i.e., independent vs. interdependent self-concept) and a negative self-concept (i.e., depressive traits) on the attentional prioritization for the own face with a visual search paradigm. Findings showed that the attentional prioritization for the own face over an unfamiliar face is not modulated by cultural differences of one’s self-concept nor one’s level of depressive traits, and individuals showed no difference in the attentional prioritization for both the own face and friend’s face, demonstrating no processing advantage for the own face over a personally familiar face. These findings suggests that the attentional prioritization for the own face is better explained by a familiar face advantage. Altogether, the findings of this thesis suggest that the own face is processed qualitatively different compared to both personally familiar and unfamiliar face, with the own face being processed in a more featural manner. However, in terms of quantitative differences, the self-face is processed differently compared to an unfamiliar face, but not to a familiar face. Although the specific face processing strategies for the own face may be due to the distinct visual experience that one has with their face, the attentional prioritization of the own face is however, better explained by a familiar face advantage rather than a self-specificity effect

    Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles

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    This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets

    Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction

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    Saliency Prediction aims to predict the attention distribution of human eyes given an RGB image. Most of the recent state-of-the-art methods are based on deep image feature representations from traditional CNNs. However, the traditional convolution could not capture the global features of the image well due to its small kernel size. Besides, the high-level factors which closely correlate to human visual perception, e.g., objects, color, light, etc., are not considered. Inspired by these, we propose a Transformer-based method with semantic segmentation as another learning objective. More global cues of the image could be captured by Transformer. In addition, simultaneously learning the object segmentation simulates the human visual perception, which we would verify in our investigation of human gaze control in cognitive science. We build an extra decoder for the subtask and the multiple tasks share the same Transformer encoder, forcing it to learn from multiple feature spaces. We find in practice simply adding the subtask might confuse the main task learning, hence Multi-task Attention Module is proposed to deal with the feature interaction between the multiple learning targets. Our method achieves competitive performance compared to other state-of-the-art methods

    Deep Unrestricted Document Image Rectification

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    In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previously proposed DocTr, a transformer-assisted network for document image rectification, also suffers from this limitation. In this work, we present DocTr++, a novel unified framework for document image rectification, without any restrictions on the input distorted images. Our major technical improvements can be concluded in three aspects. Firstly, we upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. Secondly, we reformulate the pixel-wise mapping relationship between the unrestricted distorted document images and the distortion-free counterparts. The obtained data is used to train our DocTr++ for unrestricted document image rectification. Thirdly, we contribute a real-world test set and metrics applicable for evaluating the rectification quality. To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images. Extensive experiments are conducted, and the results demonstrate the effectiveness and superiority of our method. We hope our DocTr++ will serve as a strong baseline for generic document image rectification, prompting the further advancement and application of learning-based algorithms. The source code and the proposed dataset are publicly available at https://github.com/fh2019ustc/DocTr-Plus

    Computing Interpretable Representations of Cell Morphodynamics

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    Shape changes (morphodynamics) are one of the principal ways cells interact with their environments and perform key intrinsic behaviours like division. These dynamics arise from a myriad of complex signalling pathways that often organise with emergent simplicity to carry out critical functions including predation, collaboration and migration. A powerful method for analysis can therefore be to quantify this emergent structure, bypassing the low-level complexity. Enormous image datasets are now available to mine. However, it can be difficult to uncover interpretable representations of the global organisation of these heterogeneous dynamic processes. Here, such representations were developed for interpreting morphodynamics in two key areas: mode of action (MoA) comparison for drug discovery (developed using the economically devastating Asian soybean rust crop pathogen) and 3D migration of immune system T cells through extracellular matrices (ECMs). For MoA comparison, population development over a 2D space of shapes (morphospace) was described using two models with condition-dependent parameters: a top-down model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. A variety of landscapes were discovered, describing phenotype transitions during growth, and possible perturbations in the tip growth machinery that cause this variation were identified. For interpreting T cell migration, a new 3D shape descriptor that incorporates key polarisation information was developed, revealing low-dimensionality of shape, and the distinct morphodynamics of run-and-stop modes that emerge at minute timescales were mapped. Periodically oscillating morphodynamics that include retrograde deformation flows were found to underlie active translocation (run mode). Overall, it was found that highly interpretable representations could be uncovered while still leveraging the enormous discovery power of deep learning algorithms. The results show that whole-cell morphodynamics can be a convenient and powerful place to search for structure, with potentially life-saving applications in medicine and biocide discovery as well as immunotherapeutics.Open Acces
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