A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and generate scenarios of trajectories with different lengths, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us the flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of autonomous driving. Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, to obtain further insights into the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection

Combination of deep behavioral phenotyping with brain region and cell type specific manipulations of FKBP51

The increasing prevalence of stress-related disorders, such as major depressive disorder (MDD) has become a significant global concern, with devastating effects on individuals' personal lives and societal well-being. The exposure to severe and chronic stressors is a major risk factor for the development of such disorders, and recent traumatic events have further exacerbated this mental health crisis. The susceptibility to MDD is determined by a complex interplay of genetic, epigenetic, and environmental factors. One specific gene of significance in this context is FKBP5 (Fkbp5 in rodents), encoding the co-chaperone FK506 binding protein 51 (FKBP51). The interplay between severe stress exposure and genetic risk variants of FKBP5 has been associated with an increased vulnerability to psychopathology. A significant symptom observed in individuals with MDD is social dysfunction, characterized by the avoidance of social interactions and the display of maladaptive behaviors, such as aggression or irritability. However, traditional preclinical assessment methods for stress-induced behavioral symptoms, such as social aversion, have faced criticism due to their reductionistic nature, often failing to capture ethologically relevant behavioral constructs. Advancements in high-throughput pose estimation tools have provided opportunities for comprehensive behavioral analysis through automatically annotated behavioral assessments. This thesis explores various tools for automatically annotated behavioral assessment in preclinical psychiatry research, employing both supervised classification and unsupervised clustering strategies. Applying the newly established ad validated deep phenotyping methods, the thesis further investigates the brain region and cell type specific role of FKBP51 across different stress models and uncovers the underlying neurobiological mechanisms and behavioral profiles using automatically annotated behavioral assessment. The effectiveness of both supervised classification and unsupervised clustering strategies is demonstrated in characterizing individual and social behavioral profiles in mice subjected to various stress conditions. Moreover, the thesis highlights the distinct sex-specific effects of different stress paradigms on the regulation of the hypothalamic-pituitary-adrenal (HPA) axis, including the expression of Fkbp5 in several stress-related brain regions, in particular the Locus Coeruleus (LC). Taken together, the current thesis emphasizes the importance of brain region and cell type specific regulation of Fkbp5 and underscores the benefits of automatically annotated behavioral assessment tools. This is put into perspective with future research prospects, advocating for the integration of diverse data modalities, such as in vivo measurements of stress mediators and neuronal activity recordings. This integrated approach aims to enhance our understanding of complex behaviors and the underlying molecular mechanisms. Ultimately, this can contribute to a better comprehension of the behavioral phenotypes and associated neurobiological alterations in stress-related disorders. These insights hold potential to facilitate the development of novel treatments for psychiatric disorders

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

We propose a unified deep learning framework for generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. In order to model and generate scenarios of trajectories with different length, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of self-driving cars. Second, we develop an architecture based on Recurrent Autoencoder with GANs in order to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, in order to obtain further insights on the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table