PALMER: Perception-Action Loop with Memory for Long-Horizon Planning

To achieve autonomy in a priori unknown real-world scenarios, agents should be able to: i) act from high-dimensional sensory observations (e.g., images), ii) learn from past experience to adapt and improve, and iii) be capable of long horizon planning. Classical planning algorithms (e.g. PRM, RRT) are proficient at handling long-horizon planning. Deep learning based methods in turn can provide the necessary representations to address the others, by modeling statistical contingencies between observations. In this direction, we introduce a general-purpose planning algorithm called PALMER that combines classical sampling-based planning algorithms with learning-based perceptual representations. For training these perceptual representations, we combine Q-learning with contrastive representation learning to create a latent space where the distance between the embeddings of two states captures how easily an optimal policy can traverse between them. For planning with these perceptual representations, we re-purpose classical sampling-based planning algorithms to retrieve previously observed trajectory segments from a replay buffer and restitch them into approximately optimal paths that connect any given pair of start and goal states. This creates a tight feedback loop between representation learning, memory, reinforcement learning, and sampling-based planning. The end result is an experiential framework for long-horizon planning that is significantly more robust and sample efficient compared to existing methods.Comment: Website: https://palmer.epfl.c

Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI

Purpose: To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated Magnetic Resonance Imaging (MRI) data. Methods: Scan-Specific Artifact Reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to GRAPPA and demonstrates improved robustness over other scan-specific models, such as RAKI and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave-encoded images. Results: SPARK yields 1.5x - 2x RMSE reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan-specific techniques. When applied to advanced reconstruction techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves performance by ~2x and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non-cartesian 2D and 3D wave-encoding imaging by reducing RMSE between 20-25% and providing qualitative improvements. Conclusion: SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space

Dostluktan ortaklığa giden yol : Sezai Türkeş-Fevzi Akkaya (STFA)

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2016.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Çekiç, Can Eyup

Development of light and pH-dual responsive self-quenching theranostic SPION to make EGFR overexpressing micro tumors glow and destroy

Drug resistant and undetectable tumors easily escape treatment leading metastases and/or recurrence of the lethal disease. Therefore, it is vital to diagnose and destroy micro tumors using simple yet novel approaches. Here, we present fluorescence-based detection and light-based destruction of cancer cells that are known to be resistant to standard therapies. We developed a superparamagnetic iron oxide nanoparticle (SPION)-based theranostic agent that is composed of self-quenching light activated photosensitizer (BPD) and EGFR targeting ligand (Anti-EGFR ScFv or GE11 peptide). Photosensitizer (BPD) was immobilized to PEG-PEI modified SPION with acid-labile linker. Prior to stimulation of the theranostic system by light its accumulation within cancer cells is vital since BPD phototoxicity and fluorescence is activated by lysosomal proteolysis. As BPD is cleaved, the system switches from off to on position which triggers imaging and therapy. Targeting, therapeutic and diagnostic features of the theranostic system were evaluated in high and moderate level EGFR expressing pancreatic cancer cell lines. Our results indicate that the system distinguishes high and moderate EGFR expression levels and yields up to 4.3-fold increase in intracellular fluorescence intensity. Amplification of fluorescence signal was as low as 1.3-fold in the moderate or no EGFR expressing cell lines. Anti-EGFR ScFv targeted SPION caused nearly 2-fold higher cell death via apoptosis in high EGFR expressing Panc-1 cell line. The developed system, possessing advanced targeting, enhanced imaging and effective therapeutic features, is a promising candidate for multi-mode detection and destruction of residual drug-resistant cancer cells