A Real-time Rate-distortion Oriented Joint Video Denoising and Compression Algorithm

This thesis proposes a real-time video denoising filter, a joint pre-filtering and compression algorithm, and a joint in-loop filtering and compression algorithm. A real-time video denoising filter: a great number of digital video applications motivate the research in restoration or enhancement methods to improve the visual quality in the presence of noise. Video Block-Matching and 3D collaborative filter, abbreviated as VBM3D, is one of the best current video denoising filters. We accelerate this filter for real-time applications by simplifying the algorithm as well as optimizing the codes, while preserving its good denoising performance. A joint pre-filtering and compression algorithm: pre-filtering and compression are two separate processes in traditional systems and they do not guarantee optimal filtering and quantization parameters with respect to rate-distortion framework. We propose a joint approach with pre-filtering by VBM3D and compression by H.264/AVC. For each quantization parameter, it jointly selects the optimal filtering parameter among the provided filtering parameters. Results show that this approach enhances the performance of H.264/AVC by improving subjective visual quality and using less bitrates. A joint in-loop filtering and compression algorithm: in traditional video in-loop filtering and compression systems, a deblocking filter is employed in both the encoder and decoder. However, besides blocking artifacts, videos may contain other types of noise. In order to remove other types of noise, we add a real-time filter as an enhancing part in the H.264/AVC codec after the deblocking filter. Experiments illustrate that the proposed algorithm improves the compression performance of the H.264/AVC standard by providing frames with increased PSNR values and less bitrates. /Kir1

Camera Pose Estimation from Street-view Snapshots and Point Clouds

This PhD thesis targets on two research problems: (1) How to efficiently and robustly estimate the camera pose of a query image with a map that contains street-view snapshots and point clouds; (2) Given the estimated camera pose of a query image, how to create meaningful and intuitive applications with the map data. To conquer the first research problem, we systematically investigated indirect, direct and hybrid camera pose estimation strategies. We implemented state-of-the-art methods and performed comprehensive experiments in two public benchmark datasets considering outdoor environmental changes from ideal to extremely challenging cases. Our key findings are: (1) the indirect method is usually more accurate than the direct method when there are enough consistent feature correspondences; (2) The direct method is sensitive to initialization, but under extreme outdoor environmental changes, the mutual-information-based direct method is more robust than the feature-based methods; (3) The hybrid method combines the strength from both direct and indirect method and outperforms them in challenging datasets. To explore the second research problem, we considered inspiring and useful applications by exploiting the camera pose together with the map data. Firstly, we invented a 3D-map augmented photo gallery application, where images’ geo-meta data are extracted with an indirect camera pose estimation method and photo sharing experience is improved with the augmentation of 3D map. Secondly, we designed an interactive video playback application, where an indirect method estimates video frames’ camera pose and the video playback is augmented with a 3D map. Thirdly, we proposed a 3D visual primitive based indoor object and outdoor scene recognition method, where the 3D primitives are accumulated from the multiview images

Hedgehog Signaling Antagonist GDC-0449 (Vismodegib) Inhibits Pancreatic Cancer Stem Cell Characteristics: Molecular Mechanisms

Recent evidence from in vitro and in vivo studies has demonstrated that aberrant reactivation of the Sonic Hedgehog (SHH) signaling pathway regulates genes that promote cellular proliferation in various human cancer stem cells (CSCs). Therefore, the chemotherapeutic agents that inhibit activation of Gli transcription factors have emerged as promising novel therapeutic drugs for pancreatic cancer. GDC-0449 (Vismodegib), orally administrable molecule belonging to the 2-arylpyridine class, inhibits SHH signaling pathway by blocking the activities of Smoothened. The objectives of this study were to examine the molecular mechanisms by which GDC-0449 regulates human pancreatic CSC characteristics in vitro.GDC-0499 inhibited cell viability and induced apoptosis in three pancreatic cancer cell lines and pancreatic CSCs. This inhibitor also suppressed cell viability, Gli-DNA binding and transcriptional activities, and induced apoptosis through caspase-3 activation and PARP cleavage in pancreatic CSCs. GDC-0449-induced apoptosis in CSCs showed increased Fas expression and decreased expression of PDGFRα. Furthermore, Bcl-2 was down-regulated whereas TRAIL-R1/DR4 and TRAIL-R2/DR5 expression was increased following the treatment of CSCs with GDC-0449. Suppression of both Gli1 plus Gli2 by shRNA mimicked the changes in cell viability, spheroid formation, apoptosis and gene expression observed in GDC-0449-treated pancreatic CSCs. Thus, activated Gli genes repress DRs and Fas expressions, up-regulate the expressions of Bcl-2 and PDGFRα and facilitate cell survival.These data suggest that GDC-0499 can be used for the management of pancreatic cancer by targeting pancreatic CSCs

Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving

Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360{\deg} perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large-scale and diverse multimodal dataset collected over two years in various European countries, covering an area 9x that of existing datasets. ZOD boasts the highest range and resolution sensors among comparable datasets, coupled with detailed keyframe annotations for 2D and 3D objects (up to 245m), road instance/semantic segmentation, traffic sign recognition, and road classification. We believe that this unique combination will facilitate breakthroughs in long-range perception and multi-task learning. The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping. Frames consist of 100k curated camera images with two seconds of other supporting sensor data, while the 1473 Sequences and 29 Drives include the entire sensor suite for 20 seconds and a few minutes, respectively. ZOD is the only large-scale AD dataset released under a permissive license, allowing for both research and commercial use. More information, and an extensive devkit, can be found at https://zod.zenseact.comComment: International Conference on Computer Vision (ICCV) 202

Photoinduced coupled twisted intramolecular charge transfer and excited-state proton transfer via intermolecular hydrogen bonding: a DFT/TD-DFT study

We discuss theoretically the geometric and electronic structure properties of the thiazolidinedione derivative A and its hydrogen-bonded complex in dimethylformamide (DMF) solution in the S0 and S1 states. To gain insight into the photoinduced coupled excited-state proton transfer (ESPT) and twisted intramolecular charge transfer (TICT) associated with intermolecular hydrogen bonding, the potential energy profiles are provided along the Osingle bondH bond and the twisted angle. It is predicted that TICT in S1 can facilitate ESPT initiated by intermolecular hydrogen-bond strengthening in the S1 state. The coupling of ESPT and TICT is energetically preferable

Cysteine-Conjugated Metabolites of Ginger Components, Shogaols, Induce Apoptosis through Oxidative Stress-Mediated p53 Pathway in Human Colon Cancer Cells

Shogaols, the major constituents of thermally processed ginger, have been proven to be highly effective anticancer agents. Our group has identified cysteine-conjugated shogaols (M2, M2′, and M2″) as the major metabolites of [6]-, [8]-, and [10]-shogaol in human and found that M2 is a carrier of its parent molecule [6]-shogaol in cancer cells and in mice, while being less toxic to normal colon fibroblast cells. The objectives of this study are to determine whether M2′ and M2″ behave in a similar manner to M2, in both metabolism and efficacy as anticancer agents, and to further explore the biological pro-apoptotic mechanisms of the cysteine-conjugated shogaols against human colon cancer cells HCT-116 and HT-29. Our results show that [8]- and [10]-shogaol have similar metabolic profiles to [6]-shogaol and exhibit similar toxicity toward human colon cancer cells. M2′ and M2″ both show low toxicity against normal colon cells but retain potency against colon cancer cells, suggesting that they have similar activity to M2. We further demonstrate that the cysteine-conjugated shogaols can cause cancer cell death through the activation of the mitochondrial apoptotic pathway. Our results show that oxidative stress activates a p53 pathway that ultimately leads to p53 up-regulated modulator of apoptosis (PUMA) induction and down-regulation of B-cell lymphoma 2 (Bcl-2), followed by cytochrome c release, perturbation of inhibitory interactions of X-linked inhibitor of apoptosis protein (XIAP) with caspases, and finally caspase 9 and 3 activation and cleavage. A brief screen of the markers attenuated by the proapoptotic activity of M2 revealed similar results for [8]- and [10]-shogaol and their respective cysteine-conjugated metabolites M2′ and M2″. This study highlights the cysteine-conjugated metabolites of shogaols as novel dietary colon cancer preventive agents

Physics-Informed Data Denoising for Real-Life Sensing Systems

Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reliability of the systems they support. Classic filtering-based approaches introduce strong assumptions on the time or frequency characteristics of sensory measurements, while learning-based denoising approaches typically rely on using ground truth clean data to train a denoising model, which is often challenging or prohibitive to obtain for many real-world applications. We observe that in many scenarios, the relationships between different sensor measurements (e.g., location and acceleration) are analytically described by laws of physics (e.g., second-order differential equation). By incorporating such physics constraints, we can guide the denoising process to improve even in the absence of ground truth data. In light of this, we design a physics-informed denoising model that leverages the inherent algebraic relationships between different measurements governed by the underlying physics. By obviating the need for ground truth clean data, our method offers a practical denoising solution for real-world applications. We conducted experiments in various domains, including inertial navigation, CO2 monitoring, and HVAC control, and achieved state-of-the-art performance compared with existing denoising methods. Our method can denoise data in real time (4ms for a sequence of 1s) for low-cost noisy sensors and produces results that closely align with those from high-precision, high-cost alternatives, leading to an efficient, cost-effective approach for more accurate sensor-based systems.Comment: SenSys 202

VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction

Edge-cloud collaborative video analytics is transforming the way data is being handled, processed, and transmitted from the ever-growing number of surveillance cameras around the world. To avoid wasting limited bandwidth on unrelated content transmission, existing video analytics solutions usually perform temporal or spatial filtering to realize aggressive compression of irrelevant pixels. However, most of them work in a context-agnostic way while being oblivious to the circumstances where the video content is happening and the context-dependent characteristics under the hood. In this work, we propose VaBUS, a real-time video analytics system that leverages the rich contextual information of surveillance cameras to reduce bandwidth consumption for semantic compression. As a task-oriented communication system, VaBUS dynamically maintains the background image of the video on the edge with minimal system overhead and sends only highly confident Region of Interests (RoIs) to the cloud through adaptive weighting and encoding. With a lightweight experience-driven learning module, VaBUS is able to achieve high offline inference accuracy even when network congestion occurs. Experimental results show that VaBUS reduces bandwidth consumption by 25.0%-76.9% while achieving 90.7% accuracy for both the object detection and human keypoint detection tasks

Ginger Compound [6]-Shogaol and Its Cysteine-Conjugated Metabolite (M2) Activate Nrf2 in Colon Epithelial Cells in Vitro and in Vivo

In this study, we identified Nrf2 as a molecular target of [6]-shogaol (6S), a bioactive compound isolated from ginger, in colon epithelial cells in vitro and in vivo. Following 6S treatment of HCT-116 cells, the intracellular GSH/GSSG ratio was initially diminished but was then elevated above the basal level. Intracellular reactive oxygen species (ROS) correlated inversely with the GSH/GSSG ratio. Further analysis using gene microarray showed that 6S upregulated the expression of Nrf2 target genes (AKR1B10, FTL, GGTLA4, and HMOX1) in HCT-116 cells. Western blotting confirmed upregulation, phosphorylation, and nuclear translocation of Nrf2 protein followed by Keap1 decrease and upregulation of Nrf2 target genes (AKR1B10, FTL, GGTLA4, HMOX1, and MT1) and glutathione synthesis genes (GCLC and GCLM). Pretreatment of cells with a specific inhibitor of p38 (SB202190), PI3K (LY294002), or MEK1 (PD098059) attenuated these effects of 6S. Using ultra-high-performance liquid chromatography–tandem mass spectrometry, we found that 6S modified multiple cysteine residues of Keap1 protein. In vivo 6S treatment induced Nrf2 nuclear translocation and significantly upregulated the expression of MT1, HMOX1, and GCLC in the colon of wild-type mice but not Nrf2–/– mice. Similar to 6S, a cysteine-conjugated metabolite of 6S (M2), which was previously found to be a carrier of 6S in vitro and in vivo, also activated Nrf2. Our data demonstrated that 6S and its cysteine-conjugated metabolite M2 activate Nrf2 in colon epithelial cells in vitro and in vivo through Keap1-dependent and -independent mechanisms