Street Art as an Expression of Post-Modern Consciousness

This paper explores the development and expression of street art from the end of the twentieth century to the present day. I examine the street art’s use and definition of public space and how it challenges the prevailing paradigm on property and ownership. I explore the way that street art uses the language of advertising to reach the largest amount of people, while speaking out against all that consumerism stands for. I discuss how in braking down the boundaries between high art and low, and using images from every source imaginable, street speaks to the post-modern projects desire to see beyond disciplines and deny all hierarchies. I delve into the work of fames street artist Swoon and her art collective Toyshop and see how street art is seeking to facilitate a change in consciousness from a fixed and static way of seeing reality, to an organic and flowing mode of being

Chemical Inhibitors of the Calcium Entry Channel TRPV6

ABSTRACT: Purpose: Calcium entry channels in the plasma membrane are thought to play a major role in maintaining cellular Ca2+ levels, crucial for growth and survival of normal and cancer cells. The calcium-selective channel TRPV6 is expressed in prostate, breast, and other cancer cells. Its expression coincides with cancer progression, suggesting that it drives cancer cell growth. However, no specific inhibitors for TRPV6 have been identified thus far. Methods: To develop specific TRPV6 inhibitors, we synthesized molecules based on the lead compound TH-1177, reported to inhibit calcium entry channels in prostate cancer cells in vitro and in vivo. Results: We found that one of our compounds (#03) selectively inhibited TRPV6 over five times better than TRPV5, whereas TH-1177 and the other synthesized compounds preferentially inhibited TRPV5. The IC50 value for growth inhibition by blocking endogenous Ca2+ entry channels in the LNCaP human prostate cancer cell line was 0.44 ± 0.07μM compared to TH-1177 (50 ± 0.4μM). Conclusions: These results suggest that compound #03 is a relatively selective and potent inhibitor for TRPV6 and that it is an interesting lead compound for the treatment of prostate cancer and other cancers of epithelial origi

Heuristic-based Incremental Probabilistic Roadmap for Efficient UAV Exploration in Dynamic Environments

Autonomous exploration in dynamic environments necessitates a planner that can proactively respond to changes and make efficient and safe decisions for robots. Although plenty of sampling-based works have shown success in exploring static environments, their inherent sampling randomness and limited utilization of previous samples often result in sub-optimal exploration efficiency. Additionally, most of these methods struggle with efficient replanning and collision avoidance in dynamic settings. To overcome these limitations, we propose the Heuristic-based Incremental Probabilistic Roadmap Exploration (HIRE) planner for UAVs exploring dynamic environments. The proposed planner adopts an incremental sampling strategy based on the probabilistic roadmap constructed by heuristic sampling toward the unexplored region next to the free space, defined as the heuristic frontier regions. The heuristic frontier regions are detected by applying a lightweight vision-based method to the different levels of the occupancy map. Moreover, our dynamic module ensures that the planner dynamically updates roadmap information based on the environment changes and avoids dynamic obstacles. Simulation and physical experiments prove that our planner can efficiently and safely explore dynamic environments

Molecular Response in One-Photon Absorption via Natural Thermal Light vs Pulsed Laser Excitation

Photoinduced biological processes occur via one photon absorption in natural light, which is weak, CW and incoherent, but are often studied in the laboratory using pulsed coherent light. Here we compare the response of a molecule to these two very different sources within a quantized radiation field picture. The latter is shown to induce coherent time evolution in the molecule, whereas the former does not. As a result, the coherent time dependence observed in the laboratory experiments will not be relevant to the natural biological process. Emphasis is placed on resolving confusions regarding this issue that are shown to arise from aspects of quantum measurement and from a lack of appreciation of the proper description of the absorbed photon.Comment: Revised (now published) manuscript: Replaces ArXiv:1109.002

Low computational-cost detection and tracking of dynamic obstacles for mobile robots with RGB-D cameras

Deploying autonomous robots in crowded indoor environments usually requires them to have accurate dynamic obstacle perception. Although plenty of previous works in the autonomous driving field have investigated the 3D object detection problem, the usage of dense point clouds from a heavy LiDAR and their high computation cost for learning-based data processing make those methods not applicable to small robots, such as vision-based UAVs with small onboard computers. To address this issue, we propose a lightweight 3D dynamic obstacle detection and tracking (DODT) method based on an RGB-D camera, which is designed for low-power robots with limited computing power. Our method adopts a novel ensemble detection strategy, combining multiple computationally efficient but low-accuracy detectors to achieve real-time high-accuracy obstacle detection. Besides, we introduce a new feature-based data association method to prevent mismatches and use the Kalman filter with the constant acceleration model to track detected obstacles. In addition, our system includes an optional and auxiliary learning-based module to enhance the obstacle detection range and dynamic obstacle identification. The users can determine whether or not to run this module based on the available computation resources. The proposed method is implemented in a small quadcopter, and the experiments prove that the algorithm can make the robot detect dynamic obstacles and navigate dynamic environments safely.Comment: 8 pages, 12 figures, 2 table

Categorization of species as native or nonnative using DNA sequence signatures without a complete reference library.

New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological communities, has emerged as one of the most frequently utilized methods for DNA-based species identifications. Unfortunately, the use of DNA barcoding is limited by the availability of complete reference libraries (i.e., a collection of DNA sequences from morphologically identified species), and by the fact that the vast majority of species do not have sequences present in reference databases. Such conditions are critical especially in tropical locations that are simultaneously biodiversity rich and suffer from a lack of exploration and DNA characterization by trained taxonomic specialists. To facilitate efforts to document biodiversity in regions lacking complete reference libraries, we developed a novel statistical approach that categorizes unidentified species as being either likely native or likely nonnative based solely on measures of nucleotide diversity. We demonstrate the utility of this approach by categorizing a large sample of specimens of terrestrial insects and spiders (collected as part of the Moorea BioCode project) using a generalized linear mixed model (GLMM). Using a training data set of known endemic (n = 45) and known introduced species (n = 102), we then estimated the likely native/nonnative status for 4,663 specimens representing an estimated 1,288 species (412 identified species), including both those specimens that were either unidentified or whose endemic/introduced status was uncertain. Using this approach, we were able to increase the number of categorized specimens by a factor of 4.4 (from 794 to 3,497), and the number of categorized species by a factor of 4.8 from (147 to 707) at a rate much greater than chance (77.6% accuracy). The study identifies phylogenetic signatures of both native and nonnative species and suggests several practical applications for this approach including monitoring biodiversity and facilitating biosecurity

Retrospective Search for Strongly Lensed Supernovae in the DESI Legacy Imaging Surveys

The introduction of deep wide-field surveys in recent years and the adoption of machine learning techniques have led to the discoveries of $\mathcal{O}(10^4)$ strong gravitational lensing systems and candidates. However, the discovery of multiply lensed transients remains a rarity. Lensed transients and especially lensed supernovae are invaluable tools to cosmology as they allow us to constrain cosmological parameters via lens modeling and the measurements of their time delays. In this paper, we develop a pipeline to perform a targeted lensed transient search. We apply this pipeline to 5807 strong lenses and candidates, identified in the literature, in the DESI Legacy Imaging Surveys Data Release 9 (DR9) footprint. For each system, we analyze every exposure in all observed bands (DECam $g$, $r$, and $z$). Our pipeline finds, groups, and ranks detections that are in sufficient proximity temporally and spatially. After the first round of inspection, for promising candidate systems, we further examine the newly available DR10 data (with additional $i$ and $\textrm{Y}$ bands). Here we present our targeted lensed supernova search pipeline and seven new lensed supernova candidates, including a very likely lensed supernova $-$ probably a Type Ia $-$ in a system with an Einstein radius of $\sim 1.5''$.Comment: 53 pages, 50 figures, 3 table

A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles

Tunnel construction using the drill-and-blast method requires the 3D measurement of the excavation front to evaluate underbreak locations. Considering the inspection and measurement task's safety, cost, and efficiency, deploying lightweight autonomous robots, such as unmanned aerial vehicles (UAV), becomes more necessary and popular. Most of the previous works use a prior map for inspection viewpoint determination and do not consider dynamic obstacles. To maximally increase the level of autonomy, this paper proposes a vision-based UAV inspection framework for dynamic tunnel environments without using a prior map. Our approach utilizes a hierarchical planning scheme, decomposing the inspection problem into different levels. The high-level decision maker first determines the task for the robot and generates the target point. Then, the mid-level path planner finds the waypoint path and optimizes the collision-free static trajectory. Finally, the static trajectory will be fed into the low-level local planner to avoid dynamic obstacles and navigate to the target point. Besides, our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles based on an RGB-D camera. After inspection, the Structure-from-Motion (SfM) pipeline is applied to generate the 3D shape of the target. To our best knowledge, this is the first time autonomous inspection has been realized in unknown and dynamic tunnel environments. Our flight experiments in a real tunnel prove that our method can autonomously inspect the tunnel excavation front surface.Comment: 8 pages, 8 figure