Safe and Efficient Trajectory Optimization for Autonomous Vehicles using B-spline with Incremental Path Flattening

B-spline-based trajectory optimization is widely used for robot navigation due to its computational efficiency and convex-hull property (ensures dynamic feasibility), especially as quadrotors, which have circular body shapes (enable efficient movement) and freedom to move each axis (enables convex-hull property utilization). However, using the B-spline curve for trajectory optimization is challenging for autonomous vehicles (AVs) because of their vehicle kinodynamics (rectangular body shapes and constraints to move each axis). In this study, we propose a novel trajectory optimization approach for AVs to circumvent this difficulty using an incremental path flattening (IPF), a disc type swept volume (SV) estimation method, and kinodynamic feasibility constraints. IPF is a new method that can find a collision-free path for AVs by flattening path and reducing SV using iteratively increasing curvature penalty around vehicle collision points. Additionally, we develop a disc type SV estimation method to reduce SV over-approximation and enable AVs to pass through a narrow corridor efficiently. Furthermore, a clamped B-spline curvature constraint, which simplifies a B-spline curvature constraint, is added to dynamical feasibility constraints (e.g., velocity and acceleration) for obtaining the kinodynamic feasibility constraints. Our experimental results demonstrate that our method outperforms state-of-the-art baselines in various simulated environments. We also conducted a real-world experiment using an AV, and our results validate the simulated tracking performance of the proposed approach.Comment: 14 pages, 21 figures, 4 tables, 3 algorithm

Sericea lespedeza (Lespedeza cuneata) whole plant extract enhances rat muscle mass and sperm production by increasing the activity of NO-cGMP pathway and serum testosterone

Purpose: To analyze the effects of an aqueous extract of Sericea lespedeza (SL) on rat male menopause.Methods: Levels of nitric oxide (NO), endothelial nitric oxide synthase (NOS), cGMP, and prostaglandin E2 (PGE2) in the penile corpus cavernosum of the rats were evaluated using appropriate kits. Serum levels of dihydrotestosterone (DHT), testosterone, sex hormone-binding globulin (SHBG), and 17-beta hydroxysteroid dehydrogenases (17β-HSD) were measured with enzyme-linked immunosorbent assay kits. Total and motile sperms were counted on a hemocytometer. Histological changes in rat testis and epididymis were analyzed with hematoxylin and eosin staining.Results: The levels of NO, NOS, and cGMP (but not PGE2) increased in a dose-dependent manner (p< 0.05) upon administration of an aqueous extract of SL (AESL), while levels of DHT, 17β-HSD, and testosterone increased in the group administered with 300 mg/kg of AESL. Epididymal sperm count increased by 24 % in such rats compared to controls (p < 0.05).Conclusion: The aqueous extract of SL improves sperm count and muscle mass in rats by increasing the levels of NO, NOS, cGMP and testosterone. Thus, SL extract can potentially be developed as an alternative therapeutic agent for clinical management of TDS. Keywords: NO-cGMP, Testosterone, Hormones, Sperm count, Muscle mass, Sericea lespedeza, Lespedeza cuneat

Dual Attention GANs for Semantic Image Synthesis

In this paper, we focus on the semantic image synthesis task that aims at transferring semantic label maps to photo-realistic images. Existing methods lack effective semantic constraints to preserve the semantic information and ignore the structural correlations in both spatial and channel dimensions, leading to unsatisfactory blurry and artifact-prone results. To address these limitations, we propose a novel Dual Attention GAN (DAGAN) to synthesize photo-realistic and semantically-consistent images with fine details from the input layouts without imposing extra training overhead or modifying the network architectures of existing methods. We also propose two novel modules, i.e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively. Specifically, SAM selectively correlates the pixels at each position by a spatial attention map, leading to pixels with the same semantic label being related to each other regardless of their spatial distances. Meanwhile, CAM selectively emphasizes the scale-wise features at each channel by a channel attention map, which integrates associated features among all channel maps regardless of their scales. We finally sum the outputs of SAM and CAM to further improve feature representation. Extensive experiments on four challenging datasets show that DAGAN achieves remarkably better results than state-of-the-art methods, while using fewer model parameters. The source code and trained models are available at https://github.com/Ha0Tang/DAGAN.Comment: Accepted to ACM MM 2020, camera ready (9 pages) + supplementary (10 pages

Time Series Forecasting with Hypernetworks Generating Parameters in Advance

Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i.e. time series are under temporal drifts). Existing approaches show limited performances under data drifts, and we identify the main reason: It takes time for a model to collect sufficient training data and adjust its parameters for complicated temporal patterns whenever the underlying dynamics change. To address this issue, we study a new approach; instead of adjusting model parameters (by continuously re-training a model on new data), we build a hypernetwork that generates other target models' parameters expected to perform well on the future data. Therefore, we can adjust the model parameters beforehand (if the hypernetwork is correct). We conduct extensive experiments with 6 target models, 6 baselines, and 4 datasets, and show that our HyperGPA outperforms other baselines.Comment: 7 pages, preprint (we open our code after being accepted

Plug-in nanoliter pneumatic liquid dispenser with nozzle design flexibility

This paper presents a novel plug-in nanoliter liquid dispensing system with a plugand-play interface for simple and reversible, yet robust integration of the dispenser. A plug-in type dispenser was developed to facilitate assembly and disassembly with an actuating part through efficient modularization. The entire process for assembly and operation of the plug-in dispenser is performed via the plug-and-play interface in less than a minute without loss of dispensing quality. The minimum volume of droplets pneumatically dispensed using the plug-in dispenser was 124 nl with a coefficient of variation of 1.6%. The dispensed volume increased linearly with the nozzle size. Utilizing this linear relationship, two types of multinozzle dispensers consisting of six parallel channels (emerging from an inlet) and six nozzles were developed to demonstrate a novel strategy for volume gradient dispensing at a single operating condition. The droplet volume dispensed from each nozzle also increased linearly with nozzle size, demonstrating that nozzle size is a dominant factor on dispensed volume, even for multinozzle dispensing. Therefore, the proposed plug-in dispenser enables flexible design of nozzles and reversible integration to dispense droplets with different volumes, depending on the application. Furthermore, to demonstrate the practicality of the proposed dispensing system, we developed a pencil-type dispensing system as an alternative to a conventional pipette for rapid and reliable dispensing of minute volume droplets. (C) 2015 AIP Publishing LLC.open114sciescopu

Deterministic bead-in-droplet ejection utilizing an integrated plug-in bead dispenser for single bead-based applications

This paper presents a deterministic bead-in-droplet ejection (BIDE) technique that regulates the precise distribution of microbeads in an ejected droplet. The deterministic BIDE was realized through the effective integration of a microfluidic single-particle handling technique with a liquid dispensing system. The integrated bead dispenser facilitates the transfer of the desired number of beads into a dispensing volume and the on-demand ejection of bead-encapsulated droplets. Single bead-encapsulated droplets were ejected every 3 s without any failure. Multiple-bead dispensing with deterministic control of the number of beads was demonstrated to emphasize the originality and quality of the proposed dispensing technique. The dispenser was mounted using a plug-socket type connection, and the dispensing process was completely automated using a programmed sequence without any microscopic observation. To demonstrate a potential application of the technique, bead-based streptavidin-biotin binding assay in an evaporating droplet was conducted using ultralow numbers of beads. The results evidenced the number of beads in the droplet crucially influences the reliability of the assay. Therefore, the proposed deterministic bead-in-droplet technology can be utilized to deliver desired beads onto a reaction site, particularly to reliably and efficiently enrich and detect target biomolecules.112Ysciescopu

Observation of Supercurrent in PbIn-Graphene-PbIn Josephson Junction

Superconductor-graphene-superconductor (SGS) junction provides a unique platform to study relativistic electrodynamics of Dirac fermions combined with proximity-induced superconductivity. We report observation of the Josephson effect in proximity-coupled superconducting junctions of graphene in contact with Pb1-xInx (x=0.07) electrodes for temperatures as high as T = 4.8K, with a large IcRn (~ 255 microV). This demonstrates that Pb1-xInx SGS junction would facilitate the development of the superconducting quantum information devices and superconductor-enhanced phase-coherent transport of graphene.Comment: 8 pages, 7 figures, accepted in PR