Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions

The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpag

Contextual Attention for Hand Detection in the Wild

We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce a large-scale annotated hand dataset containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on several datasets, including our hand detection benchmark and the publicly available PASCAL VOC human layout challenge. We also conduct ablation studies on hand detection to show the effectiveness of the proposed contextual attention module.Comment: 9 pages, 9 figure

Contextual Attention for Hand Detection in the Wild

We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce large-scale annotated hand datasets containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on the newly collected datasets and the publicly available PASCAL VOC human layout dataset. Data and code: https://www3.cs.stonybrook.edu/~cvl/projects/hand_det_attention

Simulating Quantum Mean Values in Noisy Variational Quantum Algorithms: A Polynomial-Scale Approach

Large-scale variational quantum algorithms possess an expressive capacity that is beyond the reach of classical computers and is widely regarded as a potential pathway to achieving practical quantum advantages. However, the presence of quantum noise might suppress and undermine these advantages, which blurs the boundaries of classical simulability. To gain further clarity on this matter, we present a novel polynomial-scale method that efficiently approximates quantum mean values in variational quantum algorithms with bounded truncation error in the presence of independent single-qubit depolarizing noise. Our method is based on path integrals in the Pauli basis. We have rigorously proved that, for a fixed noise rate $\lambda$, our method's time and space complexity exhibits a polynomial relationship with the number of qubits $n$, the circuit depth $L$, the inverse truncation error $\frac{1}{\varepsilon}$, and the inverse success probability $\frac{1}{\delta}$. Furthermore, We also prove that computational complexity becomes $\mathrm{Poly}\left(n,L\right)$ when the noise rate $\lambda$ exceeds $\frac{1}{\log{L}}$ and it becomes exponential with $L$ when the noise rate $\lambda$ falls below $\frac{1}{L}$

Examination Of The Influence Of Service Quality On Membership Renewal In Fitness Centers In San Francisco Bay Area

Corporations have to learn how to satisfy their customers’ various demands as the era of interactivity with customers has emerged (Pepper & Rogers, 1999). For fitness center, customers’ demands are increasing and diversified. Therefore, service quality is an index of quality assessment from customers for service-producing industries. Furthermore, the concept of corporate expansion and customer relationship has become the foundation of service-providers for higher profitability through customers’ renewal of membership. The main purpose of this study is to evaluate the impact of service quality on the renewal willingness of fitness center membership. Customers from four fitness centers in the San Francisco Bay Area, USA, were randomly selected for this survey. A total of 50 subjects participated in this survey. The data was analyzed by multiple regression and stepwise regression. The result indicated that the service quality has positive influence on the renewal willingness of membership

Realizing Non-Physical Actions through Hermitian-Preserving Map Exponentiation

Quantum mechanics features a variety of distinct properties such as coherence and entanglement, which could be explored to showcase potential advantages over classical counterparts in information processing. In general, legitimate quantum operations must adhere to principles of quantum mechanics, particularly the requirements of complete positivity and trace preservation. Nonetheless, non-physical maps, especially Hermitian-preserving maps, play a crucial role in quantum information science. To date, there exists no effective method for implementing these non-physical maps with quantum devices. In this work, we introduce the Hermitian-preserving map exponentiation algorithm, which can effectively realize the action of an arbitrary Hermitian-preserving map by encoding its output into a quantum process. We analyze the performances of this algorithm, including its sample complexity and robustness, and prove its optimality in certain cases. When combined with algorithms such as the Hadamard test and quantum phase estimation, it allows for the extraction of information and generation of states from outputs of Hermitian-preserving maps, enabling various applications. Utilizing positive but not completely positive maps, this algorithm provides exponential advantages in entanglement detection and quantification compared to protocols based on single-copy operations. In addition, it facilitates the recovery of noiseless quantum states from multiple copies of noisy states by implementing the inverse map of the corresponding noise channel, offering an intriguing approach to handling quantum errors. Our findings present a pathway for systematically and efficiently implementing non-physical actions with quantum devices, thereby boosting the exploration of potential quantum advantages across a wide range of information processing tasks.Comment: 34 pages, 10 figures, comments are welcom

Fabrication of self-healing injectable hyaluronic acid hydrogel for promoting angiogenesis

Objective·To construct a self-healing injectable hyaluronic acid (HA)-based hydrogel (HAPD-Cu) and investigate the effects of different copper ions on the properties of the hydrogel and its vasogenic efficacy to evaluate its feasibility for clinical wound healing.Methods·Bisphosphonated hyaluronic acid (HAPD) was prepared via a blue-light mediated thiol-ene click reaction between thiolated hyaluronic acid (HASH) and acrylated bisphosphonate (Ac-PD) in the presence of photoinitiator 2959. Then, HAPD was further interacted with Cu2+ through metal coordination to prepare HAPD-Cu hydrogels with different Cu2+ concentrations, i.e. HAPD-Cu1, HAPD-Cu2, HAPD-Cu3 and HAPD-Cu4. The molecular structures of HASH, Ac-PD, HAPD and HAPD-Cu were verified with 1HNMR and FTIR. Microscopic morphology of HAPD-Cu was observed under SEM. The shear-thinning and self-healing properties of HAPD-Cu were verified by rheometer. The Cu2+ release from HAPD-Cu was determined with ICP. Live-dead staining and CCK-8 assay were applied to evaluate the biocompatibility of HAPD-Cu. The in vitro vasculogenic activity of HAPD-Cu was determined by a tubule-forming assay with human umbilical vein vascular endothelial cells and the in vivo vasculogenic activity of HAPD-Cu was assessed by CD31 tissue staining. A rat wound defect model was established in vitro to evaluate its actual repair effect.Results·The preparation of the materials was demonstrated through chemical qualitative and quantitative analytical means. In vitro studies showed that all HAPD-Cu with a loose porous internal structure exhibited outstanding self-healing, injectability and degradability, with a one-week degradation cycle and abrupt release behavior, which can meet the needs of wound healing cycle. All HAPD-Cu showed good biocompatibility except HAPD-Cu4, due to its high Cu2+ concentrations. Moreover, its angiogenic effect in vitro or in vivo was enhanced with increasing Cu2+ concentrations within the permissible Cu2+ concentration range. In vitro wound model experiments also showed that the HAPD-Cu hydrogel significantly promoted wound healing compared with the control group.Conclusion·HAPD-Cu hydrogel constructed via the metal coordination shows excellent shape plasticity, allowing the filling of defective sites in a minimally invasive form, and the release of Cu2+ greatly facilitates the establishment of early vascular networks, with giant potential for use in the repair of clinically irregular wounds