118 research outputs found
Improving Photostability and Antifungal Performance of Bamboo with Nanostructured Zinc Oxide
We report on the formation of zinc oxide (ZnO) films with various morphologies on bamboo to simultaneously furnish it with excellent photostability and antifungal properties. A simple two-step process was adopted, consisting of generation of ZnO seeds on the bamboo surface followed by solution treatment to promote crystal growth. Effect of reaction conditions on film morphologies was systematically investigated. Results indicate morphologies of ZnO films can be tailored from nanoparticles to nanostructured networks and irregular aggregates at the micron scale with different crystallinities through specific combinations of reaction conditions. The photostability and antifungal performances of coated bamboo were greatly improved and highly dependent on both crystallinity and morphologies of ZnO films
Multi-Resource Allocation for On-Device Distributed Federated Learning Systems
This work poses a distributed multi-resource allocation scheme for minimizing
the weighted sum of latency and energy consumption in the on-device distributed
federated learning (FL) system. Each mobile device in the system engages the
model training process within the specified area and allocates its computation
and communication resources for deriving and uploading parameters,
respectively, to minimize the objective of system subject to the
computation/communication budget and a target latency requirement. In
particular, mobile devices are connect via wireless TCP/IP architectures.
Exploiting the optimization problem structure, the problem can be decomposed to
two convex sub-problems. Drawing on the Lagrangian dual and harmony search
techniques, we characterize the global optimal solution by the closed-form
solutions to all sub-problems, which give qualitative insights to
multi-resource tradeoff. Numerical simulations are used to validate the
analysis and assess the performance of the proposed algorithm
DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
Visual surveillance technology is an indispensable functional component of
advanced traffic management systems. It has been applied to perform traffic
supervision tasks, such as object detection, tracking and recognition. However,
adverse weather conditions, e.g., fog, haze and mist, pose severe challenges
for video-based transportation surveillance. To eliminate the influences of
adverse weather conditions, we propose a dual attention and dual
frequency-guided dehazing network (termed DADFNet) for real-time visibility
enhancement. It consists of a dual attention module (DAM) and a high-low
frequency-guided sub-net (HLFN) to jointly consider the attention and frequency
mapping to guide haze-free scene reconstruction. Extensive experiments on both
synthetic and real-world images demonstrate the superiority of DADFNet over
state-of-the-art methods in terms of visibility enhancement and improvement in
detection accuracy. Furthermore, DADFNet only takes ms to process a 1,920
* 1,080 image on the 2080 Ti GPU, making it highly efficient for deployment in
intelligent transportation systems.Comment: This paper is accepted by AAAI 2022 Workshop: AI for Transportatio
Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing
Quantum cloud computing (QCC) offers a promising approach to efficiently
provide quantum computing resources, such as quantum computers, to perform
resource-intensive tasks. Like traditional cloud computing platforms, QCC
providers can offer both reservation and on-demand plans for quantum resource
provisioning to satisfy users' requirements. However, the fluctuations in user
demand and quantum circuit requirements are challenging for efficient resource
provisioning. Furthermore, in distributed QCC, entanglement routing is a
critical component of quantum networks that enables remote entanglement
communication between users and QCC providers. Further, maintaining
entanglement fidelity in quantum networks is challenging due to the requirement
for high-quality entanglement routing, especially when accessing the providers
over long distances. To address these challenges, we propose a resource
allocation model to provision quantum computing and networking resources. In
particular, entangled pairs, entanglement routing, qubit resources, and
circuits' waiting time are jointly optimized to achieve minimum total costs. We
formulate the proposed model based on the two-stage stochastic programming,
which takes into account the uncertainties of fidelity and qubit requirements,
and quantum circuits' waiting time. Furthermore, we apply the Benders
decomposition algorithm to divide the proposed model into sub-models to be
solved simultaneously. Experimental results demonstrate that our model can
achieve the optimal total costs and reduce total costs at most 49.43\% in
comparison to the baseline model.Comment: 30 pages and 20 figure
Sensitivity of several selected mechanical properties of moso bamboo to moisture content change under the fibre saturation point
The moisture dependence of different mechanical properties of bamboo has not been fully understood. In this work, the longitudinal tensile modulus, bending modulus, and compressive and shearing strength parallel to the grain were determined for bamboo of ages 0.5, 1.5, 2.5, and 4.5 years under different moisture contents (MC) to elucidate the sensitivity of different mechanical properties of bamboo to MC change. The results showed that the four mechanical properties of bamboo respond differently to MC changes. Compressive and shearing strength parallel to the grain were most sensitive to MC changes, followed by longitudinal tensile modulus, then bending modulus. This can be partially explained by the different responses of the three main components in the plant cell wall to MC change. For tensile modulus and bending modulus, the effect of bamboo age on the sensitivity to MC change was insignificant, while young bamboo (0.5 years old) was more sensitive to MC changes for shear strength and less sensitive for compression strength than older bamboo
ST-CapsNet: Linking Spatial and Temporal Attention with Capsule Network for P300 Detection Improvement
A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain refined EEG signals by capturing event-related information. Then the obtained signals were fed into the capsule network for discriminative feature extraction and P300 detection. In order to quantitatively assess the performance of the proposed ST-CapsNet, two publicly-available datasets (i.e., Dataset IIb of BCI Competition 2003 and Dataset II of BCI Competition III) were applied. A new metric of averaged symbols under repetitions (ASUR) was adopted to evaluate the cumulative effect of symbol recognition under different repetitions. In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. More interestingly, the absolute values of the spatial filters learned by ST-CapsNet are higher in the parietal lobe and occipital region, which is consistent with the generation mechanism of P300
Single-cell transcriptome sequencing reveals heterogeneity of gastric cancer: progress and prospects
Gastric cancer is one of the most serious malignant tumor and threatens the health of people worldwide. Its heterogeneity leaves many clinical problems unsolved. To treat it effectively, we need to explore its heterogeneity. Single-cell transcriptome sequencing, or single-cell RNA sequencing (scRNA-seq), reveals the complex biological composition and molecular characteristics of gastric cancer at the level of individual cells, which provides a new perspective for understanding the heterogeneity of gastric cancer. In this review, we first introduce the current procedure of scRNA-seq, and discuss the advantages and limitations of scRNA-seq. We then elaborate on the research carried out with scRNA-seq in gastric cancer in recent years, and describe how it reveals cell heterogeneity, the tumor microenvironment, oncogenesis and metastasis, as well as drug response in to gastric cancer, to facilitate early diagnosis, individualized therapy, and prognosis evaluation
Exploring Blockchain Technology through a Modular Lens: A Survey
Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration provides a big picture on the endeavors made by blockchain professionals over the years to enhance the blockchain performance while the micro-level investigation details the blockchain building blocks for deep technology comprehension. More specifically, this article introduces a general modular blockchain analytic framework that decomposes a blockchain system into interacting modules and then examines the major modules to cover the essential blockchain components of network, consensus, and distributed ledger at the micro-level. The framework as well as the modular analysis jointly build a foundation for designing scalable, flexible, and application-adaptive blockchains that can meet diverse requirements. Additionally, this article explores popular technologies that can be integrated with blockchain to expand functionality and highlights major challenges. Such a study provides critical insights to overcome the obstacles in designing novel blockchain systems and facilitates the further development of blockchain as a digital infrastructure to service new applications
- …