A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.Comment: arXiv admin note: text overlap with arXiv:1006.0308 by other author

SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation

Recently, self-supervised monocular depth estimation has gained popularity with numerous applications in autonomous driving and robotics. However, existing solutions primarily seek to estimate depth from immediate visual features, and struggle to recover fine-grained scene details with limited generalization. In this paper, we introduce SQLdepth, a novel approach that can effectively learn fine-grained scene structures from motion. In SQLdepth, we propose a novel Self Query Layer (SQL) to build a self-cost volume and infer depth from it, rather than inferring depth from feature maps. The self-cost volume implicitly captures the intrinsic geometry of the scene within a single frame. Each individual slice of the volume signifies the relative distances between points and objects within a latent space. Ultimately, this volume is compressed to the depth map via a novel decoding approach. Experimental results on KITTI and Cityscapes show that our method attains remarkable state-of-the-art performance (AbsRel = $0.082$ on KITTI, $0.052$ on KITTI with improved ground-truth and $0.106$ on Cityscapes), achieves $9.9\%$, $5.5\%$ and $4.5\%$ error reduction from the previous best. In addition, our approach showcases reduced training complexity, computational efficiency, improved generalization, and the ability to recover fine-grained scene details. Moreover, the self-supervised pre-trained and metric fine-tuned SQLdepth can surpass existing supervised methods by significant margins (AbsRel = $0.043$, $14\%$ error reduction). self-matching-oriented relative distance querying in SQL improves the robustness and zero-shot generalization capability of SQLdepth. Code and the pre-trained weights will be publicly available. Code is available at \href{https://github.com/hisfog/SQLdepth-Impl}{https://github.com/hisfog/SQLdepth-Impl}.Comment: 14 pages, 9 figure

Performance-based seismic isolation design using the theory of spatially concave friction distribution

Seismic isolation devices were designed to protect three similar building structures, containing different objects with different fragilities, in a strong earthquake region. And a performance-based assessment framework, established by the PEER, was used to identify the seismic isolation efficiency of these devices. It optimized the ratios of spring part, viscous damping part and friction part in the seismic isolation devices, aiming at different functional buildings. Results show that a spatially concave friction distribution, combined with a weak spring, not only can reduce the structural acceleration response during earthquakes, but also decrease the structural residual displacement after earthquakes. Moreover, the spatially concave friction distribution can dissipate earthquake energy, but cannot hinder the recentering of structure like that of general uniform friction distributions. Consequently, the spatially concave friction distribution can partly or fully replace the viscous dampers, which are more expensive and short-lived. The reasonable combination of different components in the seismic isolation devices can satisfy different seismic requirements, aiming at different functional buildings

A Codesigned Compact Dual-Band Filtering Antenna with PIN Loaded for WLAN Applications

A codesigned compact dual-band filtering antenna incorporating a PIN diode for 2.45/5.2 GHz wireless local area network (WLAN) applications is proposed in this paper. The integrated filtering antenna system consists of a simple monopole radiator, a microstrip dual-band band-pass filter, and a PIN diode. The performance of the filtering antenna is notably promoted by optimizing the impedance between the antenna and the band-pass filter, with good selectivity and out-of-band rejection. The design process follows the approach of the synthesis of band-pass filter. In addition, the PIN diode is incorporated in the filtering antenna for further size reduction, which also widens the coverage of the bandwidth by about 230% for 2.4 GHz WLAN. With the presence of small size and good filtering performances, the proposed filtering antenna is a good candidate for the wireless communication systems. Prototypes of the proposed filtering antenna incorporating a PIN diode are fabricated and measured. The measured results including return losses and radiation patterns are presented

Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Diffusion models have demonstrated remarkable performance in image generation tasks, paving the way for powerful AIGC applications. However, these widely-used generative models can also raise security and privacy concerns, such as copyright infringement, and sensitive data leakage. To tackle these issues, we propose a method, Unlearnable Diffusion Perturbation, to safeguard images from unauthorized exploitation. Our approach involves designing an algorithm to generate sample-wise perturbation noise for each image to be protected. This imperceptible protective noise makes the data almost unlearnable for diffusion models, i.e., diffusion models trained or fine-tuned on the protected data cannot generate high-quality and diverse images related to the protected training data. Theoretically, we frame this as a max-min optimization problem and introduce EUDP, a noise scheduler-based method to enhance the effectiveness of the protective noise. We evaluate our methods on both Denoising Diffusion Probabilistic Model and Latent Diffusion Models, demonstrating that training diffusion models on the protected data lead to a significant reduction in the quality of the generated images. Especially, the experimental results on Stable Diffusion demonstrate that our method effectively safeguards images from being used to train Diffusion Models in various tasks, such as training specific objects and styles. This achievement holds significant importance in real-world scenarios, as it contributes to the protection of privacy and copyright against AI-generated content

A replicative recombinant HPV16 E7 expression virus upregulates CD36 in C33A cells

ObjectiveIn past decades, the role of high-risk HPV (HR-HPV) infection in cancer pathogenesis has been extensively studied. The viral E7 protein expressed in pre-malignant cells has been identified as an ideal target for immunological intervention. However, the cultivation of HPV in vitro remains a significant challenge, as well as the lack of methods for expressing the HPV E7 protein and generating replication-competent recombinant viral particles, which posed a major obstacle to further exploration of the function and carcinogenic mechanisms of the E7 oncoprotein. Therefore, it is imperative to investigate novel methodologies to construct replication-competent recombinant viral particles that express the HPV E7 protein to facilitate the study of its function.MethodsWe initiated the construction of recombinant viral particles by utilizing the ccdB-Kan forward/reverse screening system in conjunction with the Red/ExoCET recombinant system. We followed the infection of C33A cells with the obtained recombinant virus to enable the continuous expression of HPV16 E7. Afterwards, the total RNA was extracted and performed transcriptome sequencing using RNA-Seq technology to identify differentially expressed genes associated with HPV-induced oncogenicity.ResultsWe successfully established replicative recombinant viral particles expressing HPV16 E7 stably and continuously. The C33A cells were infected with recombinant viral particles to achieve overexpression of the E7 protein. Subsequently, RNA-Seq analysis was conducted to assess the changes in host cell gene expression. The results revealed an upregulation of the CD36 gene, which is associated with the HPV-induced oncogenic pathways, including PI3K-Akt and p53 signaling pathway. qRT-PCR analysis further identified that the upregulation of the CD36 gene due to the expression of HPV16 E7.ConclusionThe successful expression of HPV16 E7 in cells demonstrates that the replicated recombinant virus retains the replication and infection abilities of Ad4, while also upregulating the CD36 gene involved in the PI3K-Akt signaling and p53 pathways, thereby promoting cell proliferation. The outcome of this study provides a novel perspective and serves as a solid foundation for further exploration of HPV-related carcinogenesis and the development of replicative HPV recombinant vaccines capable of inducing protective immunity against HPV

A conceptual cellular interaction model of left ventricular remodelling post-MI: dynamic network with exit-entry competition strategy

Abstract Background Progressive remodelling of the left ventricle (LV) following myocardial infarction (MI) is an outcome of spatial-temporal cellular interactions among different cell types that leads to heart failure for a significant number of patients. Cellular populations demonstrate temporal profiles of flux post-MI. However, little is known about the relationship between cell populations and the interaction strength among cells post-MI. The objective of this study was to establish a conceptual cellular interaction model based on a recently established graph network to describe the interaction between two types of cells. Results We performed stability analysis to investigate the effects of the interaction strengths, the initial status, and the number of links between cells on the cellular population in the dynamic network. Our analysis generated a set of conditions on interaction strength, structure of the network, and initial status of the network to predict the evolutionary profiles of the network. Computer simulations of our conceptual model verified our analysis. Conclusions Our study introduces a dynamic network to model cellular interactions between two different cell types which can be used to model the cellular population changes post-MI. The results on stability analysis can be used as a tool to predict the responses of particular cell populations