Analysis of the Application of Artificial Intelligence in Medical Imaging

Medical imaging technology is considered one of the most critical diagnostic tools in the clinical analysis, and its imaging results provide medical interventions with the changes of patients' tissues and organs to support the diagnosis. With the development of computer technology, the application of artificial intelligence (AI) technology in clinical diagnosis has become increasingly more widespread. In order to enhance the quality of medical imaging work, this paper mainly analyzes the application effect of AI in medical imaging, further discovering the significance of the application of artificial intelligence in medical imaging

Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing

Participating in demand response programs is a promising tool for reducing energy costs in data centers by modulating energy consumption. Towards this end, data centers can employ a rich set of resource management knobs, such as workload shifting and dynamic server provisioning. Nonetheless, these knobs may not be readily available in a cloud data center (CDC) that serves cloud tenants/users, because workloads in CDCs are managed by tenants themselves who are typically charged based on a usage-based or flat-rate pricing and often have no incentive to cooperate with the CDC operator for demand response and cost saving. Towards breaking such "split incentive" hurdle, a few recent studies have tried market-based mechanisms, such as dynamic pricing, inside CDCs. However, such mechanisms often rely on complex designs that are hard to implement and difficult to cope with by tenants. To address this limitation, we propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing for cloud resources unchanged for a long period. While it charges tenants based on a Usage-based Pricing (UP) as used by today's major cloud operators, it rewards tenants proportionally based on the time length that tenants set as deadlines for completing their workloads. This new mechanism is called Usage-based Pricing with Monetary Reward (UPMR). We demonstrate the effectiveness of UPMR both analytically and empirically. We show that UPMR can reduce the CDC operator's energy cost by 12.9% while increasing its profit by 4.9%, compared to the state-of-the-art approaches used by today's CDC operators to charge their tenants

Signature of the γ\gamma+jet and dijet production mediated by an excited quark with QCD next-to-leading order accuracy at the LHC

We present a detailed study of the production and decay of the excited quark at the QCD next-to-leading order (NLO) level at the Large Hadron Collider, using the narrow width approximation and helicity amplitudes method. We find that the QCD NLO corrections can tighten the constraints on the model parameters and reduce the scale dependencies of the total cross sections. We discuss the signals of the excited quark production with decay mode $q^{\ast}\rightarrow q\gamma$ and $q^{\ast}\rightarrow qg$, and present several important kinematic distributions. Moreover, we give the upper limits of the excited quark excluded mass range and the allowed parameter space for the coupling constants and the excited quark mass.Comment: 20 pages, 13 figures; version published in PR

Multiparty Quantum Secret Sharing

Based on a quantum secure direct communication (QSDC) protocol [Phys. Rev. A69(04)052319], we propose a $(n,n)$-threshold scheme of multiparty quantum secret sharing of classical messages (QSSCM) using only single photons. We take advantage of this multiparty QSSCM scheme to establish a scheme of multiparty secret sharing of quantum information (SSQI), in which only all quantum information receivers collaborate can the original qubit be reconstructed. A general idea is also proposed for constructing multiparty SSQI schemes from any QSSCM scheme