Network Function Virtualization Service Delivery In Future Internet

This dissertation investigates the Network Function Virtualization (NFV) service delivery problems in the future Internet. With the emerging Internet of everything, 5G communication and multi-access edge computing techniques, tremendous end-user devices are connected to the Internet. The massive quantity of end-user devices facilitates various services between the end-user devices and the cloud/edge servers. To improve the service quality and agility, NFV is applied. In NFV, the customer\u27s data from these services will go through multiple Service Functions (SFs) for processing or analysis. Unlike traditional point-to-point data transmission, a particular set of SFs and customized service requirements are needed to be applied to the customer\u27s traffic flow, which makes the traditional point-to-point data transmission methods not directly used. As the traditional point-to-point data transmission methods cannot be directly applied, there should be a body of novel mechanisms that effectively deliver the NFV services with customized~requirements. As a result, this dissertation proposes a series of mechanisms for delivering NFV services with diverse requirements. First, we study how to deliver the traditional NFV service with a provable boundary in unique function networks. Secondly, considering both forward and backward traffic, we investigate how to effectively deliver the NFV service when the SFs required in forward and backward traffic is not the same. Thirdly, we investigate how to efficiently deliver the NFV service when the required SFs have specific executing order constraints. We also provide detailed analysis and discussion for proposed mechanisms and validate their performance via extensive simulations. The results demonstrate that the proposed mechanisms can efficiently and effectively deliver the NFV services under different requirements and networking conditions. At last, we also propose two future research topics for further investigation. The first topic focuses on parallelism-aware service function chaining and embedding. The second topic investigates the survivability of NFV services

Characteristics of Abjection in First Love, Last Rites

First Love, Last Rites was one of the greatest works of English writer Ian McEwan, which leaded him to fame. The book was based on eight short-stories from eight teenagers’ or youth’s point of views. Among those stories, the men suffered from different dilemmas of sexual states, in which horror, violence, death, cruelty, absurdity, mildness and sadness were mixed and interwoven. Applying Julia Kristeva’ s theory of abjection, the eight heroes in the book acted different unusual sexual behaviors because of abjection towards somebody or something in life. In the meantime,the readers could also introspect their status and identities through the stories. Thus, extreme as the plots in this novel, it is true that the mental state of abjection and disorientation still tortures people nowadays. Through this work, the readers may reflect on the life they are experiencing and build up their own self -identity.

Higher adjuvant radioactive iodine therapy dosage helps intermediate-risk papillary thyroid carcinoma patients achieve better therapeutic effect

ObjectiveThis retrospective study aims to evaluate the therapeutic effect of varying dosages of adjuvant radioactive iodine (RAI) therapy on intermediate-risk papillary thyroid carcinoma (PTC) patients.MethodsThis retrospective study involved a total of 427 intermediate-risk PTC patients, out of which 202 received a 3.7GBq dosage of RAI, and 225 received a 5.55GBq dosage. The evaluation involved assessing the therapeutic outcomes, number of treatment cycles, and successful remnant ablation rates in both dose groups, six months post-adjuvant RAI therapy. Univariate and multivariate logistic regression analyses were employed to identify factors linked with excellent response (ER). Following this, prognostic nomograms were constructed to provide a visual representation of the prediction models. Calibration curves, the concordance index (C-index), and the receiver operating characteristic (ROC) curve were employed to evaluate the predictive performance of these nomograms. The Hosmer-Lemeshow test was applied to assess the models’ goodness-of-fit. Additionally, the clinical utility of the prognostic nomograms was appraised through decision curve analysis (DCA)ResultsThe high-dose (HD) group exhibited significantly higher proportions of ER, single treatment cycles, and successful remnant ablation rates (p<0.05). Being male, receiving a 3.7GBq dose, having an N1b stage, an sTg level ≥10ng/ml, or an sTg/TSH ratio ≥0.11 were independent risk factors for Non-ER. Two prognostic nomograms, “sTg Nomogram” and “sTg/TSH Nomogram”, were established. The ranking of factors contributing to ER, in descending order, included the sTg or sTg/TSH ratio, N stage, therapy dosage, sex, and soft tissue invasion. The “sTg/TSH Nomogram” demonstrated a higher C-index compared to the “sTg Nomogram”. The calibration curves indicated excellent calibration for both nomograms. DCA demonstrated that the net benefit of the “sTg/TSH Nomogram” was higher than that of the “sTg Nomogram”.ConclusionHigher initial RAI therapy doses can improve therapeutic efficacy for intermediate-risk PTC patients. The developed nomograms, particularly the “sTg/TSH Nomogram”, could assist clinicians in optimal therapeutic decision-making

Ansor : Generating High-Performance Tensor Programs for Deep Learning

High-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However, obtaining performant tensor programs for different operators on various hardware platforms is notoriously challenging. Currently, deep learning systems rely on vendor-provided kernel libraries or various search strategies to get performant tensor programs. These approaches either require significant engineering effort to develop platform-specific optimization code or fall short of finding high-performance programs due to restricted search space and ineffective exploration strategy. We present Ansor, a tensor program generation framework for deep learning applications. Compared with existing search strategies, Ansor explores many more optimization combinations by sampling programs from a hierarchical representation of the search space. Ansor then fine-tunes the sampled programs with evolutionary search and a learned cost model to identify the best programs. Ansor can find high-performance programs that are outside the search space of existing state-of-the-art approaches. In addition, Ansor utilizes a task scheduler to simultaneously optimize multiple subgraphs in deep neural networks. We show that Ansor improves the execution performance of deep neural networks relative to the state-of-the-art on the Intel CPU, ARM CPU, and NVIDIA GPU by up to $3.8\times$, $2.6\times$, and $1.7\times$, respectively.Comment: Published in OSDI 202

Output feedback control of discrete-time systems with self-triggered controllers

This paper is concerned with the self-triggered output feedback control for discrete-time systems, where an updating instants scheduler is implemented to determine when the controller is updated. For both the full-order and reduced-order observer cases, the updating instants are determined, respectively, where only the information of the estimated state at the current updating instant is required to obtain the next updating instant. It is shown that, with the proposed self-triggered control schemes, not only the updating frequency is significantly reduced, but also the uniform ultimate boundedness of the closed-loop system is guaranteed. Finally, a numerical example is used to verify the effectiveness and the merits of the proposed approaches