Distributed access control and the prototype of the Mojoy trust policy language

In a highly distributed computing environment, people frequently move from one place to another where the new system has no previous knowledge of them at all. Traditional access control mechanisms such as access matrix and RBAC depend heavily on central management. However, the identities and privileges of the users are stored and administered in different locations in distributed systems. How to establish trust between these strange entities remains a challenge. Many efforts have been made to solve this problem. In the previous work, the decentralised administration of trust is achieved through delegation which is a very rigid mechanism. The limitation of delegation is that the identities of the delegators and delegatees must be known in advance and the privileges must be definite. In this thesis, we present a new model for decentralised administration of trust: trust empowerment. In trust empowerment, trust is defined as a set of properties. Properties can be owned and/or controlled. Owners of the properties can perform the privileges denoted by the properties. Controllers of the properties can grant the properties to other subjects but cannot gain the privileges of the properties. Each subject has its own policy to define trust empowerment. We design the Mojoy tmst policy language that supports trust empowerment. We give the syntax, semantics and an XML implementation of the language. The Mojoy trust policy language is based on XACML, which is an OASIS standard. We develop a compliance checker for the language. The responsibility of the compliance checker is to examine the certificates and policy, and return a Boolean value to indicate whether the user's request is allowed. We apply our new model, the language and the compliance checker to a case study to show that they are capable of coping with the trust issues met in the distributed systems

A hybrid cavity and parallel-plate PEEC method for analysis of complex power net area fills, and a tool development for peak distortion analysis

Modern ASICs and FPGAs are becoming more and more dense, which is causing an increasing demand of the current draw from the power distribution network (PDN). And one of the main design objectives of a power distribution network is to reduce the voltage noise ripple below a specified allowable limit. Although the target impedance is a commonly used criterion in most PDN designs, it may not be efficient because it\u27s usually rather pessimistic. Herein a time domain voltage ripple decomposition approach is proposed to avoid overdesign as well as provide design guidance to PI engineers. Based on a physics-based circuit model for PDN and a switching current generator including both high frequency switching and low frequency power gating, the total voltage ripple can be divided into several components. Each component will have a one-to-one correspondence to the real PDN geometry. Thus design curves can also be derived, which can guide PI engineers when making design decisions. Peak distortion analysis (PDA) is commonly used to find the worst-case eye diagram and data pattern. Compared to traditional long transient simulations, PDA can significantly reduce the computation time by only taking into consideration the worst case. Generally PDA is based on a superposition technique with a single bit response (SBR), which requires the system to be linear time invariant (LTI) or can be well approximated as an LTI system. SBR is no longer applicable for systems which have different rising and falling edge responses due to asymmetric I/O design or mismatches between pull-up and pull-down drivers. Also sometimes the nonlinearity can extend beyond the edge transitions which can result from the voltage noise on the power distribution network (PDN). Herein PDA based on the superposition of multiple edge responses (MER) is proposed to account for a non-LTI system as well as asymmetric rising and falling edges --Abstract, page iii

Peptides of interest:Editing of Lactococcus lactis proteolytic system to increase its bioactive potential

The goal of this thesis was to answer one question in particular: can one increase the quantity or the diversity (or both) of milk-derived bioactive peptides by engineering the Lactococcus lactis proteolytic system? The main research line explored that question itself. In addition, the three issues that derive from that main questions are separately tackled in this thesis: (i) bioactive peptides: How much do we know about bioactive peptides derived from milk-derived and, more specifically, beta-casein? (ii) L. lactis engineering: Which tools are there and are they good enough or can we develop new/better ones; (iii) 3. The L. lactis proteolytic system: what do we know about that system and, especially, what do we know about the in vivo (complementing) activities of the peptidases with respect to cellular growth and peptide degradation Chapter 1 comprehensively reviews the knowhow on β-casein-derived bioactive peptides and the potential of using lactic acid bacteria to produce such peptides; In Chapter 2, which is the founding chapter of the thesis, we engineered the L. lactis proteolytic system by making a large collection of various combinations of peptidase gene mutants and used those mutants to increase the quantity of different bioactive peptides and the diversity of different bioactivities which derived from β-casein; In Chapter 3, we broaden the knowledge about the L. lactis proteolytic system, and prove that the dipeptidase PepV plays an important role in peptidoglycan biosynthesis by acting as a link between nitrogen metabolism and cell wall synthesis. In Chapter 4, we expand the genetic toolbox for L. lactis by developing plasmid- and genome-based CRISPRi systems that will allow rapidly e.g., editing biological pathways or characterizing essential genes, as was explored in Chapter 5

A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network

In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and pooling layers, which can model a variety of compositions by the feature maps and choose the most informative compositions by the pooling layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list of candidate dependency parsing trees. The experiments show that RCNN is very effective to improve the state-of-the-art dependency parsing on both English and Chinese datasets

Editing of the proteolytic system of Lactococcus lactis increases its bioactive potential

Large-scale mass spectrometry-based peptidomics for bioactive-peptide discovery is relatively unexplored because of challenges in intracellular peptide extraction and small-peptide identification. Here, we present an analytical pipeline for large-scale intracellular peptidomics of Lactococcus lactis. It entails an optimized sample preparation protocol for L. lactis, used as an "enzyme complex" to digest γ-casein, an extraction method for its intracellular peptidome, and a peptidomics data analysis and visualization procedure. In addition, we proofread the publicly available bioactive-peptide databases and obtained an optimized database of bioactive peptides derivable from bovine γ-casein. We used the pipeline to examine cultures of L. lactis MG1363 and a set of 6 isogenic multiple peptidase mutants incubated with γ-casein. We observed a clearly strain-dependent accumulation of peptides with several bioactivities, such as angiotensin-converting enzyme (ACE)-inhibitory, dipeptidyl peptidase 4 (DPP-IV)-inhibitory, and immunoregulatory functions. The results suggest that both the number of different bioactive peptides and the bioactivity diversity can be increased by editing the proteolytic system of L. lactis. This comprehensive pipeline offers a model for discovery of bioactive peptides in combination with other proteins and might be applicable to other bacteria

Federated Learning in Competitive EV Charging Market

Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on developing FL algorithms, the effect of FL on the charging stations' operation in terms of price competition has yet to be fully understood. This paper aims to fill this gap by modeling the strategic interactions between two charging stations and EV owners as a multi-stage game. Each station first decides its FL participation strategy and charging price, and then individual EV owners decide their charging strategies. The game analysis involves solving a non-concave problem and by decomposing it into a piece-wise concave program we manage to fully characterize the equilibrium. Based on real-world datasets, our numerical results reveal an interesting insight: even if FL improves QoS, it can lead to smaller profits for both stations. The key reason is that FL intensifies the price competition between charging stations by improving stations' QoS to a similar level. We further show that the stations will participate in FL when their data distributions are mildly dissimilar.Comment: Accepted to IEEE ISGT EUROPE 202

Error reduction method for singularity point detection using Shack–Hartmann wavefront sensor

AbstractA new framework is proposed for realizing high-spatial-resolution detection of singularity points in optical vortex beams using a Shack–Hartmann wavefront sensor (SHWS). The method uses a Shack–Hartmann wavefront sensor (SHWS) to record a Hartmanngram. A map of evaluation values related to phase slope is then calculated from the Hartmanngram. We first determined the singularity's position precisely by calculating the centroid of the circulation of 3×3 crosspoints. After that, we analyzed the error distribution of it, and proposed hybrid centroiding framework for reducing its error. Optical experiments were carried out to verify the method. Good linearity was showed in detecting positions of the singularity points, and it was indicated that the accuracy of detection the position of OV was improved. The average root mean square (RMS) error over various measurements was better than correlation matching method, which we proposed before. The method not only shows higher accuracy, but also consumes much less time than our former work