Scheduling language and algorithm development study. Volume 3, phase 2: As-built specifications for the prototype language and module library

Detailed specifications of the prototype language and module library are presented. The user guide to the translator writing system is included

Varying the Money Supply of Commercial Banks

We consider the problem of financing two productive sectors in an economy through bank loans, when the sectors may experience independent demands for money but when it is desirable for each to maintain an independently determined sequence of prices. An idealized central bank is compared with a collection of commercial banks that generate profits from interest rate spreads and flow those through to a collection of consumer/owners who are also one group of borrowers and lenders in the private economy. We model the private economy as one in which both production functions and consumption preferences for the two goods are independent, and in which one production process experiences a shock in the demand for money arising from an opportunity for risky innovation of its production function. An idealized, profitless central bank can decouple the sectors, but for-profit commercial banks inherently propagate shocks in money demand in one sector into price shocks with a tail of distorted prices in the other sector. The connection of profits with efficiency-reducing propagation of shocks is mechanical in character, in that it does not depend on the particular way profits are used strategically within the banking system. In application, the tension between profits and reserve requirements is essential to enabling but also controlling the distributed perception and evaluation services provided by commercial banks. We regard the inefficiency inherent in the profit system as a source of costs that are paid for distributed perception and control in economies

Automation and Control

Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems

The Architecture of Contract Innovation

Contract law and the formal models of contract economics assume that agreements are fully customized. On the other hand, recent legal research highlights the role standardized terms play in contract design. Those lines of research overlook an important class of contracts between those extremes. Many contracts, such as the merger agreements studied here, are complex combinations of customized and standardized terms, and thereby achieve economies of both scale and scope. Such contracts are “mass customized,” to borrow a term from engineering research. This Article introduces a theoretical framework for understanding how mass customization of such complex agreements is achieved. It adds to recent scholarship that applies modularity theory to the design of complex agreements by introducing an alternative approach—flexible specialization. It then introduces empirical methods for studying the structure of complex agreements. Using hand-collected data from samples of public company merger agreements and of the teams of deal lawyers that designed them, this Article presents the results of a preliminary empirical study that finds that the architecture of mass customized contracts reflects the logic of flexible specialization rather than modular design. The picture of contract design that emerges is of agreements built upon a flexible architecture provided by a dynamic cluster of experts, more similar to the industrial districts of Emilia-Romagna in Italy than Ford Motor Company’s fabled Highland Park assembly line. Those preliminary results suggest important implications for doctrine, policy, and research. In regard to doctrine, this Article adds a missing dimension to recent attempts to articulate a non-unitary theory of U.S. contract law. With respect to policy, evidence that flexible specialization underpins the infrastructure of the mergers and acquisitions (M&A) market challenges deterministic accounts of the legal industry’s transformation, and illuminates the overlooked trade-offs presented by recent arguments to further standardize complex contracting. Finally, in regard to research, this Article provides a basis for much needed theoretical and empirical work on the interaction effects between governance mechanisms within an agreement

A Dynamic Allocation Mechanism for Network Slicing as-a-Service

In my thesis, I explore the design of a market mechanism to socially efficiently allocate resources for network slicing as-a-Service. Network slicing is a novel usage concept for the upcoming 5G network standard, allowing for isolated and customized virtual networks to operate upon a larger, physical 5G network. By providing network slices as-a-Service, where the users of the network slice do not own any of the underlying resources, a larger range of use cases can be catered to. My market mechanism is a novel amalgamation of existing mechanism design solutions from economics, and the nascent computer science literature into the technical aspects of network slicing and underlying network virtualization concepts. The existing literature in computer science is focused on the operative aspects of network slicing, while economics literature is incompatible with the unique problems network slicing poses as a market. In this thesis, I bring these two strands of literature together to create a functional allocation mechanism for the network slice market. I successfully create this market mechanism in my thesis, which is split into three phases. The first phase allows for bidder input into the network slices they bid for, overcoming a trade-off between market efficiency and tractability, making truthful valuation Bayes-Nash optimal. The second phase allocates resources to bidders based on a modified VCG mechanism that forms the multiple, non-identical resources of the market into packages that are based on bidder Quality of Service demands. Allocation is optimized to be socially efficient. The third phase re-allocates vacant resources of entitled network slices according to a Generalized Second-Price auction, while allowing for the return of resources to these entitled network slices without service interruption. As a whole, the mechanism is designed to optimize the allocation of resources as much as possible to those users that create the greatest value out of them, and successfully does so

Smart electric vehicle charging strategy in direct current microgrid

This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces

Strategizing for Corporate Social Responsibility: Wells Fargo Case

This study evaluates Wells Fargo Corporation’s Corporate Social Responsibility strategy. It employs a qualitative methodology using the instrumental case study approach, within which the broad research question is raised: How does the Wells Fargo Corporation strategize for, and implement, corporate social responsibility? Our findings revealed that Wells Fargo has a solid, well-integrated CSR strategy deeply engrained within its culture, skillfully aligned with its capabilities and resources, and that this plan has been executed expertly within the corporation. We will communicate our findings to their leadership team in hopes they continue expansion of their CSR strategies and recommend they consider seeking status as a B-Corporation

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 24th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 28 regular papers presented in this volume were carefully reviewed and selected from 88 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language