Scholars often conflate the concepts of pooling (how states make collective decisions) and delegation (authorizing an international body to act) in examining the authority of intergovernmental organizations (IGOs). We clarify the difference by showing how states “soft pool” decision-making through informal intergovernmental organizations (IIGOs) without creating legal obligations or delegating authority. IIGOs such as the G-groups are growing in prevalence and importance because soft pooling allows states to make collective decisions that are politically binding in nonlegal ways. We examine organizational characteristics of IIGOs that allow states to minimize sovereignty costs while cooperating through soft pooling – including the use of consensus to express shared expectations through declarations and memoranda of understanding and administrative structures such as rotating chairs to avoid delegating to an independent secretariat. We review these understudied organizational alternatives, explaining how soft pooling makes IIGOs authoritative even as states retain sovereignty.

Capital expenditure is not deductible under the general deduction provision in s 8-1 ITAA97 as it falls within the first negative limb of the section. This means that capital expenditure can only be deductible if it satisfies the requirements of a specific deduction provision. This chapter focuses on the ‘capital allowance regime’ in div 40 ITAA97, which allows taxpayers to deduct the cost of ‘depreciating assets’ that are used for a ‘taxable purpose’ over their ‘effective lives’. The regime operates subject to special rules that allow SBEs and certain other entities to claim immediate deductions in particular cases. It also contains ‘balancing adjustment’ rules that apply where taxpayers stop holding depreciating assets and ‘pooling’ rules that allow groups of assets to be written off together as if they were a single asset. There are also special rules that apply to primary producers and miners as well as business-related ‘blackhole’ capital expenditure.

The chapter also examines the ‘capital works regime’ in div 43 ITAA97, which provides deductions for construction expenditure on ‘capital works’ (eg buildings and structural improvements) used for income-producing and other eligible purposes.

Based on Chapter 6, in this chapter we expand the discussion of neural networks to include networks that have more than one hidden layer. Common structures such as the convolutional neural network (CNN) or the Long Short-Term Memory network (LSTM) are explained and used along with Matlab’s Deep Network Designer App as well as Matlab script to implement and train such networks. Issues such as the vanishing or exploding gradient, normalization, and training strategies are discussed. Concepts that address overfitting and the vanishing or exploding gradient are introduced, including dropout and regularization. Transfer learning is discussed and showcased using Matlab’s DND App.

Predictors can be fixed or random, and their classification affects how we fit and interpret statistical models. A mismatch between their treatment in the model and their interpretation is a common problem. This chapter focuses on categorical predictors and introduces nested or hierarchical designs that combine fixed and random effects. For these designs, we distinguish between those where the random effects correspond to replicate experimental and sampling units and those that also include multiple observational units within each replicate. We also consider factorial mixed models and introduce hybrid designs that combine factorial and nested components. We describe the fitting of these models using traditional “ANOVA” approaches using OLS and present an alternative approach used in the following chapters – linear mixed models or multilevel models. These modeling approaches are illustrated for multilevel nested designs and factorial designs with and without replication.

High performance health financing requires funding levels that are adequate, sustainable and resilient to meet country health goals, pooling that is sufficient to spread the financial risks of ill-health across the population, and spending that is both efficient and equitable to assure the desired levels of health service coverage, quality, and financial protection for all people. This chapter describes the various components of health financing and how they interact to determine how rapidly countries can approach the goal of Universal Health Coverage.

In the problems we have considered so far, we have either ignored the actual material consumption/production (sequential environments) or assumed that materials are consumed/produced at fixed proportions (network environments). There are problems, however, where the proportions in which materials are consumed can vary provided that some specifications are satisfied. This problem, which is termed multiperiod blending or simply blending, is fundamentally different from the ones discussed thus far because it leads to nonlinear models. There are two types of blending problems: (1) different streams/inputs are blended before they are processed/converted (process blending); and (2) streams/inputs are blended to produce final products (product blending). In Section 11.1, we introduce some preliminary concepts and a formal problem statement for product blending. In Section 11.2, we present two alternative formulations for product blending, and in Section 11.3, we present two approximate linear reformulations. We close, in Section 11.4, with a discussion of models for process blending. We focus on the equations necessary to account for the key new features of blending problems: (1) the selection of input materials and their blending in variable proportions, and (2) the requirement to satisfy given property specifications.

We cannot miss deep learning in a modern pattern recognition textbook, and we introduce CNN (convolutional neural networks) in this chapter. Although the mathematical derivation of CNN, especially the back-propagation process and gradient computation, is complex, we use a lot of useful tools to help readers understand what exactlyis going on in a CNN. Hence, this chapter focuses on accessibility rather than completeness. In its exercise problems, we introduce more relevant topics and methods.