Towards Effective Social Insurance in Latin America: The Importance of Countercyclical Fiscal Policy

Latin America is a volatile, crisis-prone region, with limited and inadequate social insurance. Therefore, the long-term as well as the recent poor suffer significantly during crises. Furthermore, social spending is procyclical in the region, but less so than total spending, indicating that the effectiveness of compensatory social policies designed to protect those vulnerable to crises is constrained by adjustments during recessions. The causes of procyclical fiscal policy lie in the political constraints on saving during expansions, combined with limited creditworthiness during recessions, and enhanced by economic volatility and a low share of automatic stabilizers in the budget.

Towards Effective Social Insurance in Latin America: The Importance of Countercyclical Fiscal Policy

Latin America is a volatile, crisis-prone region, with limited and inadequate social insurance. Therefore, the long-term as well as the recent poor suffer significantly during crises. Furthermore, social spending is procyclical in the region, but less so than total spending, indicating that the effectiveness of compensatory social policies designed to protect those vulnerable to crises is constrained by adjustments during recessions. The causes of procyclical fiscal policy lie in the political constraints on saving during expansions, combined with limited creditworthiness during recessions, and enhanced by economic volatility and a low share of automatic stabilizers in the budget. We evaluate policy options to reduce procyclicality of fiscal policy, such as stabilization funds, fiscal rules and reform of budget institutions, and argue in favor of integrated policy proposals based on more country-specific analysis, such as the Fiscal Responsibility Law in Brazil.

Who Decides on Public Expenditures? A Political Economy Analysis of the Budget Process: The Case of Argentina

The budget process is increasingly considered key for reform efforts to improve fiscal outcomes. In this paper we embark on a political economy analysis of the budget process in Argentina, in the spirit of the IDB project “Political Institutions, Policymaking Processes and Policy Outcomes” in order to understand who determines budget outcomes in Argentina. In particular, we seek to characterize the institutional framework that regulates the budget preparation, approval, implementation and control. Furthermore, we identify which actors are involved both formally and informally in the process at each stage, and seek to understand their incentives and interactions. We find that the President has a de facto role that is much more powerful than what the laws and institutions of the budget process stipulate. However, the rigidity of the budget process, together with other constraints such as macroeconomic shocks, fiscal rules, agreements with International Financial Institutions (IFIs) and the influence of other actors such as governors, legislators and lobbies - have limited the ability of the Executive to substantially modify the budget process. Furthermore, compared with the period of high inflation of 1983-1991, in the past decade we have witnessed dramatic improvements in the institutionalization of the budget process, both in political and administrative terms. These reforms have accompanied a strong improvement in fiscal outcomes in the 1990s compared with the 1980s, and provided some of the tools necessary to limit the depth of the recent crisis and regain macroeconomic stability.

N=2 Super Yang-Mills and the XXZ spin chain

We analyse the renormalisation properties of composite operators of scalar fields in the N=2 Super Yang-Mills theory. We compute the matrix of anomalous dimensions in the planar limit at one-loop order in the 't Hooft coupling, and show that it corresponds to the Hamiltonian of an integrable XXZ spin chain with an anisotropy parameter Delta>1. We suggest that this parameter could be related to the presence of non-trivial two-form fluxes in the dual supergravity background. We find that the running of the gauge coupling does not affect the renormalization group equations for these composite operators at one-loop order, and argue that this is a general property of gauge theories which is not related to supersymmetry.Comment: 16 pages, 2 figures; v2: references added, misprints correcte

DFL: Dynamic Federated Split Learning in Heterogeneous IoT

Federated Learning (FL) in edge Internet of Things (IoT) environments is challenging due to the heterogeneous nature of the learning environment, mainly embodied in two aspects. Firstly, the statistically heterogeneous data, usually non-independent identically distributed (non-IID), from geographically distributed clients can deteriorate the FL training accuracy. Secondly, the heterogeneous computing and communication resources in IoT devices often result in unstable training processes that slow down the training of a global model and affect energy consumption. Most existing solutions address only the unilateral side of the heterogeneity issue but neglect the joint problem of resources and data heterogeneity for the resource-constrained IoT. In this article, we propose Dynamic Federated split Learning (DFL) to address the joint problem of data and resource heterogeneity for distributed training in IoT. DFL enhances training efficiency in heterogeneous dynamic IoT through resource-aware split computing of deep neural networks and dynamic clustering of training participants based on the similarity of their sub-model layers. We evaluate DFL on a real testbed comprising heterogeneous IoT devices using two widely-adopted datasets, in various non-IID settings. Results show that DFL improves training performance in terms of training time by up to 48%, accuracy by up to 32%, and energy consumption by up to 62.8% compared to classic FL and Federated Split Learning in scenarios with both data and resource heterogeneity

ARES: Adaptive Resource-Aware Split Learning for Internet of Things

Distributed training of Machine Learning models in edge Internet of Things (IoT) environments is challenging because of three main points. First, resource-constrained devices have large training times and limited energy budget. Second, resource heterogeneity of IoT devices slows down the training of the global model due to the presence of slower devices (stragglers). Finally, varying operational conditions, such as network bandwidth, and computing resources, significantly affect training time and energy consumption. Recent studies have proposed Split Learning (SL) for distributed model training with limited resources but its efficient implementation on the resource-constrained and decentralized heterogeneous IoT devices remains minimally explored. We propose Adaptive REsource-aware Splitlearning (ARES), a scheme for efficient model training in IoT systems. ARES accelerates local training in resource-constrained devices and minimizes the effect of stragglers on the training through device-targeted split points while accounting for time-varying network throughput and computing resources. ARES takes into account application constraints to mitigate training optimization tradeoffs in terms of energy consumption and training time. We evaluate ARES prototype on a real testbed comprising heterogeneous IoT devices running a widely-adopted deep neural network and dataset. Results show that ARES accelerates model training on IoT devices by up to 48% and minimizes the energy consumption by up to 61.4% compared to Federated Learning (FL) and classic SL, without sacrificing the model convergence and accurac

REACT: A Solidarity-based Elastic Service Resource Reallocation Strategy for Multi-access Edge Computing

The Multi-access Edge Computing (MEC) paradigm promises to enhance network flexibility and scalability through resource virtualization. MEC allows telecom operators to fulfill the stringent and heterogeneous requirements of 5G applications via service deployment at the edge of the mobile network. However, current solutions to support MEC struggle to provide resource elasticity since MEC infrastructures have limited resources. The coexistence of many heterogeneous services on the distributed MEC infrastructure makes the resource scarcity problem even more challenging than it already is in traditional networks. Services need distinct resource provisioning patterns due to their diverse requirements, and we may not assume an extensive MEC infrastructure that can accommodate an arbitrary number of services. To address these aspects, we present REACT: a MEC-suppoRted sElfadaptive elAstiCiTy mechanism that leverages resource provisioning among different services running on a shared MEC environment. REACT adopts an adaptive and solidarity-based strategy to redistribute resources from over-provisioned services to under-provisioned services in MEC environments. REACT is an alternative strategy to avoid service migration due to resource scarcity. Real testbed results show that REACT outperforms Kubernetes’ elasticity strategy by accomplishing up to 18.88% more elasticity events, reducing service outages by up to 95.1%, reducing elasticity attempts by up to 95.36%, and reducing over-provisioned resources by up to 33.88%, 38.41%, and 73% for CPU cycles, RAM and bandwidth resources, respectively. Finally, REACT reduces response time by up to 15.5%

Self-assembled guanine ribbons as wide-bandgap semiconductors

We present a first principle study about the stability and the electronic properties of a new biomolecular solid-state material, obtained by the self-assembling of guanine (G) molecules. We consider hydrogen-bonded planar ribbons in isolated and stacked configurations. These aggregates present electronic properties similar to inorganic wide-bandgap semiconductors. The formation of Bloch-type orbitals is observed along the stacking direction, while it is negligible in the ribbon plane. Global band-like conduction may be affected by a dipole-field which spontaneously arises along the ribbon axis. Our results indicate that G-ribbon assemblies are promising materials for biomolecular nanodevices, consistently with recent experimental results.Comment: 7 pages, 3 figures, to be published in Physica