Debt Crises, Fast and Slow

Drawing on the theory of sovereign risk, we show that, driven by self-fulfilling expectations of default, both slow-moving and rollover (fast) crises are generically possible in models with standard features, at intermediate and high levels of debt, respectively. This is without relying on the specification of debt auctions by Cole and Kehoe (2000). A necessary condition is that debt tolerance thresholds—the time- and state- contingent levels of debt above which default becomes the preferred action by the government—respond endogenously to shifts in investors' expectations. In a sunspot equilibrium, the threat of belief-driven crises may not be enough for the government to deleverage in a recession, and bring debt to default-free levels. Unless the initial debt is close enough to the critical threshold above which the country becomes vulnerable to such crises, the government will keep borrowing, gambling on economic recovery in the future

Influence of Oxygen content on the electrochemical behavior of Ta1-xOx coatings

In this study, Ta1-xOx coatings were deposited by reactive magnetron sputtering aiming at the enhancement of the electrochemical stability stainless steel 316L. The coatings were produced using variable oxygen content in order to determine its influence on the films morphological features and corrosion resistance. Structural and morphological characteristics were correlated with the corrosion behavior in artificial saliva. Potentiodynamic and electrochemical impedance spectroscopy tests were complemented with X-ray photoelectron spectroscopy to determine the electrochemical behavior of the coatings. The results reveal a more protective behavior of the coatings as the oxygen amount increases in the films, as well as pitting inhibition in the coated stainless steel, independently of the film composition. A synergetic effect between Ta2O5 and phosphate-based passive layers is suggested as the protective mechanisms of the coatings; while the more active electrochemical behavior of low oxygen content films is evidenced as a consequence of the metallic tantalum on the surface with a more open morphology and larger density of defects on the surface.This research is sponsored by FEDER funds through the program COMPETE – Programa Operacional Factores de Competitividade – by national funds through FCT – Fundação para a Ciência e a Tecnologia , in the framework of the Strategic Projects PEST-C/FIS/UI607/2013, and PEst-C/EME/UI0285/2013, and with a PhD fellowship SFRH/BD/98199/2013. The authors thank the financial support by IAPMEI funds through QREN – Implantes dentários inteligentes – SMARTDENT, Projeto Vale Inovação n. 2012/24005 and by MCTI/CNPQ N 16/2012 TECNOLOGIAS INOVADORAS NA PRODUÇÃO, PROTOTIPAGEM E/OU AUMENTO DE ESCALA EM NANOTECNOLO- GIA – Desenvolvimento de Titânio e Liga de Titânio Nano-estruturados com Tratamentos de Superfície para Aplicação em Implantes Ósseos

Lazaroid U-74500A for warm ischemia and reperfusion injury of the canine small intestine

BACKGROUND: Although lazaroids have been shown to protect various organs from ischemia/reperfusion injury, results obtained in the small intestine have been conflicting. STUDY DESIGN: The canine small intestine was made totally ischemic for 2 hours by occluding the superior mesenteric artery and the superior mesenteric vein with interruption of the mesenteric collateral vessels. A lazaroid compound, U74500A, or a citrate vehicle was given intravenously to each of the six animals for 30 minutes before intestinal ischemia. Intestinal tissue blood flow, lipid peroxidation, neutrophil infiltration, adenine nucleotides and their catabolites, and histologic changes after reperfusion were determined. RESULTS: Lazaroid treatment attenuated decline of the mucosal and serosal blood flow after reperfusion. Accumulation of lipid peroxidation products and neutrophils in mucosal tissues was markedly inhibited by the treatment. Postischemic energy resynthesis was also augmented by lazaroid. Morphologically, mucosal architectures were better preserved with lazaroid treatment after reperfusion, and recovered to normal by postoperative day 3 in the treated group and by post-operative day 7 in control animals. CONCLUSIONS: Lazaroids protect the canine small intestine from ischemia/reperfusion injury by inhibiting lipid peroxidation and neutrophil infiltration. Dogs are tolerant of 2-hour normothermic complete intestinal ischemia

Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems

Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest or news feed. State-of-the-art recommendation models rely on sparse and dense features to represent users' profile information and the items they interact with. Although sparse features account for 99% of the total model size, there was not enough attention paid to the potential information leakage through sparse features. These sparse features are employed to track users' behavior, e.g., their click history, object interactions, etc., potentially carrying each user's private information. Sparse features are represented as learned embedding vectors that are stored in large tables, and personalized recommendation is performed by using a specific user's sparse feature to index through the tables. Even with recently-proposed methods that hides the computation happening in the cloud, an attacker in the cloud may be able to still track the access patterns to the embedding tables. This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns. We first characterize the types of attacks that can be carried out on sparse features in recommendation models in an untrusted cloud, followed by a demonstration of how each of these attacks leads to extracting users' private information or tracking users by their behavior over time

Scaling of nestedness in complex networks

Nestedness characterizes the linkage pattern of networked systems, indicating the likelihood that a node is linked to the nodes linked to the nodes with larger degrees than it. Networks of mutualistic relationship between distinct groups of species in ecological communities exhibit such nestedness, which is known to support the network robustness. Despite such importance, quantitative characteristics of nestedness is little understood. Here we take graph-theoretic approach to derive the scaling properties of nestedness in various model networks. Our results show how the heterogeneous connectivity patterns enhance nestedness. Also we find that the nestedness of bipartite networks depend sensitively on the fraction of different types of nodes, causing nestedness to scale differently for nodes of different types.Comment: 9 pages, 4 figures, final versio

GPU-based Private Information Retrieval for On-Device Machine Learning Inference

On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1-10 GBs of data, making them impractical to store on-device. To overcome this barrier, we propose the use of private information retrieval (PIR) to efficiently and privately retrieve embeddings from servers without sharing any private information. As off-the-shelf PIR algorithms are usually too computationally intensive to directly use for latency-sensitive inference tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR with the downstream ML application to obtain further speedup. Our GPU acceleration strategy improves system throughput by more than $20 \times$ over an optimized CPU PIR implementation, and our PIR-ML co-design provides an over $5 \times$ additional throughput improvement at fixed model quality. Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100,000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy