The Financial and Macroeconomic Implications of Banking Frictions and Banking Riskiness

This paper develops a model of banking frictions and banking riskiness, the importance of which is highlighted by the recent Global Financial Crisis (GFC). We propose a model-based approach to decompose the effect of a banking riskiness shock into a pure default effect and a risk effect when risk sharing among the depositors is imperfect. Although the default effect is quantitatively more important, the risk effect is not to be neglected. When the shock generates a bank spread similar in value to the peak during the GFC, the overall effect is a decline in employment by 6:57 percent. The pure default effect leads to a 4:76 percent employment decline by a “within-model” measure, and a 5:05 decline by a “between-model” measure. The remaining is attributed to the risk effect.Banking riskiness shocks; two-sided debt contract; default effects; risk effects; financial crisis.

Asset Prices, Monetary Policy, and Aggregate Fluctuations: An Empirical Investigation

This paper studies empirically the dynamic interactions between asset prices, monetary policy, and aggregate fluctuations during the Volcker-Greenspan period. Using a simple structural vector autoregression framework, we investigate the effects of monetary policy on output, inflation and asset prices, the interactions of asset prices with the aggregate economy, as well as the relationship between stock price and house price. Several robust findings emerge. The systematic response of monetary policy to output and inflation is also found to play an important role in stabilizing the aggregate economy. In addition, the results call for special attention to be paid to house price when studying the dynamic relationships between asset prices and macroeconomic fluctuations.House prices; stock prices; systematic monetary policy; structural vector autoregressions.

Real Estate, the External Finance Premium and Business Investment: A Quantitative Dynamic General Equilibrium Analysis

This paper studies the connection between the capital market and the real estate market. Empirically, we find that positive real house price shocks lower the external finance premium and stimulate nonresidential investment and real GDP. Our theoretical framework is able to mimic the volatility of the external finance premium, the relative price of real estate and capital, and the investment in real estate and capital. It also captures the cyclicality of the external finance premium and of real estate prices. The contribution of real estate price fluctuations to the variability of the external finance premium and the GDP is confirmed to be significant.External Finance Premium, Residential and Corporate Real Estate, Capital Market Imperfections, Equilibrium Default, Real Estate Price Volatility.

Picroside-I attenuated isoproterenol-induced heart damage via modification of cardio-morphology, infarct size and inflammatory cascade

Purpose: To study the effect of picroside-I (PIC-I) on isoproterenol (ISO)-induced heart damage in rats through determination of infarct size, antioxidant enzymes, cardiac/inflammatory and apoptotic markers, as well as cardio-morphology.Methods: A total of 32 rats were divided equally into 4 groups. Rats in normal control group were treated with saline only, while myocardial infarction (MI) rat model was prepared by intraperitoneal (i.p.) injection of ISO at a concentration of 100 mg/kg. Rats pretreated with PIC-Iat dose 10 mg/kg (i.p) for 28 days and administered with isoproterenol. Another group of rats was administered only with PIC-I (10 mg/kg) for 28 days.Results: After 28 days of pretreatment with PIC-I, there were significant increases in arterial blood pressure and cardiac antioxidants, as well as marked decreases in infarct size, cardiac markers, inflammatory markers and apoptotic markers in rats with ISO-induced heart damage, when compared with rats given ISO alone. Rats administered PIC-I showed better histology, with reduced necrosis and prominent cardiac fibers.Conclusion: PIC-1 pre-treatment for 28 days significantly reversed elevations in infarct size, cardiac/inflammatory and apoptotic markers, and also improved antioxidant status and cardiacmorphology in rats with ISO-induced heart damage. Keywords: Picroside-I, Isoproterenol, Infarct size, Inflammation, Apoptosi

Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach

The limited communication resources, e.g., bandwidth and energy, and data heterogeneity across devices are two of the main bottlenecks for federated learning (FL). To tackle these challenges, we first devise a novel FL framework with partial model aggregation (PMA), which only aggregates the lower layers of neural networks responsible for feature extraction while the upper layers corresponding to complex pattern recognition remain at devices for personalization. The proposed PMA-FL is able to address the data heterogeneity and reduce the transmitted information in wireless channels. We then obtain a convergence bound of the framework under a non-convex loss function setting. With the aid of this bound, we define a new objective function, named the scheduled data sample volume, to transfer the original inexplicit optimization problem into a tractable one for device scheduling, bandwidth allocation, computation and communication time division. Our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the optimal device scheduling. Compared with the state-of-the-art benchmarks, the proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets, i.e., MINIST and CIFAR-10, respectively. In addition, the proposed joint dynamic device scheduling and resource optimization approach achieve slightly higher accuracy than the considered benchmarks, but they provide a satisfactory energy and time reduction: 29% energy or 20% time reduction on the MNIST; and 25% energy or 12.5% time reduction on the CIFAR-10.Comment: 32pages, 7 figure

Real Estate, the External Finance Premium and Business Investment: A Quantitative Dynamic General Equilibrium Analysis

This paper studies the connection between the capital market and the real estate market. Empirically, we find that positive real house price shocks lower the external finance premium and stimulate nonresidential investment and real GDP. Our theoretical framework is able to mimic the volatility of the external finance premium, the relative price of real estate and capital, and the investment in real estate and capital. It also captures the cyclicality of the external finance premium and of real estate prices. The contribution of real estate price fluctuations to the variability of the external finance premium and the GDP is confirmed to be significant

Distributed Digital Twin Migration in Multi-tier Computing Systems

At the network edges, the multi-tier computing framework provides mobile users with efficient cloud-like computing and signal processing capabilities. Deploying digital twins in the multi-tier computing system helps to realize ultra-reliable and low-latency interactions between users and their virtual objects. Considering users in the system may roam between edge servers with limited coverage and increase the data synchronization latency to their digital twins, it is crucial to address the digital twin migration problem to enable real-time synchronization between digital twins and users. To this end, we formulate a joint digital twin migration, communication and computation resource management problem to minimize the data synchronization latency, where the time-varying network states and user mobility are considered. By decoupling edge servers under a deterministic migration strategy, we first derive the optimal communication and computation resource management policies at each server using convex optimization methods. For the digital twin migration problem between different servers, we transform it as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this problem, we propose a novel agent-contribution-enabled multi-agent reinforcement learning (AC-MARL) algorithm to enable distributed digital twin migration for users, in which the counterfactual baseline method is adopted to characterize the contribution of each agent and facilitate cooperation among agents. In addition, we utilize embedding matrices to code agents' actions and states to release the scalability issue under the high dimensional state in AC-MARL. Simulation results based on two real-world taxi mobility trace datasets show that the proposed digital twin migration scheme is able to reduce 23%-30% data synchronization latency for users compared to the benchmark schemes

Urban Lead: Modeling Its Distribution and Effects on Children

We model the transportation of lead from the atmosphere and from the surface of the soil simultaneously at the macroscale and mesoscale to study its health effects on children in Jersey City, NJ. We conceptualize Jersey City as an open system where lead is continuously emitted from a local smelting plant and a local power plant, deposited onto the surface soil of playgrounds, and ingested by children. The model is constructed using the diffusion-advection partial differential equation in three spatial dimensions and one temporal dimension with an initial condition and boundary conditions. The model is solved using the Crank-Nicolson numerical method at the macroscale to determine the deposition of lead from the smelting plant and the local power plant and at the mesoscale to refine the amount of lead deposition for the areas considered. We then determine the health consequences for the average child using the bioaccessibility of lead from soil to children, the bioavailability of ingested lead to the circulatory system, and the biological half-life of lead isotopes in the blood. The health effects on children from lead are directly proportional to the blood lead concentration

Adaptive Model Pruning for Communication and Computation Efficient Wireless Federated Learning

Most existing wireless federated learning (FL) studies focused on homogeneous model settings where devices train identical local models. In this setting, the devices with poor communication and computation capabilities may delay the global model update and degrade the performance of FL. Moreover, in the homogenous model settings, the scale of the global model is restricted by the device with the lowest capability. To tackle these challenges, this work proposes an adaptive model pruning-based FL (AMP-FL) framework, where the edge server dynamically generates sub-models by pruning the global model for devices’ local training to adapt their heterogeneous computation capabilities and time-varying channel conditions. Since the involvement of diverse structures of devices’ sub-models in the global model updating may negatively affect the training convergence, we propose compensating for the gradients of pruned model regions by devices’ historical gradients. We then introduce an age of information (AoI) metric to characterize the staleness of local gradients and theoretically analyze the convergence behaviour of AMP-FL. The convergence bound suggests scheduling devices with large AoI of gradients and pruning the model regions with small AoI for devices to improve the learning performance. Inspired by this, we define a new objective function, i.e., the average AoI of local gradients, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, model pruning, and resource block (RB) allocation design. Through detailed analysis, we derive the optimal model pruning strategy and transform the RB allocation problem into equivalent linear programming that can be effectively solved. Experimental results demonstrate the effectiveness and superiority of the proposed approaches. The proposed AMP-FL is capable of achieving 1.9x and 1.6x speed up for FL on MNIST and CIFAR-10 datasets in comparison with the FL schemes with homogeneous model settings