An Equilibrium Model of Lumpy Housing Investment

We formulate and solve a dynamic general equilibrium model with heterogeneous agents and lumpy housing adjustment at the household level. We use the model to ask a simple question: how does the microeconomic lumpiness of housing adjustment affect the equilibrium dynamic properties of aggregate consumption and investment? Our main conclusion is that lumpiness matters: in particular, lumpiness in housing adjustment (1) reduces the volatility of both housing and business investment; (2) increases the volatility of aggregate consumption; (3) increases the correlation of housing investment with business investment and with GDP. We also show that lumpiness of investment activity at the household level has small but significant aggregate implications, in contrast with the literature that shows that the aggregate effects of lumpy investment at the firm level are negligible.

Housing and Debt over the Life Cycle and over the Business Cycle

We study housing and debt in a quantitative general equilibrium model. In the cross-section, the model matches the wealth distribution, the age pro?les of homeownership and mortgage debt, and the frequency of housing adjustment. In the time-series, the model matches the procyclicality and volatility of housing investment, and the procyclicality of mortgage debt. We use the model to conduct two experiments. First, we investigate the consequences of higher individual income risk and lower downpayments, and ?nd that these two changes can explain, in the model and in the data, the reduced volatility of housing investment, the reduced procyclicality of mortgage debt, and a small fraction of the reduced volatility of GDP. Second, we use the model to look at the behavior of housing investment and mortgage debt in an experiment that mimics the Great Recession: we ?nd that countercyclical financial conditions can account for large drops in housing activity and mortgage debt when the economy is hit by large negative shocks.Housing, Housing Investment, Mortgage Debt, Life-cycle Models, Income Risk, Homeownership, Precautionary Savings, Borrowing Constraints

Housing and debt over the life cycle and over the business cycle

Housing and mortgage debt are studied in a quantitative general equilibrium model. The model matches wealth distribution, age profiles of homeownership and debt, and frequency of housing adjustment. Over the cycle, the model matches the cyclicality and volatility of housing investment, and the procyclicality of debt. Higher individual income risk and lower downpayments can explain the reduced volatility of housing investment, the reduced procyclicality of debt, and part of the reduced volatility of GDP. In an experiment that mimics the Great Recession, countercyclical financial conditions can account for large drops in housing activity and debt following large negative shocks

GURLS: A Least Squares Library for Supervised Learning

We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non- specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines for efficient model selection. The library is particularly well suited for multi-output problems (multi-category/multi-label). GURLS is currently available in two independent implementations: Matlab and C++. It takes advantage of the favorable properties of regularized least squares algorithm to exploit advanced tools in linear algebra. Routines to handle computations with very large matrices by means of memory-mapped storage and distributed task execution are available. The package is distributed under the BSD license and is available for download at https://github.com/LCSL/GURLS

Exploiting news to categorize tweets: Quantifying the impact of different news collections

Short texts, due to their nature which makes them full of abbreviations and new coined acronyms, are not easy to classify. Text enrichment is emerging in the literature as a potentially useful tool. This paper is a part of a longer term research that aims at understanding the effectiveness of tweet enrichment by means of news, instead of the whole web as a knowledge source. Since the choice of a news collection may contribute to produce very different outcomes in the enrichment process, we compare the impact of three features of such collections: volume, variety, and freshness. We show that all three features have a significant impact on categorization accuracy. Copyright \ua9 2016 for the individual papers by the paper's authors

The use of dynamic probing tests and cone penetration tests to verify the effectiveness of expanding polyurethane resin injections for ground improvement

Injection of expanding polyurethane resins is a popular method to improve both the stiffness and the shear strength of the ground below existing foundations. The effect of the polyurethane resin expansion is to increase the soil confining stress and density around the injection holes. An estimation of the horizontal stress and volumetric strain changes that are induced within the ground is derived from the theory of cavity expansion in elasto-plastic materials. A series of case-histories is presented to document the feasibility of different in-situ tests to evaluate the achieved ground improvement. The tests have been performed before and after the injection of polyurethane resins and the obtained results have been compared with theoretical predictions. The considered investigation methods include the dynamic probing tests and the cone penetration tests. The preliminary results that have been achieved using an experimental miniature cone penetration test are also illustrated. The advantages and limitations of different test methods are discussed and practical indications for conducting such verifications of polyurethane resin injection effectiveness are provided

GURLS: a Toolbox for Regularized Least Squares Learning

We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm. The toolbox takes advantage of all the favorable properties of least squares and is tailored to deal in particular with multi-category/multi-label problems. One of the main advantages of GURLS is that it allows training and tuning a multi-category classifier at essentially the same cost of one single binary classifier. The toolbox provides a set of basic functionalities including different training strategies and routines to handle computations with very large matrices by means of both memory-mapped storage and distributed task execution. The system is modular and can serve as a basis for easily prototyping new algorithms. The toolbox is available for download, easy to set-up and use

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

In many complex molecular systems, the macroscopic ensemble's properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g., Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g., Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g., LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales

New Insights into the Surfactant-Assisted Liquid-Phase Exfoliation of Bi2S3 for Electrocatalytic Applications

During water electrolysis, adding an electrocatalyst for the hydrogen evolution reaction (HER) is necessary to reduce the activation barrier and thus enhance the reaction rate. Metal chalcogenide-based 2D nanomaterials have been studied as an alternative to noble metal electrocatalysts because of their interesting electrocatalytic properties and low costs of production. However, the difficulty in improving the catalytic efficiency and industrializing the synthetic methods have become a problem in the potential application of these species in electrocatalysis. Liquid-phase exfoliation (LPE) is a low-cost and scalable technique for lab- and industrial-scale synthesis of 2D-material colloidal inks. In this work, we present, to the best of our knowledge, for the first time a systematic study on the surfactant-assisted LPE of bulk Bi2S3 crystalline powder to produce nanosheets (NSs). Different dispersing agents and LPE conditions have been tested in order to obtain colloidal low-dimensional Bi2S3 NSs in H2O at optimized concentrations. Eventually, colloidally stable layered nano-sized Bi2S3 suspensions can be produced with yields of up to ~12.5%. The thus obtained low-dimensional Bi2S3 is proven to be more active for HER than the bulk starting material, showing an overpotential of only 235 mV and an optimized Tafel slope of 125 mV/dec. Our results provide a facile top-down method to produce nano-sized Bi2S3 through a green approach and demonstrate that this material can have a good potential as electrocatalyst for HER