Feedback Effects and the Limits to Arbitrage

This paper identifies a limit to arbitrage that arises from the fact that a firm's fundamental value is endogenous to the act of exploiting the arbitrage. Trading on private information reveals this information to managers and helps them improve their real decisions, in turn enhancing fundamental value. While this increases the profitability of a long position, it reduces the profitability of a short position -- selling on negative information reveals that firm prospects are poor, causing the manager to cancel investment. Optimal abandonment increases firm value and may cause the speculator to realize a loss on her initial sale. Thus, investors may strategically refrain from trading on negative information, and so bad news is incorporated more slowly into prices than good news. The effect has potentially important real consequences -- if negative information is not incorporated into stock prices, negative-NPV projects may not be abandoned, leading to overinvestment.

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

A portable device for studying the effects of fluid flow on degradation properties of biomaterials inside cell incubators.

A portable device was designed and constructed for studying the properties of biomaterials in physiologically relevant fluids under controllable flow conditions that closely simulate fluid flow inside the body. The device can fit entirely inside a cell incubator; and, thus, it can be used directly under standard cell culture conditions. An impedance-driven pump was built in the sterile flow loop to control the flow rates of fluids, which made the device small and portable for easy deployment in the incubator. To demonstrate the device functions, magnesium (Mg) as a representative biodegradable material was tested in the flow device for immersion degradation under flow versus static conditions, while the flow module was placed inside a standard cell incubator. The flow rate was controlled at 0.17 ± 0.06 ml/s for this study; and, the flow rate is adjustable through the controller module outside of incubators for simulating the flow rates in the ranges of blood flow in human artery (0.05 ∼0.43 ml/s) and vein (0.02 ∼0.08 ml/s). Degradation of Mg under flow versus static conditions was characterized by measuring the changes of sample mass and thickness, and Mg2+ ion concentrations in the immersion media. Surface chemistry and morphology of Mg after immersion under flow versus static conditions were compared. The portable impedance-driven flow device is easy to fit inside an incubator and much smaller than a peristaltic pump, providing a valuable solution for studying biomaterials and implants (e.g. vascular or ureteral stents) in body fluids under flow versus static conditions with or without cells

Models of Customer Behavior: From Populations to Individuals

There have been various claims made in the marketing community about the benefits of 1-to-1 marketing versus traditional customer segmentation approaches and how much they can improve understanding of customer behavior. However, few rigorous studies exist that systematically compare these approaches. In this paper, we conducted such a systematic study and compared the performance of aggregate, segmentation, and 1-to-1 marketing approaches across a broad range of experimental settings such as multiple segmentation levels, multiple real world marketing datasets, multiple dependent variables, different types of classifiers, different segmentation techniques, and different predictive measures. Our results show that, overall, 1-to-1 modeling significantly outperforms the aggregate approach among high-volume customers and is never worse than aggregate approach among low-volume customers in our experimental settings. Moreover, the best segmentation techniques tend to outperform 1-to-1 modeling among low-volume customers.Information Systems Working Papers Serie

Identification of the salmonid IL-17A/F1a/b, IL-17A/F2b, IL-17A/F3 and IL-17N genes and analysis of their expression following in vitro stimulation and infection

Peer reviewedPostprin

BART-SIMP: a novel framework for flexible spatial covariate modeling and prediction using Bayesian additive regression trees

Prediction is a classic challenge in spatial statistics and the inclusion of spatial covariates can greatly improve predictive performance when incorporated into a model with latent spatial effects. It is desirable to develop flexible regression models that allow for nonlinearities and interactions in the covariate structure. Machine learning models have been suggested in the spatial context, allowing for spatial dependence in the residuals, but fail to provide reliable uncertainty estimates. In this paper, we investigate a novel combination of a Gaussian process spatial model and a Bayesian Additive Regression Tree (BART) model. The computational burden of the approach is reduced by combining Markov chain Monte Carlo (MCMC) with the Integrated Nested Laplace Approximation (INLA) technique. We study the performance of the method via simulations and use the model to predict anthropometric responses, collected via household cluster samples in Kenya

Improvements on Scalable Stochastic Bayesian Inference Methods for Multivariate Hawkes Process

Multivariate Hawkes Processes (MHPs) are a class of point processes that can account for complex temporal dynamics among event sequences. In this work, we study the accuracy and computational efficiency of three classes of algorithms which, while widely used in the context of Bayesian inference, have rarely been applied in the context of MHPs: stochastic gradient expectation-maximization, stochastic gradient variational inference and stochastic gradient Langevin Monte Carlo. An important contribution of this paper is a novel approximation to the likelihood function that allows us to retain the computational advantages associated with conjugate settings while reducing approximation errors associated with the boundary effects. The comparisons are based on various simulated scenarios as well as an application to the study the risk dynamics in the Standard & Poor's 500 intraday index prices among its 11 sectors