Unsupervised Bilingual POS Tagging with Markov Random Fields

In this paper, we give a treatment to the problem of bilingual part-of-speech induction with parallel data. We demonstrate that naïve optimization of log-likelihood with joint MRFs suffers from a severe problem of local maxima, and suggest an alternative – using contrastive estimation for estimation of the parameters. Our experiments show that estimating the parameters this way, using overlapping features with joint MRFs performs better than previous work on the 1984 dataset.

Quantifying the effects of harvesting on carbon fluxes and stocks in northern temperate forests

Harvest disturbance has substantial impacts on forest carbon (C) fluxes and stocks. The quantification of these effects is essential for the better understanding of forest C dynamics and informing forest management in the context of global change. We used a process-based forest ecosystem model, PnET-CN, to evaluate how, and by what mechanisms, clear-cuts alter ecosystem C fluxes, aboveground C stocks (AGC), and leaf area index (LAI) in northern temperate forests. We compared C fluxes and stocks predicted by the model and observed at two chronosequences of eddy covariance flux sites for deciduous broadleaf forests (DBF) and evergreen needleleaf forests (ENF) in the Upper Midwest region of northern Wisconsin and Michigan, USA. The average normalized root mean square error (NRMSE) and the Willmott index of agreement (d) for carbon fluxes, LAI, and AGC in the two chronosequences were 20% and 0.90, respectively. Simulated gross primary productivity (GPP) increased with stand age, reaching a maximum (1200–1500 g C m−2 yr−1) at 11–30 years of age, and leveled off thereafter (900–1000 g C m−2 yr−1). Simulated ecosystem respiration (ER) for both plant functional types (PFTs) was initially as high as 700–1000 g C m−2 yr−1 in the first or second year after harvesting, decreased with age (400–800 g C m−2 yr−1) before canopy closure at 10–25 years of age, and increased to 800–900 g C m−2 yr−1 with stand development after canopy recovery. Simulated net ecosystem productivity (NEP) for both PFTs was initially negative, with net C losses of 400–700 g C m−2 yr−1 for 6–17 years after clear-cuts, reaching peak values of 400–600 g C m−2 yr−1 at 14–29 years of age, and eventually stabilizing in mature forests (\u3e 60 years old), with a weak C sink (100–200 g C m−2 yr−1). The decline of NEP with age was caused by the relative flattening of GPP and gradual increase of ER. ENF recovered more slowly from a net C source to a net sink, and lost more C than DBF. This suggests that in general ENF may be slower to recover to full C assimilation capacity after stand-replacing harvests, arising from the slower development of photosynthesis with stand age. Our model results indicated that increased harvesting intensity would delay the recovery of NEP after clear-cuts, but this had little effect on C dynamics during late succession. Future modeling studies of disturbance effects will benefit from the incorporation of forest population dynamics (e.g., regeneration and mortality) and relationships between age-related model parameters and state variables (e.g., LAI) into the model

Impact of tax reduction policies on consumer purchase of new automobiles : an analytical investigation with real data-based experiments

We investigate and compare the impact of the tax reduction policies implemented in the United States and China to stimulate consumer purchase of new automobiles and improve manufacturers\u27 profits. The U.S. policy provides each qualifying consumer with a federal income tax deduction on state and local sales and excise taxes paid on the purchase price (up to a cutoff level), whereas the Chinese policy reduces the vehicle sales tax rate for consumers. We observe that these policy designs are consistent with the tax management system and the economic environment in the respective country. We analytically determine the effects of the two tax reduction policies on the automobile sales and the manufacturer\u27s and the retailer\u27s profits. Numerical examples are then used to provide insights on the importance of certain factors that influence the effects of the two policies. Finally, a numerical experiment with sensitivity analysis based on real data is conducted to compare the merits and characteristics of the two policies under comparable conditions. We find that the U.S. policy is better than the Chinese policy in stimulating the sales of high-end automobiles, whereas the Chinese policy is better than the U.S. policy in improving the sales of low-end automobiles. The U.S. policy is slightly more effective in increasing the profitability of the automobile supply chain; but, in general, the Chinese policy is more cost effective. The methodology developed herein can be used to evaluate other tax reduction policies such as those related to the purchase of energy-saving vehicles and to serve as a decision model to guide the choice of alternative tax reduction policies

‘Short Interest Pressure’ and Competitive Behaviour

This study introduces and examines a new-to-strategy form of Wall Street pressure – ‘short interest pressure’ – the tension felt by management caused by short sales of the firm\u27s stock. Drawing from a sample of over 5000 competitive actions carried out by competing firms over a 6-year time period, we test whether the level of short interest pressure experienced by the firm in one time period is predictive of properties of the firm\u27s competitive action repertoire in the ensuing time period. Our findings suggest that when faced with short interest pressure firms tend to carry out a higher number of competitive actions in the following time period, as well as a set of actions that deviate from the industry norm. In addition, post hoc analysis reveals that this effect is amplified for poorly performing firms. Thus, our study contributes to a deeper understanding of the relationship between capital market signals and competitive strategy

Multiscale methods for fabrication design

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 135-146).Modern manufacturing technologies such as 3D printing enable the fabrication of objects with extraordinary complexity. Arranging materials to form functional structures can achieve a much wider range of physical properties than in the constituent materials. Many applications have been demonstrated in the fields of mechanics, acoustics, optics, and electromagnetics. Unfortunately, it is difficult to design objects manually in the large combinatorial space of possible designs. Computational design algorithms have been developed to automatically design objects with specified physical properties. However, many types of physical properties are still very challenging to optimize because predictive and efficient simulations are not available for problems such as high-resolution non-linear elasticity or dynamics with friction and impact. For simpler problems such as linear elasticity, where accurate simulation is available, the simulation resolution handled by desktop workstations is still orders of magnitudes below available printing resolutions. We propose to speed up simulation and inverse design process of fabricable objects by using multiscale methods. Our method computes coarse-scale simulation meshes with data-drive material models. It improves the simulation efficiency while preserving the characteristic deformation and motion of elastic objects. The first step in our method is to construct a library of microstructures with their material properties such as Young's modulus and Poisson's ratio. The range of achievable material properties is called the material property gamut. We developed efficient sampling method to compute the gamut by focusing on finding samples near and outside the currently sampled gamut. Next, with a pre-computed gamut, functional objects can be simulated and designed using microstructures instead of the base materials. This allows us to simulate and optimize complex objects at a much coarser scale to improve simulation efficiency. The speed improvement leads to designs with as many as a trillion voxels to match printer resolutions. It also enables computational design of dynamic properties that can be faithfully reproduced in reality. In addition to efficient design optimization, the gamut representation of the microstructure envelope provides a way to discover templates of microstructures with extremal physical properties. In contrast to work where such templates are constructed by hand, our work enables the first computational method to automatically discovery microstructure templates that arise from voxel representations.by Desai Chen.Ph. D

Reducer-tuner model for translating specifications to 3D prints

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 42-45).Multi-material 3D printing allows objects to be composed of complex, heterogeneous arrangements of materials. It is often more natural to define a functional goal than to define the material composition of an object. Translating these functional requirements to fabricable 3D prints is still an open research problem. Recently, several specific instances of this problem have been explored (e.g., appearance or elastic deformation), but they exist as isolated, monolithic algorithms. In this research, I propose an abstraction mechanism that simplifies the design, development, implementation, and reuse of these algorithms. The solution relies on two new data structures: a reducer tree that efficiently parameterizes the space of material assignments and a tuner network that describes the optimization process used to compute material arrangement. I provide an application programming interface for specifying the desired object and for defining parameters for the reducer tree and tuner network. I illustrate the utility of my new framework by implementing several fabrication algorithms as well as demonstrating the manufactured results.by Desai Chen.S.M

Theory Summary and Future Directions

Summary talk at the Lepton-Photon Symposium, Cornell University, Aug. 10-15, 1993.Comment: (Talk presented at the Lepton-Photon Symposium, Cornell University, Aug. 10-15, 1993.) 19 page