Learning to Embed Words in Context for Syntactic Tasks

We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are specific to a given context, such as word sense, syntactic category, and semantic role. We explore simple, efficient token embedding models based on standard neural network architectures. We learn token embeddings on a large amount of unannotated text and evaluate them as features for part-of-speech taggers and dependency parsers trained on much smaller amounts of annotated data. We find that predictors endowed with token embeddings consistently outperform baseline predictors across a range of context window and training set sizes.Comment: Accepted by ACL 2017 Repl4NLP worksho

The Rise of State-Specific Attempts to Decipher the Sufficiency-of-a-Debtor-Name Standard Under Revised Article 9 and the End of Uniformity in Secured Transactions

This is the published version

Modeling plant-soil-atmosphere carbon dioxide exchange using optimality principles

The exchange of carbon dioxide (CO2) between terrestrial ecosystems and the atmosphere plays a central role in the ecology of the biosphere and the climate system. Towards quantification of ecosystem-atmosphere CO 2 exchange, a generalized model of plant-soil-atmosphere CO2 exchange (OPTICAL) was described and evaluated using eddy covariance measurements of net ecosystem exchange of CO2 (NEE) in arctic, boreal, temperate, and tropical landscapes. The model requires no calibration and is based on theories of plant resource optimization and plant-soil nutrient feedbacks. The model predicts canopy photosynthetic capacity (Pcmax), canopy photosynthesis (P c), plant respiration (Rp), and soil heterotrophic respiration (RH). It can be applied globally using satellite-derived estimates of canopy light absorptance (f APAR), incident radiation (PAR), and air temperature (T air). The model provides the means by which to relate satellite observations such as the Normalized Difference Vegetation Index (NDVI) to the physiological status of vegetation and to ecosystem-atmosphere carbon exchange. A unique aspect of the model is its use of a recursive filter for calculating photosynthetic acclimation based on the integrated effect of environmental conditions. Good agreement was found between modeled and observed Pcmax (r2 = 0.76), the latter derived from light response curves fit to estimates of gross ecosystem exchange (GEE). Consistent with theories of resource optimization, P cmax varied strongly with time-averaged absorbed PAR and temperature. Modeled Pcmax combined with a \u27big-leaf\u27 canopy model explained 74 to 85% of the variability in GEE. The photo-acclimation model not only performed better than a traditional time-invariant model and as good or better than calibrated site-specific models, it did not require knowledge of vegetation type. The process of photo-acclimation appeared most important during periods of greatest transition in plant physiological status (e.g. spring and fall). Agreement between modeled and observed NEE (r2 = 0.66 to 0.81) was similar to that for GEE, implying little additional error was introduced by predictions of Rp and R H. Despite excellent agreement between modeled and observed cumulative photosynthesis (r2 = 0.98) and ecosystem respiration (Rp + RH) (r 2 = 0.99), agreement for NEE was not as good (r2 = 0.75), due in part to NEE being the small difference between the two much larger fluxes of photosynthesis and ecosystem respiration

Extreme Policy Makeover: Re-Evaluating Current U.S.-Vietnam Relations under the International Religious Freedom Act

Following the signing of the Paris Peace Accord in 1973, the relationship between the United States and Vietnam remained essentially frozen. In 2000, the signing of the United States-Vietnam Bilateral Trade Agreement was an epic step in the normalization of relations. In addition, the BTA was hailed as a means of effectuating positive change in the area of Vietnam\u27s human rights. Unfortunately, the state of religious freedom in Vietnam has deteriorated while economic ties with the United States have strengthened. Despite Vietnam\u27s purported respect for religious freedom, violations continue. Vietnam restricts the practice of religion, detains religious leaders, and tolerates forced renunciations of faith by local officials. These acts violate the International Covenant on Civil and Political Rights, to which Vietnam has acceded. Vietnam\u27s violations of the right to religious freedom have also drawn the concern of the international community. Specifically, the United States has called for improvements in Vietnam\u27s religious rights record, utilizing diplomatic mean coupled with continued engagement in the hopes that Vietnam will voluntarily enact changes. However, this approach has failed to yield concrete progress. In 2004, the U.S. Department of State designated Vietnam a Country of Particular Concern as provided in the International Religious Freedom Act. Because the IRFA mandates affirmative action against violators of religious freedom, the United States must abandon constructive engagement in Vietnam. Instead, the IRFA provides the framework for opposing violations under the responsible engagement doctrine. In doing so, the United States may employ economic pressure to narrowly target violators, while allowing the liberalizing effect of engagement to continue where it does not sustain violations. By fully implementing the IRFA in accordance with the tenets of responsible engagement, the United States would actively oppose violations rather than engaging Vietnam with the hope that improvements will occur. Moreover, this extreme makeover of current policy would balance the dual interests of improved religious freedom and bilateral relations

Blockchain Stock Ledgers

American corporate law contains a seemingly innocuous mandate. Corporations must maintain appropriate books and records, including a stock ledger with the corporation\u27s shareholders and stock ownership. The importance of accurate stock ownership records is obvious. Corporations must know who owns each of its outstanding shares at any point in time. Among other things, this allows corporations to determine who receives dividends and who is entitled to vote. In theory, keeping accurate records of stock ownership should be a simple matter. But despite diligent efforts, serious share discrepancies plague corporations, and reconciliation is often functionally impossible. Doing so may require the examination of records from millions of trades, including records from hundreds of participant brokers and custodial banks (not to mention records from their individual clients). So, when disputes arise, there is frequently no easy answer. This Article charts the use of blockchain technology as a potential solution to the systemic issues hindering efforts to maintain accurate records of stock transactions. In doing so, this Article accomplishes three goals. First, it establishes that federal efforts to resolve the paperwork crisis of the 1970\u27s created a concomitant problem the lack of reliable records of stock ownership, which now threatens the exercise of shareholder rights. Second, it demonstrates that practical constraints, not legal barriers, stand as the most significant impediment to the application of blockchain technology to corporate recordkeeping and global capital markets. Third, it argues that despite reasons for skepticism, states should proactively amend corporate codes to authorize the use of blockchain technology because it enables corporate choice and facilitates efforts by private actors to assess the viability of innovative solutions. This Article concludes by drawing transferable lessons to improve law and policy as new applications of blockchain technology continue to emerge

Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

Despite the steady progress in video analysis led by the adoption of convolutional neural networks (CNNs), the relative improvement has been less drastic as that in 2D static image classification. Three main challenges exist including spatial (image) feature representation, temporal information representation, and model/computation complexity. It was recently shown by Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained on ImageNet, could be a promising way for spatial and temporal representation learning. However, as for model/computation complexity, 3D CNNs are much more expensive than 2D CNNs and prone to overfit. We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices. In particular, we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions. Rather surprisingly, best result (in both speed and accuracy) is achieved when replacing the 3D convolutions at the bottom of the network, suggesting that temporal representation learning on high-level semantic features is more useful. Our conclusion generalizes to datasets with very different properties. When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating, our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks (Kinetics, Something-something, UCF101 and HMDB), as well as two action detection (localization) benchmarks (JHMDB and UCF101-24).Comment: ECCV 2018 camera read