A Third Vaccine Success – Oxford University breakthrough on global COVID-19 vaccine

Very promising news — and now some of the key questions for 2021 …

What is the Venn between these candidate vaccines?
Hopefully it is not perfectly overlapping so a patient can take one vaccine if another vaccine proves to be ineffective.

Where is the testing regime to allow folks to explore the efficacy at the personal level?
While it is helpful to offer a characterization of the mean-field performance of a vaccine, we cannot expect folks to ‘get back to normal’ unless they have some personal assurance that the vaccine has actually worked for them.

How long does immunity last ?
This is still unknown. 6 months, 1 year, etc. Also, even if the vaccine ‘fails’ or wanes how much does it reduce the severity of COVID-19?

What about Children?
Trials for Children have yet to begin (or have only recently started). While Children appear to have had less issues with COVID-19 (perhaps because of exposure to other coronaviruses, etc.), there is still the question of how well the vaccine will perform on Children.

OpenEDGAR: Open Source Software for SEC EDGAR Analysis is published in MIT Computational Law Report

Today our Paper – “OpenEDGAR: Open Source Software for SEC EDGAR Analysis” was published in MIT Computational Law Report.

ABSTRACT:  OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC). OpenEDGAR is built on the Django application framework, supports distributed compute across one or more servers, and includes functionality to (i) retrieve and parse index and filing data from EDGAR, (ii) build tables for key metadata like form type and filer, (iii) retrieve, parse, and update CIK to ticker and industry mappings, (iv) extract content and metadata from filing documents, and (v) search filing document contents. OpenEDGAR is designed for use in both academic research and industrial applications, and is distributed under MIT License at https://github.com/LexPredict/openedgar

Data Science & Machine Learning in Containers (or Ad Hoc vs Enterprise Grade Data Products)

As Mike Bommarito, Eric Detterman and I often discuss – one of the consistent themes in the Legal Tech / Legal Analytics space is the disconnect between what might be called ‘ad hoc’ data science and proper enterprise grade products / approaches (whether B2B or B2C). As part of the organizational maturity process, many organizations who decide that they must ‘get data driven’ start with an ad hoc approach to leveraging doing data science. Over time, it then becomes apparent that a more fundamental and robust undertaking is what is actually needed.

Similar dynamics also exist within the academy as well. Many of the code repos out there would not be considered proper production grade data science pipelines.  Among other things, this makes deployment, replication and/or extension quite difficult.

Anyway, this blog post from Neptune.ai outlines just some of these issues.

Complex Societies and the Growth of the Law – Published Today in Scientific Reports (Nature Research)

Access the Full Article via Scientific Reports (Nature Research). This article is part of a special compilation for Scientific Reports devoted to Social Physics.

ABSTRACT: While many informal factors influence how people interact, modern societies rely upon law as a primary mechanism to formally control human behaviour. How legal rules impact societal development depends on the interplay between two types of actors: the people who create the rules and the people to which the rules potentially apply. We hypothesise that an increasingly diverse and interconnected society might create increasingly diverse and interconnected rules, and assert that legal networks provide a useful lens through which to observe the interaction between law and society. To evaluate these propositions, we present a novel and generalizable model of statutory materials as multidimensional, time-evolving document networks. Applying this model to the federal legislation of the United States and Germany, we find impressive expansion in the size and complexity of laws over the past two and a half decades. We investigate the sources of this development using methods from network science and natural language processing. To allow for cross-country comparisons over time, based on the explicit cross-references between legal rules, we algorithmically reorganise the legislative materials of the United States and Germany into cluster families that reflect legal topics. This reorganisation reveals that the main driver behind the growth of the law in both jurisdictions is the expansion of the welfare state, backed by an expansion of the tax state. Hence, our findings highlight the power of document network analysis for understanding the evolution of law and its relationship with society.

It has been a real pleasure to work with my transatlantic colleagues Corinna Coupette (Max Planck Institute for Informatics), Janis Beckedorf (Heidelberg University) and Dirk Hartung (Bucerius Law School). We have other projects also in the works — so stay tuned!

Election Models, Election Dynamics and Early Voting Data

As it stands today, the Biden Campaign would appear quite likely but not guaranteed to win come November 3rd (or at some point thereafter). It could end early on November 3rd (if Florida appears to be trending toward Biden). Namely, it is hard to craft a scenario whereby Trump loses Florida and wins the White House. 538 has created an interactive where you can explore the inferential dynamics between the states (we learn about the likelihood in State B from the earlier results in State A). The interactive also highlights how results in early reporting states can reduce the remaining plausible paths to victory (there are only a few paths for Trump at this point).

Of course, it should be stated that remaining events or other issues could (potentially) change the dynamics or undermine the ability to leverage polls to make a proper inference. Here are few possibilities —

(1) Another October Surprise could drop between now and Election Day (there have already been several). However, it should be noted that one implication of all of this early voting is that the impact of a late October surprise is diminished.

(2) There could be systematic bias in polling (such as an unwillingness on behalf of voters to admit to pollsters their support for Trump). Alternatively, there could be a fundamental misunderstanding of the composition of the 2020 Electorate. As has been recently noted, the Trump Campaign has spent a significant amount of time on voter registration in several key battleground states. Will these newly registered folks actually vote ?

(3) Turnout dynamics associated with the cocktail of early voting (very large numbers so far), large scale absentee ballots (including rejection of ballots, delays in mail, etc.) or fear of turning up to the polls due to our latest COVID surge (the Trump campaign is counting on a Election Day surge). Any or all could impact the final outcome.

That said, if I had to bet I would bet on Biden to win (and give far better than even money).

We do have at least some information on the state of ongoing voting thanks to the Early Voting Tracking Project by Michael McDonald.

It is unprecedented turnout thus far.  On its face this would purport to favor the Biden Campaign. However, the question remains whether this is merely a cannibalization of the normal Early In Person Voting and/or Election Day In Person Voting.  In other words, how much will net turnout increase? Will it make a difference?    

Taking Pennsylvania as a highly probable Tipping Point State, it will be interesting to see what percentage of mail in ballots are returned in the days to come.  At the County level, there is significant variation in number of returned ballots thus far (even among those who have already requested a ballot).   

Immigration Document Classification and Automated Response Generation

ABSTRACT: “In this paper, we consider the problem of organizing supporting documents vital to U.S. work visa petitions, as well as responding to Requests For Evidence (RFE) issued by the U.S.~Citizenship and Immigration Services (USCIS). Typically, both processes require a significant amount of repetitive manual effort. To reduce the burden of mechanical work, we apply machine learning methods to automate these processes, with humans in the loop to review and edit output for submission. In particular, we use an ensemble of image and text classifiers to categorize supporting documents. We also use a text classifier to automatically identify the types of evidence being requested in an RFE, and used the identified types in conjunction with response templates and extracted fields to assemble draft responses. Empirical results suggest that our approach achieves considerable accuracy while significantly reducing processing time.” Access Via arXiv — To Appear in ICDM 2020 workshop: MLLD-2020