Online Lenders Shouldn\u27t Get Mad Over Madden

The Second Circuit’s surprising decision in Madden v. Midland Funding caused consternation within the financial services industry. There, the Madden Court held that the National Bank Act’s pre-emption of state usury law did not apply to consumer debt sold by banks to third parties. Under the Second Circuit’s ruling, third-party buyers could not be certain of loan values, potentially making consumer finance markets less liquid. This decision immediately sparked concerns from the alternative finance industry, which worried that the secondary market for consumer debt would dry up and reduce consumer credit availability. It also alarmed financial technology startups such as online lenders, that originate loans via their bank partners before buying those loans back on their own balance sheets. The Supreme Court recently denied certiorari making Madden good law in the Second Circuit. This Note critiques the Madden court’s reasoning and develops an approach that avoids the pitfalls of the court’s unduly narrow interpretation of the National Bank Act. This Note also distinguishes the relationship between online lenders and banks from that of alternative finance companies and banks, to argue that online lending arrangements should not run afoul of Madden. Finally, this Note proposes several options lenders can take to minimize Madden’s impact

Comparative investigation of the freezing phenomena for quantum correlations under nondissipative decoherence

We show that the phenomenon of frozen discord, exhibited by specific classes of two-qubit states under local nondissipative decoherent evolutions, is a common feature of all known bona fide measures of general quantum correlations. All those measures, despite inducing typically inequivalent orderings on the set of nonclassically correlated states, return a constant value in the considered settings. Every communication protocol which relies on quantum correlations as resource will run with a performance completely unaffected by noise in the specified dynamical conditions. We provide a geometric interpretation of thisComment: 7 pages, 2 figures, 1 table; title changed to match published versio

Hierarchy and dynamics of trace distance correlations

We define and analyze measures of correlations for bipartite states based on trace distance. For Bell diagonal states of two qubits, in addition to the known expression for quantum correlations using this metric, we provide analytic expressions for the classical and total correlations. The ensuing hierarchy of correlations based on trace distance is compared to those based on relative entropy and Hilbert–Schmidt norm. Although some common features can be found, the trace distance measure is shown to differentiate from the others in that the closest uncorrelated state to a given bipartite quantum state is not given by the product of the marginals, and further, the total correlations are strictly smaller than the sum of the quantum and classical correlations. We compare the various correlation measures in two dynamical non-Markovian models, locally applied phase-flip channels and random external fields. It is shown that the freezing behavior, observed across all known valid measures of quantum correlations for Bell diagonal states under local phase-flip channels, occurs for a larger set of starting states for the trace distance than for the other metrics

ITSS: Interactive Web-Based Authoring and Playback Integrated Environment for Programming Tutorials

Video-based programming tutorials are a popular form of tutorial used by authors to guide learners to code. Still, the interactivity of these videos is limited primarily to control video flow. There are existing works with increased interactivity that are shown to improve the learning experience. Still, these solutions require setting up a custom recording environment and are not well-integrated with the playback environment. This paper describes our integrated ITSS environment and evaluates the ease of authoring and playback of our interactive programming tutorials. Our environment is designed to run within the browser sandbox and is less intrusive to record interactivity actions. We develop a recording approach that tracks the author's interactivity actions (e.g., typing code, highlighting words, scrolling panels) on the browser and stored in text and audio formats. We replay these actions using the recorded artefacts for learners to have a more interactive, integrated and realistic playback of the author's actions instead of watching video frames. Our design goals are 1) efficient recording and playback, 2) extensible interactivity features to help students learn better, and 3) a scalable web-based environment. Our first user study of 20 participants who carry out the author tasks agree that it is efficient and easy to author interactive videos in our environment with no additional software needed. Our second user study of 84 students using the environment agrees that the increased interactivity can help them learn better over a video-based tutorial. Our performance test shows that the environment can scale to support up to 500 concurrent users. We hope our open-source environment enable more educators to create interactive programming tutorials

A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents

Many real-world applications (e.g., note taking, search) require extracting a sentence or paragraph from a document and showing that snippet to a human outside of the source document. Yet, users may find snippets difficult to understand as they lack context from the original document. In this work, we use language models to rewrite snippets from scientific documents to be read on their own. First, we define the requirements and challenges for this user-facing decontextualization task, such as clarifying where edits occur and handling references to other documents. Second, we propose a framework that decomposes the task into three stages: question generation, question answering, and rewriting. Using this framework, we collect gold decontextualizations from experienced scientific article readers. We then conduct a range of experiments across state-of-the-art commercial and open-source language models to identify how to best provide missing-but-relevant information to models for our task. Finally, we develop QaDecontext, a simple prompting strategy inspired by our framework that improves over end-to-end prompting. We conclude with analysis that finds, while rewriting is easy, question generation and answering remain challenging for today's models.Comment: 19 pages, 2 figures, 8 tables, EMNLP202

LIMEADE: A General Framework for Explanation-Based Human Tuning of Opaque Machine Learners

Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow humans to tune a model in response to the explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA2Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, no method for tuning opaque models in response to explanations has been user-tested to date. This paper introduces LIMEADE, a general framework for tuning an arbitrary machine learning model based on an explanation of the model's prediction. We demonstrate the generality of our approach with two case studies. First, we successfully utilize LIMEADE for the human tuning of opaque image classifiers. Second, we apply our framework to a neural recommender system for scientific papers on a public website and report on a user study showing that our framework leads to significantly higher perceived user control, trust, and satisfaction. Analyzing 300 user logs from our publicly-deployed website, we uncover a tradeoff between canonical greedy explanations and diverse explanations that better facilitate human tuning.Comment: 16 pages, 7 figure