Using Inherent Judicial Power in a State-Level Budget Dispute

State courts are in financial crisis. Since the mid-1990s, state legislatures have allowed funding for their judicial systems to stagnate or dwindle. With diminished resources, state courts have struggled to provide adequate access to justice and dispute resolution. The solution to this crisis may lie in the doctrine of inherent judicial power. Courts have historically used inherent power to request additional funds from local legislative bodies for discrete expenditures. The use of inherent power to challenge the overall sufficiency of a judicial budget, however, has proven troubling. Under the current formulation of the inherent-power doctrine, a state court contesting the adequacy of a statewide judicial budget runs into two problems. First, by invoking its inherent power to compel additional funding, the court may usurp the appropriation power of the legislature. Second, state courts threaten their own legitimacy by taking a portion of the state budget out of the political process. In response to these problems, this Note proposes a reformulation of the inherent-power doctrine. Specifically, state courts should invoke inherent power against a legislature only under a standard of absolute necessity to perform the duties required by federal and state constitutional law. This new standard limits the use of inherent power to situations that threaten the judiciary\u27s ability to perform its constitutionally mandated functions. By cabining the permitted uses of inherent power, the standard respects the separation of powers and preserves the judiciary\u27s public legitimacy

Theories and quantification of thymic selection

The peripheral T cell repertoire is sculpted from prototypic T cells in the thymus bearing randomly generated T cell receptors (TCR) and by a series of developmental and selection steps that remove cells that are unresponsive or overly reactive to self-peptide–MHC complexes. The challenge of understanding how the kinetics of T cell development and the statistics of the selection processes combine to provide a diverse but self-tolerant T cell repertoire has invited quantitative modeling approaches, which are reviewed here

DE-PACRR: Exploring Layers Inside the PACRR Model

Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval. However, deep models have a reputation for being black boxes, and the roles of a neural IR model's components may not be obvious at first glance. In this work, we attempt to shed light on the inner workings of a recently proposed neural IR model, namely the PACRR model, by visualizing the output of intermediate layers and by investigating the relationship between intermediate weights and the ultimate relevance score produced. We highlight several insights, hoping that such insights will be generally applicable.Comment: Neu-IR 2017 SIGIR Workshop on Neural Information Retrieva

Citizens' demand for permits and Kwerel''s incentive compatible mechanism for pollution control

An interesting feature of pollution permit markets is that citizens may purchase permits to directly lower the levels of pollution. Kwerel's mechanism (Review of Economic Studies~1977) is not incentive compatible when citizens demand permits. We show that a modification of Kwerel''s mechainism, the minimum-price mechanism, is incentive compatible when citizens demand permits, even in the case where there is uncertainty about the damages from pollution.

Depression and Self-Harm Risk Assessment in Online Forums

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4, FastText baseline, and CNN-

Microwave Background Anisotropies from Alfven waves

We investigate microwave background anisotropies in the presence of primordial magnetic fields. We show that a homogeneous field with fixed direction can amplify vector perturbations. We calculate the correlations of $\delta T/T$ explicitly and show that a large scale coherent field induces correlations between $a_{\ell-1,m}$ and $a_{\ell+1,m}$. We discuss constraints on amplitude and spectrum of a primordial magnetic field imposed by observations of CMB anisotropies.Comment: 18 page LaTeX file, 4 postscript figs. included, submitted to PR

Listening between the Lines: Learning Personal Attributes from Conversations

Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.Comment: published in WWW'1

Triaging Content Severity in Online Mental Health Forums

Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for Information Science and Technology (2017

CEDR: Contextualized Embeddings for Document Ranking

Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.Comment: Appeared in SIGIR 2019, 4 page