ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U

Moving Targets: Geographically Routed Human Movement Networks

We introduce a new communication paradigm, Human-to-human Mobile Ad hoc Networking (HuManet), that exploits smartphone capabilities and human behavior to create decentralized networks for smartphone-to-smartphone message delivery. HuManets support stealth command-and-control messaging for mobile BotNets, covert channels in the presence of an observer who monitors all cellular communication, and distributed protocols for querying the state or content of targeted mobile devices. In this paper, we introduce techniques for constructing HumaNets and describe protocols for efficiently routing and addressing messages. In contrast to flooding or broadcast schemes that saturate the network and aggressively consume phone resources (e.g., batteries), our protocols exploit human mobility patterns to significantly increase communication efficiency while limiting the exposure of HuManets to mobile service providers. Our techniques leverage properties of smartphones – in particular, their highly synchronized clocks and ability to discern location information – to construct location profiles for each device. HuManets’ fully-distributed and heuristic-based routing protocols route messages towards phones with location profiles that are similar to those of the intended receiver, enabling efficient message delivery with limited effects to end-to-end latency

A Framework for Quality K-12 Engineering Education: Research and Development

Recent U.S. national documents have laid the foundation for highlighting the connection between science, technology, engineering and mathematics at the K-12 level. However, there is not a clear definition or a well-established tradition of what constitutes a quality engineering education at the K-12 level. The purpose of the current work has been the development of a framework for describing what constitutes a quality K-12 engineering education. The framework presented in this paper is the result of a research project focused on understanding and identifying the ways in which teachers and schools implement engineering and engineering design in their classrooms. The development of the key indicators that are included in the framework were determined based on an extensive review of the literature, established criteria for undergraduate and professional organizations, document content analysis of state academic content standards in science, mathematics, and technology, and in consultation with experts in the fields of engineering and engineering education. The framework is designed to be used as a tool for evaluating the degree to which academic standards, curricula, and teaching practices address the important components of a quality K-12 engineering education. Additionally, this framework can be used to inform the development and structure of future K-12 engineering and STEM education standards and initiatives

Do We Still Need Clinical Language Models?

Although recent advances in scaling large language models (LLMs) have resulted in improvements on many NLP tasks, it remains unclear whether these models trained primarily with general web text are the right tool in highly specialized, safety critical domains such as clinical text. Recent results have suggested that LLMs encode a surprising amount of medical knowledge. This raises an important question regarding the utility of smaller domain-specific language models. With the success of general-domain LLMs, is there still a need for specialized clinical models? To investigate this question, we conduct an extensive empirical analysis of 12 language models, ranging from 220M to 175B parameters, measuring their performance on 3 different clinical tasks that test their ability to parse and reason over electronic health records. As part of our experiments, we train T5-Base and T5-Large models from scratch on clinical notes from MIMIC III and IV to directly investigate the efficiency of clinical tokens. We show that relatively small specialized clinical models substantially outperform all in-context learning approaches, even when finetuned on limited annotated data. Further, we find that pretraining on clinical tokens allows for smaller, more parameter-efficient models that either match or outperform much larger language models trained on general text. We release the code and the models used under the PhysioNet Credentialed Health Data license and data use agreement

Rectifiability of Optimal Transportation Plans

The purpose of this note is to show that the solution to the Kantorovich optimal transportation problem is supported on a Lipschitz manifold, provided the cost is $C^{2}$ with non-singular mixed second derivative. We use this result to provide a simple proof that solutions to Monge's optimal transportation problem satisfy a change of variables equation almost everywhere

Isolation of human intrahepatic leukocytes for phenotypic and functional characterization by flow cytometry

With the growing appreciation of tissue-resident immunity, studying tissue-specific immune cells contributing to both homeostasis and disease is imperative. Here, we provide a protocol for the isolation of human intrahepatic leukocytes (IHL) maximizing viability, purity, and yield. Our protocol is scalable by tissue weight, allowing for reproducible and efficient IHL liberation suitable for functional characterization, cell isolation, and profiling by flow (or mass) cytometry. Furthermore, we provide a "guide" to determine an expected IHL yield per gram of tissue processed. For complete details on the use and execution of this protocol, please refer to Stegmann et al. (2016), Pallett et al. (2017), Easom et al. (2018), Swadling et al. (2020), Pallett et al. (2020), and Zakeri et al. (2022)

Financing U.S. Graduate Medical Education: A Policy Position Paper of the Alliance for Academic Internal Medicine and the American College of Physicians

In this position paper, the Alliance for Academic Internal Medicine and the American College of Physicians examine the state of graduate medical education (GME) financing in the United States and recent proposals to reform GME funding. They make a series of recommendations to reform the current funding system to better align GME with the needs of the nation's health care workforce. These recommendations include using Medicare GME funds to meet policy goals and to ensure an adequate supply of physicians, a proper specialty mix, and appropriate training sites; spreading the costs of financing GME across the health care system; evaluating the true cost of training a resident and establishing a single per-resident amount; increasing transparency and innovation; and ensuring that primary care residents receive training in well-functioning ambulatory settings that are financially supported for their training roles