Since the start of the COVID-19 pandemic, social distancing has been key to curb the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Social distancing policies have largely been made based on reported case data, which are often underreported. There is little to no understanding of how underreporting in case data may affect the performance of social distancing policies designed using such data. This article aims to fill this gap. We evaluate three policy-making rationales (called “controllers”): a flattening-the-curve (FC) controller that maximizes allowable transmission rate while ensuring hospital capacity is not exceeded, a time-limited-intervention (TLI) controller to minimize the number of infectious people with policies of fixed durations, and a minimizing-health-and-economic-costs-with-reinforcement-learning (MC-RL) controller to minimize weighted public health and economic costs. Each controller is applied in six scenarios where there is no underreporting, underreporting without correcting it, and underreporting with corrections. It is found that both the recommended policies by a controller and the resulting disease trajectories would be different under different scenarios, in which direction underreporting affects policies and disease trajectories depending on three factors: time elapsed since start of pandemic, local hospital capacity, and disease severity. Our findings demonstrate the high stakes of ignoring the underreporting issue or simply upscaling the case data: policies designed cannot achieve preset control objectives and could bring unexpected public health and economic outcomes.

Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allows these models to learn rich, contextual relationships between elements of a sequence. The main idea behind our method is to visualize a joint embedding of the query and key vectors used by transformer models to compute attention. Unlike previous attention visualization techniques, our approach enables the analysis of global patterns across multiple input sequences. We create an interactive visualization tool, AttentionViz (demo: http://attentionviz.com), based on these joint query-key embeddings, and use it to study attention mechanisms in both language and vision transformers. We demonstrate the utility of our approach in improving model understanding and offering new insights about query-key interactions through several application scenarios and expert feedback.

We describe a family of recursive methods for the synthesis of qubit permutations on quantum computers with limited qubit connectivity. Two objectives are of importance: circuit size and depth. In each case we combine a scalable heuristic with a non-scalable, yet exact, synthesis.

Understanding user behavior is crucial for the success of many emerging applications that aim to provide personalized services for target users, such as many patient-centered health apps and transportation apps. Models based on the random utility maximization (RUM) theory are widely used in learning and understanding behavioral preferences on the population level but find difficult to estimate individuals’ preferences, particularly when individuals’ data are limited and fragmented. To address this problem, our framework builds on the concepts such as canonical structure and membership vectors invented in recent works on collaborative learning and is suitable for modeling heterogeneous population with insufficient data from each individual. We further propose an extension of the collaborative learning framework using pairwise-fusion regularization as a knowledge discovery tool for real-world applications where the canonical structure is uneven, e.g., some canonical models may only represent minor subpopulations. Computationally competent algorithms are developed to solve the corresponding optimization challenges. Extensive simulation studies and a real-world application in smart transportation demand management (TDM) show the effectiveness of our proposed methods.Note to Practitioners—The proposed methods in this article can learn a distinct behavior model for each user and understand his/her own preferences, even when each of the user’s data is limited. With personalized models, it will help nowadays’ personalized service apps to understand, explain, and change user behavior in a more targeted and efficient way. The utility of the method is illustrated by an application on a real-world transportation demand management problem where personalized incentives are assigned to users to change their travel behavior.