Given the ubiquity of negative campaigning in recent political elections, we find it important to study its properties from a theoretical computational perspective. To this end, we present a model where elections can be manipulated by convincing voters to demote specific non-favored candidates, and study its properties in the classic setting of scoring rules. When the goal is constructive (making a preferred candidate win), we prove that finding such a demotion strategy is easy for Plurality and Veto, while generally hard for t-approval and Borda. We also provide a min(t, m - t)-factor approximation for t-approval for every t ∈ {1,..., m - 1} (where m is the number of candidates), and a 3-factor approximation algorithm for Borda. Interestingly enough---following recent trends in political science that show that the effectiveness of negative campaigning depends on the type of candidate and demographic---when assigning varying prices to different possible demotion operations, we are able to provide inapproximability results. When the goal is destructive (making the leading opponent lose), we show that the problem is easy for a broad class of scoring rules and provide an FPTAS for the general case.

Fixed-time strategy is a common approach in signal traffic control, characterized by simple and periodic signal plans that are easy to implement without detection mechanisms. A major step in the design of such plans refers to the grouping of the day hours such that the same plan applies for several consecutive hours. The efficacy of the plan, measured by vehicle delays, relies on the matching of traffic with the fixed plan. Accordingly, the time-of-day breakpoints between plans are selected based on the variability of the traffic within each group. The paper studies the selection of time-of-day breakpoints (TODs) based on real traffic characteristics from two cities. Motivated by the Google Green Light project, this study presents an approach to compute TODs based on aggregated traffic statistics computed from anonymized trajectories from navigation applications. We evaluate an optimal dynamic programming algorithm to compute time-of-day breakpoints at an intersection, based on traffic variability among hours. We analyze typical forms of efficient time-of-day breakpoints and examine the impact of the number of daily plans on the ability to predict traffic behavior. We refer to various metrics to measure the variability of the traffic within sets of hours concerning the amount of traffic and its distribution among various movements. We measure the dissimilarity of the time-of-day breakpoints when computed for the different metrics. We also address the joint computation of TODs in adjacent intersections to improve coordination potential.

Although large neural language models (LMs) like BERT can be finetuned to yield state-of-the-art results on many NLP tasks, it is often unclear what these models actually learn. Here we study using such LMs to fill in entities in human-authored comparative questions, like ``Which country is older, India or _____?''---i.e., we study the ability of neural LMs to ask (not answer) reasonable questions. We show that accuracy in this fill-in-the-blank task is well-correlated with human judgements of whether a question is reasonable, and that these models can be trained to achieve nearly human-level performance in completing comparative questions in three different subdomains. However, analysis shows that what they learn fails to model any sort of broad notion of which entities are semantically comparable or similar---instead the trained models are very domain-specific, and performance is highly correlated with co-occurrences between specific entities observed in the training set. This is true both for models that are pretrained on general text corpora, as well as models trained on a large corpus of comparison questions. Our study thus reinforces recent results on the difficulty of making claims about a deep model's world knowledge or linguistic competence based on performance on specific benchmark problems. We make our evaluation datasets publicly available to foster future research on complex understanding and reasoning in such models at standards of human interaction.