Recent research on instant runoff voting (IRV) shows that it exhibits a striking combinatorial property in one-dimensional preference spaces: there is an "exclusion zone" around the median voter such that if a candidate from the exclusion zone is on the ballot, then the winner must come from the exclusion zone. Thus, in one dimension, IRV cannot elect an extreme candidate as long as a sufficiently moderate candidate is running. In this work, we examine the mathematical structure of exclusion zones as a broad phenomenon in more general preference spaces. We prove that with voters uniformly distributed over any $d$-dimensional hyperrectangle (for $d > 1$), IRV has no nontrivial exclusion zone. However, we also show that IRV exclusion zones are not solely a one-dimensional phenomenon. For irregular higher-dimensional preference spaces with fewer symmetries than hyperrectangles, IRV can exhibit nontrivial exclusion zones. As a further exploration, we study IRV exclusion zones in graph voting, where nodes represent voters who prefer candidates closer to them in the graph. Here, we show that IRV exclusion zones present a surprising computational challenge: even checking whether a given set of positions is an IRV exclusion zone is NP-hard. We develop an efficient randomized approximation algorithm for checking and finding exclusion zones. We also report on computational experiments with exclusion zones in two directions: (i) applying our approximation algorithm to a collection of real-world school friendship networks, we find that about 60% of these networks have probable nontrivial IRV exclusion zones; and (ii) performing an exhaustive computer search of small graphs and trees, we also find nontrivial IRV exclusion zones in most graphs. While our focus is on IRV, the properties of exclusion zones we establish provide a novel method for analyzing voting systems in metric spaces more generally.

An anomaly, or an outlier, is a data point that is significantly different from the remaining data [Aggarwal, 2017], to such an extent that it was likely generated by a different mechanism [Hawkins, 1980]. From the perspective of machine learning, Anomaly Detection (AD) wants to determine, from a set of examples, which ones are likely anomalies, typically through a score. This problem finds applications in many different fields, such as medicine Salem et al. [2013], cyber-security Siddiqui et al. [2019], and system monitoring Isermann [2006], to name a few. As reviewed in Han et al. [2022], existing techniques for AD are usually divided into unsupervised, semi-supervised and supervised approaches, with an increasing need for labeled data. In this paper, we focus on unsupervised AD, which does not need further labeling effort in constituting datasets. As discussed in Livernoche et al. [2024], the growing number of applications involving high-dimensional and complex data begs the need for non-parametric algorithms.

Microswimmers are sub-millimeter swimming microrobots that show potential as a platform for controllable locomotion in applications including targeted cargo delivery and minimally invasive surgery. To be viable for these target applications, microswimmers will eventually need to be able to navigate in environments with dynamic fluid flows and forces. Experimental studies with microswimmers towards this goal are currently rare because of the difficulty isolating intentional microswimmer motion from environment-induced motion. In this work, we present a method for measuring microswimmer locomotion within a complex flow environment using fiducial microspheres. By tracking the particle motion of ferromagnetic and non-magnetic polystyrene fiducial microspheres, we capture the effect of fluid flow and field gradients on microswimmer trajectories. We then determine the field-driven translation of these microswimmers relative to fluid flow and demonstrate the effectiveness of this method by illustrating the motion of multiple microswimmers through different flows.

This article addresses the challenge of UAV survey coverage path planning for areas that are complex concave polygons, containing exclusion zones or obstacles. While standard drone path planners typically generate coverage paths for simple convex polygons, this study proposes a method to manage more intricate regions, including boundary splits, merges, and interior holes. To achieve this, polygonal decomposition techniques are used to partition the target area into convex sub-regions. The sub-polygons are then merged using a depth-first search algorithm, followed by the generation of continuous Boustrophedon paths based on connected components. Polygonal offset by the straight skeleton method was used to ensure a constant safe distance from the exclusion zones. This approach allows UAV path planning in environments with complex geometric constraints.

Instant runoff voting (IRV) has recently gained popularity as an alternative to plurality voting for political elections, with advocates claiming a range of advantages, including that it produces more moderate winners than plurality and could thus help address polarization. However, there is little theoretical backing for this claim, with existing evidence focused on case studies and simulations. In this work, we prove that IRV has a moderating effect relative to plurality voting in a precise sense, developed in a 1-dimensional Euclidean model of voter preferences. We develop a theory of exclusion zones, derived from properties of the voter distribution, which serve to show how moderate and extreme candidates interact during IRV vote tabulation. The theory allows us to prove that if voters are symmetrically distributed and not too concentrated at the extremes, IRV cannot elect an extreme candidate over a moderate. In contrast, we show plurality can and validate our results computationally. Our methods provide new frameworks for the analysis of voting systems, deriving exact winner distributions geometrically and establishing a connection between plurality voting and stick-breaking processes.

This paper addresses exact approaches to multi-agent collective construction problem which tasks a group of cooperative agents to build a given structure in a blocksworld under the gravity constraint. We propose a generalization of the existing exact model based on mixed integer linear programming by accommodating varying agent action durations. We refer to the model as a fraction-time model. The generalization by introducing action duration enables one to create a more realistic model for various domains. It provides a significant reduction of plan execution duration at the cost of increased computational time, which rises steeply the closer the model gets to the exact real-world action duration. We also propose a makespan estimation function for the fraction-time model. This can be used to estimate the construction time reduction size for the purpose of cost-benefit analysis. The fraction-time model and the makespan estimation function have been evaluated in a series of experiments using a set of benchmark structures. The results show a significant reduction of plan execution duration for non-constant duration actions due to decreasing synchronization overhead at the end of each action. According to the results, the makespan estimation function provides a reasonably accurate estimate of the makespan.

The rain was pouring as game director Alex Dracott drove through the wilderness of the Pacific north-west. There wasn't anyone in the car with him, but nonetheless, Dracott didn't feel alone in his trusty station wagon – a dependable, durable vehicle he'd been driving ever since he was a teenager. As the game maker was bludgeoned by the elements, he describes feeling a "camaraderie with the car", sheltered by its windshield and the metal of its body. This experience inspired Pacific Drive, the game Dracott has been making for the past three years with his team at Ironwood Studios in Seattle, capital of the famously verdant region. He describes it as a "run-based driving survival game," played in first-person.

The catastrophic Fukushima nuclear disaster in 2011 caused an estimated 250,000 people to evacuate their homes, but scientists have determined certain wildlife species in the area are thriving, suggesting people could eventually return to the region, according to a new study. Researchers at Colorado State University, the University of Georgia and Fukushima University's Institute of Environmental Radioactivity have found that multiple generations of wild boar and rat snakes have not suffered from any significant adverse health effects. Multiple generations of animals have been exposed to radiation levels above the threshold for human occupancy, but have suffered no ill effects. That may be due to the fact that cesium-134, one of the major radioactive materials released during the accident, saw its levels decrease by almost 90 percent. The researchers looked at biomarkers of DNA damage and stress to determine that the boar and snakes were thriving in the area. The researchers looked at the wild boars and snakes between 2016 and 2018, or five to seven years after the earthquake and resulting tsunami destroyed the Fukushima Dai-ichi Nuclear Power Plant, releasing massive amounts of radioactive material in the environment.

Japan's catastrophic Fukushima disaster in 2011 has resulted in a unique species of boar-pig, a new study reveals. Researchers investigating the effects of the nuclear disaster on animals in the area report that radiation has had no adverse effects on their genetics. However, wild boars (Sus scrofa leucomystax) have proliferated in the area, after being left to roam freely from the lack of humans. The boars have bred with domestic pigs (Sus scrofa domesticus) that escaped from nearby properties after farmers had to flee, creating a new hybrid species. Rare spotted wild boar observed inside the evacuated area of Fukushima, Japan, indicative of the'introgression' - the transfer of genetic information from one species to another - with domestic pigs Images from remotely-operated cameras indicate wildlife is flourishing in Fukushima's exclusion zone. Wildlife ecologist James Beasley of the University of Georgia and colleagues used a network of 106 remote cameras to capture images of the wildlife in the area over a four-month period.

It's been a tough road for Spot, Boston Dynamics' growingly dependable robot dog, having to creep out of the shadows of Black Mirror's "Metalhead" episode in its continual search for love and approval. Sure, helping out during the coronavirus pandemic has been good for the bot's overall image, but now Spot is going one step further by heading to Chernobyl to serve as an autonomous safety checker and help finally decommission its infamous Reactor 4. Boston Dynamics' Ferrari of a four-legged robot is designed to do the dirty work that would put their human counterparts at risk, so it's perfectly suited for seeking out radioactive dust and mapping out and taking measurements in the most dangerous areas still festering in Chernobyl's Exclusion Zone. To that end, engineers from the University of Bristol recently visited the site. While testing a number of remotely controlled robots, they found that Spot's ability to minimize the kicking up radioactive dust while navigating complex terrain (like the stones and debris still prevalent in the now sealed area), obstacles, sloping surfaces, and stairs made it uniquely qualified for a remote radiation survey. It also helped that, unlike many robotic turtles, Spot can flip upright after accidentally turning on its back.