Catastrophic planetesimal disruptions offer a unique opportunity to study and characterize large planetesimal populations in exoplanetary systems that are not currently detectable by modern observatories. The unexpected discovery of a second collision event in the Fomalhaut system raises important questions about the planetesimal population and dynamical state inside the Fomalhaut main belt that led to two collisions in 20 years. We present a statistical model developed and applied to the archetypal Fomalhaut system to provide new constraints on the bulk properties of the planetesimals in Fomalhaut's main belt. Utilizing the constraints provided by the spatially resolved Fomalhaut cs1 and cs2 collision events, we retrieve the belt parameters that best reproduce the observed collision rate while remaining consistent with the system's age and dust mass. Our best-fit model suggests a total main belt mass of 200-360 $M_{\oplus}$, with the transition from a collisionally evolved to a primordial planetesimal population occurring at a radius of $115_{-10}^{+30}$ km and a maximum planetesimal radius of $380_{-202}^{+643}$ km. We estimate a catastrophic collision rate of $0.086_{-0.048}^{+0.067}$ collision events per year for planetesimals with radii $\ge$ 100 km in the region interior to the main belt. Our findings show that further observable collisions are likely, motivating continued monitoring of Fomalhaut and other nearby debris disks.
Scott E. Perkins, Peter McGill, William A. Dawson
et al.
The dark and dynamic parts of the Galaxy, including the bulk shape and movement of the Galactic Bulge and characteristics of dark compact object populations, such as a hypothetical population of primordial black holes (PBHs), are difficult to study directly by their very nature, but are critical to our understanding of the universe. Fortunately, all of these mysteries can be uniquely studied via gravitational microlensing, a method of astronomical detection that traces mass and dynamics as opposed to light. Using the OGLE-IV microlensing survey bulge fields, we apply a Bayesian hierarchical model to jointly infer properties of the Galaxy, the characteristics of compact objects, and and test PBHs with an extended mass distribution as a test PBHs as a viable explanation of dark matter, extending work focused on the Small and Large Magellanic Clouds, both with much lower event-rates. We infer a preference within the data for a lower patternspeed in the galactic model and a wider mass spectrum for compact objects. When adding a PBH component to the favored astrophysical model from our initial investigations, we find a Bayes factor of $\ln\mathcal{B} = 20.23$ preferring the PBH model. Upon further investigation of these results, we find the critical feature in the PBH model to be the velocity distribution, which is fundamentally different than the velocity distribution of astrophysical objects and uniquely able to explain a large number of low parallax, low timescale microlensing events. Noting that this effect is not unique to PBHs, we consider the implications of these results as applied to a hypothetical population of PBHs and discuss alternative explanations, including a variety of other possible astrophysical and survey or analysis systematics.
Sandro M. Reia, P. Suresh C. Rao, Marc Barthelemy
et al.
We show here that population growth, resolved at the county level, is spatially heterogeneous both among and within the U.S. metropolitan statistical areas. Our analysis of data for over 3,100 U.S. counties reveals that annual population flows, resulting from domestic migration during the 2015 - 2019 period, are much larger than natural demographic growth, and are primarily responsible for this heterogeneous growth. More precisely, we show that intra-city flows are generally along a negative population density gradient, while inter-city flows are concentrated in high-density core areas. Intra-city flows are anisotropic and generally directed towards external counties of cities, driving asymmetrical urban sprawl. Such domestic migration dynamics are also responsible for tempering local population shocks by redistributing inflows within a given city. This "spill-over" effect leads to a smoother population dynamics at the county level, in contrast to that observed at the city level. Understanding the spatial structure of domestic migration flows is a key ingredient for analyzing their drivers and consequences, thus representing a crucial knowledge for urban policy makers and planners.
Josef Tkadlec, Kamran Kaveh, Krishnendu Chatterjee
et al.
Evolution occurs in populations of reproducing individuals. It is well known that population structure can affect evolutionary dynamics. Traditionally, natural selection is studied between mutants that differ in reproductive rate, but are subject to the same population structure. Here we study how natural selection acts on mutants that have the same reproductive rate, but experience different population structures. In our framework, mutation alters population structure, which is given by a graph that specifies the dispersal of offspring. Reproduction can be either genetic or cultural. Competing mutants disperse their offspring on different graphs. A more connected graph implies higher motility. We show that enhanced motility tends to increase an invader's fixation probability, but there are interesting exceptions. For island models, we show that the magnitude of the effect depends crucially on the exact layout of the additional links. Finally, we show that for low-dimensional lattices, the effect of altered motility is comparable to that of altered fitness: in the limit of large population size, the invader's fixation probability is either constant or exponentially small, depending on whether it is more or less motile than the resident.
Models of population growth and extinction are an increasingly popular subject of study. However, consequences of stochasticity and noise in shaping distributions and outcomes are not sufficiently explored. Here we consider a distributed population with logistic growth at each location, subject to "seascape" noise, wherein the population's fitness randomly varies with {\it location and time}. Despite its simplicity, the model actually incorporates variants of directed percolation, and directed polymers in random media, within a mean-field perspective. Probability distributions of the population can be computed self-consistently; and the extinction transition is shown to exhibit novel critical behavior with exponents dependent on the ratio of the strengths of migration and noise amplitudes. The results are compared and contrasted with the more conventional choice of demographic noise due to stochastic changes in reproduction.
Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events. We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new events which fit into an existing temporally-ordered sequence. We use a BART-based conditional generation model that can capture both temporality and common event co-occurrence, meaning it can be flexibly applied to different tasks in this space. Our model is trained as a denoising autoencoder: we take temporally-ordered event sequences, shuffle them, delete some events, and then attempt to recover the original event sequence. This task teaches the model to make inferences given incomplete knowledge about the events in an underlying scenario. On the temporal ordering task, we show that our model is able to unscramble event sequences from existing datasets without access to explicitly labeled temporal training data, outperforming both a BERT-based pairwise model and a BERT-based pointer network. On event infilling, human evaluation shows that our model is able to generate events that fit better temporally into the input events when compared to GPT-2 story completion models.
Current gravitational microlensing surveys are observing hundreds of millions of stars in the Galactic bulge - which makes finding rare microlensing events a challenging tasks. In almost all previous works, microlensing events have been detected either by applying very strict selection cuts or manually inspecting tens of thousands of light curves. However, the number of microlensing events expected in the future space-based microlensing experiments forces us to consider fully-automated approaches. They are especially important for selecting binary-lens events that often exhibit complex light curve morphologies and are otherwise difficult to find. There are no dedicated selection algorithms for binary-lens events in the literature, which hampers their statistical studies. Here, we present two simple neural-network-based classifiers for detecting single and binary microlensing events. We demonstrate their robustness using OGLE-III and OGLE-IV data sets and show they perform well on microlensing events detected in data from the Zwicky Transient Facility (ZTF). Classifiers are able to correctly recognize ~98% of single-lens events and 80-85% of binary-lens events.
Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.
This work introduces a general method for automatically finding the locations where political events in text occurred. Using a novel set of 8,000 labeled sentences, I create a method to link automatically extracted events and locations in text. The model achieves human level performance on the annotation task and outperforms previous event geolocation systems. It can be applied to most event extraction systems across geographic contexts. I formalize the event--location linking task, describe the neural network model, describe the potential uses of such a system in political science, and demonstrate a workflow to answer an open question on the role of conventional military offensives in causing civilian casualties in the Syrian civil war.
In Reinforcement Learning we look for meaning in the flow of input/output information. If we do not find meaning, the information flow is not more than noise to us. Before we are able to find meaning, we should first learn how to discover and identify objects. What is an object? In this article we will demonstrate that an object is an event-driven model. These models are a generalization of action-driven models. In Markov Decision Process we have an action-driven model which changes its state at each step. The advantage of event-driven models is their greater sustainability as they change their states only upon the occurrence of particular events. These events may occur very rarely, therefore the state of the event-driven model is much more predictable.
Ecological resilience refers to the ability of a system to retain its state when subject to state variables perturbations or parameter changes. While understanding and quantifying resilience is crucial to anticipate the possible regime shifts, characterizing the influence of the system parameters on resilience is the first step towards controlling the system to avoid undesirable critical transitions. In this paper, we apply tools of qualitative theory of differential equations to study the resilience of competing populations as modeled by the classical Lotka-Volterra system. Within the high interspecific competition regime, such model exhibits bistability, and the boundary between the basins of attraction corresponding to exclusive survival of each population is the stable manifold of a saddle-point. Studying such manifold and its behavior in terms of the model parameters, we characterized the populations resilience: while increasing competitiveness leads to higher resilience, it is not always the case with respect to reproduction. Within a pioneering context where both populations initiate with few individuals, increasing reproduction leads to an increase in resilience; however, within an environment previously dominated by one population and then invaded by the other, an increase in resilience is obtained by decreasing the reproduction rate. Besides providing interesting insights for the dynamics of competing population, this work brings near to each other the theoretical concepts of ecological resilience and the mathematical methods of differential equations and stimulates the development and application of new mathematical tools for ecological resilience.
Alex R. Pettitt, Fumi Egusa, Clare L. Dobbs
et al.
With the advent of modern observational efforts providing extensive giant molecular cloud catalogues, understanding the evolution of such clouds in a galactic context is of prime importance. While numerous previous numerical and theoretical works have focused on the cloud properties in isolated discs, few have looked into the cloud population in an interacting disc system. We present results of the first study investigating the evolution of the cloud population in galaxy experiencing an M51-like tidal fly-by using numerical simulations including star formation, interstellar medium cooling and stellar feedback. We see the cloud population shift to large unbound clouds in the wake of the companion passage, with the largest clouds appearing as fleeting short-lived agglomerations of smaller clouds within the tidal spiral arms, brought together by large scale streaming motions. These are then sheared apart as they leave the protection of the spiral arms. Clouds appear to lead diverse lives, even within similar environments, with some being born from gas shocked by filaments streaming into the spiral arms, and others from effectively isolated smaller colliding pairs. Overall this cloud population produces a shallower mass function than the disc in isolation, especially in the arms compared to the inter-arm regions. Direct comparisons to M51 observations show similarities between cloud populations, though models tailored to the mass and orbital models of M51 appear necessary to precisely reproduce the cloud population.
This paper continues our study of the foreground population to the Orion molecular clouds. The goal is to characterize the foreground population north of NGC 1981 and to investigate the star formation history in the large Orion star-forming region. We focus on a region covering about 25 square degrees, centered on the $ε$ Orionis supergiant (HD 37128, B0\,Ia) and covering the Orion Belt asterism. We used a combination of optical (SDSS) and near-infrared (2MASS) data, informed by X-ray (\textit{XMM-Newton}) and mid-infrared (WISE) data, to construct a suite of color-color and color-magnitude diagrams for all available sources. We then applied a new statistical multiband technique to isolate a previously unknown stellar population in this region. We identify a rich and well-defined stellar population in the surveyed region that has about 2\,000 objects that are mostly M stars. We infer the age for this new population to be at least 5\, Myr and likely $\sim10$\,Myr and estimate a total of about 2\,500 members, assuming a normal IMF. This new population, which we call the Orion Belt population, is essentially extinction-free, disk-free, and its spatial distribution is roughly centered near $ε$ Ori, although substructure is clearly present. The Orion Belt population is likely the low-mass counterpart to the Ori OB Ib subgroup. Although our results do not rule out Blaauw's sequential star formation scenario for Orion, we argue that the recently proposed blue streams scenario provides a better framework on which one can explain the Orion star formation region as a whole. We speculate that the Orion Belt population could represent the evolved counterpart of an Orion nebula-like cluster.
A new strategy is introduced for estimating population size and networked population characteristics. Sample selection is based on a multi-wave snowball sampling design. A generalized stochastic block model is posited for the population's network graph. Inference is based on a Bayesian data augmentation procedure. Applications are provided to an empirical and simulated populations. The results demonstrate that statistically efficient estimates of the size and distribution of the population can be achieved.
Andrej Fischer, Ignacio Vazquez-Garcia, Ville Mustonen
Populations can evolve in order to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has quickly become a key challenge for global health. Therefore, ideas of how to control evolving populations in order to overcome this threat are valuable. Here we use the mathematical concepts of stochastic optimal control to study what is needed to control evolving populations. Following established routes to calculate control strategies, we first study how a polymorphism can be maintained in a finite population by adaptively tuning selection. We then introduce a minimal model of drug resistance in a stochastically evolving cancer cell population and compute adaptive therapies, where decisions are based on monitoring the response of the tumor, which can outperform established therapy paradigms. For both case studies, we demonstrate the importance of high-resolution monitoring of the target population in order to achieve a given control objective: to control one must monitor.
Population-based evolutionary algorithms (EAs) have been widely applied to solve various optimization problems. The question of how the performance of a population-based EA depends on the population size arises naturally. The performance of an EA may be evaluated by different measures, such as the average convergence rate to the optimal set per generation or the expected number of generations to encounter an optimal solution for the first time. Population scalability is the performance ratio between a benchmark EA and another EA using identical genetic operators but a larger population size. Although intuitively the performance of an EA may improve if its population size increases, currently there exist only a few case studies for simple fitness functions. This paper aims at providing a general study for discrete optimisation. A novel approach is introduced to analyse population scalability using the fundamental matrix. The following two contributions summarize the major results of the current article. (1) We demonstrate rigorously that for elitist EAs with identical global mutation, using a lager population size always increases the average rate of convergence to the optimal set; and yet, sometimes, the expected number of generations needed to find an optimal solution (measured by either the maximal value or the average value) may increase, rather than decrease. (2) We establish sufficient and/or necessary conditions for the superlinear scalability, that is, when the average convergence rate of a $(μ+μ)$ EA (where $μ\ge2$) is bigger than $μ$ times that of a $(1+1)$ EA.