Medial Prefrontal Cortex Predicts Intertemporal Choice

People often make shortsighted decisions to receive small benefits in the present rather than large benefits in the future, that is, to favor their current selves over their future selves. In two studies using fMRI, we demonstrated that people make such decisions in part because they fail to engage in the same degree of self-referential processing when thinking about their future selves. When participants predicted how much they would enjoy an event in the future, they showed less activity in brain regions associated with introspective self-reference—such as the ventromedial pFC (vMPFC)—than when they predicted how much they would enjoy events in the present. Moreover, the magnitude of vMPFC reduction predicted the extent to which participants made shortsighted monetary decisions several weeks later. In light of recent findings that the vMPFC contributes to the ability to simulate future events from a first-person perspective, these data suggest that shortsighted decisions result in part from a failure to fully imagine the subjective experience of one's future self.Psycholog

Safe, Remote-Access Swarm Robotics Research on the Robotarium

This paper describes the development of the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-agent research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium as well as connects these to the particular considerations one must take when making complex hardware remotely accessible. In particular, safety must be built in already at the design phase without overly constraining which coordinated control programs the users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.Comment: 13 pages, 7 figures, 3 code samples, 72 reference

Occurrence of the Old World bug Megacopta cribraria (Fabricius) (Heteroptera: Plataspidae) in Georgia: a serious home invader and potential legume pest

Specimens of Megacopta cribraria (Fabricius) were collected in northern Georgia in late October 2009, where they were invading homes in large numbers. This is the first known occurrence of this species and the family Plataspidae in the New World. Megacopta cribraria was previously known from Asia and Australia. A key is provided to separate Plataspidae from other families of Pentatomoidea in America North of Mexico. A diagnosis and figures are provided to facilitate recognition of M. cribraria. Reported host plants and other aspects of the biology of this species are reviewed. Megacopta cribraria is considered a pest of numerous legumes in Asia, has the potential to provide biological control of kudzu, Pueraria montana var. lobata (Willd.) Ohwi, (Fabaceae) and likely will continue to be a household pest in the vicinity of kudzu fields as well as become a pest of North American legume crops

Weak-form Sparse Identification of Differential Equations from Noisy Measurements

Data-driven modeling refers to the use of measurement data to infer the parameters and structure ofa mathematical model. While currently an active area research, data-driven modeling is characterized by the cycle of hypothesis, observation, and conclusion, which is none other than the scientific method, and can be traced back to Aristotle. For millenia, attempts have been made to distill governing laws from the observations made on a given system, with the hope of both explaining the observations and predicting future observations. Major accomplishments in this vein include Archimedes’ principle of buoyancy, Newtonian physics, and Röntgen’s X-rays. In each of these cases, the observations of a peculiar phenomena, often accidental, compelled the researcher to develop models. It is this map from observations to models that is at the heart of this thesis.The paradigm shift in recent years, driven by increased computing power, availability of large quan-tities of data, and the development of advanced mathematical techniques and algorithms, has been to automate the process of data-driven modeling. In the terminology of hypothesis, observation, and conclu- sion, automation can occur at the level of developing hypotheses about possible mathematical models, or the design of experiments which differentiate between the many possible models, or the map which takes experimental data and returns a mathematical model. To complete the cycle, one could also consider algorithms which generate potential nearby models given the model that is found to best fit the data. In this dissertation, we explore algorithms which automate the map from observations to governing equations, specifically differential equations. Our key contribution is the development of algorithms which identify differential equations in a weak form, which loosely refers to integrating the differential equation against arbitrary functions. We will show that the weak form is an ideal framework for identifying models from data if the criteria are robustness to data corruptions, highly accurate model recovery when corruption levels are low, and computational efficiency.We will demonstrate the superiority of our weak-form sparse identification for nonlinear dynamicsalgorithm (WSINDy) in the discovery of correct underlying model equations in a variety of differential equation and data corruption scenarios. We start with the simplest case, of ordinary differential equations (ODEs) depending only on time. We then move to partial differential equations (PDEs), where state variables change in both time and space. We then bridge the two previous regimes by considering interacting particle systems (IPSs), where the weak form is used to identify a mean-field PDE using data that can also be described as a large system of ODEs. Finally, we establish feasibility of weak- form identification of PDEs in an online context, where data is streamed in. In the online setting, we demonstrate the possibility of identifying time-varying models as well as models from data in four dimensions (3 space + 1 time)

Conservation status assessment of the highest forests in the world: Polylepis flavipila forests as a case study

Polylepis forests are one of the most threatened high Andean ecosystems, with 15 species and eight subspecies being categorized as critically endangered, vulnerable or near threatened by IUCN. However, their conservation status is poorly evaluated and could be outdated. As a case study, we evaluated Polylepis flavipila, a species endemic to the Peruvian central Andes, that is categorized as Vulnerable in Peru and is not mentioned in the Global Threatened Species Red List. We used two methods to categorize P. flavipila: (1) a species-level assessment using criteria proposed by IUCN and (2) a population-level assessment of four forests using the more specific criteria proposed by Navarro and collaborators. We recorded 350 relicts of P. flavipila forests as identified from herbariums and other sources. Forest cover was reduced 53% over 45 years as evaluated using satellite images from 1975 and 2020 and we estimated a total area of 458 and 216 km2, respectively. Thus, according to the IUCN criteria, P. flavipila should be classified as Endangered. At the population level, the application of the criteria of Navarro and collaborators results in different threat categories: one of the studied forests is classified as Critically Endangered, two forests as Vulnerable and one as Least Concern. We stress the need for updated categorizations for the 45 described Polylepis tree and shrub species based on the following facts: the only species we tested should change category, the IUCN categorizations were performed 16 to 22 years ago, and there have been many changes in the taxonomy of the genus. The assessment using IUCN criteria should also be complemented with more detailed evaluations at the population level since important differences were detected at a smaller scale, which could help target conservation and restoration resources more efficiently.Fil: Ames Martínez, Fressia Nathalie. Universidad Continental Huancayo; PerúFil: Quispe Melgar, Harold Rusbelth. Asociación ANDINUS; PerúFil: Renison, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentin

HydroShare GIS: Visualizing Spatial Data in the Cloud

Cloud-based data management systems are more conducive to collaborative efforts when they are integrated with cloud-computing tools that interact with their stored data. HydroShare, a web based data management system for climate and water data, has implemented an Application Programming Interface and a web application platform deployed using Tethys Platform to encourage the development of apps that interact with its data. HydroShare GIS is the result of one such development effort to provide cloud-based visualization of spatial data stored in HydroShare. It functions by accessing the spatial metadata contained within the HydroShare resource data model and overlaying datasets as layers within the OpenLayers JavaScript library. Data are passed from the app’s server to a GeoServer data server and shared as web mapping service layers. Thus, users can easily build map projects from data sources registered in HydroShare and save them back to HydroShare as map project resources, which can both be shared with others and re-opened in HydroShare GIS. This paper will describe the design of the HydroShare GIS app and the cyber-infrastructure that supports it, and evaluate its efficacy as a web based mapping tool

Vicarious Shame and Guilt

Participants recalled instances when they felt vicariously ashamed or guilty for another’s wrongdoing and rated their appraisals of the event and resulting motivations. The study tested aspects of social association that uniquely predict vicarious shame and guilt. Results suggest that the experience of vicarious shame and vicarious guilt are distinguishable. Vicarious guilt was predicted by one’s perceived interdependence with the wrongdoer (e.g. high interpersonal interaction), an appraisal of control over the event, and a motivation to repair the other person’s wrongdoing. Vicarious shame was predicted by the relevance of the event to a shared social identity with the wrongdoer, an appraisal of self-image threat, and a motivation to distance from the event. Implications for intergroup behavior and emotion are discussed