Food Webs: Experts Consuming Families of Experts

The question what determines the structure of natural food webs has been listed among the nine most important unanswered questions in ecology. It arises naturally from many problems related to ecosystem stability and resilience. The traditional view is that population-dynamical stability is crucial for understanding the observed structures. But phylogeny (evolutionary history) has also been suggested as the dominant mechanism. Here we show that observed topological features of predatory food webs can be reproduced to unprecedented accuracy by a mechanism taking into account only phylogeny, size constraints, and the heredity of the trophically relevant traits of prey and predators. The analysis reveals a tendency to avoid resource competition rather than apparent competition. In food webs with many parasites this pattern is reversed.Comment: 16 pages, 3 figures, 1 table + Appendix of 36 pages, 18 figures. movie available from http://ag.rossberg.net/matching.mp

Neurological modeling of what experts vs. non-experts find interesting

The P3 and related ERP's have a long history of use to identify stimulus events in subjects as part of oddball-style experiments. In this work we describe the ongoing development of oddball style experiments which attempt to capture what a subject finds of interest or curious, when presented with a set of visual stimuli i.e. images. This joint work between Dublin City University (DCU) and the European Space Agency's Advanced Concepts Team (ESA ACT) is motivated by the challenges of autonomous space exploration where the time lag for sending data back to earth for analysis and then communicating an action or decision back to the spacecraft means that decision-making is slow. Also, when extraterrestrial sensors capture data, the determination of what data to send back to earth is driven by an expertly devised rule set, that is scientists need to determine apriori what will be of interest. This cannot adapt to novel or unexpected data that a scientist may find curious. Our work is attempting to determine if it is possible to capture what a scientist (subject) finds of interest (curious) in a stream of image data through EEG measurement. One of the our challenges is to determine the difference between an expert and a lay subject response to stimulus. To investigate the theorized difference, we use a set of lifelog images as our dataset. Lifelog images are first person images taken by a small wearable camera which continuously records images whilst it is worn. We have devised two key experiments for use with this data and two classes of subjects. Our subjects are a person who has worn the personal camera, from which our collection of lifelog images is taken and who becomes our expert, and the remaining subjects are people who have no association with the captured images. Our first experiment is a traditional oddball experiment where the oddballs are people having coffee, and can be thought of as a directed information seeking task. The second experiment is to present a stream of lifelog images to the subjects and record which images cause a stimulus response. Once the data from these experiments has been captured our task is to compare the responses between the expert and lay subject groups, to determine if there are any commonalities between these groups or any distinct differences. If the latter outcome is the case the objective is then to investigate methods for capturing properties of images which cause an expert to be interested in a presented image. Further novelty is added to our work by the fact we are using entry-level off-the-shelf EEG devices, consisting of 4 nodes with a sampling rate of 255Hz

Global experts 'off radar'

This issue of ABE Journal, which takes inspiration from a 2008 conference session as well as from the many conversations that took place within one of the working groups of the European funded COST-action “European Architecture beyond Europe,”1 seeks to contribute to a more thorough understanding of a particular type of professional who emerged in architecture and planning milieus from 1945 onwards: the “global expert”. Through a series of contributions, some resulting from long-lasting, in-depth study while others draw on work-in-progress research, a number of individuals are brought to the fore who, despite their often extensive production or prominent roles on a global scale, have remained “off the radar”. Included in this issue are discussions pertaining to people such as Michel Kalt, Henri-Jean Calsat, David Oakley, Erica Mann, or Max Lock, as well as other, more well-known figures such as Louis Kahn, Jacqueline Tyrwhitt and Hassan Fathy. Through this variety, this ABE-journal issue stresses the need to distinguish between various types of such “global experts”, from embedded practitioners to foreign consultants just passing through. More importantly, the issue also seeks to outline some of the challenges confronting architectural historians in writing the history of this new kind of professional. This is done explicitly in the lengthy editorial, which, through a discussion of recent literature, serves as an introduction to the current state of research on the theme. As such, we hope that this issue will help set a possible research agenda on a topic that in the last several years has triggered scholarly attention, yet still requires a sound theoretical and methodological framing

Competing with Gaussian linear experts

We study the problem of online regression. We prove a theoretical bound on the square loss of Ridge Regression. We do not make any assumptions about input vectors or outcomes. We also show that Bayesian Ridge Regression can be thought of as an online algorithm competing with all the Gaussian linear experts

'Becoming experts': learning through mediation

Purpose – This study is largely founded on Vygotsky’s sociocultural theory, Feuerstein’s theory of Mediated Learning Experience and Lave and Wenger’s ‘community of practice’, which concerned building a community of learners that places mediation as central in learning and teaching. While the overall study involved Malaysian Year One English and Mathematics classrooms, this article focuses only on the latter. Two research questions were posed: 1) How does the teacher/peers mediate learning? 2) How does mediation influence the individual’s identity? Method – This qualitative study was conducted within a period of three months. Data collection included intense classroom observations, interviews, classroom discourse and dialogic discussions with teachers and pupils. Microgenetic analyses of transcripts were made to show moment-to moment changes observed.Findings – Four types of mediation emerged from the data : Environmental mediation, cognitive mediation, affective mediation and metacognitive mediation (i.e., an ECAM model for mediation).Findings suggest that mediation enabled the Mathematics teacher to change, to take ownership and to sustain her new pedagogical approaches within the classroom. This re-focusing benefited her pupils, and dramatically changed a particular less able pupil from one who was initially ‘lost in his world,’ into one who was able to engage in the learning process, take ownership of his own learning, as well as mediate other pupils’ learning. Value – Hence it is argued that the ECAM model for mediation provided opportunities for this teacher and her pupil to expand their capacity to learn and develop their identities as individuals capable of learning and becoming ‘experts’

Forecasting Distributions with Experts Advice

This paper considers forecasts of the distribution of data whose distribution function is possibly time varying. The forecast is achieved via time varying combinations of experts’ forecasts. We derive theoretical worse case bounds for general algorithms based on multiplicative updates of the combination weights. The bounds are useful to study the properties of forecast combinations when data are nonstationary and there is no unique best model. An application with an empirical study is used to highlight the results in practice

Hire the Experts: Combinatorial Auction Based Scheme for Experts Selection in E-Healthcare

During the last decade, scheduling the healthcare services (such as staffs and OTs) inside the hospitals have assumed a central role in healthcare. Recently, some works are addressed in the direction of hiring the expert consultants (mainly doctors) for the critical healthcare scenarios from outside of the medical unit, in both strategic and non-strategic settings under monetary and non-monetary perspectives. In this paper, we have tried to investigate the experts hiring problem with multiple patients and multiple experts; where each patient reports a preferred set of experts which is private information alongwith their private cost for consultancy. To the best of our knowledge, this is the first step in the direction of modeling the experts hiring problem in the combinatorial domain. In this paper, the combinatorial auction based scheme is proposed for hiring experts from outside of the hospitals to have expertise by the preferred doctors set to the patients.Comment: 7 Page

Online Learning with Low Rank Experts

We consider the problem of prediction with expert advice when the losses of the experts have low-dimensional structure: they are restricted to an unknown $d$-dimensional subspace. We devise algorithms with regret bounds that are independent of the number of experts and depend only on the rank $d$. For the stochastic model we show a tight bound of $\Theta(\sqrt{dT})$, and extend it to a setting of an approximate $d$ subspace. For the adversarial model we show an upper bound of $O(d\sqrt{T})$ and a lower bound of $\Omega(\sqrt{dT})$