The evolution of power and standard Wikidata editors: comparing editing behavior over time to predict lifespan and volume of edits

Knowledge bases are becoming a key asset leveraged for various types of applications on the Web, from search engines presenting ‘entity cards’ as the result of a query, to the use of structured data of knowledge bases to empower virtual personal assistants. Wikidata is an open general-interest knowledge base that is collaboratively developed and maintained by a community of thousands of volunteers. One of the major challenges faced in such a crowdsourcing project is to attain a high level of editor engagement. In order to intervene and encourage editors to be more committed to editing Wikidata, it is important to be able to predict at an early stage, whether an editor will or not become an engaged editor. In this paper, we investigate this problem and study the evolution that editors with different levels of engagement exhibit in their editing behaviour over time. We measure an editor’s engagement in terms of (i) the volume of edits provided by the editor and (ii) their lifespan (i.e. the length of time for which an editor is present at Wikidata). The large-scale longitudinal data analysis that we perform covers Wikidata edits over almost 4 years. We monitor evolution in a session-by-session- and monthly-basis, observing the way the participation, the volume and the diversity of edits done by Wikidata editors change. Using the findings in our exploratory analysis, we define and implement prediction models that use the multiple evolution indicators

Ferromagnetism without flat bands in thin armchair nanoribbons

Describing by a Hubbard type of model a thin armchair graphene ribbon in the armchair hexagon chain limit, one shows in exact terms, that even if the system does not have flat bands at all, at low concentration a mesoscopic sample can have ferromagnetic ground state, being metallic in the same time. The mechanism is connected to a common effect of correlations and confinement.Comment: 37 pages, 12 figures, in press at Eur. Phys. Jour.

All those wasted hours: On task abandonment in crowdsourcing

Crowdsourcing has become a standard methodology to collect manually annotated data such as relevance judgments at scale. On crowdsourcing platforms like Amazon MTurk or FigureEight, crowd workers select tasks to work on based on different dimensions such as task reward and requester reputation. Requesters then receive the judgments of workers who self-selected into the tasks and completed them successfully. Several crowd workers, however, preview tasks, begin working on them, reaching varying stages of task completion without finally submitting their work. Such behavior results in unrewarded effort which remains invisible to requesters. In this paper, we conduct the first investigation into the phenomenon of task abandonment, the act of workers previewing or beginning a task and deciding not to complete it. We follow a threefold methodology which includes 1) investigating the prevalence and causes of task abandonment by means of a survey over different crowdsourcing platforms, 2) data-driven analyses of logs collected during a large-scale relevance judgment experiment, and 3) controlled experiments measuring the effect of different dimensions on abandonment. Our results show that task abandonment is a widely spread phenomenon. Apart from accounting for a considerable amount of wasted human effort, this bears important implications on the hourly wages of workers as they are not rewarded for tasks that they do not complete. We also show how task abandonment may have strong implications on the use of collected data (for example, on the evaluation of IR systems)

DNA methylation epi-signature is associated with two molecularly and phenotypically distinct clinical subtypes of Phelan-McDermid syndrome

Background: Phelan-McDermid syndrome is characterized by a range of neurodevelopmental phenotypes with incomplete penetrance and variable expressivity. It is caused by a variable size and breakpoint microdeletions in the distal long arm of chromosome 22, referred to as 22q13.3 deletion syndrome, including the SHANK3 gene. Genetic defects in a growing number of neurodevelopmental genes have been shown to cause genome-wide disruptions in epigenomic profiles referred to as epi-signatures in affected individuals. Results: In this study we assessed genome-wide DNA methylation profiles in a cohort of 22 individuals with Phelan-McDermid syndrome, including 11 individuals with large (2 to 5.8 Mb) 22q13.3 deletions, 10 with small deletions (\u3c 1 Mb) or intragenic variants in SHANK3 and one mosaic case. We describe a novel genome-wide DNA methylation epi-signature in a subset of individuals with Phelan-McDermid syndrome. Conclusion: We identified the critical region including the BRD1 gene as responsible for the Phelan-McDermid syndrome epi-signature. Metabolomic profiles of individuals with the DNA methylation epi-signature showed significantly different metabolomic profiles indicating evidence of two molecularly and phenotypically distinct clinical subtypes of Phelan-McDermid syndrome

A survey of task-oriented crowdsourcing

Since the advent of artificial intelligence, researchers have been trying to create machines that emulate human behaviour. Back in the 1960s however, Licklider (IRE Trans Hum Factors Electron 4-11, 1960) believed that machines and computers were just part of a scale in which computers were on one side and humans on the other (human computation). After almost a decade of active research into human computation and crowdsourcing, this paper presents a survey of crowdsourcing human computation systems, with the focus being on solving micro-tasks and complex tasks. An analysis of the current state of the art is performed from a technical standpoint, which includes a systematized description of the terminologies used by crowdsourcing platforms and the relationships between each term. Furthermore, the similarities between task-oriented crowdsourcing platforms are described and presented in a process diagram according to a proposed classification. Using this analysis as a stepping stone, this paper concludes with a discussion of challenges and possible future research directions.This work is part-funded by ERDF-European Regional Development Fund through the COMPETE Programme (Operational Programme for Competitiveness) and by National Funds through the FCT-Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within the Ph.D. Grant SFRH/BD/70302/2010 and by the Projects AAL4ALL (QREN11495), World Search (QREN 13852) and FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012). The authors also thank Jane Boardman for her assistance proof reading the document.info:eu-repo/semantics/publishedVersio

Exact solution of the Falicov-Kimball model with dynamical mean-field theory

The Falicov-Kimball model was introduced in 1969 as a statistical model for metal-insulator transitions; it includes itinerant and localized electrons that mutually interact with a local Coulomb interaction and is the simplest model of electron correlations. It can be solved exactly with dynamical mean-field theory in the limit of large spatial dimensions which provides an interesting benchmark for the physics of locally correlated systems. In this review, we develop the formalism for solving the Falicov-Kimball model from a path-integral perspective, and provide a number of expressions for single and two-particle properties. We examine many important theoretical results that show the absence of fermi-liquid features and provide a detailed description of the static and dynamic correlation functions and of transport properties. The parameter space is rich and one finds a variety of many-body features like metal-insulator transitions, classical valence fluctuating transitions, metamagnetic transitions, charge density wave order-disorder transitions, and phase separation. At the same time, a number of experimental systems have been discovered that show anomalies related to Falicov-Kimball physics [including YbInCu4, EuNi2(Si[1-x]Gex)2, NiI2 and TaxN].Comment: 51 pages, 40 figures, submitted to Reviews of Modern Physic

SHANK proteins limit integrin activation by directly interacting with Rap1 and R-Ras

SHANK3, a synaptic scaffold protein and actin regulator, is widely expressed outside of the central nervous system with predominantly unknown function. Solving the structure of the SHANK3 N-terminal region revealed that the SPN domain is an unexpected Ras-association domain with high affinity for GTP-bound Ras and Rap G-proteins. The role of Rap1 in integrin activation is well established but the mechanisms to antagonize it remain largely unknown. Here, we show that SHANK1 and SHANK3 act as integrin activation inhibitors by sequestering active Rap1 and R-Ras via the SPN domain and thus limiting their bioavailability at the plasma membrane. Consistently, SHANK3 silencing triggers increased plasma membrane Rap1 activity, cell spreading, migration and invasion. Autism-related mutations within the SHANK3 SPN domain (R12C and L68P) disrupt G-protein interaction and fail to counteract integrin activation along the Rap1-RIAM-talin axis in cancer cells and neurons. Altogether, we establish SHANKs as critical regulators of G-protein signalling and integrin-dependent processes

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts