Drag it together with Groupie: making RDF data authoring easy and fun for anyone

One of the foremost challenges towards realizing a “Read-write Web of Data” [3] is making it possible for everyday computer users to easily find, manipulate, create, and publish data back to the Web so that it can be made available for others to use. However, many aspects of Linked Data make authoring and manipulation difficult for “normal” (ie non-coder) end-users. First, data can be high-dimensional, having arbitrary many properties per “instance”, and interlinked to arbitrary many other instances in a many different ways. Second, collections of Linked Data tend to be vastly more heterogeneous than in typical structured databases, where instances are kept in uniform collections (e.g., database tables). Third, while highly flexible, the problem of having all structures reduced as a graph is verbosity: even simple structures can appear complex. Finally, many of the concepts involved in linked data authoring - for example, terms used to define ontologies are highly abstract and foreign to regular citizen-users.To counter this complexity we have devised a drag-and-drop direct manipulation interface that makes authoring Linked Data easy, fun, and accessible to a wide audience. Groupie allows users to author data simply by dragging blobs representing entities into other entities to compose relationships, establishing one relational link at a time. Since the underlying representation is RDF, Groupie facilitates the inclusion of references to entities and properties defined elsewhere on the Web through integration with popular Linked Data indexing services. Finally, to make it easy for new users to build upon others’ work, Groupie provides a communal space where all data sets created by users can be shared, cloned and modified, allowing individual users to help each other model complex domains thereby leveraging collective intelligence

States Of Wisdom In Marketing During An Era Of Economic Turbulence

In 1975, Cundiff (1975) wrote an editorial in the Journal of Marketing titled, “What is the Role of Marketing in a Recession?”  The 1974/1975 recession was more damaging to the economy than any recession since the Great Depression.  Implicit in his editorial was the momentary concern corporations would emphasize cost reduction over marketing innovation to insure their short-term survival. Numerous articles were published in response to his article about how marketing and consumers appeared to be changing during the period

Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation

Reinforcement learning (RL) is an agent-based approach for teaching robots to navigate within the physical world. Gathering data for RL is known to be a laborious task, and real-world experiments can be risky. Simulators facilitate the collection of training data in a quicker and more cost-effective manner. However, RL frequently requires a significant number of simulation steps for an agent to become skilful at simple tasks. This is a prevalent issue within the field of RL-based visual quadrotor navigation where state dimensions are typically very large and dynamic models are complex. Furthermore, rendering images and obtaining physical properties of the agent can be computationally expensive. To solve this, we present a simulation framework, built on AirSim, which provides efficient parallel training. Building on this framework, Ape-X is modified to incorporate decentralised training of AirSim environments to make use of numerous networked computers. Through experiments we were able to achieve a reduction in training time from 3.9 hours to 11 minutes using the aforementioned framework and a total of 74 agents and two networked computers. Further details including a github repo and videos about our project, PRL4AirSim, can be found at https://sites.google.com/view/prl4airsim/homeComment: This work has been submitted to the IEEE International Conference on Robotics and Automation (ICRA) for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Maximal cocliques and cohomology in rank one linear groups

In this thesis, we investigate certain aspects of $\mathrm{PSL}_2(q)$. We begin by looking at the generating graph of $\mathrm{PSL}_2(q)$, a structure which may be used to encode certain information about the group, which was first introduced by Liebeck and Shalev and further investigated by many others. We provide a classification of maximal cocliques (independent sets) in the generating graph of $\mathrm{PSL}_2(q)$ when $q$ is a prime and provide a family of examples to show that this result does not directly extend to the prime-power case. After this, we instead investigate the cohomology of finite groups and prove a general result relating the first cohomology of any module to the structure of some fixed module and a generalisation of this result to higher cohomology. We then completely determine the cohomology $\mathrm{H}^n(G,V)$ and its generalisation, $\mathrm{Ext}_G^n(V,W)$, for irreducible modules $V$, $W$ for $G = \mathrm{PSL}_2(q)$ for all $q$ in all non-defining characteristics before doing the same for the Suzuki groups

GA-Auto-PU: A genetic algorithm-based automated machine learning system for Positive-Unlabeled learning

Positive-Unlabeled (PU) learning is a growing field of machine learning that now consists of numerous algorithms; the number is now so large that considering an extensive manual search to select the best algorithm for a given task is impractical. As such, the area of PU learning could benefit from an Automated Machine Learning (Auto-ML) system, which selects the best algorithm for a given input dataset, among a pre-defined set of candidate algorithms. This work proposes such with GA-Auto-PU, a Genetic Algorithm-based Auto-ML system that can generate PU learning algorithms. Experiments with 20 real-world datasets show that GA-Auto-PU significantly outperformed a state-of-the-art PU learning method

MicroRNAs Can Regulate Human APP Levels

A number of studies have shown that increased APP levels, resulting from either a genomic locus duplication or alteration in APP regulatory sequences, can lead to development of early-onset dementias, including Alzheimer's disease (AD). Therefore, understanding how APP levels are regulated could provide valuable insight into the genetic basis of AD and illuminate novel therapeutic avenues for AD. Here we test the hypothesis that APP protein levels can be regulated by miRNAs, evolutionarily conserved small noncoding RNA molecules that play an important role in regulating gene expression. Utilizing human cell lines, we demonstrate that miRNAs hsa-mir-106a and hsa-mir-520c bind to their predicted target sequences in the APP 3'UTR and negatively regulate reporter gene expression. Over-expression of these miRNAs, but not control miRNAs, results in translational repression of APP mRNA and significantly reduces APP protein levels. These results are the first to demonstrate that levels of human APP can be regulated by miRNAs

Stay by thy neighbor? Social organization determines the efficiency of biodiversity markets with spatial incentives

Market-based conservation instruments, such as payments, auctions or tradable permits, are environmental policies that create financial incentives for landowners to engage in voluntary conservation on their land. But what if ecological processes operate across property boundaries and land use decisions on one property influence ecosystem functions on neighboring sites? This paper examines how to account for such spatial externalities when designing market-based conservation instruments. We use an agent-based model to analyze different spatial metrics and their implications on land use decisions in a dynamic cost environment. The model contains a number of alternative submodels which differ in incentive design and social interactions of agents, the latter including coordinating as well as cooperating behavior of agents. We find that incentive design and social interactions have a strong influence on the spatial allocation and the costs of the conservation market.Comment: 11 pages, 6 figure

MicroRNAs can regulate human APP levels

A number of studies have shown that increased APP levels, resulting from either a genomic locus duplication or alteration in APP regulatory sequences, can lead to development of early-onset dementias, including Alzheimer's disease (AD). Therefore, understanding how APP levels are regulated could provide valuable insight into the genetic basis of AD and illuminate novel therapeutic avenues for AD. Here we test the hypothesis that APP protein levels can be regulated by miRNAs, evolutionarily conserved small noncoding RNA molecules that play an important role in regulating gene expression. Utilizing human cell lines, we demonstrate that miRNAs hsa-mir-106a and hsa-mir-520c bind to their predicted target sequences in the APP 3'UTR and negatively regulate reporter gene expression. Over-expression of these miRNAs, but not control miRNAs, results in translational repression of APP mRNA and significantly reduces APP protein levels. These results are the first to demonstrate that levels of human APP can be regulated by miRNAs