A social networking-enabled framework for autonomous robot skill development

University of Technology Sydney. Faculty of Engineering and Information Technology.Intelligent service robots will need to adapt to unforeseen situations when performing tasks for humans. To do this, they will be expected to continuously develop new skills. Existing frameworks that address robot learning of new skills for a particular task often follow a task-specific design approach. Task-specific design is unable to support robots to adapt new skills to new tasks. This is largely due to the inability of skill specification in task-specific design to be extended or to be easily changed. This dissertation provides an innovative task-independent framework that allows robots to develop new skills on their own. The idea is to create an online social network platform called Numbots that enables robots to learn new skills autonomously from their social circles. This platform integrates a state-of-the-art approach to learning from experience, called Constructing Skill Trees (CST), with a state-of-the-art framework for knowledge sharing, called RoboEarth. Based on this integration, a new logic model for online Robot-Robot Interaction (RRI) is developed. The principal focus of this dissertation is the analysis of, and solutions to three underlying technical challenges required to achieve the RRI model: (i) skill representation; (ii) autonomous skill recognition and sharing; and (iii) skill transfer. We focus on motion skills required to interact with and manipulate objects where a robot performs a series of motions to attain a goal given by humans. Skills formalise robot activities, which may involve an object (for example, kicking a ball, lifting a box, or passing a bottle of water to a person). Skills may also include robot activities that do not involve objects (for example, raising hands or walking forward). The first challenge concerns how to create a new skill representation that can represent robot skills independently of robot species, tasks and environments. We develop a generic robot skill representation, which characterises three key dimensions of a robot skill in the focused domain: the changing relationship, the spatial relationship and the temporal relationship between the robot and a possible object. The new representation takes a spatial-temporal perspective similar to that found in RoboEarth, and uses the concepts of “agent space” and “object space” from the CST approach. The second challenge concerns how to enable robots to autonomously recognise and share their experiences with other robots that are in their social network. We propose an effect-based skill recognition mechanism that enables robots to recognise skills based on the effects that result from their action. We introduce two types of autonomous skill recognition: (i) recognition of a chain of existing skill primitives; (ii) recognition of a chain of unknown skills. All recognised skills are generalised and packed into a JSON file to share across Numbots. The third challenge is how to enable shared generic robot skills to be interpreted by a robot learner for its own problem solving. We introduce an effect-based skill transfer mechanism, an algorithm to decompose and customise the downloaded generic robot skill into a set of executable action commands for the robot learner's own problem solving. After the introduction of three technical challenges of the RRI model and our solutions, a simulation is undertaken. It demonstrates that a skill recognised and shared by a PR2 robot can be reused and transferred by a NAO robot for a different problem solving. In addition, we also provide a series of comparisons with RoboEarth with a use case study “ServeADrink” to demonstrate the key advantages of the newly created generic robot skill representation over the limited skill representation in RoboEarth. Even though implementation of Numbots and the RRI model on a real robot remains as future work, the proposed analysis and solutions in this dissertation have demonstrated the potential to enable robots to develop new skills on their own, in the absence of human/robot demonstrators and to perform a task for which the robot was not explicitly programmed

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Voltammetric determination of catechol based on a glassy carbon electrode modified with a composite consisting of graphene oxide and polymelamine

The authors describe an voltammetric catechol (CC) assay based on the use of a glassy carbon electrode (GCE) modified with a composite consisting of graphene oxide and polymelamine (GO/PM). The modified GCE was characterized by field emission scanning electron microscopy, elemental analysis, Raman spectroscopy and FTIR. Cyclic voltammetry reveals a well-defined response to CC, with an oxidation peak current that is distinctly enhanced compared to electrodes modified with GO or PM only. The combined synergetic activity of GO and PM in the composite also results in a lower oxidation potential. Differential pulse voltammetry (DPV) shows a response that is linear in the 0.03 to 138 μM CC concentration range. The detection limit is 8 nM, and the sensitivity is 0.537 μA⋅μM−1 ⋅cm−2 . The sensor is selective for CC even in the presence of potentially interfering compounds including hydroquinone, resorcinol and dopamine. The modified GCE is highly reproducible, stable, sensitive, and shows an excellent practicability for detection of CC in water samples

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation