Evolution of honesty in higher-order social networks

Sender-receiver games are simple models of information transmission that provide a formalism to study the evolution of honest signaling and deception between a sender and a receiver. In many practical scenarios, lies often affect groups of receivers, which inevitably entangles the payoffs of individuals to the payoffs of other agents in their group, and this makes the formalism of pairwise sender-receiver games inapt for where it might be useful the most. We therefore introduce group interactions among receivers and study how their interconnectedness in higher-order social networks affects the evolution of lying. We observe a number of counterintuitive results that are rooted in the complexity of the underlying evolutionary dynamics, which has thus far remained hidden in the realm of pairwise interactions. We find conditions for honesty to persist even when there is a temptation to lie, and we observe the prevalence of moral strategy profiles even when lies favor the receiver at a cost to the sender. We confirm the robustness of our results by further performing simulations on hypergraphs created from real-world data using the SocioPatterns database. Altogether, our results provide persuasive evidence that moral behavior may evolve on higher-order social networks, at least as long as individuals interact in groups that are small compared to the size of the network

Rehabilitation of recurrent unicystic ameloblastoma using distraction osteogenesis and dental implants

Ameloblastoma is a true neoplasm of odontogenic epithelial origin. Surgical resection of the ameloblastoma is well documented and an accepted treatment modality. Vertical distraction of the alveolar process is an efficient method for augmentation. This method of providing additional bone and soft tissue for implant placement is becoming more common. This clinical report describes the use of distraction osteogenesis and fixed implant supported prosthesis to treat a postsurgical alveolar defect as a result of the resection of a unicystic ameloblastoma in the anterior mandibular region. As a result of alveolar distraction a segment of mature bone was transported vertically in order to lengthen the crest, for better implant anchorage. Further clinical and experimental studies of the technique with long-term follow-up are needed, to confirm bone and implant stability, as it relates to alveolar height

Distribution of Contact Pressure and Stresses under Skirted Footings

Skirted footings posses many novel characteristics which render them eminently suitable for construction of structures in situations involving heavy loads and poor soil conditions with promise of economy. The results of the present investigations will help considerably to understand a detailed picture of the complex phenomenon of contact pressure distribution and vertical stress distribution in soil under skirted footings

Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation

Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot

Compartment syndrome of the hand: A case report and review of literature

© 2017 The Authors Elevation of pressure within tightly bound myofascial compartments has detrimental consequences if not treated promptly, leading to a loss of circulation, ischemia, myonecrosis, nerve damage, and limb loss. They are commonly seen in the distal upper and lower extremities; however, compartment syndrome of the hand is rarely encountered and prompt recognition can prevent permanent damage and tissue loss. This case study presents a complicated case of compartment syndrome of the hand and discusses the interrelationship between compartment syndrome and rhabdomyolysis. An emphasis is placed on pathophysiology of this relationship to allow a better understanding of the imaging features as well as early clinical recognition of compartment syndrome. Magnetic resonance imaging findings are specifically discussed as it remains the best imaging tool to evaluate the extent of the damage and surgical planning

Experimental Results of Concurrent Learning Adaptive Controllers

Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very di fferent. Concurrent learning adaptive control uses specifi cally selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT's RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110688)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 0645960)Boeing Scientific Research Laboratorie

Paradoxical Emboli Secondary to Hepatic Pathology: Common or Coincidental?

Paradoxical cerebral emboli from cardiac and pulmonary sources are well described in the peer-reviewed literature. We outline a case with a hepatic etiology and describe diagnostic and management options. Though this paper represents the first documentation of such, we believe that transpulmonary shunting with concurrent paradoxical cerebral microemboli is more prevalent than recognized. We introduce this case report to compel practitioners to consider paradoxical emboli in selected cirrhotic patients since it can often be difficult to elicit subtle neurologic changes on clinical examination of patients with end stage liver disease

Structural and functional-annotation of an equine whole genome oligoarray

<p>Abstract</p> <p>Background</p> <p>The horse genome is sequenced, allowing equine researchers to use high-throughput functional genomics platforms such as microarrays; next-generation sequencing for gene expression and proteomics. However, for researchers to derive value from these functional genomics datasets, they must be able to model this data in biologically relevant ways; to do so requires that the equine genome be more fully annotated. There are two interrelated types of genomic annotation: structural and functional. Structural annotation is delineating and demarcating the genomic elements (such as genes, promoters, and regulatory elements). Functional annotation is assigning function to structural elements. The Gene Ontology (GO) is the <it>de facto </it>standard for functional annotation, and is routinely used as a basis for modelling and hypothesis testing, large functional genomics datasets.</p> <p>Results</p> <p>An Equine Whole Genome Oligonucleotide (EWGO) array with 21,351 elements was developed at Texas A&M University. This 70-mer oligoarray was designed using the approximately 7× assembled and annotated sequence of the equine genome to be one of the most comprehensive arrays available for expressed equine sequences. To assist researchers in determining the biological meaning of data derived from this array, we have structurally annotated it by mapping the elements to multiple database accessions, including UniProtKB, Entrez Gene, NRPD (Non-Redundant Protein Database) and UniGene. We next provided GO functional annotations for the gene transcripts represented on this array. Overall, we GO annotated 14,531 gene products (68.1% of the gene products represented on the EWGO array) with 57,912 annotations. GAQ (GO Annotation Quality) scores were calculated for this array both before and after we added GO annotation. The additional annotations improved the <it>meanGAQ </it>score 16-fold. This data is publicly available at <it>AgBase </it><url>http://www.agbase.msstate.edu/</url>.</p> <p>Conclusion</p> <p>Providing additional information about the public databases which link to the gene products represented on the array allows users more flexibility when using gene expression modelling and hypothesis-testing computational tools. Moreover, since different databases provide different types of information, users have access to multiple data sources. In addition, our GO annotation underpins functional modelling for most gene expression analysis tools and enables equine researchers to model large lists of differentially expressed transcripts in biologically relevant ways.</p

Identifying Prognostic Groups Using Machine Learning Tools in Patients Undergoing Chemoradiation for Inoperable Locally Advanced Nonsmall Cell Lung Carcinoma

Introduction Unresectable stage III nonsmall cell lung cancer (NSCLC) continues to have dismal 5-year overall survival (OS) rate. However, a subset of the patients treated with chemoradiation show significantly better outcome. Prediction of treatment outcome can be improved by utilizing machine learning tools, such as cluster analysis (CA), and is capable of identifying complex interactions among many variables. We have utilized CA to identify a cluster with good prognosis within stage III NSCLC. Materials and Methods Retrospective analysis of treatment outcomes was done for 92 patients who underwent chemoradiation for inoperable locally advanced NSCLC from 2012 to 2018. Using various patient- and treatment-related variables, an exploratory factor analysis was performed to extract factors with eigenvalue &gt; 1. An appropriate number of homogeneous groups were identified using agglomerative hierarchical cluster analysis. Further K-mean cluster analysis was applied to classify each patient into their homogeneous clusters. The newly formed cluster variable was used as an independent variable to estimate survival over time using Kaplan–Meier method. Results With a median follow-up of 18 months, median OS was 14 months. Using CA, three prognostic clusters were obtained. Cluster 2 with 36 patients had a median OS of 36 months, whereas Cluster 1 with 34 patients had a median OS of 20 months (p = 0.004). Conclusion A cluster could thus be identified with a relatively good prognosis within stage III NSCLC. Using CA, we have attempted to create a model which may provide more specific prognostic information in addition to that provided by tumor node metastasis-based models