Modeling the Context of the Problem Domain of Time Series with Type-2 Fuzzy Sets

Data analysis in the context of the features of the problem domain and the dynamics of processes are significant in various industries. Uncertainty modeling based on fuzzy logic allows building approximators for solving a large class of problems. In some cases, type-2 fuzzy sets in the model are used. The article describes constructing fuzzy time series models of the analyzed processes within the context of the problem domain. An algorithm for fuzzy modeling of the time series was developed. A new time series forecasting scheme is proposed. An illustrative example of the time series modeling is presented. The benefits of contextual modeling are demonstrated.

Pan-cancer organoid validation of tumor outlier chromosomal amplification events

Somatic copy number gains are pervasive in many cancer types, yet their roles in oncogenesis are often poorly explored. This lack of understanding is in part due to broad extensions of copy gains across cancer genomes spanning large chromosomal regions, obscuring causal driver loci. Here we employed a multi-tissue pan-organoid modeling approach to validate candidate oncogenic loci identified within pan-cancer TCGA data by the overlap of extreme copy number amplifications with extreme expression dysregulation for each gene. The candidate outlier loci nominated by this integrative computational analysis were functionally validated by infecting cancer type-specific barcoded full length cDNA lentiviral libraries into cognate minimally transformed human and mouse organoids from the oral cavity, esophagus, colon, stomach, pancreas and lung. Presumptive amplification oncogenes were identified by barcode enrichment as a proxy for increased proliferation. Iterative analysis validated DYRK2 at 12q15, encoding a serine-threonine kinase, as an amplified head and neck squamous carcinoma oncogene in p53-/- oral mucosal organoids. Similarly, FGF3, amplified at 11q13 in 41% of esophageal squamous carcinomas, was validated in p53-/- esophageal organoids in vitro and in vivo with pharmacologic inhibition by small molecule and soluble receptor FGFR antagonists. Our studies establish the feasibility of pan-organoid contextual modeling of pan-cancer candidate genomic drivers, enabling oncogene discovery and preclinical therapeutic modeling.

Testing of Image Compression Methods

Seven console codecs BMF 2.01 (BMF.exe), WebP (cwebp.exe, dwebp.exe), PAQ8L (paq-8l_intel.exe), PAQ8P (paq8p_sse2.exe), JPEG-LS (jpeg.exe), JPEG2000 (convert.exe), PNG (opting.exe) are tested on a set of 19 grayscale images and 10 color images (widespread images used in testing compression methods). An image compression program tester was developed. The tester receives images and executable files of image compression programs, and programs are started for each input image to compress and restore the image. The program operation results are contained in an HTML/CSS file, which includes, among other information, the bitrates achieved by the compression programs and the results of checking how successfully the compressed files were restored. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods have been made to compare the effectiveness of the multipredictors that lie at the heart of the Blend-A13+, Blend-16, Blend-20 methods. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods consist of multipredictors used in the Blend-A13+, Blend-16, Blend-20, methods, and 13 and 16 elementary predictors used in the Blend-A13+ and Blend-16 methods, respectively. The predictor GAP+ is replaced by the predictor GAP; the modeling like JPEG-LS is replaced by contextual modeling with quantization of a context from differences of pixels from the vicinity of the coded pixel, the arithmetic coder, and the reversible intercolor RGB-YUV transformation from JPEG2000. For many images, the obtained partial clones outperformed the results yielded by the JPEG-LS, JPEG2000, PNG methods and gave results at the level provided by the PAQ8L and WebP compression methods. In general, the BMF2.01 method demonstrated the best results on the test set of images. On the test set of images, the multipredictors from Blend-16 and Blend-20 unexpectedly provided color image compression results poorer than the multipredictor from the Blend-A13+ method. In compressing grayscale images, the multipredictor from Blend-20 yielded better results than the multipredictors from Blend-16, Blend-A13+.

Bridges Don’t Make Themselves: Using Community-Based Theater to Reshape Relationships: Rethinking the Idea of Abundance in ABCD

Community-based theater has a variety of manifestations, and the plurality with which these manifestations are occurring is increasing. As such, the diversity and complexity derived from these social sites of public engagement requires further understanding. This article is based upon a multi-case study of two community-based theaters: one in Middle Appalachia, and the other on the Gulf Coast of Florida. Together these sites of performative expression are acting as social interventions for differing reasons within their respective contexts. Through intensive and communicative processes, the theaters provide examples of how co-created performances at the community level simultaneously catalyze relationships and alter how relationships are experienced to engage community members in discussion and performances. As a complex behavioral interaction, the two theaters simultaneously manifest dimensions of ‘abundance’, as well as expand upon normative conceptions of asset-based community development. Through process and contextual modeling, the work provides in-depth exploration to these interpersonal endeavors to assist in how socio-cultural differences as well as narrative reconstruction co-join to enact the individuality of identity across working groups as an overall discursive process.

Context Aware Sequence Modeling

Context modeling helps understand the data, such as sentence or user behavior. Contextual information captures the important underlying feature, and it enhances the relationship between data instances or hidden representations. As the importance of the sequential model grows, so does the importance of the sequential contextual modeling. Under the sequential data, we need to consider the context change over time. In this paper, we present our research works on context modeling and its dynamics modeling over time. Furthermore, we extend our research to handle the multi-granularity of sequential context modeling to consider rich context representations.