Object Detection and Tracking with Post-Merge Motion Estimation

This publication describes techniques and apparatuses that enable an electronic device (e.g., a smartphone) with at least one camera to capture, render, and/or process frames. As the smartphone captures frames of a scene, the smartphone determines frames to be rendered and/or processed. Initially, the smartphone performs a global (fast) motion estimation to correctly render and/or process the captured frames of the scene. Information from the global motion estimation enables the smartphone to merge the captured frames, and the smartphone can generate one merged frame or a set of merged frame candidates. Information from the global motion estimation also enables the smartphone to perform target (object) detection on the merged frame. If the smartphone fails to detect a target (e.g., inside a target-detection box), the smartphone proceeds to render and/or process a next merged frame of the set of the merged frame candidates. If the smartphone, however, detects a target inside the target-detection box, the smartphone uses the necessary resources to perform local (accurate, detailed) motion estimation inside the target-detection box. The smartphone may use the information from the local motion estimation to merge the target, generate a local frame patch, and/or verify the target. The local motion estimation helps reduce or remove undesired artifacts (e.g., ghosting). Lastly, the smartphone projects the location of the target to a corresponding merged frame to generate a resulting frame with increased signal and/or increased signal-to-noise ratio (SNR)

GRADUAL CHANGES IN SNOW PEAKS IN UPPER INDUS BASIN, PAKISTAN: A GOOGLE EARTH BASED REVIEW

The hydrology and climate of mid to high-latitude mountainous areas are significantly impacted by snow cover. Since adding or removing snow cover significantly impacts the snowpack’s capacity to operate as a reservoir for water storage, the snowfall-dominated basins of mid- to higher latitudes are anticipated to see the largest shifts in the hydrological cycle because of global warming. By moving the time slider in the historical imagery feature of Google Earth Pro, the Upper Indus Basin study area was examined from the years 1984 to 2020 to track changes in the snow cover. All observations were made with longitude and latitude at 35o, 34', 51.79" N and 74o, 34', 24.21" E, and the eye altitude at 344.46 miles. Google Earth captured pictures of all the observations on December 31st of every year. The data from 1984 to 2020 was examined keenly, and it was observed that as time goes on, global warming is showing its effects and producing climate changes, which has a negative impact on the region's snow and glacier availability. The Landsat images make it abundantly evident that the lower areas of the upper Indus Basin's snow cover are more negatively impacted than the downstream side areas due to the variation in altitude. The authors also referred to the research work by other researchers in the study to compare with their work. The study observed that some areas were utterly showing no snow in 2020 as compared to 1984 as time moved on with an increase in global warming in 36 years

Intertwined magnetism and charge density wave order in kagome FeGe

Electron correlations often lead to emergent orders in quantum materials. Kagome lattice materials are emerging as an exciting platform for realizing quantum topology in the presence of electron correlations. This proposal stems from the key signatures of electronic structures associated with its lattice geometry: flat band induced by destructive interference of the electronic wavefunctions, topological Dirac crossing, and a pair of van Hove singularities (vHSs). A plethora of correlated electronic phases have been discovered amongst kagome lattice materials, including magnetism, charge density wave (CDW), nematicity, and superconductivity. These materials can be largely organized into two types: those that host magnetism and those that host CDW order. Recently, a CDW order has been discovered in the magnetic kagome FeGe, providing a new platform for understanding the interplay between CDW and magnetism. Here, utilizing angle-resolved photoemission spectroscopy, we observe all three types of electronic signatures of the kagome lattice: flat bands, Dirac crossings, and vHSs. From both the observation of a temperature-dependent shift of the vHSs towards the Fermi level as well as guidance via first-principle calculations, we identify the presence of the vHSs near the Fermi level (EF) to be driven by the development of underlying magnetic exchange splitting. Furthermore, we show spectral evidence for the CDW order as gaps that open on the near-EF vHS bands, as well as evidence of electron-phonon coupling from a kink on the vHS band together with phonon hardening observed by inelastic neutron scattering. Our observation points to the magnetic interaction-driven band modification resulting in the formation of the CDW order, indicating an intertwined connection between the emergent magnetism and vHS charge order in this moderately-correlated kagome metal.Comment: submitted on April 22, 202

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

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

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

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