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RescueNet: A High Resolution UAV Semantic Segmentation Dataset for Natural Disaster Damage Assessment
No full textAbstractRecent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we present RescueNet, a meticulously curated high-resolution post-disaster dataset that includes detailed classification and semantic segmentation annotations. This dataset aims to facilitate comprehensive scene understanding in the aftermath of natural disasters. RescueNet comprises post-disaster images collected after Hurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs) from multiple impacted regions. The uniqueness of RescueNet lies in its provision of high-resolution post-disaster imagery, accompanied by comprehensive annotations for each image. Unlike existing datasets that offer annotations limited to specific scene elements such as buildings, RescueNet provides pixel-level annotations for all classes, including buildings, roads, pools, trees, and more. Furthermore, we evaluate the utility of the dataset by implementing state-of-the-art segmentation models on RescueNet, demonstrating its value in enhancing existing methodologies for natural disaster damage assessment.</jats:p
DAG: Dual Attention Graph Representation Learning for Node Classification
No full textTransformer-based graph neural networks have accomplished notable achievements by utilizing the self-attention mechanism for message passing in various domains. However, traditional methods overlook the diverse significance of intra-node representations, focusing solely on internode interactions. To overcome this limitation, we propose a DAG (Dual Attention Graph), a novel approach that integrates both intra-node and internode dynamics for node classification tasks. By considering the information exchange process between nodes from dual branches, DAG provides a holistic understanding of information propagation within graphs, enhancing the interpretability of graph-based machine learning applications. The experimental evaluations demonstrate that DAG excels in node classification tasks, outperforming current benchmark models across ten datasets.</jats:p
Endocytosis and Endocytic Motifs across the Connexin Gene Family
No full textProteins fated to be internalized by clathrin-mediated endocytosis require an endocytic motif, where AP-2 or another adaptor protein can bind and recruit clathrin. Tyrosine and di-leucine-based sorting signals are such canonical motifs. Connexin 43 (Cx43) has three canonical tyrosine-based endocytic motifs, two of which have been previously shown to recruit clathrin and mediate its endocytosis. In addition, di-leucine-based motifs have been characterized in the Cx32 C-terminal domain and shown to mediate its endocytosis. Here, we examined the amino acid sequences of all 21 human connexins to identify endocytic motifs across the connexin gene family. We find that although there is limited conservation of endocytic motifs between connexins, 14 of the 21 human connexins contain one or more canonical tyrosine or di-leucine-based endocytic motif in their C-terminal or intracellular loop domain. Three connexins contain non-canonical (modified) di-leucine motifs. However, four connexins (Cx25, Cx26, Cx31, and Cx40.1) do not harbor any recognizable endocytic motif. Interestingly, live cell time-lapse imaging of different GFP-tagged connexins that either contain or do not contain recognizable endocytic motifs readily undergo endocytosis, forming clearly identifiable annular gap junctions when expressed in HeLa cells. How connexins without defined endocytic motifs are endocytosed is currently not known. Our results demonstrate that an array of endocytic motifs exists in the connexin gene family. Further analysis will establish whether the sites we identified in this in silico analysis are legitimate endocytic motifs.</jats:p
Scrutinizing formally Ni<sup>IV</sup>centers through the lenses of core spectroscopy, molecular orbital theory, and valence bond theory
No full textNickel K- and L2,3-edge X-ray absorption spectra (XAS) are discussed for coordinated nickel centers spanning formal oxidation states from II to IV. The ensuing analysis indicates that a physical “+4” oxidation state is untenable for coordinated Ni.</jats:p
Measurement of electrons from open heavy-flavor hadron decays in Au+Au collisions at = 200 GeV with the STAR detector
No full textAbstract
We report a new measurement of the production of electrons from open heavy-flavor hadron decays (HFEs) at mid-rapidity (y< 0.7) in Au+Au collisions at
s
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= 200 GeV. Invariant yields of HFEs are measured for the transverse momentum range of 3.5 < pT< 9 GeV/c in various configurations of the collision geometry. The HFE yields in head-on Au+Au collisions are suppressed by approximately a factor of 2 compared to that in p + p collisions scaled by the average number of binary collisions, indicating strong interactions between heavy quarks and the hot and dense medium created in heavy-ion collisions. Comparison of these results with models provides additional tests of theoretical calculations of heavy quark energy loss in the quark-gluon plasma.</jats:p
An Inexact Feasible Quantum Interior Point Method for Linearly Constrained Quadratic Optimization
No full textQuantum linear system algorithms (QLSAs) have the potential to speed up algorithms that rely on solving linear systems. Interior point methods (IPMs) yield a fundamental family of polynomial-time algorithms for solving optimization problems. IPMs solve a Newton linear system at each iteration to compute the search direction; thus, QLSAs can potentially speed up IPMs. Due to the noise in contemporary quantum computers, quantum-assisted IPMs (QIPMs) only admit an inexact solution to the Newton linear system. Typically, an inexact search direction leads to an infeasible solution, so, to overcome this, we propose an inexact-feasible QIPM (IF-QIPM) for solving linearly constrained quadratic optimization problems. We also apply the algorithm to ‚Ñì1-norm soft margin support vector machine (SVM) problems, and demonstrate that our algorithm enjoys a speedup in the dimension over existing approaches. This complexity bound is better than any existing classical or quantum algorithm that produces a classical solution.</jats:p
