Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational ap...
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All‐solid‐state sodium batteries (ASSSBs) with nonflammable electrolytes and ubiquitous sodium resource are a promising solution to the safety and cost concerns for lithium‐ion batteries. However, the intrinsic mis...
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All‐solid‐state sodium batteries (ASSSBs) with nonflammable electrolytes and ubiquitous sodium resource are a promising solution to the safety and cost concerns for lithium‐ion batteries. However, the intrinsic mismatch between low anodic decomposition potential of superionic sulfide electrolytes and high operating potentials of sodium‐ion cathodes leads to a volatile cathode–electrolyte interface and undesirable cell performance. Here we report a high‐capacity organic cathode, Na 4 C 6 O 6 , that is chemically and electrochemically compatible with sulfide electrolytes. A bulk‐type ASSSB shows high specific capacity (184 mAh g −1 ) and one of the highest specific energies (395 Wh kg −1 ) among intercalation compound‐based ASSSBs. The capacity retentions of 76 % after 100 cycles at 0.1 C and 70 % after 400 cycles at 0.2 C represent the record stability for ASSSBs. Additionally, Na 4 C 6 O 6 functions as a capable anode material, enabling a symmetric all‐organic ASSSB with Na 4 C 6 O 6 as both cathode and anode materials.
In this paper, a novel technique for tight outer-approximation of the intersection region of a finite number of ellipses in 2-dimensional (2D) space is proposed. First, the vertices of a tight polygon that contains th...
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Using the full four-year SPTpol 500 deg2 dataset in both the 95 GHz and 150 GHz frequency bands, we present measurements of the temperature and E-mode polarization of the cosmic microwave background (CMB), as well as ...
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Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and th...
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Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal bes...
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Among learning-based hashing methods, supervised hashing tries to find hash codes which preserve semantic similarities of original data. Recent years have witnessed much efforts devoted to design objective functions a...
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Among learning-based hashing methods, supervised hashing tries to find hash codes which preserve semantic similarities of original data. Recent years have witnessed much efforts devoted to design objective functions and optimization methods for supervised hashing learning, in order to improve search accuracy and reduce training cost. In this paper, we propose a very straightforward supervised hashing algorithm and demonstrate its superiority over several state-of-the-art methods. The key idea of our approach is to treat label vectors as binary codes and to learn target codes which have similar structure to label vectors. To circumvent direct optimization on large n × n Gram matrices, we identify an inner-product-preserving transformation and use it to bring close label vectors and hash codes without changing the structure. The optimization process is very efficient and scales well. In our experiment, training 16-bit and 96-bit code on NUS-WIDE cost respectively only 3 and 6 minutes.
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