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A ranking hashing algorithm based on listwise supervision
Recently,learning to hash technology has been used for the similarity search of large-scale data.It can simultaneous increase the search speed and reduce the storage cost through transforming the data into binary codes.At present,most ranking hashing ...
Anbang YANG +3 more
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Hamming distance for conjugates
Let x, y be strings of equal length. The Hamming distance h(x,y) between x and y is the number of positions in which x and y differ. If x is a cyclic shift of y, we say x and y are conjugates. We consider f(x,y), the Hamming distance between the conjugates xy and yx.
openaire +3 more sources
We show that the majority of the 18 analyzed recurrent cancer‐associated ERBB4 mutations are transforming. The most potent mutations are activating, co‐operate with other ERBB receptors, and are sensitive to pan‐ERBB inhibitors. Activating ERBB4 mutations also promote therapy resistance in EGFR‐mutant lung cancer.
Veera K. Ojala +15 more
wiley +1 more source
The bi-orthogonal codes for embedding Side-Information (SI) in data-based blind SLM (BSLM) proposed in Joo et al. (2012) produce better bit error rate (BER) and SI error rate (SIER) performance compared to binary codes.
Adnan Haider Yusef Sa'd +2 more
doaj +1 more source
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
The Power of Asymmetry in Binary Hashing [PDF]
When approximating binary similarity using the hamming distance between short binary hashes, we show that even if the similarity is symmetric, we can have shorter and more accurate hashes by using two distinct code maps. I.e.
Makarychev, Yury +4 more
core +2 more sources
Hamming Distance Kernelisation via Topological Quantum Computation [PDF]
We present a novel approach to computing Hamming distance and its kernelisation within Topological Quantum Computation. This approach is based on an encoding of two binary strings into a topological Hilbert space, whose inner product yields a natural Hamming distance kernel on the two strings.
Di Pierro, Alessandra +3 more
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ABSTRACT Background Accessing brain magnetic resonance imaging (MRI) can be challenging, especially for underserved patients, which may lead to disparities in neurological diagnosis. Method This mixed‐methods study enrolled adults with one of four neurological disorders: mild cognitive impairment or dementia of the Alzheimer type, multiple sclerosis ...
Maya L. Mastick +19 more
wiley +1 more source
This article investigates the probabilistic relationship between quantum classification of Boolean functions and their Hamming distance. By integrating concepts from quantum computing, information theory, and combinatorics, we explore how Hamming ...
Theodore Andronikos +4 more
doaj +1 more source
Image Retrieval Using a Deep Attention-Based Hash
Image retrieval is becoming more and more important due to the rapid increase of the number of images on the web. To improve the efficiency of computing the similarity of images, hashing has moved into the focus of research.
Xinlu Li +6 more
doaj +1 more source

