Efficient quantum key distribution scheme with pre-announcing the basis

We devise a new quantum key distribution scheme that is more efficient than the BB84 protocol. By pre-announcing basis, Alice and Bob are more likely to use the same basis to prepare and measure the qubits, thus achieves a higher efficiency. The error analysis is revised and its security against any eavesdropping is proven briefly. Furthermore we show that, compared with the LCA scheme, our modification can be applied in more quantum channels

Improved Neural Radiance Fields Using Pseudo-depth and Fusion

Since the advent of Neural Radiance Fields, novel view synthesis has received tremendous attention. The existing approach for the generalization of radiance field reconstruction primarily constructs an encoding volume from nearby source images as additional inputs. However, these approaches cannot efficiently encode the geometric information of real scenes with various scale objects/structures. In this work, we propose constructing multi-scale encoding volumes and providing multi-scale geometry information to NeRF models. To make the constructed volumes as close as possible to the surfaces of objects in the scene and the rendered depth more accurate, we propose to perform depth prediction and radiance field reconstruction simultaneously. The predicted depth map will be used to supervise the rendered depth, narrow the depth range, and guide points sampling. Finally, the geometric information contained in point volume features may be inaccurate due to occlusion, lighting, etc. To this end, we propose enhancing the point volume feature from depth-guided neighbor feature fusion. Experiments demonstrate the superior performance of our method in both novel view synthesis and dense geometry modeling without per-scene optimization

Development of Diagnostic SCAR Markers for Genomic DNA Amplifications in Breast Carcinoma by DNA Cloning of High-GC RAMP-PCR Fragments

Cancer is genetically heterogeneous regarding to molecular genetic characteristics and pathogenic pathways. A wide spectrum of biomarkers, including DNA markers, is used in determining genomic instability, molecular subtype determination and disease prognosis, and estimating sensitivity to different drugs in clinical practice. In a previous study, we developed highly effective DNA markers using improved random amplified polymorphic DNA (RAPD) with high-GC primers, which is a valuable approach for the genetic authentication of medicinal plants. In this study, we applied this effective DNA marker technique to generate genetic fingerprints that detect genomic alterations in human breast cancer tissues and then developed sequence-characterized amplified region (SCAR) markers. Three SCAR markers (BC10-1, BC13-4 and BC31-2) had high levels of genomic DNA amplification in breast cancer. The PHKG2 and RNF40 genes are either overlapping or close to the sequences of SCAR marker BC13-4, while SCAR marker BC10-1 is in the intron and overlap the DPEP1 gene, suggesting that alterations in the expression of these genes could contribute to cancer progression. Screening of breast cancer cell lines showed that the mRNA expression levels for the PHKG2 and DPEP1 were lower in non-tumorigenic mammary epithelial cell MCF10A, but elevated in other cell lines. The DPEP1 mRNA level in invasive ductal carcinoma specimens was significantly higher than that of the adjacent normal tissues in women. Taken together, high-GC RAMP-PCR provides greater efficacy in measuring genomic DNA amplifications, deletion or copy number variations. Furthermore, SCAR markers BC10-1 and BC13-4 might be useful diagnostic markers for breast cancer carcinomas

Assessing r2SCAN meta-GGA functional for structural parameters, cohesive energy, mechanical modulus and thermophysical properties of 3d, 4d and 5d transition metals

The recent development of the accurate and efficient semilocal density functionals on the third rung of Jacob's ladder of density functional theory such as the revised regularized strongly constrained and appropriately normed (r2SCAN) density functional could enable the rapid and highly reliable prediction of the elasticity and temperature dependence of thermophysical parameters of refractory elements and their intermetallic compounds using quasi-harmonic approximation (QHA). Here, we present a comparative evaluation of the equilibrium cell volumes, cohesive energy, mechanical moduli, and thermophysical properties (Debye temperature and thermal expansion coefficient) for 22 transition metals using semilocal density functionals, including local density approximation (LDA), the Perdew-Burke-Ernzerhof (PBE) and PBEsol generalized gradient approximations (GGA), and the r2SCAN meta-GGA. PBEsol and r2SCAN deliver the same level of accuracies for structural, mechanical and thermophysical properties. Otherwise, PBE and r2SCAN perform better than LDA and PBEsol for calculating cohesive energies of transition metals. Among the tested density functionals, r2SCAN provides an overall well-balanced performance for reliably computing the cell volumes, cohesive energies, mechanical properties, and thermophysical properties of various 3d, 4d, and 5d transition metals using QHA. Therefore, we recommend that r2SCAN could be employed as a workhorse method to evaluate the thermophysical properties of transition metal compounds and alloys in the high throughput workflows

MDAS: a new multimodal benchmark dataset for remote sensing

In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieval. With current satellite missions, such as ESA Copernicus programme, various data will be accessible at an affordable cost. Future applications will have many options for data sources. Such a privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. There are two reasons; first, different combinations of data sources face different scientific challenges. For example, the fusion of synthetic aperture radar (SAR) data and optical images needs to handle the geometric difference, while the fusion of hyperspectral and multispectral images deals with different resolutions on spatial and spectral domains. Second, nowadays, it is still both financially and labour expensive to acquire multiple data sources for the same region at the same time. In this paper, we provide the community with a benchmark multimodal data set, MDAS, for the city of Augsburg, Germany. MDAS includes synthetic aperture radar data, multispectral image, hyperspectral image, digital surface model (DSM), and geographic information system (GIS) data. All these data are collected on the same date, 7 May 2018. MDAS is a new benchmark data set that provides researchers rich options on data selections. In this paper, we run experiments for three typical remote sensing applications, namely, resolution enhancement, spectral unmixing, and land cover classification, on MDAS data set. Our experiments demonstrate the performance of representative state-of-the-art algorithms whose outcomes can serve as baselines for further studies. The dataset is publicly available at https://doi.org/10.14459/2022mp1657312 (Hu et al., 2022a) and the code (including the pre-trained models) at https://doi.org/10.5281/zenodo.7428215 (Hu et al., 2022b)

Use of PETRA-MRA to assess intracranial arterial stenosis: Comparison with TOF-MRA, CTA, and DSA

Background and purposeNon-invasive and accurate assessment of intracranial arterial stenosis (ICAS) is important for the evaluation of intracranial atherosclerotic disease. This study aimed to evaluate the performance of 3D pointwise encoding time reduction magnetic resonance angiography (PETRA-MRA) and compare its performance with that of 3D time-of-flight (TOF) MRA and computed tomography angiography (CTA), using digital subtraction angiography (DSA) as the reference standard in measuring the degree of stenosis and lesion length.Materials and methodsThis single-center, prospective study included a total of 52 patients (mean age 57 ± 11 years, 27 men, 25 women) with 90 intracranial arterial stenoses who underwent PETRA-MRA, TOF-MRA, CTA, and DSA within 1 month. The degree of stenosis and lesion length were measured independently by two radiologists on these four datasets. The degree of stenosis was classified according to DSA measurement. Severe stenosis was defined as a single lesion with >70% diameter stenosis. The smaller artery stenosis referred to the stenosis, which occurred at the anterior cerebral artery, middle cerebral artery, and posterior cerebral artery, except for the first segment of them. The continuous variables were compared using paired t-test or Wilcoxon signed rank test. The intraclass correlation coefficients (ICCs) were used to assess the agreement between MRAs/CTA and DSA as well as inter-reader variabilities. The ICC value >0.80 indicated excellent agreement. The agreement of data was assessed further by Bland–Altman analysis and Spearman's correlation coefficients. When the difference between MRAs/CTA and DSA was statistically significant in the degree of stenosis, the measurement of MRAs/CTA was larger than that of DSA, which referred to the overestimation of MRAs/CTA for the degree of stenosis.ResultsThe four imaging methods exhibited excellent inter-reader agreement [intraclass correlation coefficients (ICCs) > 0.80]. PETRA-MRA was more consistent with DSA than with TOF-MRA and CTA in measuring the degree of stenosis (ICC = 0.94 vs. 0.79 and 0.89) and lesion length (ICC = 0.99 vs. 0.97 and 0.73). PETRA-MRA obtained the highest specificity and positive predictive value (PPV) than TOF-MRA and CTA for detecting stenosis of >50% and stenosis of >75%. TOF-MRA and CTA overestimated considerably the degree of stenosis compared with DSA (63.0% ± 15.8% and 61.0% ± 18.6% vs. 54.0% ± 18.6%, P < 0.01, respectively), whereas PETRA-MRA did not overestimate (P = 0.13). The degree of stenosis acquired on PETRA-MRA was also more consistent with that on DSA than with that on TOF-MRA and CTA in severe stenosis (ICC = 0.78 vs. 0.30 and 0.57) and smaller artery stenosis (ICC = 0.95 vs. 0.70 and 0.80). In anterior artery circulation stenosis, PETRA-MRA also achieved a little bigger ICC than TOF-MRA and CTA in measuring the degree of stenosis (0.93 vs. 0.78 and 0.88). In posterior artery circulation stenosis, PETRA-MRA had a bigger ICC than TOF-MRA (0.94 vs. 0.71) and a comparable ICC to CTA (0.94 vs. 0.91) in measuring the degree of stenosis.ConclusionPETRA-MRA is more accurate than TOF-MRA and CTA for the evaluation of intracranial stenosis and lesion length when using DSA as a reference standard. PETRA-MRA is a promising non-invasive tool for ICAS assessment

DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation

Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires both time-series data and pixel-wise segmentations. To that end, we propose the DynamicEarthNet dataset that consists of daily, multi-spectral satellite observations of 75 selected areas of interest distributed over the globe with imagery from Planet Labs. These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes. DynamicEarthNet is the first dataset that provides this unique combination of daily measurements and high-quality labels. In our experiments, we compare several established baselines that either utilize the daily observations as additional training data (semi-supervised learning) or multiple observations at once (spatio-temporal learning) as a point of reference for future research. Finally, we propose a new evaluation metric SCS that addresses the specific challenges associated with time-series semantic change segmentation. The data is available at: https://mediatum.ub.tum.de/1650201

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors