A quantum-inspired classical algorithm for separable Non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting

PACE Solver Description: Hust-Solver - A Heuristic Algorithm of Directed Feedback Vertex Set Problem

A directed graph is formed by vertices and arcs from one vertex to another. The feedback vertex set problem (FVSP) consists in making a given directed graph acyclic by removing as few vertices as possible. In this write-up, we outline the core techniques used in the heuristic feedback vertex set algorithm, submitted to the heuristic track of the 2022 PACE challenge

CDCA3 Is a Novel Prognostic Biomarker Associated with Immune Infiltration in Hepatocellular Carcinoma

Cell division cycle-associated protein-3 (CDCA3) contributes to the regulation of the cell cycle. CDCA3 plays an important role in the carcinogenesis of various cancers; however, the association between CDCA3 expression, prognosis of patients, and immune infiltration in the tumor microenvironment is still unknown. Here, we demonstrated that CDCA3 was differentially expressed between the tumor tissues and corresponding normal tissues using in silico analysis in the ONCOMINE and Tumor Immune Estimation Resource (TIMER) databases. We analyzed the relationship between the expression of CDCA3 and prognosis of patients with hepatocellular carcinoma (HCC) using the Kaplan–Meier plotter database and Gene Expression Profiling Interactive Analysis (GEPIA). Furthermore, we determined the prognostic value of CDCA3 expression using univariate and multivariate analyses. We observed that CDCA3 expression closely correlated with immune infiltration and gene markers of infiltrating immune cells in the TIMER database. CDCA3 was highly expressed in the tumor tissues than in the adjacent normal tissues in various cancers, including HCC. Increased expression of CDCA3 was accompanied by poorer overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease-specific survival (DSS). The correlation between CDCA3 expression and OS and disease-free survival (DFS) was also studied using GEPIA. CDCA3 expression was associated with the levels of immune cell infiltration and was positively correlated with tumor purity. Moreover, CDCA3 expression was associated with gene markers such as PD-1, CTLA4, LAG3, and TIM-3 from exhausted T cells, CD3D, CD3E, and CD2 from T cells, and TGFB1 and CCR8 located on the surface of Tregs. Thus, we demonstrated that CDCA3 may be a potential target and biomarker for the management and diagnosis of HCC

A quantum-inspired classical algorithm for separable Non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, the separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting

Effect of high-pressure homogenization on the structure of cassava starch

Cassava starch suspension was homogenized at different pressures (0, 20, 40, 60, 80, and 100 MPa) with a high-pressure homogenizer. To investigate the effect of high-pressure homogenization on the structure of cassava starch, the samples were characterized using microscopy, laser scattering, and X-ray diffraction techniques, with native and heat gelatinized cassava starches as controlled samples. The temperature of starch suspension increased linearly with applied pressure at a rate of 0.187 degrees C/MPa. Microscopy studies showed that cassava starch was partly gelatinized after high-pressure homogenization, and the degree of gelatinization increased with homogenizing pressure. Results of laser scattering measurements suggested a considerable increase in particle size after homogenization at 100 MPa as a result of granule swelling. The Xray diffraction pattern showed that there was no evident change after homogenization suggesting that the crystalline structure of starch granules was resistant to high-pressure homogenization

Effects of Defatted Flaxseed Addition on Rheological Properties of Wheat Flour Slurry

Rheological property of defatted flaxseed-added wheat flour dispersions was investigated as a function of defatted flaxseed concentration (0-20%), NaCl concentration (0, 0.6, and 1.2%), sucrose concentration (0, 5, and 10%), and slurry concentration (33and 62.5%). Frequency sweep tests at 20 degrees C and temperature sweep tests from 20 to 90 degrees C were applied to the samples. The experimental measurements demonstrated that the viscoelastic moduli of samples increased with the increase in defatted flaxseed concentration from 0 to 20% and decreased with the increase in NaCl, sucrose, and water concentration at 20 degrees C. The gelatinization temperatures of the defatted flaxseed-wheat slurry samples were delayed with the addition of defatted flaxseed, NaCl, and sucrose but hastened with the addition of water

Degradation Trend Prediction of Hydropower Units Based on a Comprehensive Deterioration Index and LSTM

Deterioration trend prediction of hydropower units helps to detect abnormal conditions of hydropower units and can prevent early failures. The reliability and accuracy of the prediction results are crucial to ensure the safe operation of the units and promote the stable operation of the power system. In this paper, the long short-term neural network (LSTM) is introduced, a comprehensive deterioration index (CDI) trend prediction model based on the time–frequency domain is proposed, and the prediction accuracy of the situation trend of hydropower units is improved. Firstly, the time–domain health model (THM) is constructed with back-propagation neural network (BPNN) and condition parameters of active power, guide vane opening and blade opening and the time–domain indicators. Subsequently, a frequency-domain health model (FHM) is established based on ensemble empirical mode decomposition (EEMD), approximate entropy (ApEn), and k-means clustering algorithm. Later, the time–domain degradation index (TDI) is developed according to THM, the frequency-domain degradation index (FDI) is constructed according to FHM, and the CDI is calculated as a weighted sum by TDI and FDI. Finally, the prediction model of LSTM is proposed based on the CDI to achieve degradation trend prediction. In order to validate the effectiveness of the CDI and the accuracy of the prediction model, the vibration waveform dataset of a hydropower plant in China is taken as a case study and compared with four different prediction models. The results demonstrate that the proposed model outperforms other comparison models in terms of predicting accuracy and stability

Security follows the vehicle of Pope John Paul II

A security car follows the vehicle of John Paul II as it speeds by Pius XII Memorial Library. (27 January 1999) [Photo by Randy R. McGuire, Assistant SLU Archivist. Original photo identification number is PHO 3.355.17