Exact multi-parameter persistent homology of time-series data: one-dimensional reduction of multi-parameter persistence theory

In various applications of data classification and clustering problems, multi-parameter analysis is effective and crucial because data are usually defined in multi-parametric space. Multi-parameter persistent homology, an extension of persistent homology of one-parameter data analysis, has been developed for topological data analysis (TDA). Although it is conceptually attractive, multi-parameter persistent homology still has challenges in theory and practical applications. In this study, we consider time-series data and its classification and clustering problems using multi-parameter persistent homology. We develop a multi-parameter filtration method based on Fourier decomposition and provide an exact formula and its interpretation of one-dimensional reduction of multi-parameter persistent homology. The exact formula implies that the one-dimensional reduction of multi-parameter persistent homology of the given time-series data is equivalent to choosing diagonal ray (standard ray) in the multi-parameter filtration space. For this, we first consider the continuousization of time-series data based on Fourier decomposition towards the construction of the exact persistent barcode formula for the Vietoris-Rips complex of the point cloud generated by sliding window embedding. The proposed method is highly efficient even if the sliding window embedding dimension and the length of time-series data are large because the method precomputes the exact barcode and the computational complexity is as low as the fast Fourier transformation of $O(N \log N)$. Further the proposed method provides a way of finding different topological inferences by trying different rays in the filtration space in no time.Comment: 29 page

Supervised low-rank semi-nonnegative matrix factorization with frequency regularization for forecasting spatio-temporal data

We propose a novel methodology for forecasting spatio-temporal data using supervised semi-nonnegative matrix factorization (SSNMF) with frequency regularization. Matrix factorization is employed to decompose spatio-temporal data into spatial and temporal components. To improve clarity in the temporal patterns, we introduce a nonnegativity constraint on the time domain along with regularization in the frequency domain. Specifically, regularization in the frequency domain involves selecting features in the frequency space, making an interpretation in the frequency domain more convenient. We propose two methods in the frequency domain: soft and hard regularizations, and provide convergence guarantees to first-order stationary points of the corresponding constrained optimization problem. While our primary motivation stems from geophysical data analysis based on GRACE (Gravity Recovery and Climate Experiment) data, our methodology has the potential for wider application. Consequently, when applying our methodology to GRACE data, we find that the results with the proposed methodology are comparable to previous research in the field of geophysical sciences but offer clearer interpretability.Comment: 34 page

Customer process management A framework for using customer-related data to create customer value

Purpose The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services. Design/methodology/approach This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature. Findings The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature. Originality/value "Using data to advance service" is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company's use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data

Disposable Integrated Microfluidic Biochip for Blood Typing by Plastic Microinjection Moulding

Blood typing is the most important test for both transfusion recipients and blood donors. In this paper, a low cost disposable blood typing integrated microfluidic biochip has been designed, fabricated and characterized. In the biochip, flow splitting microchannels, chaotic micromixers, reaction microchambers and detection microfilters are fully integrated. The loaded sample blood can be divided by 2 or 4 equal volumes through the flow splitting microchannel so that one can perform 2 or 4 blood agglutination tests in parallel. For the purpose of obtaining efficient reaction of agglutinogens on red blood cells (RBCs) and agglutinins in serum, we incorporated a serpentine laminating micromixer into the biochip, which combines two chaotic mixing mechanisms of splitting/recombination and chaotic advection. Relatively large area reaction microchambers were also introduced for the sake of keeping the mixture of the sample blood and serum during the reaction time before filtering. The gradually decreasing multi-step detection microfilters were designed in order to effectively filter the reacted agglutinated RBCs, which show the corresponding blood group. To achieve the cost-effectiveness of the microfluidic biochip for disposability, the biochip was realized by the microinjection moulding of COC (cyclic olefin copolymer) and thermal bonding of two injection moulded COC substrates in mass production with a total fabrication time of less than 20 min. Mould inserts of the biochip for the microinjection moulding were fabricated by SU-8 photolithography and the subsequent nickel electroplating process. Human blood groups of A, B and AB have been successfully determined with the naked eye, with 3 mu l of the whole sample bloods, by means of the fabricated biochip within 3 min.X11100104sciescopu

Multidirectional Instability Accompanying an Inferior Labral Cyst

Paralabral cyst of the shoulder joint can be observed in 2% to 4% of the general population, particularly in men during the third and fourth decade. On average, these cysts measure 10 mm to 20 mm in diameter and are located preferentially on the postero-superior aspect of the glenoid. The MRI has increased the frequency of the diagnosis of paralabral cysts of the shoulder joint. Paralabral cysts of the shoulder joint usually develop in the proximity of the labrum. The relationship between shoulder instability and labral tears is well known, however, the association of shoulder instability with a paralabral cyst is rare. Shoulder instability may cause labral injury or labral injury may cause shoulder instability, and then injured tear develops paralabral cyst. In our patient, the inferior paralabral cyst may be associated with inferior labral tears and instability MRI

Significance of the Nanograin Size on the H 2

CuO-SnO2 composite nanofibers with various nanograin sizes were synthesized for investigating their sensing properties with respect to H2S gas. The nanograin size in the CuO-SnO2 composite nanofibers was controlled by changing the thermal treatment duration under isothermal conditions. The nanograin size was found to be critical for the sensing ability of the composite nanofibers. The CuO-SnO2 composite nanofibers comprised of small-sized nanograins were more sensitive to H2S than those with larger-sized nanograins. The superior sensing properties of the CuO-SnO2 composite nanofibers with the smaller nanograins were attributed to the formation of the larger number of p-CuO-n-SnO2 junctions and their transformation to metallic-CuS-n-SnO2 contacts upon exposure to H2S gas. The results suggest that smaller nanograins are conducive to obtaining superior H2S-sensing properties in CuO-SnO2 composite nanofibers

Automatic segmentation of kidney and kidney tumors using the cascaded dense network combined with cLSTM in CT scan

In this study, we develop the cascaded deep neural network model for automatic segmentation of the kidney and kidney tumors in CT scans. We used the fully dense network (to extract inner-slice image features) combined with bi-cLSTM (to extract inter-slice image features) for segmentation of kidney and kidney tumors. The whole CT scan is preprocessing for resizing (256*256) and intensity normalization (clipping between -600 and 400 and then normalizing between 0 and 1), and then entered into 1 st neural network (growth factor = 8, and the consecutive slices = 3) for segmentation of kidney. And the output of 1 st network is resized into 96*96, and entered into 2 nd neural network, which is the same architecture of 1 st network excepting growth factor = 16, and the consecutive slices = 5. Our cascaded deep neural network showed the dice scores of 0.932 (train), and 0.884 (validation) for segmentation of kidney and the dice scores of 0.845 (train), and 0.696 (validation) for segmentation of kidney tumors