Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.Comment: This revised version fixes two small typos in the published versio

MicroRNA-383 located in frequently deleted chromosomal locus 8p22 regulates CD44 in prostate cancer.

A major genomic alteration in prostate cancer (PCa) is frequent loss of chromosome (chr) 8p with a common region of loss of heterozygosity (LOH) at chr8p22 locus. Genomic studies implicate this locus in the initiation of clinically significant PCa and with progression to metastatic disease. However, the genes within this region have not been fully characterized to date. Here we demonstrate for the first time that a microRNA component of this region-miR-383-is frequently downregulated in prostate cancer, has a critical role in determining tumor-initiating potential and is involved in prostate cancer metastasis via direct regulation of CD44, a ubiquitous marker of PCa tumor-initiating cells (TICs)/stem cells. Expression analyses of miR-383 in PCa clinical tissues established that low miR-383 expression is associated with poor prognosis. Functional data suggest that miR-383 regulates PCa tumor-initiating/stem-like cells via CD44 regulation. Ectopic expression of miR-383 inhibited tumor-initiating capacity of CD44+ PCa cells. Also, 'anti-metastatic' effects of ectopic miR-383 expression were observed in a PCa experimental metastasis model. In view of our results, we propose that frequent loss of miR-383 at chr8p22 region leads to tumor initiation and prostate cancer metastasis. Thus, we have identified a novel finding that associates a long observed genomic alteration to PCa stemness and metastasis. Our data suggest that restoration of miR-383 expression may be an effective therapeutic modality against PCa. Importantly, we identified miR-383 as a novel PCa tissue diagnostic biomarker with a potential that outperforms that of serum PSA

A fast tag identification anti-collision algorithm for RFID systems

© 2019 John Wiley & Sons, Ltd. In this work, we propose a highly efficient binary tree-based anti-collision algorithm for radio frequency identification (RFID) tag identification. The proposed binary splitting modified dynamic tree (BS-MDT) algorithm employs a binary splitting tree to achieve accurate tag estimation and a modified dynamic tree algorithm for rapid tag identification. We mathematically evaluate the performance of the BS-MDT algorithm in terms of the system efficiency and the time system efficiency based on the ISO/IEC 18000-6 Type B standard. The derived mathematical model is validated using computer simulations. Numerical results show that the proposed BS-MDT algorithm can provide the system efficiency of 46% and time system efficiency of 74%, outperforming all other well-performed algorithms

Thermal one- and two-graviton Green's functions in the temporal gauge

The thermal one- and two-graviton Green's function are computed using a temporal gauge. In order to handle the extra poles which are present in the propagator, we employ an ambiguity-free technique in the imaginary-time formalism. For temperatures T high compared with the external momentum, we obtain the leading T^4 as well as the subleading T^2 and log(T) contributions to the graviton self-energy. The gauge fixing independence of the leading T^4 terms as well as the Ward identity relating the self-energy with the one-point function are explicitly verified. We also verify the 't Hooft identities for the subleading T^2 terms and show that the logarithmic part has the same structure as the residue of the ultraviolet pole of the zero temperature graviton self-energy. We explicitly compute the extra terms generated by the prescription poles and verify that they do not change the behavior of the leading and sub-leading contributions from the hard thermal loop region. We discuss the modification of the solutions of the dispersion relations in the graviton plasma induced by the subleading T^2 contributions.Comment: 17 pages, 5 figures. Revised version to be published in Phys. Rev.

Impurity Effects on Superconductivity on Surfaces of Topological Insulators

A two-dimensional superconductor (SC) on surfaces of topological insulators (TIs) is a mixture of s-wave and helical p-wave components when induced by s-wave interactions, since spin and momentum are correlated. On the basis of the Abrikosov-Gor'kov theory, we reveal that unconventional SCs on the surfaces of TIs are stable against time-reversal symmetric (TRS) impurities within a region of small impurity concentration. Moreover, we analyze the stability of the SC on the surfaces of TIs against impurities beyond the perturbation theory by solving the real-space Bogoliubov-de Gennes equation for an effective tight-binding model of a TI. We find that the SC is stable against strong TRS impurities. The behaviors of bound states around an impurity suggest that the SC on the surfaces of TIs is not a topological SC.Comment: 17 pages, 14 figures, to appear in J. Phys. Soc. Jp

Dirac Neutrino Dark Matter

We investigate the possibility that dark matter is made of heavy Dirac neutrinos with mass in the range [O(1) GeV- a few TeV] and with suppressed but non-zero coupling to the Standard Model Z as well as a coupling to an additional Z' gauge boson. The first part of this paper provides a model-independent analysis for the relic density and direct detection in terms of four main parameters: the mass, the couplings to the Z, to the Z' and to the Higgs. These WIMP candidates arise naturally as Kaluza-Klein states in extra-dimensional models with extended electroweak gauge group SU(2)_L* SU(2)_R * U(1). They can be stable because of Kaluza-Klein parity or of other discrete symmetries related to baryon number for instance, or even, in the low mass and low coupling limits, just because of a phase-space-suppressed decay width. An interesting aspect of warped models is that the extra Z' typically couples only to the third generation, thus avoiding the usual experimental constraints. In the second part of the paper, we illustrate the situation in details in a warped GUT model.Comment: 35 pages, 25 figures; v2: JCAP version; presentation and plots improved, results unchange

Generalization of the NpNnN_pN_n Scheme and the Structure of the Valence Space

The $N_pN_n$ scheme, which has been extensively applied to even-even nuclei, is found to be a very good benchmark for odd-even, even-odd, and doubly-odd nuclei as well. There are no apparent shifts in the correlations for these four classes of nuclei. The compact correlations highlight the deviant behavior of the Z=78 nuclei, are used to deduce effective valence proton numbers near Z=64, and to study the evolution of the Z=64 subshell gap.Comment: 10 pages, 4 figure

Two novel approaches for photometric redshift estimation based on SDSS and 2MASS databases

We investigate two training-set methods: support vector machines (SVMs) and Kernel Regression (KR) for photometric redshift estimation with the data from the Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey databases. We probe the performances of SVMs and KR for different input patterns. Our experiments show that the more parameters considered, the accuracy doesn't always increase, and only when appropriate parameters chosen, the accuracy can improve. Moreover for different approaches, the best input pattern is different. With different parameters as input, the optimal bandwidth is dissimilar for KR. The rms errors of photometric redshifts based on SVM and KR methods are less than 0.03 and 0.02, respectively. Finally the strengths and weaknesses of the two approaches are summarized. Compared to other methods of estimating photometric redshifts, they show their superiorities, especially KR, in terms of accuracy.Comment: accepted for publication in ChJA