Multiscale Technicolor and b→sγb \to s \gamma

Correction to the $b\rightarrow s\gamma$ branching ratio in the multiscale walking technicolor model (MWTCM) is examined. For the original MWTCM, the correction is too large to explain the recent CLEO data. We show that if topcolor is further introduced, the branching ratio in the topcolor assisted MWTCM can be in agreement with the CLEO data for a certain range of the parameters.Comment: 11 pages, Latex, no macros, 3 figures, hard copy is available upon request. to appear in Z. Phys.

Robustness meets low-rankness: unified entropy and tensor learning for multi-view subspace clustering

In this paper, we develop the weighted error entropy-regularized tensor learning method for multi-view subspace clustering (WETMSC), which integrates the noise disturbance removal and subspace structure discovery into one unified framework. Unlike most existing methods which focus only on the affinity matrix learning for the subspace discovery by different optimization models and simply assume that the noise is independent and identically distributed (i.i.d.), our WETMSC method adopts the weighted error entropy to characterize the underlying noise by assuming that noise is independent and piecewise identically distributed (i.p.i.d.). Meanwhile, WETMSC constructs the self-representation tensor by storing all self-representation matrices from the view dimension, preserving high-order correlation of views based on the tensor nuclear norm. To solve the proposed nonconvex optimization method, we design a half-quadratic (HQ) additive optimization technology and iteratively solve all subproblems under the alternating direction method of multipliers framework. Extensive comparison studies with state-of-the-art clustering methods on real-world datasets and synthetic noisy datasets demonstrate the ascendancy of the proposed WETMSC method

Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering

Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method. However, we observe that these LRR-based methods would suffer from two issues: limited clustering performance and high computational cost since (1) they usually adopt the nuclear norm with biased estimation to explore the low-rank structures; (2) the singular value decomposition of large-scale matrices is inevitably involved. Moreover, LRR may not achieve low-rank properties in both intra-views and interviews simultaneously. To address the above issues, this paper proposes the Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering (BTMSC). Specifically, BTMSC constructs a third-order tensor from the view dimension to explore the high-order correlation and the subspace structures of multi-view features. The Bi-Nuclear Quasi-Norm (BiN) factorization form of the Schatten-p norm is utilized to factorize the third-order tensor as the product of two small-scale thirdorder tensors, which not only captures the low-rank property of the third-order tensor but also improves the computational efficiency. Finally, an efficient alternating optimization algorithm is designed to solve the BTMSC model. Extensive experiments with ten datasets of texts and images illustrate the performance superiority of the proposed BTMSC method over state-of-the-art methods

Interactive Virtual Reality Game for Online Learning of Science Subject in Primary Schools

Education plays an important role in nurturing children. COVID-19 pandemic brings challenges or disruptions to school education, due to school closures in some countries. Science subject in primary schools is unique as hands-on experiments are important learning components. Its learning process may be affected, as a new norm of online learning or home-based learning. This research project creates a serious game on science subject for primary school students aging within 10 to 11 years old using virtual reality (VR) technology. It consists of three virtual learning phases. Phase 1 explains theories of science topics on electricity and electric circuits. Phase 2 provides interactive hands-on experiment exercises where students can practice theory knowledge learned in the previous phase. An interactive quiz session is offered to reinforce the learning in Phase 3. Interactive VR features enable primary school students learning abstract science concepts in an interesting way compared to conventional classroom settings. Meticulous design attentions have been placed in the details such as visual instructions, voice instructions, speech tempo, animations, and colorful graphics to create a sense of realism and keep students actively engaged. Preliminary case study has been conducted with 10 students at primary schools in Singapore to evaluate learning effectiveness in this research

Ionic cluster effect in suppression on superconductivity in Ni- and Co-doped YBCO systems

We adopted the x-ray diffraction, oxygen contents, positron annihilation technology as well as simulation methods to investigate systemically YBa₂Cu₃–x(Ni,Co)xO₇–δ (x = 0–0.5). The simulated results show that ions distribute in dispersive form in little doped concentration. As doped concentration increases, ions combine into clusters in the crystal lattice. The calculated results and oxygen contents, together with the impure phases and the local electron density ne, show the ionic cluster effect, which not only causes the local electron density to reach the saturation, but also suppress the superconductivity significantly

Study on the rare radiative decay Bc→Ds∗γB_c \to D_s^*\gamma in the standard model and multiscale walking technicolor model

Applying the perturbative QCD ( PQCD ) method, we study the decay $B_c\rightarrow D_s^*\gamma$ in the standard model and multiscale walking technicolor model. In the SM, we find that the contribution of weak annihilation is more important than that of the electromagnetic penguin. The presence of Pseudo-Goldstone-Bosons in the MWTCM leads to a large enhancement in the rate of $B_c\rightarrow D_s^*\gamma$, but this model is in conflict with the branching ratio of $Z\rightarrow b\overline b$ ( $R_b$ ) and the CLEO data on the branching ratio BR ( $b\rightarrow s\gamma$ ). If topcolor is further introduced, the calculated results in the topcolor assisted MWTCM can be suppressed and be in agreement with the CLEO data for a certain range of the parameters.Comment: 16 pages, Latex, no macros, 1 figure(in Latex), hard copy is available upon request. to appear in Phys. Rev.

Booming or sinking: How does an emission trading scheme affect enterprise value?

Unlike prior research that shows climate policy improves enterprise value, this study reveals the negative impact of emission trading schemes (ETSs) on enterprise value under China’s unique institutional backdrop and identifies the mechanism through which this impact occurs. Data from a sample of 1 267 listed companies in the Chinese stock market from 2005 to 2018 models are analyzed using difference-in-differences (DID) and propensity score matching methods (PSM). The results suggest that ETSs have an average short-term negative impact on enterprise value, which peaks in the second year of the ETS and diminishes from the fourth year. Further analysis reveals that ETSs did not cause significant operating losses for firms but reduced their value through the market response mechanism. ETS enterprises experienced significant declines in their annual stock transaction amounts and in returns on individual shares. This indicates that investors expect ETSs to adversely affect pilot enterprises and accordingly adopt disinvestment strategies. Despite the short-term negative effect, ETSs effectively encourage enterprises to innovate green technologies to mitigate long-term carbon risk