On the Nature of X(4260)

We study the property of $X(4260)$ resonance by re-analyzing all experimental data available, especially the $e^+e^- \rightarrow J/\psi\,\pi^+\pi^-,\,\,\,\omega\chi_{c0}$ cross section data. The final state interactions of the $\pi\pi$, $K\bar K$ couple channel system are also taken into account. A sizable coupling between the $X(4260)$ and $\omega\chi_{c0}$ is found. The inclusion of the $\omega\chi_{c0}$ data indicates a small value of $\Gamma_{e^+e^-}=23.30\pm 3.55$eV.Comment: Refined analysis with new experimental data included. 13 page

Deep factorization for speech signal

Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors. An intuitive idea is to factorize each speech frame into individual informative factors, though it turns out to be highly difficult. Recently, we found that speaker traits, which were assumed to be long-term distributional properties, are actually short-time patterns, and can be learned by a carefully designed deep neural network (DNN). This discovery motivated a cascade deep factorization (CDF) framework that will be presented in this paper. The proposed framework infers speech factors in a sequential way, where factors previously inferred are used as conditional variables when inferring other factors. We will show that this approach can effectively factorize speech signals, and using these factors, the original speech spectrum can be recovered with a high accuracy. This factorization and reconstruction approach provides potential values for many speech processing tasks, e.g., speaker recognition and emotion recognition, as will be demonstrated in the paper.Comment: Accepted by ICASSP 2018. arXiv admin note: substantial text overlap with arXiv:1706.0177

Molecular Lines of 13 Galactic Infrared Bubble Regions

We investigated the physical properties of molecular clouds and star formation processes around infrared bubbles which are essentially expanding HII regions. We performed observations of 13 galactic infrared bubble fields containing 18 bubbles. Five molecular lines, 12CO (J=1-0), 13CO (J=1-0), C18O(J=1-0), HCN (J=1-0), and HCO+ (J=1-0), were observed, and several publicly available surveys, GLIMPSE, MIPSGAL, ATLASGAL, BGPS, VGPS, MAGPIS, and NVSS, were used for comparison. We find that these bubbles are generally connected with molecular clouds, most of which are giant. Several bubble regions display velocity gradients and broad shifted profiles, which could be due to the expansion of bubbles. The masses of molecular clouds within bubbles range from 100 to 19,000 solar mass, and their dynamic ages are about 0.3-3.7 Myr, which takes into account the internal turbulence pressure of surrounding molecular clouds. Clumps are found in the vicinity of all 18 bubbles, and molecular clouds near four of these bubbles with larger angular sizes show shell-like morphologies, indicating that either collect-and-collapse or radiation-driven implosion processes may have occurred. Due to the contamination of adjacent molecular clouds, only six bubble regions are appropriate to search for outflows, and we find that four of them have outflow activities. Three bubbles display ultra-compact HII regions at their borders, and one of them is probably responsible for its outflow. In total, only six bubbles show star formation activities in the vicinity, and we suggest that star formation processes might have been triggered.Comment: 55 Pages, 32 figures. Accepted for publication in A

Unidirectional emission and nanoparticle detection in a deformed circular square resonator

We propose a novel deformed square resonator which has four asymmetric circular sides. Photons leak out from specific points, depending on the interplay between stable islands and unstable manifolds in phase space. By carefully breaking the mirror reflection symmetry, optical modes with strong chirality approaching 1 and unidirectional emission can be achieved simultaneously. Upon binding of a nanoparticle, the far-field emission pattern of the deformed microcavity changes drastically. Due to the EP point of the degenerate mode pairs in the deformed cavity, chirality-based far-field detection of nanoparticles with ultra-small size can be realized

The Lifecycle and Cascade of WeChat Social Messaging Groups

Social instant messaging services are emerging as a transformative form with which people connect, communicate with friends in their daily life - they catalyze the formation of social groups, and they bring people stronger sense of community and connection. However, research community still knows little about the formation and evolution of groups in the context of social messaging - their lifecycles, the change in their underlying structures over time, and the diffusion processes by which they develop new members. In this paper, we analyze the daily usage logs from WeChat group messaging platform - the largest standalone messaging communication service in China - with the goal of understanding the processes by which social messaging groups come together, grow new members, and evolve over time. Specifically, we discover a strong dichotomy among groups in terms of their lifecycle, and develop a separability model by taking into account a broad range of group-level features, showing that long-term and short-term groups are inherently distinct. We also found that the lifecycle of messaging groups is largely dependent on their social roles and functions in users' daily social experiences and specific purposes. Given the strong separability between the long-term and short-term groups, we further address the problem concerning the early prediction of successful communities. In addition to modeling the growth and evolution from group-level perspective, we investigate the individual-level attributes of group members and study the diffusion process by which groups gain new members. By considering members' historical engagement behavior as well as the local social network structure that they embedded in, we develop a membership cascade model and demonstrate the effectiveness by achieving AUC of 95.31% in predicting inviter, and an AUC of 98.66% in predicting invitee.Comment: 10 pages, 8 figures, to appear in proceedings of the 25th International World Wide Web Conference (WWW 2016

Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation

Post-click Conversion Rate (CVR) prediction task plays an essential role in industrial applications, such as recommendation and advertising. Conventional CVR methods typically suffer from the data sparsity problem as they rely only on samples where the user has clicked. To address this problem, researchers have introduced the method of multi-task learning, which utilizes non-clicked samples and shares feature representations of the Click-Through Rate (CTR) task with the CVR task. However, it should be noted that the CVR and CTR tasks are fundamentally different and may even be contradictory. Therefore, introducing a large amount of CTR information without distinction may drown out valuable information related to CVR. This phenomenon is called the curse of knowledge problem in this paper. To tackle this issue, we argue that a trade-off should be achieved between the introduction of large amounts of auxiliary information and the protection of valuable information related to CVR. Hence, we propose a Click-aware Structure Transfer model with sample Weight Assignment, abbreviated as CSTWA. It pays more attention to the latent structure information, which can filter the input information that is related to CVR, instead of directly sharing feature representations. Meanwhile, to capture the representation conflict between CTR and CVR, we calibrate the representation layer and reweight the discriminant layer to excavate the click bias information from the CTR tower. Moreover, it incorporates a sample weight assignment algorithm biased towards CVR modeling, to make the knowledge from CTR would not mislead the CVR. Extensive experiments on industrial and public datasets have demonstrated that CSTWA significantly outperforms widely used and competitive models

SPATIAL-TEMPORAL PATTERN OF VEGETATION INDEX CHANGE AND THE RELATIONSHIP TO LAND SURFACE TEMPERATURE IN ZOIGE

The Zoige wetland is the largest alpine peat wetland in China, and it has been degrading since 1960s. MODIS Enhance Vegetation Index (EVI) and Land Surface Temperature (LST) products in late august from 2000 to 2014 were employed to explore vegetation index and land surface temperature change tendency and to perform Temperature Vegetation Dryness Index (TVDI). The correlation between the annual mean of EVI and annual mean of LST was also calculated at pixel scale. The main purpose of this study is to explore the relationship between wetland degradation and climate change. The main conclusions are as follows: (1) Average EVI in Zoige plateau tended to be decreasing from 2000 to 2014, especially after 2007. In wetland areas, the annual mean of EVI were negative, while the slope were positive. It showed that the water storage of wetlands in Zoige plateau had been decreasing in the past 15 years and will keep decreasing in the future. (2) Overall, LST in the whole Zoige plateau had been increasing since 2000. While the minimum TVDI increased from 2000 to 2008 and then decreased. The change of TVDI suggested that drought should be a main factor that lead to wetland degradation in Zoige. (3) The uneven distribution of the correlation between EVI and LST suggested that LST is also one of the main reasons of wetland degradation