User Donations in a Crowdsourced Video System

Crowdsourced video systems like YouTube and Twitch.tv have been a major internet phenomenon and are nowadays entertaining over a billion users. In addition to video sharing and viewing, over the years they have developed new features to boost the community engagement and some managed to attract users to donate, to the community as well as to other users. User donation directly reflects and influences user engagement in the community, and has a great impact on the success of such systems. Nevertheless, user donations in crowdsourced video systems remain trade secrets for most companies and to date are still unexplored. In this work, we attempt to fill this gap, and we obtain and provide a publicly available dataset on user donations in one crowdsourced video system named BiliBili. Based on information on nearly 40 thousand donators, we examine the dynamics of user donations and their social relationships, we quantitively reveal the factors that potentially impact user donation, and we adopt machine-learned classifiers and network representation learning models to timely and accurately predict the destinations of the majority and the individual donations.Comment: 8 page

A Distributed Incremental Update Scheme for Probability Distribution of Wind Power Forecast Error

Due to the uncertainty of distributed wind generations (DWGs), a better understanding of the probability distributions (PD) of their wind power forecast errors (WPFEs) can help market participants (MPs) who own DWGs perform better during trading. Under the premise of an accurate PD model, considering the correlation among DWGs and absorbing the new information carried by the latest data are two ways to maintain an accurate PD. These two ways both require the historical and latest wind power and forecast data of all DWGs. Each MP, however, only has access to the data of its own DWGs and may refuse to share these data with MPs belonging to other stakeholders. Besides, because of the endless generation of new data, the PD updating burden increases sharply. Therefore, we use the distributed strategy to deal with the data collection problem. In addition, we further apply the incremental learning strategy to reduce the updating burden. Finally, we propose a distributed incremental update scheme to make each MP continually acquire the latest conditional PD of its DWGs' WPFE. Specifically, we first use the Gaussian-mixture-model-based (GMM-based) joint PD to characterize the correlation among DWGs. Then, we propose a distributed modified incremental GMM algorithm to enable MPs to update the parameters of the joint PD in a distributed and incremental manner. After that, we further propose a distributed derivation algorithm to make MPs derive their conditional PD of WPFE from the joint one in a distributed way. Combining the two original algorithms, we finally achieve the complete distributed incremental update scheme, by which each MP can continually obtain its latest conditional PD of its DWGs' WPFE via neighborhood communication and local calculation with its own data. The effectiveness, correctness, and efficiency of the proposed scheme are verified using the dataset from the NREL

A congruence involving harmonic sums modulo pαqβp^{\alpha}q^{\beta}

In 2014, Wang and Cai established the following harmonic congruence for any odd prime $p$ and positive integer $r$, \begin{equation*} Z(p^{r})\equiv-2p^{r-1}B_{p-3} ~(\bmod ~ p^{r}), \end{equation*} where $Z(n)=\sum\limits_{i+j+k=n\atop{i,j,k\in\mathcal{P}_{n}}}\frac{1}{ijk}$ and $\mathcal{P}_{n}$ denote the set of positive integers which are prime to $n$. In this note, we obtain a congruence for distinct odd primes $p,~q$ and positive integers $\alpha,~\beta$, \begin{equation*} Z(p^{\alpha}q^{\beta})\equiv 2(2-q)(1-\frac{1}{q^{3}})p^{\alpha-1}q^{\beta-1}B_{p-3}\pmod{p^{\alpha}} \end{equation*} and the necessary and sufficient condition for \begin{equation*} Z(p^{\alpha}q^{\beta})\equiv 0\pmod{p^{\alpha}q^{\beta}}. \end{equation*} Finally, we raise a conjecture that for $n>1$ and odd prime power $p^{\alpha}||n$, $\alpha\geq1$, \begin{eqnarray} \nonumber Z(n)\equiv \prod\limits_{q|n\atop{q\neq p}}(1-\frac{2}{q})(1-\frac{1}{q^{3}})(-\frac{2n}{p})B_{p-3}\pmod{p^{\alpha}}. \end{eqnarray

Low dose CT reconstruction assisted by an image manifold prior

X-ray Computed Tomography (CT) is an important tool in medical imaging to obtain a direct visualization of patient anatomy. However, the x-ray radiation exposure leads to the concern of lifetime cancer risk. Low-dose CT scan can reduce the radiation exposure to patient while the image quality is usually degraded due to the appearance of noise and artifacts. Numerous studies have been conducted to regularize CT image for better image quality. Yet, exploring the underlying manifold where real CT images residing on is still an open problem. In this paper, we propose a fully data-driven manifold learning approach by incorporating the emerging deep-learning technology. An encoder-decoder convolutional neural network has been established to map a CT image to the inherent low-dimensional manifold, as well as to restore the CT image from its corresponding manifold representation. A novel reconstruction algorithm assisted by the leant manifold prior has been developed to achieve high quality low-dose CT reconstruction. In order to demonstrate the effectiveness of the proposed framework, network training, testing, and comprehensive simulation study have been performed using patient abdomen CT images. The trained encoder-decoder CNN is capable of restoring high-quality CT images with average error of ~20 HU. Furthermore, the proposed manifold prior assisted reconstruction scheme achieves high-quality low-dose CT reconstruction, with average reconstruction error of < 30 HU, more than five times and two times lower than that of filtered back projection method and total-variation based iterative reconstruction method, respectively

Discussion of Parameters Setting for A Distributed Probabilistic Modeling Algorithm

This manuscript provides additional case analysis for the parameters setting of the distributed probabilistic modeling algorithm for the aggregated wind power forecast error

Experimental review of the Υ(1S,2S,3S)\Upsilon(1S,2S,3S) physics at e+e−e^+e^- colliders and the LHC

The three lowest-lying $\Upsilon$ states, i.e. $\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$, composed of $b\bar b$ pairs and below the $B\bar B$ threshold, provide a good platform for the researches of hadronic physics and physics beyond the Standard Model. They can be produced directly in $e^+e^-$ colliding experiments, such as CLEO, Babar, and Belle, with low continuum backgrounds. In these experiments, many measurements of the exclusive $\Upsilon(1S)$ and $\Upsilon(2S)$ decays into light hadrons, which shed light on the "80\% rule" for the Okubo-Zweig-Iizuka suppressed decays in the bottomonium sector, were carried out. Meanwhile, many studies of the charmonium and bottomonium productions in $\Upsilon(1S,2S,3S)$ decays were performed, to distinguish different Quantum Chromodynamics (QCD) models. Besides, exotic states and new physics were also extensively explored in $\Upsilon(1S,2S,3S)$ decays at CLEO, BaBar, and Belle. The $\Upsilon(1S,2S,3S)$ states can also be produced in $pp$ collisions and in collisions involving heavy ions. The precision measurements of their cross sections and polarizations at the large hadron collider (LHC), especially in the CMS, ATLAS, and LHCb experiments, help to understand $\Upsilon$ production mechanisms in $pp$ collisions. The observation of the sequential $\Upsilon$ suppression in heavy ion collisions at CMS is of great importance for verifying the quark-gluon plasma predicted by QCD. In this article, we review the experimental results on $\Upsilon(1S,2S,3S)$ at $e^+e^-$ colliders and the LHC, and summarize their prospects at Belle II and the LHC.Comment: 42 pages, 40 figures; revised version, accepted by Frontiers of Physic

Electromagnetic fingerprints of the Little Bang

Measurements of thermal photons emitted from the rapidly expanding hot and dense medium ("Little Bang") formed in ultra relativistic heavy-ion collisions, and their current theoretical interpretation, are reviewed.Comment: 6 pages, 4 figures. Invited talk at Hard Probes 2013, Stellenbosch, South Africa, Nov. 4-8, 2013. To be published in the Proceedings by Nuclear Physics

Homeomorphic approximation of the intersection curve of two rational surfaces

We present an approach of computing the intersection curve $\mathcal{C}$ of two rational parametric surface $\S_1(u,s)$ and $\S_2(v,t)$, one being projectable and hence can easily be implicitized. Plugging the parametric surface to the implicit surface yields a plane algebraic curve $G(v,t)=0$. By analyzing the topology graph \G of $G(v,t)=0$ and the singular points on the intersection curve $\mathcal{C}$ we associate a space topology graph to $\mathcal{C}$, which is homeomorphic to $\mathcal{C}$ and therefore leads us to an approximation for $\mathcal{C}$ in a given precision.Comment: 18 pages,15 figure

Pre-equilibrium evolution effects on heavy-ion collision observables

In order to investigate the importance of pre-equilibrium dynamics on relativistic heavy-ion collision observables, we match a highly non-equilibrium early evolution stage, modeled by free-streaming partons generated from the Monte Carlo Kharzeev-Levin-Nardi (MC-KLN) and Monte Carlo Glauber (MC-Glb) models, to a locally approximately thermalized later evolution stage described by viscous hydrodynamics, and study the dependence of final hadronic transverse momentum distributions, in particular their underlying radial and anisotropic flows, on the switching time between these stages. Performing a 3-parameter fit of the measured values for the average transverse momenta $\langle p_\perp \rangle$ for pions, kaons and protons as well as the elliptic and triangular flows of charged hadrons $v_{2,3}^\mathrm{ch}$, with the switching time $\tau_s$, the specific shear viscosity $\eta/s$ during the hydrodynamic stage, and the kinetic decoupling temperature $T_\mathrm{dec}$ as free parameters, we find that the preferred "thermalization" times $\tau_s$ depend strongly on the model of the initial conditions. MC-KLN initial conditions require an earlier transition to hydrodynamic behavior (at $\tau_s \approx$ 0.13 fm/$c$) , followed by hydrodynamic evolution with a larger specific shear viscosity $\eta/s\approx$ 0.2, than MC-Glb initial conditions which prefer switching at a later time ($\tau_s\approx$ 0.6 fm/$c$) followed by a less viscous hydrodynamic evolution with $\eta/s\approx$ 0.16. These new results including pre-equilibrium evolution are compared to fits without a pre-equilbrium stage where all dynamic evolution before the onset of hydrodynamic behavior is ignored. In each case, the quality of the dynamical descriptions for the optimized parameter sets, as well as the observables which show the strongest constraining power for the thermalization time, are discussed

Cultivating Online: Question Routing in a Question and Answering Community for Agriculture

Community-based Question and Answering (CQA) platforms are nowadays enlightening over a billion people with crowdsourced knowledge. A key design issue in CQA platforms is how to find the potential answerers and to provide the askers timely and suitable answers, i.e., the so-called \textit{question routing} problem. State-of-art approaches often rely on extracting topics from the question texts. In this work, we analyze the question routing problem in a CQA system named Farm-Doctor that is exclusive for agricultural knowledge. The major challenge is that its questions contain limited textual information. To this end, we conduct an extensive measurement and obtain the whole knowledge repository of Farm-Doctor that consists of over 690 thousand questions and over 3 million answers. To remedy the text deficiency, we model Farm-Doctor as a heterogeneous information network that incorporates rich side information and based on network representation learning models we accurately recommend for each question the users that are highly likely to answer it. With an average income of fewer than 6 dollars a day, over 300 thousands farmers in China seek online in Farm-Doctor for agricultural advices. Our method helps these less eloquent farmers with their cultivation and hopefully provides a way to improve their lives