Cross-Promotion in Social Media: Choosing the Right Allies

This paper investigates the strategic use of cross-promotion for content producers in social media. In particular, we study how a producer chooses other producers to cross-promote so as to maximize the expected benefits of them cross-promoting him/her in return. Theories on homophily effect and social influence suggest that cross-promoted producers are more likely to cross-promote the initiator in return when they are in the similar categories or share more common friends and when the initiator has higher status. However, the cross-promotion from producers of different categories and social groups (i.e., share fewer common friends) tend to benefit the initiator more. The benefits also increase as the status of the initiator increases. We collected a panel of data consisting of 27,356 producers’ profile and status information, content categories, and their cross-promotion activities over a period of two months from YouTube. To test our hypotheses, we first employ a cox proportional hazard model to estimate the probability of cross-promotion in return. Then, we use a difference-in-differences method with panel fixed effects to evaluate the effect of cross-promotion in return on the initiator. Our results strongly support our hypotheses and provide valuable insights for both content producers and social media platforms

Coastal Wave Generation and Wave Breaking over Terrain: Two Problems in Mesoscale Wave Dynamics

Two problems in mesoscale wave dynamics are addressed: (i) wave-turbulence interaction in a breaking mountain wave and (ii) gravity wave generation associated with coastal heating gradients. The mean and turbulent structures in a breaking mountain wave are considered using an ensemble of high-resolution (essentially LES) wave-breaking calculations. A turbulent kinetic energy budget for the wave shows that the turbulence production is almost entirely due to the mean shear. Most of the production is at the top of the leeside shooting ow, where the mean- ow Richardson number is persistently less than 0:25. Computation of the turbulent heat and momentum uxes shows that the dissipation of mean- ow wave energy is due primarily to the momentum uxes. The resulting drag on the leeside shooting ow leads to a loss of mean ow Bernoulli function as well as a cross-stream PV ux. The dependence of both the resolved-scale and subgrid turbulent uxes on the grid spacings is examined by computing a series of ensembles with varying grid spacings. The subgrid parameterization is shown to produce an overestimate of the PV ux at low grid resolution. The generation of gravity waves by coastal heating gradients is explored using linear theory calculations and idealized numerical modeling. The linear theory for ow without terrain shows that the solution depends on two parameters: a nondimensional coastal width L and a nondimensional wind speed U. For U 6= 0 the solution is composed of three distinct wave branches. Two of these branches correspond to the no-wind solution of Rotunno, except with Doppler shifting and dispersion. The third branch exists only for U 6= 0 and is shown to be broadly similar to ow past a steady heat source or a topographic obstacle. The relative importance of this third branch is determined largely by the parameter combination U=L. The e ect of terrain is explored in the linear context using an idealized linear model and associated diagnostic computations. These results are then extended to the nonlinear problem using idealized nonlinear model runs

A 100-m-Scale Modeling Study of a Gale Event on the Lee Side of a Long Narrow Mountain

In this study, a gale event that occurred on the lee side of a long narrow mountain was investigated, together with the associated mountain flows, using a realistic-case large-eddy simulation (LES) that is based on the Weather Research and Forecasting Model. The mountain is located on the southeastern Tibetan Plateau, where approximately 58 gales occur annually, mostly in the afternoons during the winter season. Benefitting from realistic topography and high horizontal resolution as fine as 111 m, the LES can replicate features similar to the wind fields observed during the gale period. Investigation of the early morning wind structure over the mountain revealed that weak inflows were blocked, reversed, and divided in the upstream area and that some weak lee waves, rotors, and two clear lee vortices were evident downstream. As the upstream wind accelerated and the boundary layer developed during the daytime, the lee waves became amplified with severe downslope wind and rotors. The interaction and coherent structure of the downslope wind, rotor, and vortices were investigated to show the severe wind distribution. The mountain drags associated with blocking and amplified lee waves are displayed to show the potential impact on the large-scale model. The linear lee-wave theory was adopted to explain the wave evolution during this event together with a discussion of the uncertainty around low-level nonlinear processes

Online Content Consumption: Social Endorsements, Observational Learning and Word-of-Mouth

The consumption of online content can occur through observational learning (OL) whereby consumers follow previous consumers’ choices or social endorsement (SE) wherein consumers receive content sharing from their social ties. As users consume content, they also generate post-consumption word-of-mouth (WOM) signals. OL, SE and WOM together shape the diffusion of the content. This study examines the drivers of SE and the effect of SE on content consumption and post-consumption WOM. In particular, we compare SE with OL. Using a random sample of 8,945 new videos posted on YouTube, we collected a multi-platform dataset consisting of data on video consumption and WOM from YouTube and data on tweet sharing of the video from Twitter. Applying a panel vector autoregression (PVAR) model, we find that OL increases consumption significantly more than SE in the short run. However, SE has a stronger effect on content consumption in the long run. This can be attributed to the impact of SE on WOM signals, which also increase content consumption. While OL and SE leads to similar amount of positive WOM, SE generates significantly more negative WOM than OL. Our results also show that SE is driven by WOM (i.e., likes and dislikes) but not content popularity. We further confirm the effects of OL vs. SE on content consumption and WOM using a randomized experiment at the individual consumer level. Implications for content providers and social media platforms are derived accordingly

Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution

Since the number of incident energies is limited, it is difficult to directly acquire hyperspectral images (HSI) with high spatial resolution. Considering the high dimensionality and correlation of HSI, super-resolution (SR) of HSI remains a challenge in the absence of auxiliary high-resolution images. Furthermore, it is very important to extract the spatial features effectively and make full use of the spectral information. This paper proposes a novel HSI super-resolution algorithm, termed dual-domain network based on hybrid convolution (SRDNet). Specifically, a dual-domain network is designed to fully exploit the spatial-spectral and frequency information among the hyper-spectral data. To capture inter-spectral self-similarity, a self-attention learning mechanism (HSL) is devised in the spatial domain. Meanwhile the pyramid structure is applied to increase the acceptance field of attention, which further reinforces the feature representation ability of the network. Moreover, to further improve the perceptual quality of HSI, a frequency loss(HFL) is introduced to optimize the model in the frequency domain. The dynamic weighting mechanism drives the network to gradually refine the generated frequency and excessive smoothing caused by spatial loss. Finally, In order to better fully obtain the mapping relationship between high-resolution space and low-resolution space, a hybrid module of 2D and 3D units with progressive upsampling strategy is utilized in our method. Experiments on a widely used benchmark dataset illustrate that the proposed SRDNet method enhances the texture information of HSI and is superior to state-of-the-art methods

Investigation on the methane adsorption capacity in coals : considerations from nanopores by multifractal analysis

ACKNOWLEDGEMENTS This research was funded by the National Natural Science Foundation of China (grant numbers 41830427, 41922016, and 41772160).Peer reviewedPostprin

VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images

The precise combination of image sensor and micro-lens array enables lenslet light field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from light field images. In turn, 3D models of the recorded objects can be recovered, which is a great advantage over other imaging system. However, reflective and texture-less areas in light field images have complicated conditions, making it hard to correctly calculate disparity with existing algorithms. To tackle this problem, we introduce a novel end-to-end network VommaNet to retrieve multi-scale features from reflective and texture-less regions for accurate disparity estimation. Meanwhile, our network has achieved similar or better performance in other regions for both synthetic light field images and real-world data compared to the state-of-the-art algorithms. Currently, we achieve the best score for mean squared error (MSE) on HCI 4D Light Field Benchmark

The roles and mechanisms of gut microbiome and metabolome in patients with cerebral infarction

As the most common type of stroke, ischemic stroke, also known as cerebral infarction (CI), with its high mortality and disability rate, has placed a huge burden on social economy and public health. Treatment methods for CI mainly include thrombectomy, thrombolysis, drug therapy, and so on. However, these treatments have certain timeliness and different side effects. In recent years, the gut-brain axis has become a hot topic, and its role in nervous system diseases has been confirmed by increasing evidences. The intestinal microbiota, as an important part of the gut-brain axis, has a non-negligible impact on the progression of CI through mechanisms such as inflammatory response and damage-associated molecular patterns, and changes in the composition of intestinal microbiota can also serve as the basis for predicting CI. At the same time, the diagnosis of CI requires more high-throughput techniques, and the analysis method of metabolomics just fits this demand. This paper reviewed the changes of intestinal microbiota in patients within CI and the effects of the intestinal microbiota on the course of CI, and summarized the therapeutic methods of the intervention with the intestinal microbiota. Furthermore, metabolic changes of CI patients were also discussed to reveal the molecular characteristics of CI and to elucidate the potential pathologic pathway of its interference

Super-resolution imaging through a multimode fiber: the physical upsampling of speckle-driven

Following recent advancements in multimode fiber (MMF), miniaturization of imaging endoscopes has proven crucial for minimally invasive surgery in vivo. Recent progress enabled by super-resolution imaging methods with a data-driven deep learning (DL) framework has balanced the relationship between the core size and resolution. However, most of the DL approaches lack attention to the physical properties of the speckle, which is crucial for reconciling the relationship between the magnification of super-resolution imaging and the quality of reconstruction quality. In the paper, we find that the interferometric process of speckle formation is an essential basis for creating DL models with super-resolution imaging. It physically realizes the upsampling of low-resolution (LR) images and enhances the perceptual capabilities of the models. The finding experimentally validates the role played by the physical upsampling of speckle-driven, effectively complementing the lack of information in data-driven. Experimentally, we break the restriction of the poor reconstruction quality at great magnification by inputting the same size of the speckle with the size of the high-resolution (HR) image to the model. The guidance of our research for endoscopic imaging may accelerate the further development of minimally invasive surgery