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

LncRNAs as Biomarkers for Melanoma

Melanoma is the most aggressive and serious type of skin cancer. Known for being highly malignant and metastatic, melanoma typically has low survival rates. Prognosis can be improved with an early diagnosis and a good monitoring of the disease. However, current melanoma biomarkers display severe limitations, making them inadequate for early detection of the malignancy. Therefore, it is of urgent matter for us to characterize and establish novel biomarkers with a direct application to daily clinics in order to accurately detect early american joint committee on cancer (AJCC) stages in melanoma patients, efficiently monitor the disease progression, and reliably predict the response to therapies, survival, and likely future recurrence. Long non-coding RNAs (lncRNAs) are a promising biomarker and regulator of tumor progression for many cancers. They are secreted into the bloodstream inside exosomes by a wide range of malignant cells and several of them have actually been validated as promising circulating molecular signatures of other cancer types, but not melanoma. However, in recent years there has been much research into lncRNA melanoma biomarkers, and many of them have been characterized as potentially clinically relevant

Committed emissions from existing energy infrastructure jeopardize 1.5 °C climate target.

Net anthropogenic emissions of carbon dioxide (CO2) must approach zero by mid-century (2050) in order to stabilize the global mean temperature at the level targeted by international efforts1-5. Yet continued expansion of fossil-fuel-burning energy infrastructure implies already 'committed' future CO2 emissions6-13. Here we use detailed datasets of existing fossil-fuel energy infrastructure in 2018 to estimate regional and sectoral patterns of committed CO2 emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of the associated infrastructure. We estimate that, if operated as historically, existing infrastructure will cumulatively emit about 658 gigatonnes of CO2 (with a range of 226 to 1,479 gigatonnes CO2, depending on the lifetimes and utilization rates assumed). More than half of these emissions are predicted to come from the electricity sector; infrastructure in China, the USA and the 28 member states of the European Union represents approximately 41 per cent, 9 per cent and 7 per cent of the total, respectively. If built, proposed power plants (planned, permitted or under construction) would emit roughly an extra 188 (range 37-427) gigatonnes CO2. Committed emissions from existing and proposed energy infrastructure (about 846 gigatonnes CO2) thus represent more than the entire carbon budget that remains if mean warming is to be limited to 1.5 degrees Celsius (°C) with a probability of 66 to 50 per cent (420-580 gigatonnes CO2)5, and perhaps two-thirds of the remaining carbon budget if mean warming is to be limited to less than 2 °C (1,170-1,500 gigatonnes CO2)5. The remaining carbon budget estimates are varied and nuanced14,15, and depend on the climate target and the availability of large-scale negative emissions16. Nevertheless, our estimates suggest that little or no new CO2-emitting infrastructure can be commissioned, and that existing infrastructure may need to be retired early (or be retrofitted with carbon capture and storage technology) in order to meet the Paris Agreement climate goals17. Given the asset value per tonne of committed emissions, we suggest that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternatives are available and affordable4,18

The inhibition of high ammonia to in vitro rumen fermentation is pH dependent

Ammonia is an important rumen internal environment indicator. In livestock production, feeding a large amount of non-protein nitrogen to ruminants will create high ammonia stress to the animals, which increases the risk of ammonia toxicity. However, the effects of ammonia toxicity on rumen microbiota and fermentation are still unknown. In this study, an in vitro rumen fermentation technique was used to investigate the effects of different concentrations of ammonia on rumen microbiota and fermentation. To achieve the four final total ammonia nitrogen (TAN) concentrations of 0, 8, 32, and 128 mmol/L, ammonium chloride (NH4Cl) was added at 0, 42.8, 171.2, and 686.8 mg/100 mL, and urea was added at 0, 24, 96, and 384 mg/100 mL. Urea hydrolysis increased, while NH4Cl dissociation slightly reduced the pH. At similar concentrations of TAN, the increased pH of the rumen culture by urea addition resulted in a much higher free ammonia nitrogen (FAN) concentration compared to NH4Cl addition. Pearson correlation analysis revealed a strong negative correlation between FAN and microbial populations (total bacteria, protozoa, fungi, and methanogens) and in vitro rumen fermentation profiles (gas production, dry matter digestibility, total volatile fatty acid, acetate, propionate, etc.), and a much weaker correlation between TAN and the above indicators. Additionally, bacterial community structure changed differently in response to TAN concentrations. High TAN increased Gram-positive Firmicutes and Actinobacteria but reduced Gram-negative Fibrobacteres and Spirochaetes. The current study demonstrated that the inhibition of in vitro rumen fermentation by high ammonia was pH-dependent and was associated with variations of rumen microbial populations and communities

BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization

Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy function serves as input features to an upper-level objective of interest. These optimal features typically depend on tunable parameters of the lower-level energy in such a way that the entire bilevel pipeline can be trained end-to-end. Although not generally presented as such, this paper demonstrates how a variety of graph learning techniques can be recast as special cases of bilevel optimization or simplifications thereof. In brief, building on prior work we first derive a more flexible class of energy functions that, when paired with various descent steps (e.g., gradient descent, proximal methods, momentum, etc.), form graph neural network (GNN) message-passing layers; critically, we also carefully unpack where any residual approximation error lies with respect to the underlying constituent message-passing functions. We then probe several simplifications of this framework to derive close connections with non-GNN-based graph learning approaches, including knowledge graph embeddings, various forms of label propagation, and efficient graph-regularized MLP models. And finally, we present supporting empirical results that demonstrate the versatility of the proposed bilevel lens, which we refer to as BloomGML, referencing that BiLevel Optimization Offers More Graph Machine Learning. Our code is available at https://github.com/amberyzheng/BloomGML. Let graph ML bloom.Comment: Publication at AISTATS 202

OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation

As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images. To significantly reduce annotation efforts while attaining competitive segmentation accuracy, we propose a self-learning and one-shot learning based framework for 3D medical image segmentation by annotating only one slice of each 3D image. Our approach takes two steps: (1) self-learning of a reconstruction network to learn semantic correspondence among 2D slices within 3D images, and (2) representative selection of single slices for one-shot manual annotation and propagating the annotated data with the well-trained reconstruction network. Extensive experiments verify that our new framework achieves comparable performance with less than 1% annotated data compared with fully supervised methods and generalizes well on several out-of-distribution testing sets