Mining and Predicting Temporal Patterns in the Quality Evolution of Wikipedia Articles

Online open collaboration systems like Wikipedia are complex adaptive systems within which large numbers of individual agents and artifacts interact and co-evolve over time. A key issue in these systems is the quality of the co-created artifacts and the processes through which high-quality artifacts are produced. In this paper, we took a dynamic approach to uncover common patterns in the temporal evolution of 6,057 Wikipedia articles in the domains of roads, films, and battles. Using Dynamic Time Warping, an advanced time-series clustering method, we identified three distinctive growth patterns, namely, stalled, plateaued, and sustained. Multinomial logistic regressions to predict these different clusters suggest that the path that an article follows is determined by both its inherent attributes, such as topic importance, and the contribution and coordination of editors who collaborated on the article. Our results also suggest that different factors matter at different stages of an article’s life cycle

Role of Cytokines and Chemokines in Alcohol-Induced Tumor Promotion

Excessive chronic alcohol consumption has become a worldwide health problem. The oncogenic effect of chronic alcohol consumption is one of the leading concerns. The mechanisms of alcohol-induced tumorigenesis and tumor progression are largely unknown, although many factors have been implicated in the process. This review discusses the recent progress in this research area with concentration on alcohol-induced dysregulation of cytokines and chemokines. Based on the available evidence, we propose that alcohol promotes tumor progression by the dysregulation of the cytokine/chemokine system. In addition, we discuss specific transcription factors and signaling pathways that are involved in the action of these cytokines/chemokines and the oncogenic effect of alcohol. This review provides novel insight into the mechanisms of alcohol-induced tumor promotion

A Search for Spectral Galaxy Pairs of Overlapping Galaxies based on Fuzzy Recognition

The Spectral Galaxy Pairs (SGPs) are defined as the composite galaxy spectra which contain two independent redshift systems. These spectra are useful for studying dust properties of the foreground galaxies. In this paper, a total of 165 spectra of SGPs are mined out from Sloan Digital Sky Survey (SDSS) Data Release 9 (DR9) using the concept of membership degree from the fuzzy set theory particularly defined to be suitable for fuzzily identifying emission lines. The spectra and images of this sample are classified according to the membership degree and their image features, respectively. Many of these 2nd redshift systems are too small or too dim to select from the SDSS images alone, making the sample a potentially unique source of information on dust effects in low-luminosity or low-surface-brightness galaxies that are underrepresented in morphological pair samples. The dust extinction of the objects with high membership degree is also estimated by Balmer decrement. Additionally, analyses for a series of spectroscopic observations of one SGP from 165 systems indicate that a newly star-forming region of our Milky Way might occur.Comment: 16pages, 6figure

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

Learning and predicting user responses, such as clicks and conversions, are crucial for many Internet-based businesses including web search, e-commerce, and online advertising. Typically, a user response model is established by optimizing the prediction accuracy, e.g., minimizing the error between the prediction and the ground truth user response. However, in many practical cases, predicting user responses is only part of a rather larger predictive or optimization task, where on one hand, the accuracy of a user response prediction determines the final (expected) utility to be optimized, but on the other hand, its learning may also be influenced from the follow-up stochastic process. It is, thus, of great interest to optimize the entire process as a whole rather than treat them independently or sequentially. In this paper, we take real-time display advertising as an example, where the predicted user's ad click-through rate (CTR) is employed to calculate a bid for an ad impression in the second price auction. We reformulate a common logistic regression CTR model by putting it back into its subsequent bidding context: rather than minimizing the prediction error, the model parameters are learned directly by optimizing campaign profit. The gradient update resulted from our formulations naturally fine-tunes the cases where the market competition is high, leading to a more cost-effective bidding. Our experiments demonstrate that, while maintaining comparable CTR prediction accuracy, our proposed user response learning leads to campaign profit gains as much as 78.2% for offline test and 25.5% for online A/B test over strong baselines

A wear-resistant metastable CoCrNiCu high-entropy alloy with modulated surface and subsurface structures

Sliding friction-induced subsurface structures and severe surface oxidation can be the major causes influencing the wear resistance of ductile metallic materials. Here, we demonstrated the role of subsurface and surface structures in enhancing the wear resistance of an equiatomic metastable CoCrNiCu high-entropy alloy (HEA). The CoCrNiCu HEA is composed of a CoCrNi-rich face-centered cubic (FCC) dendrite phase and a Cu-rich FCC inter-dendrite phase. Copious Cu-rich nano-precipitates are formed and distributed uniformly inside the dendrites after tuning the distribution and composition of the two phases by thermal annealing. Although the formation of nano-precipitates decreases the hardness of the alloy due to the loss of solid solution strengthening, these nano-precipitates can be deformed to form continuous Cu-rich nanolayers during dry sliding, leading to a self-organized nano-laminated microstructure and extensive hardening in the subsurface. In addition, the nano-precipitates can facilitate the formation of continuous and compacted glaze layers on the worn surface, which are also beneficial for the reduction of the wear rate of CoCrNiCu. The current work can be extended to other alloy systems and might provide guidelines for designing and fabricating wear-resistant alloys in general

Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image

Reconstructing interacting hands from a single RGB image is a very challenging task. On the one hand, severe mutual occlusion and similar local appearance between two hands confuse the extraction of visual features, resulting in the misalignment of estimated hand meshes and the image. On the other hand, there are complex spatial relationship between interacting hands, which significantly increases the solution space of hand poses and increases the difficulty of network learning. In this paper, we propose a decoupled iterative refinement framework to achieve pixel-alignment hand reconstruction while efficiently modeling the spatial relationship between hands. Specifically, we define two feature spaces with different characteristics, namely 2D visual feature space and 3D joint feature space. First, we obtain joint-wise features from the visual feature map and utilize a graph convolution network and a transformer to perform intra- and inter-hand information interaction in the 3D joint feature space, respectively. Then, we project the joint features with global information back into the 2D visual feature space in an obfuscation-free manner and utilize the 2D convolution for pixel-wise enhancement. By performing multiple alternate enhancements in the two feature spaces, our method can achieve an accurate and robust reconstruction of interacting hands. Our method outperforms all existing two-hand reconstruction methods by a large margin on the InterHand2.6M dataset.Comment: Accepted to ICCV 2023 (Oral

Long-Lasting Phosphorescence in BaSi\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e2\u3c/sub\u3eN\u3csub\u3e2\u3c/sub\u3e:Eu\u3csup\u3e2+\u3c/sup\u3e and Ba\u3csub\u3e2\u3c/sub\u3eSiO\u3csub\u3e4\u3c/sub\u3e:Eu\u3csup\u3e2+\u3c/sup\u3e Phases for X-Ray and Cathode Ray Tubes

We report the long-lasting bluish-green phosphorescence for X-ray or cathode ray tubes in the phosphors with compositions of either Ba2SiO4:0.01Eu2+–xSi3N4 (x=0–1) or 2BaCO3–ySi3N4:0.01Eu2+(y=1/6–1) synthesized by a solid-state reaction. By tuning the Si3N4content, the phosphorescence may originate from Eu2+ in BaSi2O2N2(peaking at 490 nm), Ba2SiO4 (505 nm), and Ba3SiO5 (590 nm) phases. The strong phosphorescence of the Ba2SiO4:Eu2+ phase in 2BaCO3–ySi3N4:0.01Eu2+ is attributed to N substitution for O to generate a shallow trap. In Ba2SiO4:0.01Eu2+–xSi3N4 , however, N prefers reacting with Ba2SiO4 to form BaSi2O2N2 , thereby exhibiting a strong phosphorescence of the BaSi2O2N2:Eu2+ phase but a weak phosphorescence of the Ba2SiO4:Eu2+ phase

RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to jointly optimize them in order to achieve mutual improvement. In this paper, we propose a novel joint training approach for dense passage retrieval and passage re-ranking. A major contribution is that we introduce the dynamic listwise distillation, where we design a unified listwise training approach for both the retriever and the re-ranker. During the dynamic distillation, the retriever and the re-ranker can be adaptively improved according to each other's relevance information. We also propose a hybrid data augmentation strategy to construct diverse training instances for listwise training approach. Extensive experiments show the effectiveness of our approach on both MSMARCO and Natural Questions datasets. Our code is available at https://github.com/PaddlePaddle/RocketQA.Comment: EMNLP 202