Incremental Group-Level Popularity Prediction in Online Social Networks

Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.

Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition

As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering “temporal-spatial-periodic” characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. Then, we predict the future traffic flow data by using the proposed Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. Additionally, the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method.

Carbamoyl phosphate and its substitutes for the uracil synthesis in origins of life scenarios

The first step of pyrimidine synthesis along the orotate pathway is studied to test the hypothesis of geochemical continuity of protometabolic pathways at the origins of life. Carbamoyl phosphate (CP) is the first high-energy building block that intervenes in the in vivo synthesis of the uracil ring of UMP. Thus, the likelihood of its occurrence in prebiotic conditions is investigated herein. The evolution of carbamoyl phosphate in water and in ammonia aqueous solutions without enzymes was characterised using ATR-IR, 31P and 13C spectroscopies. Carbamoyl phosphate initially appears stable in water at ambient conditions before transforming to cyanate and carbamate/hydrogenocarbonate species within a matter of hours. Cyanate, less labile than CP, remains a potential carbamoylating agent. In the presence of ammonia, CP decomposition occurs more rapidly and generates urea. We conclude that CP is not a likely prebiotic reagent by itself. Alternatively, cyanate and urea may be more promising substitutes for CP, because they are both “energy-rich” (high free enthalpy molecules in aqueous solutions) and kinetically inert regarding hydrolysis. Energy-rich inorganic molecules such as trimetaphosphate or phosphoramidates were also explored for their suitability as sources of carbamoyl phosphate. Although these species did not generate CP or other carbamoylating agents, they exhibited energy transduction, specifically the formation of high-energy P–N bonds. Future efforts should aim to evaluate the role of carbamoylating agents in aspartate carbamoylation, which is the following reaction in the orotate pathway.

Carbamoyl phosphate and its substitutes for the uracil synthesis in origins of life scenarios

The first step of pyrimidine synthesis along the orotate pathway is studied to test the hypothesis of geochemical continuity of protometabolic pathways at the origins of life. Carbamoyl phosphate (CP) is the first high-energy building block that intervenes in the in vivo synthesis of the uracil ring of UMP. Thus, the likelihood of its occurrence in prebiotic conditions is investigated herein. The evolution of carbamoyl phosphate in water and in ammonia aqueous solutions without enzymes was characterised using ATR-IR, 31P and 13C spectroscopies. Carbamoyl phosphate initially appears stable in water at ambient conditions before transforming to cyanate and carbamate/hydrogenocarbonate species within a matter of hours. Cyanate, less labile than CP, remains a potential carbamoylating agent. In the presence of ammonia, CP decomposition occurs more rapidly and generates urea. We conclude that CP is not a likely prebiotic reagent by itself. Alternatively, cyanate and urea may be more promising substitutes for CP, because they are both “energy-rich” (high free enthalpy molecules in aqueous solutions) and kinetically inert regarding hydrolysis. Energy-rich inorganic molecules such as trimetaphosphate or phosphoramidates were also explored for their suitability as sources of carbamoyl phosphate. Although these species did not generate CP or other carbamoylating agents, they exhibited energy transduction, specifically the formation of high-energy P-N bonds. Future efforts should aim to evaluate the role of carbamoylating agents in aspartate carbamoylation, which is the following reaction in the orotate pathway

Online Tensor-Based Learning Model for Structural Damage Detection

The online analysis of multi-way data stored in a tensor has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.

Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.