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.

DeepRoute+: Modeling Couriers’ Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction

Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers’ package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and overdue-risk evaluation, by leveraging the predicted routes to improve those downstream tasks. In the package pick-up scene, the decision-making of a courier is affected by strict spatial-temporal constraints (e.g., package location, promised pick-up time, current time, and courier’s current location). Furthermore, couriers have different decision preferences on various factors (e.g., time factor, distance factor, and balance of both), based on their own perception of the environments and work experience. In this article, we propose a novel model, named DeepRoute+, to predict couriers’ future package pick-up routes according to the couriers’ decision experience and preference learned from the historical behaviors. Specifically, DeepRoute+ consists of three layers: (1) The representation layer produces experience- and preference-aware representations for the unpicked-up packages, in which a decision preference module can dynamically adjust the importance of factors that affects the courier’s decision under the current situation. (2) The transformer encoder layer encodes the representations of packages while considering the spatial-temporal correlations among them. (3) The attention-based decoder layer uses the attention mechanism to generate the whole pick-up route recurrently. Experiments on a real-world logistics dataset demonstrate the state-of-the-art performance of our model.

Knowledge Preserving and Distribution Alignment for Heterogeneous Domain Adaptation

Domain adaptation aims at improving the performance of learning tasks in a target domain by leveraging the knowledge extracted from a source domain. To this end, one can perform knowledge transfer between these two domains. However, this problem becomes extremely challenging when the data of these two domains are characterized by different types of features, i.e., the feature spaces of the source and target domains are different, which is referred to as heterogeneous domain adaptation (HDA). To solve this problem, we propose a novel model called Knowledge Preserving and Distribution Alignment (KPDA), which learns an augmented target space by jointly minimizing information loss and maximizing domain distribution alignment. Specifically, we seek to discover a latent space, where the knowledge is preserved by exploiting the Laplacian graph terms and reconstruction regularizations. Moreover, we adopt the Maximum Mean Discrepancy to align the distributions of the source and target domains in the latent space. Mathematically, KPDA is formulated as a minimization problem with orthogonal constraints, which involves two projection variables. Then, we develop an algorithm based on the Gauss–Seidel iteration scheme and split the problem into two subproblems, which are solved by searching algorithms based on the Barzilai–Borwein (BB) stepsize. Promising results demonstrate the effectiveness of the proposed method.

The Electrostatic Basis of Diacylglycerol Pyrophosphate—Protein Interaction

Diacylglycerol pyrophosphate (DGPP) is an anionic phospholipid formed in plants, yeast, and parasites under multiple stress stimuli. It is synthesized by the phosphorylation action of phosphatidic acid (PA) kinase on phosphatidic acid, a signaling lipid with multifunctional properties. PA functions in the membrane through the interaction of its negatively charged phosphomonoester headgroup with positively charged proteins and ions. DGPP, like PA, can interact electrostatically via the electrostatic-hydrogen bond switch mechanism but differs from PA in its overall charge and shape. The formation of DGPP from PA alters the physicochemical properties as well as the structural dynamics of the membrane. This potentially impacts the molecular and ionic binding of cationic proteins and ions with the DGPP enriched membrane. However, the results of these important interactions in the stress response and in DGPP’s overall intracellular function is unknown. Here, using 31P MAS NMR, we analyze the effect of the interaction of low DGPP concentrations in model membranes with the peptides KALP23 and WALP23, which are flanked by positively charged Lysine and neutral Tryptophan residues, respectively. Our results show a significant effect of KALP23 on the charge of DGPP as compared to WALP23. There was, however, no significant effect on the charge of the phosphomonoester of DGPP due to the interaction with positively charged lipids, dioleoyl trimethylammonium propane (DOTAP) and dioleoyl ethyl-phosphatidylcholine (EtPC). Divalent calcium and magnesium cations induce deprotonation of the DGPP headgroup but showed no noticeable differences on DGPP’s charge. Our results lead to a novel model for DGPP—protein interaction.