Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate~(CTR) prediction, whose goal is to estimate the probability of the user clicks, has become one of the core tasks in advertising systems. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external environment and the internal cognition, user interest evolves over time dynamically. There are several CTR prediction methods for interest modeling, while most of them regard the representation of behavior as the interest directly, and lack specially modeling for latent interest behind the concrete behavior. Moreover, few work consider the changing trend of interest. In this paper, we propose a novel model, named Deep Interest Evolution Network~(DIEN), for CTR prediction. Specifically, we design interest extractor layer to capture temporal interests from history behavior sequence. At this layer, we introduce an auxiliary loss to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, we propose interest evolving layer to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution. In the experiments on both public and industrial datasets, DIEN significantly outperforms the state-of-the-art solutions. Notably, DIEN has been deployed in the display advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201

Extended imaginary gauge transformation in a general nonreciprocal lattice

Imaginary gauge transformation (IGT) provides a clear understanding of the non-Hermitian skin effect by transforming the non-Hermitian Hamiltonians with real spectra into Hermitian ones. In this work, we extend this approach to the complex spectrum regime in a general nonreciprocal lattice model. We unveil the validity of IGT hinges on a class of pseudo-Hermitian symmetry. The generalized Brillouin zone of Hamiltonian respect such pseudo-Hermiticity is demonstrated to be a circle, which enables easy access to the continuum bands, localization length of skin modes, and relevant topological numbers. Furthermore, we investigate the applicability of IGT and the underlying pseudo-Hermiticity beyond nearest-neighbour hopping, offering a graphical interpretation. Our theoretical framework is applied to establish bulk-boundary correspondence in the nonreciprocal trimer Su-Schrieffer-Heeger model and analyze the localization behaviors of skin modes in the two-dimensional Hatano-Nelson model.Comment: 16 pages, 6 figure

Mycobacterium smegmatis Induces Neurite Outgrowth and Differentiation in an Autophagy-Independent Manner in PC12 and C17.2 Cells

Both pathogenic and non-pathogenic Mycobacteria can induce the differentiation of immune cells into dendritic cells (DC) or DC-like cells. In addition, pathogenic Mycobacteria is found to stimulate cell differentiation in the nerves system. Whether non-pathogenic Mycobacteria interacts with nerve cells remains unknown. In this study, we found that co-incubation with fast-growing Mycobacteria smegmatis induced neuron-like morphological changes of PC12 and C17.2 cells. Moreover, the M. smegmatis culture supernatant which was ultrafiltrated through a membrane with a 10 kDa cut-off, induced neurite outgrowth and differentiation in an autophagy-independent pathway in PC12 and C17.2 cells. Further analysis showed that IFN-γ production and activation of the PI3K-Akt signaling pathway were involved in the neural differentiation. In conclusion, our finding demonstrated that non-pathogenic M. smegmatis was able to promote neuronal differentiation by its extracellular proteins, which might provide a novel therapeutic strategy for the treatment of neurodegenerative disorders

Chemical Bonding of Transition-metal Co13_{13} Clusters with Graphene

We carried out density functional calculation to study Co$_{13}$ clusters on graphene. We deposit several free isomers in different disposition respect to hexagonal lattice nodes, studying even the $hcp$ $2d$ isomer recently obtained as the most stable one. Surprisingly, Co$_{13}$ clusters bonded to graphene prefer $icosahedron-like$ structures where the low lying isomer is much distorted, because it is linked with more bonds than in previous works. For any isomer the most stable position binds to graphene by the Co atoms that can lose electrons. We find that the charge transfers between graphene and clusters are small enough to conclude that the Co-graphene binding is not ionic-like but chemical. Besides, the same order of stability among the different isomers on doped graphene is well kept. These findings could also be of interest for magnetic clusters on graphenic nanostructures such as ribbons and nanotubes.Comment: 12 pages, 6 figure

Regulatory controls of duplicated gene expression during fiber development in allotetraploid cotton.

Polyploidy complicates transcriptional regulation and increases phenotypic diversity in organisms. The dynamics of genetic regulation of gene expression between coresident subgenomes in polyploids remains to be understood. Here we document the genetic regulation of fiber development in allotetraploid cotton Gossypium hirsutum by sequencing 376 genomes and 2,215 time-series transcriptomes. We characterize 1,258 genes comprising 36 genetic modules that control staged fiber development and uncover genetic components governing their partitioned expression relative to subgenomic duplicated genes (homoeologs). Only about 30% of fiber quality-related homoeologs show phenotypically favorable allele aggregation in cultivars, highlighting the potential for subgenome additivity in fiber improvement. We envision a genome-enabled breeding strategy, with particular attention to 48 favorable alleles related to fiber phenotypes that have been subjected to purifying selection during domestication. Our work delineates the dynamics of gene regulation during fiber development and highlights the potential of subgenomic coordination underpinning phenotypes in polyploid plants. [Abstract copyright: © 2023. The Author(s).