Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability

GAN inversion aims to invert an input image into the latent space of a pre-trained GAN. Despite the recent advances in GAN inversion, there remain challenges to mitigate the tradeoff between distortion and editability, i.e. reconstructing the input image accurately and editing the inverted image with a small visual quality drop. The recently proposed pivotal tuning model makes significant progress towards reconstruction and editability, by using a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code. Here, we show that both reconstruction and editability can be improved by a proper design of the pivot code. We present a simple yet effective method, named cycle encoding, for a high-quality pivot code. The key idea of our method is to progressively train an encoder in varying spaces according to a cycle scheme: W->W+->W. This training methodology preserves the properties of both W and W+ spaces, i.e. high editability of W and low distortion of W+. To further decrease the distortion, we also propose to refine the pivot code with an optimization-based method, where a regularization term is introduced to reduce the degradation in editability. Qualitative and quantitative comparisons to several state-of-the-art methods demonstrate the superiority of our approach

CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services

Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items. Unlike universal e-commerce platforms such as Taobao and Amazon, user behaviors and interests on the OFD platform are more location and time-sensitive due to limited delivery ranges and regional commodity supplies. However, existing CTR prediction algorithms in OFD scenarios concentrate on capturing interest from historical behavior sequences, which fails to effectively model the complex spatiotemporal information within features, leading to poor performance. To address this challenge, this paper introduces the Contrastive Sres under different search states using three modules: contrastive spatiotemporal representation learning (CSRL), spatiotemporal preference extractor (StPE), and spatiotemporal information filter (StIF). CSRL utilizes a contrastive learning framework to generate a spatiotemporal activation representation (SAR) for the search action. StPE employs SAR to activate users' diverse preferences related to location and time from the historical behavior sequence field, using a multi-head attention mechanism. StIF incorporates SAR into a gating network to automatically capture important features with latent spatiotemporal effects. Extensive experiments conducted on two large-scale industrial datasets demonstrate the state-of-the-art performance of CSPM. Notably, CSPM has been successfully deployed in Alibaba's online OFD platform Ele.me, resulting in a significant 0.88% lift in CTR, which has substantial business implications

Link-aware semi-supervised hypergraph

Abstract(#br)Hypergraph learning has been widely applied to various learning tasks. To ensure learning accuracy, it is essential to construct an informative hypergraph structure that effectively modulates data correlations. However, existing hypergraph construction methods essentially resort to an unsupervised learning paradigm, which ignores supervisory information, such as pairwise links/non-links. In this article, to exploit the supervisory information, we propose a novel link-aware hypergraph learning model, which modulates high-order correlations of data samples in a semi-supervised manner. To construct a hypergraph, a coefficients matrix of the entire dataset is first calculated by solving a linear regression problem. Then, pairwise link constraints are exploited and propagated to the unconstrained samples, upon which the coefficients matrix is adjusted accordingly. Finally, the adjusted coefficients are used to generate a set of the hyperedges, as well as calculate the corresponding weights. We have validated the proposed link-aware semi-supervised hypergraph model on the problem of image clustering. Superior performance over the state-of-the-art methods demonstrates the effectiveness of the proposed hypergraph model

Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor