Modification Problems Toward Proper (Helly) Circular-Arc Graphs

We present a $9^k\cdot n^{O(1)}$-time algorithm for the proper circular-arc vertex deletion problem, resolving an open problem of van 't Hof and Villanger [Algorithmica 2013] and Crespelle et al. [arXiv:2001.06867]. Our structural study also implies parameterized algorithms for modification problems toward proper Helly circular-arc graphs

Finite-Temperature Simulations of Quantum Lattice Models with Stochastic Matrix Product States

In this work, we develop a stochastic matrix product state (stoMPS) approach that combines the MPS technique and Monte Carlo samplings and can be applied to simulate quantum lattice models down to low temperature. In particular, we exploit a procedure to unbiasedly sample the local tensors in the matrix product states, which has one physical index of dimension $d$ and two geometric indices of dimension $D$, and find the results can be continuously improved by enlarging $D$. We benchmark the methods on small system sizes and then compare the results to those obtained with minimally entangled typical thermal states, finding that stoMPS has overall better performance with finite $D$. We further exploit the MPS sampling to simulate long spin chains, as well as the triangular and square lattices with cylinder circumference $W$ up to 4. Our results showcase the accuracy and effectiveness of stochastic tensor networks in finite-temperature simulations

PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments validate the effectiveness of the innovative mechanisms integrated within PhoGAD.Comment: Accepted by WSDM 202

A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model

In online advertising scenario, sellers often create multiple creatives to provide comprehensive demonstrations, making it essential to present the most appealing design to maximize the Click-Through Rate (CTR). However, sellers generally struggle to consider users preferences for creative design, leading to the relatively lower aesthetics and quantities compared to Artificial Intelligence (AI)-based approaches. Traditional AI-based approaches still face the same problem of not considering user information while having limited aesthetic knowledge from designers. In fact that fusing the user information, the generated creatives can be more attractive because different users may have different preferences. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. The ranking model can predict the CTR score for each creative considering user features. However, the two above stages are regarded as two different tasks and are optimized separately. In this paper, we proposed a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage. Our contributions have 4 parts: 1) The inpainting mode in stable diffusion is firstly applied to creative generation task in online advertising scene. A self-cyclic generation pipeline is proposed to ensure the convergence of training. 2) Prompt model is designed to generate individualized creatives for different user groups, which can further improve the diversity and quality. 3) Reward model comprehensively considers the multimodal features of image and text to improve the effectiveness of creative ranking task, and it is also critical in self-cyclic pipeline. 4) The significant benefits obtained in online and offline experiments verify the significance of our proposed method

Movement Law of Overlying Strata and Abutment Pressure Redistribution Characteristic Based on Rigid Block

AbstractRoof movement induced by coal excavation is the immediate cause of rock pressure redistribution and strata behavior. The rigid block in PFC3D was used to generate a multijointed rock mass, and the PFC3D–FLAC3D coupling model was used to study the movement law of the highly developed structural plane of the overlying strata. Strata movement and abutment pressure redistribution characteristics were obtained. The numerical simulation results showed that the multijointed rock mass model reproduced a rock mass with highly developed structural planes. After coal seam mining, the immediate roof caved and filled the goaf, forming an irregular and regular caved zone. The immediate roof shear slipped along the coal wall. The fracture of the basic roof formed a fractured zone, and the maximum height of the fractured zone first increased and then decreased, exhibiting continuous slow subsidence. The fluctuation of the front abutment pressure was reduced, and the abutment pressure in the goaf jumps was discontinuous. The abutment pressure in the goaf was high in the middle and low on both sides. After the initial fracture of the basic roof, the stress concentration of some rock blocks in the goaf exceeded the in-situ stress, and the average abutment pressure increased with the working face advancing length. With the coal wall of the working face gradually moving away from the goaf, the abutment pressure of the goaf first increased and then remained unchanged; the porosity first decreased sharply and then declined slowly; the coordination number of particles rose sharply and then increased slowly, indicating that the goaf gradually stabilized. Similar simulation results indicated that the variation law of abutment pressure, caving characteristics of the immediate roof, and continuous slow subsidence of the basic roof were the same as those of the numerical simulation

Tracing blastomere fate choices of early embryos in single cell culture

Blastomeres of early vertebrate embryos undergo numerous fate choices for division, motility, pluripotency maintenance and restriction culminating in various cell lineages. Tracing blastomere fate choices at the single cell level in vitro has not been possible because of the inability to isolate and cultivate early blastomeres as single cells. Here we report the establishment of single cell culture system in the fish medaka, enabling the isolation and cultivation of individual blastomeres from 16- to 64-cell embryos for fate tracing at the single cell level in vitro. Interestingly, these blastomeres immediately upon isolation exhibit motility, lose synchronous divisions and even stop dividing in ≥50% cases, suggesting that the widely accepted nucleocytoplasmic ratio controlling synchronous divisions in entire embryos does not operate on individual blastomeres. We even observed abortive division, endomitosis and cell fusion. Strikingly, ~5% of blastomeres in single cell culture generated extraembryonic yolk syncytial cells, embryonic stem cells and neural crest-derived pigment cells with timings mimicking their appearance in embryos. We revealed the maternal inheritance of key lineage regulators and their differential expression in cleavage embryos. Therefore, medaka blastomeres possess the accessibility for single cell culture, previously unidentified heterogeneity in motility, division, gene expression and intrinsic ability to generate major extraembryonic and embryonic lineages without positioning cues. Our data demonstrate the fidelity and potential of the single cell culture system for tracking blastomere fate decisions under defined conditions in vitro

Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification

Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes (quantity-imbalance). Existing studies on topology-imbalance focus on the location or the local neighborhood structure of nodes, ignoring the global underlying hierarchical properties of the graph, i.e., hierarchy. In the real-world scenario, the hierarchical structure of graph data reveals important topological properties of graphs and is relevant to a wide range of applications. We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments. It is well known that hyperbolic geometry has a unique advantage in representing the hierarchical structure of graphs. Therefore, we attempt to explore the hierarchy-imbalance issue for node classification of graph neural networks with a novelty perspective of hyperbolic geometry, including its characteristics and causes. Then, we propose a novel hyperbolic geometric hierarchy-imbalance learning framework, named HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven hierarchy-levels and cross-hierarchy connectivity patterns of labeled nodes.Extensive experimental results demonstrate the superior effectiveness of HyperIMBA for hierarchy-imbalance node classification tasks.Comment: Accepted by Web Conference (WWW) 202