Bootstrapping Witten diagrams via differential representation in Mellin space

We explore the use of the differential representation of AdS amplitudes to compute Witten diagrams. The differential representation expresses AdS amplitudes in terms of conformal generators acting on contact Witten diagrams, which allows us to construct differential equations for Witten diagrams. These differential equations can then be transformed into difference equations in Mellin space, which can be solved recursively. Using this method, we efficiently re-computed scalar four-point amplitudes and obtained new results for scalar six-point amplitudes mediated by gluons and scalars, as well as two examples of scalar eight-point amplitudes from gluon exchange

A New Method for Piecewise Linear Representation of Time Series Data

AbstractIn various methods of modeling of time series, the piecewise linear representation has the advantage of being simple, straightforward and supporting dynamic incremental update of time series. This paper proposed a new method of Piecewise Linear Representation of Time Series based on Slope Change Threshold (SCT). Detailed experiments on real datasets from various fields show that STC representation, compared with several other Piecewise Linear Representations, can be easily calculated and has a high degree of fitting

Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism

Traditional search engines usually provide identical search results for all users, overlooking individual preferences. To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs. Deep learning-based personalized search methods have shown promise, but they rely heavily on abundant training data, making them susceptible to data sparsity challenges. This paper proposes a Cognitive Personalized Search (CoPS) model, which integrates Large Language Models (LLMs) with a cognitive memory mechanism inspired by human cognition. CoPS employs LLMs to enhance user modeling and user search experience. The cognitive memory mechanism comprises sensory memory for quick sensory responses, working memory for sophisticated cognitive responses, and long-term memory for storing historical interactions. CoPS handles new queries using a three-step approach: identifying re-finding behaviors, constructing user profiles with relevant historical information, and ranking documents based on personalized query intent. Experiments show that CoPS outperforms baseline models in zero-shot scenarios.Comment: Accepted by WWW 202

MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features

Identifying moving objects is an essential capability for autonomous systems, as it provides critical information for pose estimation, navigation, collision avoidance, and static map construction. In this paper, we present MotionBEV, a fast and accurate framework for LiDAR moving object segmentation, which segments moving objects with appearance and motion features in the bird's eye view (BEV) domain. Our approach converts 3D LiDAR scans into a 2D polar BEV representation to improve computational efficiency. Specifically, we learn appearance features with a simplified PointNet and compute motion features through the height differences of consecutive frames of point clouds projected onto vertical columns in the polar BEV coordinate system. We employ a dual-branch network bridged by the Appearance-Motion Co-attention Module (AMCM) to adaptively fuse the spatio-temporal information from appearance and motion features. Our approach achieves state-of-the-art performance on the SemanticKITTI-MOS benchmark. Furthermore, to demonstrate the practical effectiveness of our method, we provide a LiDAR-MOS dataset recorded by a solid-state LiDAR, which features non-repetitive scanning patterns and a small field of view

A Comparison of Molecular Biology Mechanism of Shewanella putrefaciens between Fresh and Terrestrial Sewage Wastewater

Municipal and industrial wastewater is often discharged into the environment without appropriate treatment, especially in developing countries. As a result, many rivers and oceans are contaminated. It is urgent to control and administer treatments to these contaminated rivers and oceans. However, most mechanisms of bacterial colonization in contaminated rivers and oceans were unknown, especially in sewage outlets. We found Shewanella putrefaciens to be the primary bacteria in the terrestrial sewage wastewater outlets around Ningbo City, China. Therefore, in this study, we applied a combination of differential proteomics, metabolomics, and real-time fluorescent quantitative PCR techniques to identify bacteria intracellular metabolites. We found S. putrefaciens had 12 different proteins differentially expressed in freshwater culture than when grown in wastewater, referring to the formation of biological membranes (Omp35, OmpW), energy metabolism (SOD, deoxyribose-phosphate pyrophosphokinase), fatty acid metabolism (beta-ketoacyl synthase), secondary metabolism, TCA cycle, lysine degradation (2-oxoglutarate reductase), and propionic acid metabolism (succinyl coenzyme A synthetase). The sequences of these 12 differentially expressed proteins were aligned with sequences downloaded from NCBI. There are also 27 differentially concentrated metabolites detected by NMR, including alcohols (ethanol, isopropanol), amines (dimethylamine, ethanolamine), amino acids (alanine, leucine), amine compounds (bilinerurine), nucleic acid compounds (nucleosides, inosines), organic acids (formate, acetate). Formate and ethanolamine show significant difference between the two environments and are possibly involved in energy metabolism, glycerophospholipid and ether lipids metabolism to provide energy supply and material basis for engraftment in sewage. Because understanding S. putrefaciens’s biological mechanism of colonization (protein, gene express and metabolites) in terrestrial sewage outlets is so important to administering and improving contaminated river and to predicting and steering performance, we delved into the biological mechanism that sheds light on the effect of environmental conditions on metabolic pathways

Trajectory-Based Spatiotemporal Entity Linking

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a k-nearest neighbor (k-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency.Comment: 15 pages, 3 figures, 15 table