Bilinear Graph Neural Network with Neighbor Interactions

Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co-occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well-known GCN and GAT, respectively. Empirical results on three public benchmarks of semi-supervised node classification verify the effectiveness of BGNN -- BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5%) in classification accuracy.Codes are available at: https://github.com/zhuhm1996/bgnn.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserve

On the Calibration of Large Language Models and Alignment

As large language models attract increasing attention and find widespread application, concurrent challenges of reliability also arise at the same time. Confidence calibration, an effective analysis method for gauging the reliability of deep models, serves as a crucial tool for assessing and improving their reliability. However, such investigation has been comparatively underexplored. In this work, we conduct a systematic examination of the calibration of aligned language models throughout the entire construction process, including pretraining and alignment training. At each stage, we investigate how different training settings, such as parameter scales and training data, affect model calibration. To thoroughly assess model calibration, we evaluate models on three most concerned aspects: generation, factuality and understanding. Our work sheds light on whether popular LLMs are well-calibrated and how the training process influences model calibration.Comment: to be published in findings of EMNLP-202

Minimum-Energy Bivariate Wavelet Frame with Arbitrary Dilation Matrix

In order to characterize the bivariate signals, minimum-energy bivariate wavelet frames with arbitrary dilation matrix are studied, which are based on superiority of the minimum-energy frame and the significant properties of bivariate wavelet. Firstly, the concept of minimum-energy bivariate wavelet frame is defined, and its equivalent characterizations and a necessary condition are presented. Secondly, based on polyphase form of symbol functions of scaling function and wavelet function, two sufficient conditions and an explicit constructed method are given. Finally, the decomposition algorithm, reconstruction algorithm, and numerical examples are designed

Epidemiology and associations with climatic conditions of Mycoplasma pneumoniae and Chlamydophila pneumoniae infections among Chinese children hospitalized with acute respiratory infections

BACKGROUND: The incidence of severe acute respiratory tract infections in children caused by Mycoplasma pneumoniae (syn. Schizoplasma pneumoniae) and Chlamydophila pneumoniae (formerly Chlamydia pneumoniae) varies greatly from year to year and place to place around the world. This study investigated the epidemiology of M. pneumoniae and C. pneumoniae infections among children hospitalized with acute respiratory infections in Suzhou, China in the year 2006, and associations between incidence rates and climatic conditions. METHODS: Nasopharyngeal aspirates obtained from 1598 patients (aged 26.4 ± 28.3 months; range, 1 month to 13 years) were analyzed with real-time PCR and ELISA. Meteorological data were obtained from the weather bureau. RESULTS: About 18.5% of patients were infected with M. pneumoniae and, C. pneumoniae, or both. Isolated M. pneumoniae infection was positively correlated with increasing age (χ(2) = 34.76, P < 0.0001). Incidence of M. pneumoniae infection was seasonal with a peak in summer (P < 0.0001) and minimum in winter (P = 0.0001), whereas C. pneumoniae infection was low only in autumn (P = 0.02). Monthly mean temperature was strongly correlated with the incidence of M. pneumoniae infection (r = 0.825, P = 0.001). CONCLUSIONS: M. pneumoniae and C. pneumoniae are important infectious agents in hospitalized children with acute respiratory tract infections. M. pneumoniae infection showed a strong direct correlation with environmental temperature

AI-Oriented Two-Phase Multi-Factor Authentication in SAGINs: Prospects and Challenges

Space-air-ground integrated networks (SAGINs), which have emerged as an expansion of terrestrial networks, provide flexible access, ubiquitous coverage, high-capacity backhaul, and emergency/disaster recovery for mobile users (MUs). While the massive benefits brought by SAGIN may improve the quality of service, unauthorized access to SAGIN entities is potentially dangerous. At present, conventional crypto-based authentication is facing challenges, such as the inability to provide continuous and transparent protection for MUs. In this article, we propose an AI-oriented two-phase multi-factor authentication scheme (ATMAS) by introducing intelligence to authentication. The satellite and network control center collaborate on continuous authentication, while unique spatial-temporal features, including service features and geographic features, are utilized to enhance the system security. Our further security analysis and performance evaluations show that ATMAS has proper security characteristics which can meet various security requirements. Moreover, we shed light on lightweight and efficient authentication mechanism design through a proper combination of spatial-temporal factors.Comment: Accepted by IEEE Consumer Electronics Magazin

Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception

Collaboration by leveraging the shared semantic information plays a crucial role in overcoming the perception capability limitations of isolated agents. However, existing collaborative perception methods tend to focus solely on the spatial features of semantic information, while neglecting the importance of the temporal dimension. Consequently, the potential benefits of collaboration remain underutilized. In this article, we propose Select2Col, a novel collaborative perception framework that takes into account the {s}patial-t{e}mpora{l} importanc{e} of semanti{c} informa{t}ion. Within the Select2Col, we develop a collaborator selection method that utilizes a lightweight graph neural network (GNN) to estimate the importance of semantic information (IoSI) in enhancing perception performance, thereby identifying contributive collaborators while excluding those that bring negative impact. Moreover, we present a semantic information fusion algorithm called HPHA (historical prior hybrid attention), which integrates multi-scale attention and short-term attention modules to capture the IoSI in feature representation from the spatial and temporal dimensions respectively, and assigns IoSI-consistent weights for efficient fusion of information from selected collaborators. Extensive experiments on two open datasets demonstrate that our proposed Select2Col significantly improves the perception performance compared to state-of-the-art approaches. The code associated with this research is publicly available at https://github.com/huangqzj/Select2Col/

Simulation of External Ion Injection, Cooling and Extraction Processes with SIMION 6.0 for the Ion Trap/Reflectron Time-of-flight Mass Spectrometer

In this work we have developed a PC-based simulation to study ion injection, cooling and extraction processes for multiple ions in an ion trap/reflectron time-of-flight (IT/reTOF) system. This simulation is based upon SIMION 6.0 with user written programs in which a 3D collision model is used to describe ion – buffer gas molecule interactions. The results of various simulations describing the relation between the trapping efficiency for external injection of ions into the trap and the RF phase, and the effects of initial kinetic energy and ramp-up rate on dynamic trapping of externally produced ions are discussed. Further, single-pulsing and bipolar-pulsing schemes for ejecting ions from the trap are examined. The simulations show that bipolar pulsing can markedly improve the resolution. In the bipolar ejection mode the relation between resolution and the extraction voltages and RF ramp-off rate are studied. © 1997 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/35068/1/54_ftp.pd

Increased concentrations of soluble B7-H3 and interleukin 36 in bronchoalveolar lavage fluid of Children with Mycoplasma pneumoniae pneumonia

Supporting data. (XLS 58 kb

Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning

Large-scale LP problems from industry usually contain much redundancy that severely hurts the efficiency and reliability of solving LPs, making presolve (i.e., the problem simplification module) one of the most critical components in modern LP solvers. However, how to design high-quality presolve routines -- that is, the program determining (P1) which presolvers to select, (P2) in what order to execute, and (P3) when to stop -- remains a highly challenging task due to the extensive requirements on expert knowledge and the large search space. Due to the sequential decision property of the task and the lack of expert demonstrations, we propose a simple and efficient reinforcement learning (RL) framework -- namely, reinforcement learning for presolve (RL4Presolve) -- to tackle (P1)-(P3) simultaneously. Specifically, we formulate the routine design task as a Markov decision process and propose an RL framework with adaptive action sequences to generate high-quality presolve routines efficiently. Note that adaptive action sequences help learn complex behaviors efficiently and adapt to various benchmarks. Experiments on two solvers (open-source and commercial) and eight benchmarks (real-world and synthetic) demonstrate that RL4Presolve significantly and consistently improves the efficiency of solving large-scale LPs, especially on benchmarks from industry. Furthermore, we optimize the hard-coded presolve routines in LP solvers by extracting rules from learned policies for simple and efficient deployment to Huawei's supply chain. The results show encouraging economic and academic potential for incorporating machine learning to modern solvers