Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks

Predicting critical nodes of Opportunistic Sensor Network (OSN) can help us not only to improve network performance but also to decrease the cost in network maintenance. However, existing ways of predicting critical nodes in static network are not suitable for OSN. In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined. We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM). It takes RC to present the dependence of regions on Ferry nodes. TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node. The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better

Outcome-Oriented Predictive Process Monitoring to Predict Unplanned ICU Readmission in MIMIC-IV Database

Unplanned readmission entails unnecessary risks for patients and avoidable waste of medical resources, especially intensive care unit (ICU) readmissions, which increases likelihood of length of stay and more severely mortality. Identifying patients who are likely to suffer unplanned ICU readmission can benefit both patients and hospitals. Readmission is typically predicted by statistical features extracted from completed ICU stays. The development of predictive process monitoring (PPM) technique aims to learn from complete traces and predict the outcome of ongoing ones. In this paper, we adopt PPM to view ICU stay from electronic health record (EHR) as a continuous process trace to enable us to discover clinical and also process features to predict likelihood of readmission. Using events logs extracted from MIMIC-IV database, the results show that our approach can achieve up to 65% accuracy during the early stage of each ICU stay and demonstrate the feasibility of applying PPM to unplanned ICU readmission prediction

Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo

Low-precision training has emerged as a promising low-cost technique to enhance the training efficiency of deep neural networks without sacrificing much accuracy. Its Bayesian counterpart can further provide uncertainty quantification and improved generalization accuracy. This paper investigates low-precision sampling via Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) with low-precision and full-precision gradient accumulators for both strongly log-concave and non-log-concave distributions. Theoretically, our results show that, to achieve $\epsilon$-error in the 2-Wasserstein distance for non-log-concave distributions, low-precision SGHMC achieves quadratic improvement ($\widetilde{\mathbf{O}}\left({\epsilon^{-2}{\mu^*}^{-2}\log^2\left({\epsilon^{-1}}\right)}\right)$) compared to the state-of-the-art low-precision sampler, Stochastic Gradient Langevin Dynamics (SGLD) ($\widetilde{\mathbf{O}}\left({{\epsilon}^{-4}{\lambda^{*}}^{-1}\log^5\left({\epsilon^{-1}}\right)}\right)$). Moreover, we prove that low-precision SGHMC is more robust to the quantization error compared to low-precision SGLD due to the robustness of the momentum-based update w.r.t. gradient noise. Empirically, we conduct experiments on synthetic data, and {MNIST, CIFAR-10 \& CIFAR-100} datasets, which validate our theoretical findings. Our study highlights the potential of low-precision SGHMC as an efficient and accurate sampling method for large-scale and resource-limited machine learning

Process Mining to Discover and Preserve Infrequent Relations in Event Logs: An Application to Understand the Laboratory Test Ordering Process Using the MIMIC-III Dataset

Process mining techniques can provide insights into the healthcare domain with the rapid growth of electrical health records. Process mining is about understanding the sequence of activities in event logs, where directly-follows relations identify pairs of activities that follow each other directly. Existing research explores frequent relations, while infrequent relations are often seen as noises and filtered out during discovery. However, important insights may be revealed through these infrequent relations, especially in healthcare processes. This paper aims to use process mining techniques to discover and preserve value-based conditional infrequent relations. We adopt the L* life-cycle methodology and Data-aware Heuristic Miner (DHM) as tools to provide a worded example based on extracted data from the MIMIC-III dataset, which is a publicly available database containing a large amount of electrical health records (EHR), to show how process mining can be used to analyse infrequent relations in a laboratory test’s ordering process

Pyridine-2-carboximidamidate chloride monohydrate

The title compound, C6H8N3 +·Cl−·H2O, crystallizes with three formula units in the asymmetric unit. The cations are non-planar with the –C(NH2)2 groups twisted out of the ring planes. Each pyridine carboximidamidate cation is linked to another cation through N—H⋯N hydrogen bonds, to chloride ions by N—H⋯Cl hydrogen bonds, and to water mol­ecules by N—H⋯O hydrogen bonds. Water mol­ecules and chloride ions are also linked together via O—H⋯Cl hydrogen bonds. In the crystal, all these inter­molecular inter­actions result in a three-dimensional network

Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality

Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning, leading to state-of-the-art models for various downstream multimodal tasks. However, recent research has highlighted severe limitations of these models in their ability to perform compositional reasoning over objects, attributes, and relations. Scene graphs have emerged as an effective way to understand images compositionally. These are graph-structured semantic representations of images that contain objects, their attributes, and relations with other objects in a scene. In this work, we consider the scene graph parsed from text as a proxy for the image scene graph and propose a graph decomposition and augmentation framework along with a coarse-to-fine contrastive learning objective between images and text that aligns sentences of various complexities to the same image. Along with this, we propose novel negative mining techniques in the scene graph space for improving attribute binding and relation understanding. Through extensive experiments, we demonstrate the effectiveness of our approach that significantly improves attribute binding, relation understanding, systematic generalization, and productivity on multiple recently proposed benchmarks (For example, improvements upto $18\%$ for systematic generalization, $16.5\%$ for relation understanding over a strong baseline), while achieving similar or better performance than CLIP on various general multimodal tasks.Comment: 16 pages, 12 figures, 7 Tables. Pre-prin