How Does Imperfect Automatic Indexing Affect Semantic Search Performance?

Documents in the health domain are often annotated with semantic concepts (i.e., terms) from controlled vocabularies. As the volume of these documents gets large, the annotation work is increasingly done by algorithms. Compared to humans, automatic indexing algorithms are imperfect and may assign wrong terms to documents, which affect subsequent search tasks where queries contain these terms. In this work, we aim to understand the performance impact of using imperfectly assigned terms in Boolean semantic searches. We used MeSH terms and biomedical literature search as a case study. We implemented multiple automatic indexing algorithms on real-world Boolean queries that consist of MeSH terms, and found that (1) probabilistic logic can handle inaccurately assigned terms better than traditional Boolean logic, (2) query-level performance is mostly limited by lowest-performing terms in a query, and (3) mixing a small amount of human indexing with automatic indexing can regain excellent query-level performance. These findings provide important implications for future work on automatic indexing.Comment: 9 pages, 4 figures, HealthNLP 202

Normalization of Process Safety Metrics

This study is aimed at exploring new process safety metrics for measuring the process safety performance in processing industries. Following a series of catastrophic incidents such as the Bhopal chemical tragedy (1984) and Phillips 66 explosion (1989), process safety became a more important subject than ever. These incidents triggered the development and promulgation of the Process Safety Management (PSM) standard in 1992. While PSM enables management to optimize their process safety programs and organizational risks, there is an emerging need to evaluate the process safety implementation across an organization through measurements. Thus, the process safety metric is applied as a powerful tool that measures safety activities, status, and performance within PSM. In this study, process safety lagging metrics were introduced to describe the contribution of process related parameters in determining the safety performance of an organization. Lagging metrics take process safety incidents as the numerator and divide it by different process-related denominators. Currently a process lagging metric (uses work hours as denominator) introduced by the Center for Chemical Process Safety (CCPS) has been used to evaluate the safety performance in processing industries. However, this lagging metric doesn't include enough process safety information. Therefore, modified denominators are proposed in this study and compared with the existing time-based denominator to validate the effectiveness and applicability of the new metrics. Each proposed metric was validated using available industry data. Statistical unitization method has converted incident rates of different ranges for the convenience of comparison. Trend line analysis was the key indication for determining the appropriateness of new metrics. Results showed that some proposed process-related metrics have the potential as alternatives, along with the time-based metric, to evaluate process safety performance within organizations

Intuitionistic linguistic multi-attribute decision making algorithm based on integrated distance measure

This study aims to integrate the intuitionistic linguistic multi-attribute decision making (MADM) method which builds upon an integrated distance measure into supplier evaluation and selection problems. More specifically, an intuitionistic linguistic integrated distance measure based on ordered weighted averaging operator (OWA) and weighted average approach is presented and applied. The desirable characteristics and families of the developed distance operator are further explored. In addition, based on the proposed distance measure, a supplier selection problem for an automobile factory is used to test the practicality of its framework. The effectiveness and applicability of the presented framework for supplier selection are examined by carrying comparative analysis against the existing techniques of aggregation

Clustering Analysis of User Loyalty Based on K-means

In recent years, the rise of machine learning has made it possible to further explore large data in various fields. In order to explore the attributes of loyalty of public transport travelers and divide these people into different clustering clusters, this paper uses K-means clustering algorithm (K-means) to cluster the holding time, recharge amount and swiping frequency of bus travelers. Then we use Kernel Density Estimation Algorithms (KDE) to analyze the density distribution of the data of holding time, recharge amount and swipe frequency, and display the results of the two algorithms in the way of data visualization. Finally, according to the results of data visualization, the loyalty of users is classified, which provides theoretical and data support for public transport companies to determine the development potential of users

The application and sustainable development of coral in traditional medicine and its chemical composition, pharmacology, toxicology, and clinical research

This review discusses the variety, chemical composition, pharmacological effects, toxicology, and clinical research of corals used in traditional medicine in the past two decades. At present, several types of medicinal coral resources are identified, which are used in 56 formulas such as traditional Chinese medicine, Tibetan medicine, Mongolian medicine, and Uyghur medicine. A total of 34 families and 99 genera of corals are involved in medical research, with the Alcyoniidae family and Sarcophyton genus being the main research objects. Based on the structural types of compounds and the families and genera of corals, this review summarizes the compounds primarily reported during the period, including terpenoids, steroids, nitrogen-containing compounds, and other terpenoids dominated by sesquiterpene and diterpenes. The biological activities of coral include cytotoxicity (antitumor and anticancer), anti-inflammatory, analgesic, antibacterial, antiviral, immunosuppressive, antioxidant, and neurological properties, and a detailed summary of the mechanisms underlying these activities or related targets is provided. Coral toxicity mostly occurs in the marine ornamental soft coral Zoanthidae family, with palytoxin as the main toxic compound. In addition, nonpeptide neurotoxins are extracted from aquatic corals. The compatibility of coral-related preparations did not show significant acute toxicity, but if used for a long time, it will still cause toxicity to the liver, kidneys, lungs, and other internal organs in a dose-dependent manner. In clinical applications, individual application of coral is often used as a substitute for orthopedic materials to treat diseases such as bone defects and bone hyperplasia. Second, coral is primarily available in the form of compound preparations, such as Ershiwuwei Shanhu pills and Shanhu Qishiwei pills, which are widely used in the treatment of neurological diseases such as migraine, primary headache, epilepsy, cerebral infarction, hypertension, and other cardiovascular and cerebrovascular diseases. It is undeniable that the effectiveness of coral research has exacerbated the endangered status of corals. Therefore, there should be no distinction between the advantages and disadvantages of listed endangered species, and it is imperative to completely prohibit their use and provide equal protection to help them recover to their normal numbers. This article can provide some reference for research on coral chemical composition, biological activity, chemical ecology, and the discovery of marine drug lead compounds. At the same time, it calls for people to protect endangered corals from the perspectives of prohibition, substitution, and synthesis

An Empirical Analysis of Range for 3D Object Detection

LiDAR-based 3D detection plays a vital role in autonomous navigation. Surprisingly, although autonomous vehicles (AVs) must detect both near-field objects (for collision avoidance) and far-field objects (for longer-term planning), contemporary benchmarks focus only on near-field 3D detection. However, AVs must detect far-field objects for safe navigation. In this paper, we present an empirical analysis of far-field 3D detection using the long-range detection dataset Argoverse 2.0 to better understand the problem, and share the following insight: near-field LiDAR measurements are dense and optimally encoded by small voxels, while far-field measurements are sparse and are better encoded with large voxels. We exploit this observation to build a collection of range experts tuned for near-vs-far field detection, and propose simple techniques to efficiently ensemble models for long-range detection that improve efficiency by 33% and boost accuracy by 3.2% CDS.Comment: Accepted to ICCV 2023 Workshop - Robustness and Reliability of Autonomous Vehicles in the Open-Worl

Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation

Resource-constrained perception systems such as edge computing and vision-for-robotics require vision models to be both accurate and lightweight in computation and memory usage. While knowledge distillation is a proven strategy to enhance the performance of lightweight classification models, its application to structured outputs like object detection and instance segmentation remains a complicated task, due to the variability in outputs and complex internal network modules involved in the distillation process. In this paper, we propose a simple yet surprisingly effective sequential approach to knowledge distillation that progressively transfers the knowledge of a set of teacher detectors to a given lightweight student. To distill knowledge from a highly accurate but complex teacher model, we construct a sequence of teachers to help the student gradually adapt. Our progressive strategy can be easily combined with existing detection distillation mechanisms to consistently maximize student performance in various settings. To the best of our knowledge, we are the first to successfully distill knowledge from Transformer-based teacher detectors to convolution-based students, and unprecedentedly boost the performance of ResNet-50 based RetinaNet from 36.5% to 42.0% AP and Mask R-CNN from 38.2% to 42.5% AP on the MS COCO benchmark.Comment: ICML 202

Networked Time Series Prediction with Incomplete Data

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%