A penalty ADMM with quantized communication for distributed optimization over multi-agent systems

summary:In this paper, we design a distributed penalty ADMM algorithm with quantized communication to solve distributed convex optimization problems over multi-agent systems. Firstly, we introduce a quantization scheme that reduces the bandwidth limitation of multi-agent systems without requiring an encoder or decoder, unlike existing quantized algorithms. This scheme also minimizes the computation burden. Moreover, with the aid of the quantization design, we propose a quantized penalty ADMM to obtain the suboptimal solution. Furthermore, the proposed algorithm converges to the suboptimal solution with an $O(\frac{1}{k})$ convergence rate for general convex objective functions, and with an R-linear rate for strongly convex objective functions

Bridging Encounter Forms and Electronic Medical Record Databases: Annotation, Mapping, and Integration

Abstract-Forms are a major source of input for getting data into the underlying medical databases of electronic health/medical record (EHR/EMR) systems. Standardizing encounter forms and integrating data collected from different forms into a single database would greatly reduce heterogeneity. In this paper, we describe a framework, the fEHR-plus system, that annotates, maps, and integrates user-specified encounter forms into a single database. The development of the framework incorporates machine learning, standard medical terminology, and the principles of database design. We conduct an empirical study with 52 forms collected from 6 medical institutions for evaluating the performance of the fEHR-plus system. The overall results show that the system is promising towards improving interoperability among electronic health record systems

Robust STAP for MIMO Radar Based on Direct Data Domain Approach

The detection performance of direct data domain (D3) space-time adaptive processing (STAP) will be extremely degraded when there are mismatches between the actual and the presumed signal steering vectors. In this paper, a robust D3 STAP method for multiple-input multiple-output (MIMO) radar is developed. The proposed method utilizes the worst-case performance optimization (WCPO) to prevent the target self-nulling effect. An upper bound for the norm of the signal steering vector error is given to ensure that the WCPO problem has an admissible solution. Meanwhile, to obtain better detection performance in the low signal-to-noise ratio (SNR) environment, the proposed method gives a modified objective function to minimize the array noise while mitigating the interferences. Simulation results demonstrate the validity of our proposed method

Fine structure interpretation and reservoir forming characteristics analysis of Jurassic Badaowan Formation in Madong area of Xiayan fault zone

Fine structural interpretation is one of the effective methods to find favorable structural traps and targets. Aiming at the fault interpretation of Jurassic Badaowan Fm in Madong area of Xiayan fault zone is not fine and structural characteristics are not implemented, the structural characteristics of Jurassic Badaowan Fm are implemented and favorable traps are found through the study of seismic, well logging and geological comprehensive methods. The results show that the structure of Jurassic Badaowan Fm in Madong area is high in the north and low in the south. The first member of Badaowan Fm is a delta front subaqueous distributary channel deposit, which is a hydrocarbon enrichment zone. Oil and gas are adjusted along the relay of deep and shallow faults, and the great fault zone and the inherited uplifting zone are favorable accumulation areas of Jurassic Badaowan Fm in Madong area. In this paper, the relationship between deep and shallow fault system and hydrocarbon distribution of Badaowan Fm in Madong area is systematically reviewed for the first time. Combined with the palaeohigh distribution, three favorable hydrocarbon accumulation zones are identified, which provides favorable targets for exploring the hydrocarbon accumulation potential of Badaowan Fm

Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG)

We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured databases and knowledge extracted from the literature. To enhance MOF-KG accessibility for domain experts, we aim to develop a natural language interface for querying the knowledge graph. We have developed a benchmark comprised of 161 complex questions involving comparison, aggregation, and complicated graph structures. Each question is rephrased in three additional variations, resulting in 644 questions and 161 KG queries. To evaluate the benchmark, we have developed a systematic approach for utilizing ChatGPT to translate natural language questions into formal KG queries. We also apply the approach to the well-known QALD-9 dataset, demonstrating ChatGPT's potential in addressing KGQA issues for different platforms and query languages. The benchmark and the proposed approach aim to stimulate further research and development of user-friendly and efficient interfaces for querying domain-specific materials science knowledge graphs, thereby accelerating the discovery of novel materials.Comment: In 17th International Conference on Metadata and Semantics Research, October 202

Metadata for Scientific Experiment Reporting: A Case Study in Metal-Organic Frameworks

Research methods and procedures are core aspects of the research process. Metadata focused on these components is critical to supporting the FAIR principles, particularly reproducibility. The research reported on in this paper presents a methodological framework for metadata documentation supporting the reproducibility of research producing Metal Organic Frameworks (MOFs). The MOF case study involved natural language processing to extract key synthesis experiment information from a corpus of research literature. Following, a classification activity was performed by domain experts to identify entity-relation pairs. Results include: 1) a research framework for metadata design, 2) a metadata schema that includes nine entities and two relationships for reporting MOF synthesis experiments, and 3) a growing database of MOF synthesis reports structured by our metadata scheme. The metadata schema is intended to support discovery and reproducibility of metal-organic framework research and the FAIR principles. The paper provides background information, identifies the research goals and objectives, research design, results, a discussion, and the conclusion.Comment: Accepted by the 17th International Conference on Metadata and Semantics Researc