63 research outputs found
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Knowledge Graphs (KGs) play a pivotal role in advancing various AI
applications, with the semantic web community's exploration into multi-modal
dimensions unlocking new avenues for innovation. In this survey, we carefully
review over 300 articles, focusing on KG-aware research in two principal
aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal
tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into
the MMKG realm. We begin by defining KGs and MMKGs, then explore their
construction progress. Our review includes two primary task categories:
KG-aware multi-modal learning tasks, such as Image Classification and Visual
Question Answering, and intrinsic MMKG tasks like Multi-modal Knowledge Graph
Completion and Entity Alignment, highlighting specific research trajectories.
For most of these tasks, we provide definitions, evaluation benchmarks, and
additionally outline essential insights for conducting relevant research.
Finally, we discuss current challenges and identify emerging trends, such as
progress in Large Language Modeling and Multi-modal Pre-training strategies.
This survey aims to serve as a comprehensive reference for researchers already
involved in or considering delving into KG and multi-modal learning research,
offering insights into the evolving landscape of MMKG research and supporting
future work.Comment: Ongoing work; 41 pages (Main Text), 55 pages (Total), 11 Tables, 13
Figures, 619 citations; Paper list is available at
https://github.com/zjukg/KG-MM-Surve
Multilocal programming and applications
Preprint versionMultilocal programming aims to identify all local minimizers of unconstrained
or constrained nonlinear optimization problems. The multilocal programming
theory relies on global optimization strategies combined with simple ideas
that are inspired in deflection or stretching techniques to avoid convergence to the
already detected local minimizers. The most used methods to solve this type of problems
are based on stochastic procedures and a population of solutions. In general,
population-based methods are computationally expensive but rather reliable in identifying
all local solutions. In this chapter, a review on recent techniques for multilocal
programming is presented. Some real-world multilocal programming problems
based on chemical engineering process design applications are described.Fundação para a Ciência e a Tecnologia (FCT
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional knowledge graph embedding methods map elements in a knowledge graph, including entities and relations, into continuous vector spaces by assigning them one or multiple specific embeddings (i.e., vector representations). Thus the number of embedding parameters increases linearly as the growth of knowledge graphs. In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities. To obtain the embeddings for the full set of entities, we encode their distinguishable information from their connected relations, k-nearest reserved entities, and multi-hop neighbors. We learn universal and entity-agnostic encoders for transforming distinguishable information into entity embeddings. This approach allows our proposed EARL to have a static, efficient, and lower parameter count than conventional knowledge graph embedding methods. Experimental results show that EARL uses fewer parameters and performs better on link prediction tasks than baselines, reflecting its parameter efficiency
Temperature dependent thermal conductivity of pure silica MEL and MFI zeolite thin films
This paper reports the temperature dependent cross-plane thermal conductivity of pure silica zeolite (PSZ) MFI and MEL thin films measured using the 3ω method between 30 and 315 K. PSZ MFI thin films were b-oriented, fully crystalline, and had a 33% microporosity. PSZ MEL thin films consisted of MEL nanoparticles embedded in a non-uniform and porous silica matrix. They featured porosity, relative crystallinity, and particle size ranging from 40% to 59%, 23% to 47%, and 55 to 80 nm, respectively. Despite their crystallinity, MFI films were found to have thermal conductivity smaller than that of amorphous silica due to strong phonon scattering by micropores. In addition, the effects of increased relative crystallinity and particle size on thermal conductivity of MEL thin films were compensated by the simultaneous increase in porosity. Finally, thermal conductivity of MFI zeolite was predicted and discussed using the Callaway model based on the Debye approximation
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Enabling Efficient Water Splitting with Advanced Materials Designed for High pH Membrane Interface
This research focuses on durable, high-performance materials and interfaces for advanced water splitting, enabling a clear pathway for achieving <$2/KgH2 (on scale) via anion exchange membrane (AEM)-based electrolysis. We aim to advance this goal via an improved fundamental understanding of both hydrogen and oxygen evolution reactions (HER/OER) leading to platinum group metal (PGM)-free catalyst materials. Here, we use NiFeCo and NiMo as OER and HER catalysts, respectively, in a full alkaline electrolysis cell. A thermally stable, multi-cation AEM is used to operate the cell at elevated temperatures thereby lowering the operating potential by increasing the kinetics of the respective catalytic reactions. The transition metal catalysts, paired with this novel AEM, have been used to achieve an operating potential of 1.79 V at 1 A/cm2. This potential is 300 mV lower than similar electrolysis cells equipped with PGM-based catalysts. We have also demonstrated the efficacy of the less caustic potassium carbonate solution as a replacement for potassium hydroxide as an electrolyte. By using a carbonate-based electrolyte, the hydroxide ion required for the anodic reaction is continuously replenished by re-establishing an equilibrium with water. This consideration, coupled with the reduced alkalinity of the carbonate solution, yields an electrolysis cell capable of sustained operation with minimal increase in operating potential
Recommended from our members
Enabling Efficient Water Splitting with Advanced Materials Designed for High pH Membrane Interface
This research focuses on durable, high-performance materials and interfaces for advanced water splitting, enabling a clear pathway for achieving <$2/KgH2 (on scale) via anion exchange membrane (AEM)-based electrolysis. We aim to advance this goal via an improved fundamental understanding of both hydrogen and oxygen evolution reactions (HER/OER) leading to platinum group metal (PGM)-free catalyst materials. Here, we use NiFeCo and NiMo as OER and HER catalysts, respectively, in a full alkaline electrolysis cell. A thermally stable, multi-cation AEM is used to operate the cell at elevated temperatures thereby lowering the operating potential by increasing the kinetics of the respective catalytic reactions. The transition metal catalysts, paired with this novel AEM, have been used to achieve an operating potential of 1.79 V at 1 A/cm2. This potential is 300 mV lower than similar electrolysis cells equipped with PGM-based catalysts. We have also demonstrated the efficacy of the less caustic potassium carbonate solution as a replacement for potassium hydroxide as an electrolyte. By using a carbonate-based electrolyte, the hydroxide ion required for the anodic reaction is continuously replenished by re-establishing an equilibrium with water. This consideration, coupled with the reduced alkalinity of the carbonate solution, yields an electrolysis cell capable of sustained operation with minimal increase in operating potential
Recommended from our members
Enabling Efficient Water Splitting with Advanced Materials Designed for High pH Membrane Interface
This research focuses on durable, high-performance materials and interfaces for advanced water splitting, enabling a clear pathway for achieving <$2/KgH2 (on scale) via anion exchange membrane (AEM)-based electrolysis. We aim to advance this goal via an improved fundamental understanding of both hydrogen and oxygen evolution reactions (HER/OER) leading to platinum group metal (PGM)-free catalyst materials. Here, we use NiFeCo and NiMo as OER and HER catalysts, respectively, in a full alkaline electrolysis cell. A thermally stable, multi-cation AEM is used to operate the cell at elevated temperatures thereby lowering the operating potential by increasing the kinetics of the respective catalytic reactions. The transition metal catalysts, paired with this novel AEM, have been used to achieve an operating potential of 1.79 V at 1 A/cm2. This potential is 300 mV lower than similar electrolysis cells equipped with PGM-based catalysts. We have also demonstrated the efficacy of the less caustic potassium carbonate solution as a replacement for potassium hydroxide as an electrolyte. By using a carbonate-based electrolyte, the hydroxide ion required for the anodic reaction is continuously replenished by re-establishing an equilibrium with water. This consideration, coupled with the reduced alkalinity of the carbonate solution, yields an electrolysis cell capable of sustained operation with minimal increase in operating potential
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