Deeper understanding of cobalt-doped SiC nanowires as excellent electromagnetic wave absorbers

Doping is a facile and effective technique that plays a key role in the function of many semiconductor materials. Unraveling the regulatory mechanism of doping can offer useful guidance for the design of the material structure and fabricating novel functional composites invalid in pure phase structures, which extents their applications in catalysts, light emitting devices, and environmental protection. Especially, transition metal doping related to the spin and charge introduces foreign states to the electronic structures in the host materials and endows the composites with intriguing properties. However, most of the reported papers are limited on the fabrication of the doped composites with enhanced performance. Little progress has been made to clarify the underlying mechanism for those improvements. Herein, Co-doped SiC nanowires with different Co contents were successfully fabricated by a simple carbothermal reduction method. The Co-doped SiC nanowires were characterized in terms of microstructure, electronic structure, and electromagnetic (EM) parameters to study the effects of doping on enhancing the EM wave absorption ability. Both the microstructure analysis and density functional theory calculations indicated that the incorporation of Co into SiC nanowires inhibited the formation of defective structures but increased their conductivity. Thus, the improved electronic transportation ability was dominant in enhancing the dielectric loss. The Co dopants also imparted the Co-doped SiC nanowires with magnetic property, which could generate magnetic resonance to attenuate EM wave and achieve superior impedance matching. The induced synergistic effects between Co dopants and SiC nanowires endowed Co-doped SiC nanowires with excellent EM wave absorption ability. Their minimum reflection loss was -50 dB, and the effective absorption bandwidth was up to 4.0 GHz at 1.5 mm sample thickness. Therefore, the fabricated Co-doped SiC nanowires are potential candidates for high-efficiency EM wave absorption materials. The findings of this research provide a guideline for other doped functional composites. Please click Additional Files below to see the full abstract

Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding

Logical reasoning remains a pivotal component within the realm of artificial intelligence. The recent evolution of large language models (LLMs) has marked significant progress in this domain. The adoption of strategies like chain-of-thought (CoT) has enhanced the performance of LLMs across diverse reasoning tasks. Nonetheless, logical reasoning that involves proof planning, specifically those that necessitate the validation of explanation accuracy, continues to present stumbling blocks. In this study, we first evaluate the efficacy of LLMs with advanced CoT strategies concerning such tasks. Our analysis reveals that LLMs still struggle to navigate complex reasoning chains, which demand the meticulous linkage of premises to derive a cogent conclusion. To address this issue, we finetune a smaller-scale language model, equipping it to decompose proof objectives into more manageable subgoals. We also introduce contrastive decoding to stepwise proof generation, making use of negative reasoning paths to strengthen the model's capacity for logical deduction. Experiments on EntailmentBank underscore the success of our method in augmenting the proof planning abilities of language models

Give Me More Details: Improving Fact-Checking with Latent Retrieval

Evidence plays a crucial role in automated fact-checking. When verifying real-world claims, existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. Such methods ignore the challenges of collecting evidence and may not provide sufficient information to verify real-world claims. Aiming at building a better fact-checking system, we propose to incorporate full text from source documents as evidence and introduce two enriched datasets. The first one is a multilingual dataset, while the second one is monolingual (English). We further develop a latent variable model to jointly extract evidence sentences from documents and perform claim verification. Experiments indicate that including source documents can provide sufficient contextual clues even when gold evidence sentences are not annotated. The proposed system is able to achieve significant improvements upon best-reported models under different settings.Comment: Fixed minor issues, 11 page

Scene Graph Modification as Incremental Structure Expanding

A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene. Scene graphs play an important role in many cross modality tasks, as they are able to capture the interactions between images and texts. In this paper, we focus on scene graph modification (SGM), where the system is required to learn how to update an existing scene graph based on a natural language query. Unlike previous approaches that rebuilt the entire scene graph, we frame SGM as a graph expansion task by introducing the incremental structure expanding (ISE). ISE constructs the target graph by incrementally expanding the source graph without changing the unmodified structure. Based on ISE, we further propose a model that iterates between nodes prediction and edges prediction, inferring more accurate and harmonious expansion decisions progressively. In addition, we construct a challenging dataset that contains more complicated queries and larger scene graphs than existing datasets. Experiments on four benchmarks demonstrate the effectiveness of our approach, which surpasses the previous state-of-the-art model by large margins.Comment: In COLING 2022 as a long paper. Code and data available at https://github.com/THU-BPM/SG

Multimodal Automated Fact-Checking: A Survey

Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned image. Multimodal misinformation is perceived as more credible by humans, and spreads faster than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on text. In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation. Furthermore, we discuss related terms used in different communities and map them to our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and discuss limitations and promising directions for future researchComment: The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP): Finding

Optimization with a Genetic Algorithm for Multilayer Electromagnetic Wave Absorption Cement Mortar Filled with Expended Perlite

Abstract: Due to the complexity of the design of multilayer electromagnetic (EM) wave absorbing materials, it is difficult to establish the relationship between material parameters (type and filling ratios) and EM properties using traditional trial and error methods. Based on the measured EM parameters within a few materials and Boltzmann mixing theory, a database of EM parameters was thereafter built up. In this study, the genetic algorithm (GA) was used to design the multilayer wave-absorbing cement mortar. In order to verify this method, a multilayer mortar was fabricated and measured. The simulated and measured results are well consistent, which convincingly verifies computer-aided design. In addition, the optimized result expresses that the first layer as a matching layer guides EM waves into the interior of the material, while the other layers as absorption layers attenuate EM waves. The multilayer material may not meet the impedance gradient principle but still exhibits better EM wave absorption performance. The reflection loss (RL) of all optimized three layer sample is below –6.89 dB in the full frequency band and the minimum RL is –26.21 dB. This composite absorbing material and the GA method provide more design ideas for the design of future cement-based wave-absorbing materials and save a lot of time and material cost

GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

Graph is powerful for representing various types of real-world data. The topology (edges' presence) and edges' features of a graph decides the message passing mechanism among vertices within the graph. While most existing approaches only manually define a single-value edge to describe the connectivity or strength of association between a pair of vertices, task-specific and crucial relationship cues may be disregarded by such manually defined topology and single-value edge features. In this paper, we propose the first general graph representation learning framework (called GRATIS) which can generate a strong graph representation with a task-specific topology and task-specific multi-dimensional edge features from any arbitrary input. To learn each edge's presence and multi-dimensional feature, our framework takes both of the corresponding vertices pair and their global contextual information into consideration, enabling the generated graph representation to have a globally optimal message passing mechanism for different down-stream tasks. The principled investigation results achieved for various graph analysis tasks on 11 graph and non-graph datasets show that our GRATIS can not only largely enhance pre-defined graphs but also learns a strong graph representation for non-graph data, with clear performance improvements on all tasks. In particular, the learned topology and multi-dimensional edge features provide complementary task-related cues for graph analysis tasks. Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data. Our code is made publicly available at https://github.com/SSYSteve/Learning-Graph-Representation-with-Task-specific-Topology-and-Multi-dimensional-Edge-Features