GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs

Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging problem due to massive and complicated structures in many KGs. Recently, many promising works projected entities and queries into a geometric space to efficiently find answers. However, it remains challenging to model the negation and union operator. The negation operator has no strict boundaries, which generates overlapped embeddings and leads to obtaining ambiguous answers. An additional limitation is that the union operator is non-closure, which undermines the model to handle a series of union operators. To address these problems, we propose a novel probabilistic embedding model, namely Gamma Embeddings (GammaE), for encoding entities and queries to answer different types of FOL queries on KGs. We utilize the linear property and strong boundary support of the Gamma distribution to capture more features of entities and queries, which dramatically reduces model uncertainty. Furthermore, GammaE implements the Gamma mixture method to design the closed union operator. The performance of GammaE is validated on three large logical query datasets. Experimental results show that GammaE significantly outperforms state-of-the-art models on public benchmarks

Power of Information Channels: Participation in e-Government Discourse

This study examines the collective use of the electronic information and communication channels and their impact on citizen participation for public discourse. Using both quantitative and qualitative research methods, we investigate public communication channels available for government service provision in a large metropolis in China. Specifically, four electronic communication channels are analyzed to assess the impacts of diverse dimensions for electronic participation from citizens to governmental discourse. Upon completion, the study will provide a useful framework with insights for both researchers and practitioners in the power of electronic information and communication channels in electronic participation in the public discourse

The Seismic Acquisition Method Researching for the Complex Mountainous Terrain in YXL Area Qaidam Basin

YXL area is the concentration area of exploration activity with classic complicate surface feature in Qaidam Basin. So, its interference wave is development and the seismic data is in low signal-to-noise ratio (SNR) in the area. Through multiple seismic exploration collecting means, Acquisition techniques has obtained great breakthrough, and array technique has showed great affection. The geological tasks and seismic exploration difficulties of target area is aimed in the paper. The remained problems in the past seismic exploration is dissected, studying the noise interference feature and the effects for the array noise attenuation. And the positive roles of the stack response for the noise attenuation is discussed and to supply the high quality and the high precision data for the seismic in this area.Key words: Shot-receiving array; Stack array response; Geometry; Direction effect; Array weighted average effect; Signal-to-noise rati

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models

Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel in various tasks, such as arithmetic reasoning, question answering, summarization, relation extraction, machine translation, and sentiment analysis. Researchers have been actively exploring different prompt engineering strategies, such as Chain of Thought (CoT), Zero-CoT, and In-context learning. However, an unresolved problem arises from the fact that current approaches lack a solid theoretical foundation for determining optimal prompts. To address this issue in prompt engineering, we propose a new and effective approach called Prompt Space. Our methodology utilizes text embeddings to obtain basis vectors by matrix decomposition, and then constructs a space for representing all prompts. Prompt Space significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. Notably, without the help of the CoT method and the prompt "Let's think step by step", Prompt Space shows superior performance over the few-shot method. Overall, our approach provides a robust and fundamental theoretical framework for selecting simple and effective prompts. This advancement marks a significant step towards improving prompt engineering for a wide variety of applications in LLMs.Comment: Natural language processing (NLP

Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been recently proposed for feature extraction in hyperspectral remote sensing. With the help of hidden nodes in deep layers, a high-level abstraction is achieved for data reduction whilst maintaining the key information of the data. As hidden nodes in SAEs have to deal simultaneously with hundreds of features from hypercubes as inputs, this increases the complexity of the process and leads to limited abstraction and performance. As such, segmented SAE (S-SAE) is proposed by confronting the original features into smaller data segments, which are separately processed by different smaller SAEs. This has resulted in reduced complexity but improved efficacy of data abstraction and accuracy of data classification

Simultaneous Localization and Mapping with Power Network Electromagnetic Field

Various sensing modalities have been exploited for indoor location sensing, each of which has well understood limitations, however. This paper presents a first systematic study on using the electromagnetic field (EMF) induced by a building's electric power network for simultaneous localization and mapping (SLAM). A basis of this work is a measurement study showing that the power network EMF sensed by either a customized sensor or smartphone's microphone as a side-channel sensor is spatially distinct and temporally stable. Based on this, we design a SLAM approach that can reliably detect loop closures based on EMF sensing results. With the EMF feature map constructed by SLAM, we also design an efficient online localization scheme for resource-constrained mobiles. Evaluation in three indoor spaces shows that the power network EMF is a promising modality for location sensing on mobile devices, which is able to run in real time and achieve sub-meter accuracy

Evaluation of Anti-Inflammatory Activities of Qingre-Qushi Recipe (QRQS) against Atopic Dermatitis: Potential Mechanism of Inhibition of IL-33/ST2 Signal Transduction

To evaluate the anti-inflammatory activities of QRQS against AD and the inhibitory molecular mechanisms of IL-33/ST2 signal transduction, BALB/c mice were divided into six groups (normal control, OVA control, low-dose of QRQS, middle-dose of QRQS, high-dose of QRQS, and cetirizine) and epicutaneously exposed to ovalbumin or PBS for 3 weeks and treated with QRQS for 2 weeks. Skin biopsies and blood samples were obtained for histological study, antibody analysis, and RNA isolation. HaCaT cells, stimulated by TNF-α and IFN-γ, were treated with QRQS to evaluate mRNA and protein expression by RT-PCR and ELISA. QRQS decreased both epidermal and dermal thickness, alleviated dermatitis, and reduced IL-33 and ST2 positive cell numbers. The concentration of specific IgE, IgG, IgG1, and IgG2a antibodies in serum and the expression of IL-33, ST2, IL-1RAcP, IL-4, and IL-13 mRNA in the skin were suppressed. No significant difference exists in TNF-α or IFN-γ. QRQS decreased IL-33 mRNA and protein secretion in HaCaT cells exposed to TNF-α and IFN-γ in a time- and concentration-dependent manner. QRQS regulates related molecule expression of ovalbumin-induced dermatitis involved in the IL-33/ST2 signaling axis in the treatment of acute AD

Simvastatin suppresses the DNA replication licensing factor MCM7 and inhibits the growth of tamoxifen-resistant breast cancer cells

Acquired tamoxifen resistance (TamR) remains a major challenge in breast cancer endocrine therapy. The mechanism of acquiring tamoxifen resistance remains elusive, and no effective drugs are available. In this investigation, we determined that the expression of the DNA damage marker γH2AX is upregulated under minichromosome maintenance protein 7 (MCM7) knockdown in phospho Ser807/811-retinoblastoma protein (p-Rb) defect cells. In addition, the expression of p-Rb was lower in TamR cells than in parental cells, and the expression of γH2AX was significantly upregulated when MCM7 was knocked down in TamR cells. Simvastatin, an agent for hypercholesterolemia treatment, activated the MCM7/p-RB/γH2AX axis and induced DNA damage in TamR cells, especially when combined with tamoxifen. Finally, in vitro and in vivo experiments demonstrated that simvastatin combined with tamoxifen increased TamR cell apoptosis and inhibited xenograft growth. In conclusion, simvastatin may suppress TamR cell growth by inhibiting MCM7 and Rb and subsequently inducing DNA damage