Abstract Physical hydrogels crosslinked by noncovalent interactions are promising in flexible zinc–metal batteries for their component controllability and environmental friendliness. However, the compatibility of PVA‐based electrolyte kosmotropic salt remains a challenge, which shows an unclear mechanism. Here, “good‐to‐poor” solvent substitution strategy is adopted to develop PVA hydrogel with good (ZnSO 4 ). Stretching polymer conformation preshielding strong intrachain hydrogen bonds...

Use of various deterioration models in the area infrastructure management has provided decision makers with a vehicle for predicting future deterioration. This paper presents methodology likelihood that particular system is deficient state, using logistic regression models, special case linear regression. What distinguishes these two outcome variable model binary or dichotomous and assumes Bernoulli distribution. The illustrated study involving evaluation local sewer Edmonton, Alta. Canada....

Classification is valuable and necessary in spectral analysis, especially for data-driven mining. Along with the rapid development of surveys, a variety classification techniques have been successfully applied to astronomical data processing. However, it difficult select an appropriate method practical scenarios due different algorithmic ideas characteristics. Here, we present second work mining series - review techniques. This also consists three parts: systematic overview current...

Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume variety of research emerged, addressing datasets diverse modalities, formats, scales, resolutions across various industries. However, experienced data analysts often find themselves overwhelmed by intricate details in ad-hoc solutions or attempts the semantics grounded properly. This makes it difficult maintain scale more...

Abstract Carbon stars are chemically peculiar with high carbon abundance, showing strong molecule bands and rare emission lines in their spectra. This paper explores the stellar activity of by identifying line features An outlier detection method based on morphological feature extraction interval representation is used to identify 88 targets presenting from 3546 star Of these, 55 present Balmer series emissions, 35 show forbidden ([O i ] λ 6301 Å, [O iii ], [N ii [S only 2 spectra that not...

ABSTRACT Clustering is an effective tool for astronomical spectral analysis, to mine clustering patterns among data. With the implementation of large sky surveys, many methods have been applied tackle spectroscopic and photometric data effectively automatically. Meanwhile, performance under different characteristics varies greatly. aim summarizing algorithms laying foundation further research, this work gives a review spectra in three parts. First, are investigated analysed theoretically,...

The extensive growth of data quantity has posed many challenges to analysis and retrieval. Noise redundancy are typical representatives the above-mentioned challenges, which may reduce reliability retrieval results increase storage computing overhead. To solve above problems, a two-stage pre-processing framework for noise identification reduction, called ARIS, is proposed in this article. first stage identifies removes noises by following steps: First, influence space (IS) introduced...

E-OLCN photocatalyst was synthesized by oxygen doping of low molecular weight carbon nitride (LCN) with ethanol solvent stripping. The enhanced light absorption, fast electron transport rate, and photogenerated carrier separation efficiency leads to the excellent photocatalytic degradation performance compared original materials. synergistic effect stripping plays a significant role for modulation electronic structural properties prepared catalysts. Methyl orange (MO) rhodamine B (RhB) are...

Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implementations, such as processing and design. Recent advancements in pre-trained large models (LLMs) are opening new avenues generating visualizations from language. However, the lack of comprehensive reliable benchmark hinders our understanding LLMs' capabilities generation. In this paper, we address gap by proposing...

Abstract Density clustering, an effective data analysis tool, performs well on arbitrary shapes and non-convex datasets. However, it still has some limitations in identifying the cluster structures of datasets with irregular uneven density distribution. Aiming at above problem, this paper proposes a clustering method based k -nearest neighbor propagation. Firstly, theory neighbor, nearest hub points are defined to replace sample within their neighbors, boundary is ascertained relying...

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

We present LLM-ABR, the first system that utilizes generative capabilities of large language models (LLMs) to autonomously design adaptive bitrate (ABR) algorithms tailored for diverse network characteristics. Operating within a reinforcement learning framework, LLM-ABR empowers LLMs key components such as states and neural architectures. evaluate across settings, including broadband, satellite, 4G, 5G. consistently outperforms default ABR algorithms.

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implementations, such as processing and design. Recent advancements in pre-trained large models (LLMs) are opening new avenues generating visualizations from language. However, the lack of comprehensive reliable benchmark hinders our understanding LLMs' capabilities generation. In this paper, we address gap by proposing...

Despite deep learning models have made significant breakthroughs in the magnification and precision of single image super-resolution (SISR) reconstruction. However, current methods which focus on extracting rich texture details, always ignores influence byproduct artifacts high factor construction. Therefore, a method based improved diffusion probabilistic model (DPM) is proposed to eliminate while obtain details. Firstly, residual skip path (ResPath) designed enhance constrain initial...