The roles of inter-fuel substitution and inter-market contagion in driving energy prices: evidences from China’s coal market

Coal has been dominating energy supply and consumption in China, with the country becoming the largest energy supplier and consumer worldwide. Due to inter-fuel substitution of crude oil and inter-market contagion of international coal market, China's coal price might be interrelated with crude oil price and international coal price. However, the precise roles of these two effects in determining China's coal price are unknown. This paper contributes to previous literature by investigating this issue. We find that co-movements between China's coal price and crude oil price largely hinge on the shares of oil and coal in China’s energy mix, while its co-movements with international coal price depend on scales of coal trade. Inter-fuel substitution dominated the interaction of China's coal market with other energy types, but the importance of inter-market contagion has been increasing. We also find that China might have become an originator for driving the returns of crude oil and international coal, in particular after 2008. Furthermore, China's coal market is still a net volatility recipient for shocks from both crude oil market and international coal market. Given the increased integration of global energy markets, we anticipate this paper to provide a better understanding on the dynamic changes in China's coal prices

LED Lighting System Reliability Modeling and Inference via Random Effects Gamma Process and Copula Function

Light emitting diode (LED) lamp has attracted increasing interest in the field of lighting systems due to its low energy and long lifetime. For different functions (i.e., illumination and color), it may have two or more performance characteristics. When the multiple performance characteristics are dependent, it creates a challenging problem to accurately analyze the system reliability. In this paper, we assume that the system has two performance characteristics, and each performance characteristic is governed by a random effects Gamma process where the random effects can capture the unit to unit differences. The dependency of performance characteristics is described by a Frank copula function. Via the copula function, the reliability assessment model is proposed. Considering the model is so complicated and analytically intractable, the Markov chain Monte Carlo (MCMC) method is used to estimate the unknown parameters. A numerical example about actual LED lamps data is given to demonstrate the usefulness and validity of the proposed model and method

CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought

Unsupervised sentence representation learning aims to transform input sentences into fixed-length vectors enriched with intricate semantic information while obviating the reliance on labeled data. Recent progress within this field, propelled by contrastive learning and prompt engineering, has significantly bridged the gap between unsupervised and supervised strategies. Nonetheless, the potential utilization of Chain-of-Thought, remains largely untapped within this trajectory. To unlock latent capabilities within pre-trained models, such as BERT, we propose a two-stage approach for sentence representation: comprehension and summarization. Subsequently, the output of the latter phase is harnessed as the vectorized representation of the input sentence. For further performance enhancement, we meticulously refine both the contrastive learning loss function and the template denoising technique for prompt engineering. Rigorous experimentation substantiates our method, CoT-BERT, transcending a suite of robust baselines without necessitating other text representation models or external databases

Effects of Rain Events on Carbon Fluxes from Biological Soil Crusts

In dry ecosystems, biological soil crusts (BSCs) have been suggested as one of the factors responsible for the large rate of annual CO2 net uptake (Xie et al. 2009). However, most studies carried out on carbon (C) fluxes in arid and semi-arid ecosystems, such as soil respiration, have neglected the carbon fluxes from BSCs. Although BSCs are a vital component of the dry-land soil C cycle, few studies have parameterized the conditions required for photosynthesis in BSCs or determined BSCs respiration (Elbert et al. 2009, Castillo-Monroy et al. 2011). Precipitation in dry land is dominated by small events (Lauenroth and Bradford 2009). Even the smallest events will influence the carbon fluxes of BSCs, while intermediate pulses might wet the subsurface biotic community, and typically only larger events are used by plants for carbon gain or growth of roots or shoots (Belnap et al. 2005). As BSCs dry quickly and are hence very responsive to moisture pulses, the pulsed nature of precipitation can lead to highly variable carbon fluxes from BSCs (Bowling et al. 2011). Therefore, it is very important to study the effect of rain events upon carbon fluxes through BSCs in the dry ecosystem

Application of coherence analysis study on identification of vehicle noise sources

Structure-Air noise sources in different frequencies were identified based on analysis of frequency and testing of vibration and noise under idling condition, and a method for signal sources priority was developed under identifying the kinds of noise sources. The partial coherence equations of the six input and single output systems were derived based on the theory of coherence. Coefficient of partial coherence of the test data of vibration and noise in vehicle was calculated by using MATLAB. Coherence analysis results show that working engine incentive transferred to the driving cab in low frequency range caused structure noise, engine RH mounting is the main noise source; The noise in middle frequency range is caused by the coupling effects of vibration of engine left mounting and noise of the engine compartment to the driving cab, between which left hanging mount vibration affected more; Engine compartment noise in high frequency leaked through the air to the cab, engine noise is the main source of noise inside

Compositional Feature Augmentation for Unbiased Scene Graph Generation

Scene Graph Generation (SGG) aims to detect all the visual relation triplets in a given image. With the emergence of various advanced techniques for better utilizing both the intrinsic and extrinsic information in each relation triplet, SGG has achieved great progress over the recent years. However, due to the ubiquitous long-tailed predicate distributions, today's SGG models are still easily biased to the head predicates. Currently, the most prevalent debiasing solutions for SGG are re-balancing methods, e.g., changing the distributions of original training samples. In this paper, we argue that all existing re-balancing strategies fail to increase the diversity of the relation triplet features of each predicate, which is critical for robust SGG. To this end, we propose a novel Compositional Feature Augmentation (CFA) strategy, which is the first unbiased SGG work to mitigate the bias issue from the perspective of increasing the diversity of triplet features. Specifically, we first decompose each relation triplet feature into two components: intrinsic feature and extrinsic feature, which correspond to the intrinsic characteristics and extrinsic contexts of a relation triplet, respectively. Then, we design two different feature augmentation modules to enrich the feature diversity of original relation triplets by replacing or mixing up either their intrinsic or extrinsic features from other samples. Due to its model-agnostic nature, CFA can be seamlessly incorporated into various SGG frameworks. Extensive ablations have shown that CFA achieves a new state-of-the-art performance on the trade-off between different metrics.Comment: Accepted by ICCV 202

MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification

Vision Transformer (ViT) models have recently emerged as powerful and versatile models for various visual tasks. Recently, a work called PMF has achieved promising results in few-shot image classification by utilizing pre-trained vision transformer models. However, PMF employs full fine-tuning for learning the downstream tasks, leading to significant overfitting and storage issues, especially in the remote sensing domain. In order to tackle these issues, we turn to the recently proposed parameter-efficient tuning methods, such as VPT, which updates only the newly added prompt parameters while keeping the pre-trained backbone frozen. Inspired by VPT, we propose the Meta Visual Prompt Tuning (MVP) method. Specifically, we integrate the VPT method into the meta-learning framework and tailor it to the remote sensing domain, resulting in an efficient framework for Few-Shot Remote Sensing Scene Classification (FS-RSSC). Furthermore, we introduce a novel data augmentation strategy based on patch embedding recombination to enhance the representation and diversity of scenes for classification purposes. Experiment results on the FS-RSSC benchmark demonstrate the superior performance of the proposed MVP over existing methods in various settings, such as various-way-various-shot, various-way-one-shot, and cross-domain adaptation.Comment: SUBMIT TO IEEE TRANSACTION