118 research outputs found
How Volatile is ENSO?
The El Niños Southern Oscillations (ENSO) is a periodical phenomenon of climatic interannual variability, which could be measured through either the Southern Oscillation Index (SOI) or the Sea Surface Temperature (SST) Index. The main purpose of this paper is to analyze these two indexes in order to capture the volatility inherent in ENSO. The empirical results show that both the ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility accurately. The empirical results show that 1998 is a turning point, which indicates that the ENSO strength has increased since 1998. Moreover, the increasing ENSO strength is due to the increase in greenhouse gas emissions. The ENSO strengths for SST are predicted for the year 2030 to increase from 29.62% to 81.5% if global CO2 emissions increase by 40% to 110%, respectively. This indicates that we will be faced with an even stronger El Nino or La Nina in the future if global greenhouse gas emissions continue to increase unabated.ENSO; SOI; SOT; Greenhouse Gas Emissions; Volatility; GARCH; GJR; EGARCH
How Volatile is ENSO?
The El Niños Southern Oscillations (ENSO) is a periodical phenomenon of climatic interannual variability, which could be measured through either the Southern Oscillation Index (SOI) or the Sea Surface Temperature (SST) Index. The main purpose of this paper is to analyze these two indexes in order to capture the volatility inherent in ENSO. The empirical results show that both the ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility accurately. The empirical results show that 1998 is a turning point, which indicates that the ENSO strength has increased since 1998. Moreover, the increasing ENSO strength is due to the increase in greenhouse gas emissions. The ENSO strengths for SST are predicted for the year 2030 to increase from 29.62% to 81.5% if global CO2 emissions increase by 40% to 110%, respectively. This indicates that we will be faced with an even stronger El Nino or La Nina in the future if global greenhouse gas emissions continue to increase unabated.ENSO, SOI, SOT, Greenhouse Gas Emissions, Volatility, GARCH, GJR, EGARCH.
Identification of Two Thermotolerance-Related Genes in Agaricus bisporus
To characterize thermotolerance-related genes in Agaricus bisporus strain 02, we employed differential display PCR (DD-PCR) to analyze total RNA samples extracted from the mycelia grown at different temperatures. Two partial DNA fragments (023-11A and 023-11B) were cloned thus far, the expression of which was correlated with the culturing temperature. The sequences of the two DNA fragments were determined and the results showed that the nucleotide sequence of 023-11A was unknown, and 023-11B was highly similar in nucleotide sequence (identities 24 %, positives 45 %) to a gene coding for the karyopherin docking complex of the nuclear pore complex of Saccharomyces cerevisiae. It is possible to use the two fragments for further characterization of full-length coding sequences, which can potentially be used for generating new thermotolerant mushroom strains by transgenic technique
Identification of Two Thermotolerance-Related Genes in Agaricus bisporus
To characterize thermotolerance-related genes in Agaricus bisporus strain 02, we employed differential display PCR (DD-PCR) to analyze total RNA samples extracted from the mycelia grown at different temperatures. Two partial DNA fragments (023-11A and 023-11B) were cloned thus far, the expression of which was correlated with the culturing temperature. The sequences of the two DNA fragments were determined and the results showed that the nucleotide sequence of 023-11A was unknown, and 023-11B was highly similar in nucleotide sequence (identities 24 %, positives 45 %) to a gene coding for the karyopherin docking complex of the nuclear pore complex of Saccharomyces cerevisiae. It is possible to use the two fragments for further characterization of full-length coding sequences, which can potentially be used for generating new thermotolerant mushroom strains by transgenic technique
How Volatile is ENSO?
The El Niños Southern Oscillations (ENSO) is a periodical phenomenon of climatic interannual variability, which could be measured through either the Southern Oscillation Index (SOI) or the Sea Surface Temperature (SST) Index. The main purpose of this paper is to analyze these two indexes in order to capture the volatility inherent in ENSO. The empirical results show that both the ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility accurately. The empirical results show that 1998 is a turning point, which indicates that the ENSO strength has increased since 1998. Moreover, the increasing ENSO strength is due to the increase in greenhouse gas emissions. The ENSO strengths for SST are predicted for the year 2030 to increase from 29.62% to 81.5% if global CO2 emissions increase by 40% to 110%, respectively. This indicates that we will be faced with an even stronger El Nino or La Nina in the future if global greenhouse gas emissions continue to increase unabated.ENSO, SOI, SOT, Greenhouse Gas Emissions, Volatility, GARCH, GJR, EGARCH.
Domain-Specific Bias Filtering for Single Labeled Domain Generalization
Conventional Domain Generalization (CDG) utilizes multiple labeled source
datasets to train a generalizable model for unseen target domains. However, due
to expensive annotation costs, the requirements of labeling all the source data
are hard to be met in real-world applications. In this paper, we investigate a
Single Labeled Domain Generalization (SLDG) task with only one source domain
being labeled, which is more practical and challenging than the CDG task. A
major obstacle in the SLDG task is the discriminability-generalization bias:
the discriminative information in the labeled source dataset may contain
domain-specific bias, constraining the generalization of the trained model. To
tackle this challenging task, we propose a novel framework called
Domain-Specific Bias Filtering (DSBF), which initializes a discriminative model
with the labeled source data and then filters out its domain-specific bias with
the unlabeled source data for generalization improvement. We divide the
filtering process into (1) feature extractor debiasing via k-means
clustering-based semantic feature re-extraction and (2) classifier
rectification through attention-guided semantic feature projection. DSBF
unifies the exploration of the labeled and the unlabeled source data to enhance
the discriminability and generalization of the trained model, resulting in a
highly generalizable model. We further provide theoretical analysis to verify
the proposed domain-specific bias filtering process. Extensive experiments on
multiple datasets show the superior performance of DSBF in tackling both the
challenging SLDG task and the CDG task.Comment: Accepted by International Journal of Computer Vision (IJCV
Modality-invariant and Specific Prompting for Multimodal Human Perception Understanding
Understanding human perceptions presents a formidable multimodal challenge
for computers, encompassing aspects such as sentiment tendencies and sense of
humor. While various methods have recently been introduced to extract
modality-invariant and specific information from diverse modalities, with the
goal of enhancing the efficacy of multimodal learning, few works emphasize this
aspect in large language models. In this paper, we introduce a novel multimodal
prompt strategy tailored for tuning large language models. Our method assesses
the correlation among different modalities and isolates the modality-invariant
and specific components, which are then utilized for prompt tuning. This
approach enables large language models to efficiently and effectively
assimilate information from various modalities. Furthermore, our strategy is
designed with scalability in mind, allowing the integration of features from
any modality into pretrained large language models. Experimental results on
public datasets demonstrate that our proposed method significantly improves
performance compared to previous methods
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
Whole Slide Image (WSI) classification remains a challenge due to their
extremely high resolution and the absence of fine-grained labels. Presently,
WSIs are usually classified as a Multiple Instance Learning (MIL) problem when
only slide-level labels are available. MIL methods involve a patch embedding
process and a bag-level classification process, but they are prohibitively
expensive to be trained end-to-end. Therefore, existing methods usually train
them separately, or directly skip the training of the embedder. Such schemes
hinder the patch embedder's access to slide-level labels, resulting in
inconsistencies within the entire MIL pipeline. To overcome this issue, we
propose a novel framework called Iteratively Coupled MIL (ICMIL), which bridges
the loss back-propagation process from the bag-level classifier to the patch
embedder. In ICMIL, we use category information in the bag-level classifier to
guide the patch-level fine-tuning of the patch feature extractor. The refined
embedder then generates better instance representations for achieving a more
accurate bag-level classifier. By coupling the patch embedder and bag
classifier at a low cost, our proposed framework enables information exchange
between the two processes, benefiting the entire MIL classification model. We
tested our framework on two datasets using three different backbones, and our
experimental results demonstrate consistent performance improvements over
state-of-the-art MIL methods. Code will be made available upon acceptance
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