TRUSS STRUCTURE OPTIMIZATION BASED ON IMPROVED WOLF PACK ALGORITHM

Aiming at the optimization of truss structure, a wolf pack algorithm based on chaos and improved search strategy was proposed. The mathematical model of truss optimization was constructed, and the classical truss structure was optimized. The results were compared with those of other optimization algorithms. When selecting and updating the initial position of wolves, chaos idea was used to distribute the initial value evenly in the solution space; phase factor was introduced to optimize the formula of wolf detection; information interaction between wolves is increased and the number of runs is reduced. The numerical results show that the improved wolf pack algorithm has the characteristics of fewer parameters, simple programming, easy implementation, fast convergence speed, and can quickly find the optimal solution. It is suitable for the optimization design of the section size of space truss structures

Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation

In speaker-independent speech emotion recognition, the training and testing samples are collected from diverse speakers, leading to a multi-domain shift challenge across the feature distributions of data from different speakers. Consequently, when the trained model is confronted with data from new speakers, its performance tends to degrade. To address the issue, we propose a Dynamic Joint Distribution Adaptation (DJDA) method under the framework of multi-source domain adaptation. DJDA firstly utilizes joint distribution adaptation (JDA), involving marginal distribution adaptation (MDA) and conditional distribution adaptation (CDA), to more precisely measure the multi-domain distribution shifts caused by different speakers. This helps eliminate speaker bias in emotion features, allowing for learning discriminative and speaker-invariant speech emotion features from coarse-level to fine-level. Furthermore, we quantify the adaptation contributions of MDA and CDA within JDA by using a dynamic balance factor based on $\mathcal{A}$-Distance, promoting to effectively handle the unknown distributions encountered in data from new speakers. Experimental results demonstrate the superior performance of our DJDA as compared to other state-of-the-art (SOTA) methods.Comment: Accepted by ICASSP 202

Learning Local to Global Feature Aggregation for Speech Emotion Recognition

Transformer has emerged in speech emotion recognition (SER) at present. However, its equal patch division not only damages frequency information but also ignores local emotion correlations across frames, which are key cues to represent emotion. To handle the issue, we propose a Local to Global Feature Aggregation learning (LGFA) for SER, which can aggregate longterm emotion correlations at different scales both inside frames and segments with entire frequency information to enhance the emotion discrimination of utterance-level speech features. For this purpose, we nest a Frame Transformer inside a Segment Transformer. Firstly, Frame Transformer is designed to excavate local emotion correlations between frames for frame embeddings. Then, the frame embeddings and their corresponding segment features are aggregated as different-level complements to be fed into Segment Transformer for learning utterance-level global emotion features. Experimental results show that the performance of LGFA is superior to the state-of-the-art methods.Comment: This paper has been accepted on INTERSPEECH 202

Metasomatized lithospheric mantle for Mesozoic giant gold deposits in the North China craton

The origin of giant lode gold deposits of Mesozoic age in the North China craton (NCC) is enigmatic because high-grade metamorphic ancient crust would be highly depleted in gold. Instead, lithospheric mantle beneath the crust is the likely source of the gold, which may have been anomalously enriched by metasomatic processes. However, the role of gold enrichment and metasomatism in the lithospheric mantle remains unclear. Here, we present comprehensive data on gold and platinum group element contents of mantle xenoliths (n = 28) and basalts (n = 47) representing the temporal evolution of the eastern NCC. The results indicate that extensive mantle metasomatism and hydration introduced some gold (<1–2 ppb) but did not lead to a gold-enriched mantle. However, volatile-rich basalts formed mainly from the metasomatized lithospheric mantle display noticeably elevated gold contents as compared to those from the asthenosphere. Combined with the significant inheritance of mantle-derived volatiles in auriferous fluids of ore bodies, the new data reveal that the mechanism for the formation of the lode gold deposits was related to the volatile-rich components that accumulated during metasomatism and facilitated the release of gold during extensional craton destruction and mantle melting. Gold-bearing, hydrous magmas ascended rapidly along translithospheric fault zones and evolved auriferous fluids to form the giant deposits in the crust

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins

Emotion-Aware Contrastive Adaptation Network for Source-Free Cross-Corpus Speech Emotion Recognition

Cross-corpus speech emotion recognition (SER) aims to transfer emotional knowledge from a labeled source corpus to an unlabeled corpus. However, prior methods require access to source data during adaptation, which is unattainable in real-life scenarios due to data privacy protection concerns. This paper tackles a more practical task, namely source-free cross-corpus SER, where a pre-trained source model is adapted to the target domain without access to source data. To address the problem, we propose a novel method called emotion-aware contrastive adaptation network (ECAN). The core idea is to capture local neighborhood information between samples while considering the global class-level adaptation. Specifically, we propose a nearest neighbor contrastive learning to promote local emotion consistency among features of highly similar samples. Furthermore, relying solely on nearest neighborhoods may lead to ambiguous boundaries between clusters. Thus, we incorporate supervised contrastive learning to encourage greater separation between clusters representing different emotions, thereby facilitating improved class-level adaptation. Extensive experiments indicate that our proposed ECAN significantly outperforms state-of-the-art methods under the source-free cross-corpus SER setting on several speech emotion corpora.Comment: Accepted by ICASSP 202