Bis(μ-2-hydroxy­benozato)-κ3 O,O′:O′;κ3 O:O,O′-bis­[(2-hydroxy­benozato-κ2 O,O′)(1,10-phenanthroline-κ2 N,N′)cadmium(II)]

The dinuclear title compound, [Cd2(C7H5O3)4(C12H8N2)2], is located on a crystallographic rotation twofold axis. The two CdII ions are connected by two tridentate bridging 2-hydroxy­benzoate anions. Each CdII ion is seven-coordinated by five O atoms from three 2-hydroxy­benzoate ligands and two N atoms from 1,10-phenanthroline. The 2-hydroxy­benzoate mol­ecules adopt two kinds of coordination mode, bidentate chelating and tridentate bridging–chelating. Intra­molecular hydrogen bonds between hydr­oxy and carboxyl­ate groups from 2-hydroxy­benzoate groups and π–π stacking interactions between parallel 1,10-phenanthroline ligands [centroid–centroid distances = 3.707 (3) and 3.842 (3) Å] are observed. Furthermore, adjacent benzene rings from 2-hydroxy­benzoate ligands are involved in π–π inter­actions with inter­planar distances of 3.642 (3) Å, thereby forming a chain along the a axis direction

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

Increased Salt Tolerance with Overexpression of Cation/Proton Antiporter 1 Genes: A Meta-Analysis

Cation/proton antiporter 1 (CPA1) genes encode cellular Na+/H+ exchanger proteins, which act to adjust ionic balance. Overexpression of CPA1s can improve plant performance under salt stress. However, the diversified roles of the CPA1 family and the various parameters used in evaluating transgenic plants over-expressing CPA1s make it challenging to assess the complex functions of CPA1s and their physiological mechanisms in salt tolerance. Using meta-analysis, we determined how overexpression of CPA1s has influenced several plant characteristics involved in response and resilience to NaCl stress. We also evaluated experimental variables that favor or reduce CPA1 effects in transgenic plants. Viewed across studies, overexpression of CPA1s has increased the magnitude of 10 of the 19 plant characteristics examined, by 25% or more.Among the ten moderating variables, several had substantial impacts on the extent of CPA1 influence: type of culture media, donor and recipient type and genus, and gene family. Genes from monocotyledonous plants stimulated root K+, root K+/Na+, total chlorophyll, total dry weight and root length much more than genes from dicotyledonous species. Genes transformed to or from Arabidopsis have led to smaller CPA1-induced increases in plant characteristics than genes transferred to or from other genera. Heterogeneous expression of CPA1s led to greater increases in leaf chlorophyll and root length than homologous expression. These findings should help guide future investigations into the function of CPA1s in plant salt tolerance and the use of genetic engineering for breeding of resistance

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