Multimodal Emotion Recognition in Conversation (MERC) is a critical task for comprehending complex emotions from multiple sources in human-computer interaction. Despite existing methods having made progress, they still struggle to capture hierarchy characteristics and high-order interactions between modalities. Recent research adopts Euclidean space geometry for multimodal representation learning and fusion, resulting in representation and distance distortions. To tackle these challenges, we propose a multimodal hyperbolic embedding and hyperbolic hypergraph fusion framework. A hyperbolic embedding learning module projects modality representations into the Poincaré ball model to perceive hierarchies. To enhance representation discriminative and semantic consistency, it incorporates radial and angular contrastive learning objectives. A hyperbolic hypergraph fusion module further exploits high-order latent relationships and improves fusion results. Experimental results demonstrate that the proposed framework outperforms state-of-the-art methods and augments stability on IEMOCAP and MELD datasets.

SUMMARY: RNA viruses are ubiquitous across a broad spectrum of ecosystems. Therefore, beyond their significant implications for public health, RNA viruses are also key players in ecological processes. High-through sequencing has accelerated the discovery of RNA viruses. Nevertheless, many of these viruses lack taxonomic annotation, posing a challenge to functional inference and evolutionary study. In particular, virus classification at the genus level remains difficult due to the limited reference data and ambiguous boundaries between some closely related genera. We introduce VirTAXA, a robust classification tool that combines remote homology search and tree-based validation to enhance the genus-level taxonomic classification of RNA viruses. VirTAXA is able to predict the genus label of an assembled viral contig and provide evidence type for each prediction. It achieves comparable accuracy to state-of-the-art methods while assigning genus labels to a greater number of sequences. Specifically, on the Global Ocean RNA metatranscriptomic data, VirTAXA can assign genus labels for 18% more contigs than the second-best classification tool. Furthermore, we demonstrated that VirTAXA can be conveniently extended to other types of viruses. AVAILABILITY AND IMPLEMENTATION: The source code and data of VirTAXA are available via https://github.com/JudithEllyn/VirTAXA.

The battery-less sensor nodes provide a solution to power and environment issues, but they face some challenges while being applied to biosensing systems, such as varying power and communication distance. This work presents a battery-less biosensing system for solar-cell-powered continuous glucose monitoring contact lenses featuring a 2.4 GHz transmitter. The modulation method combines frequency shift keying and pulse interval modulation to send radio frequency signals with two-dimensional information including the solar cell's voltage, which aims to overcome the impact of unstable solar power. The charge-pump-based energy harvester converts the solar-cell voltage above 1.8 V to achieve a transmission distance longer than 40 cm, which allows communications between contact lenses and handsets. The fabricated chip in a 65 nm CMOS process consumes 126 nW at 0.37-V supply, which is manageable by on-lens solar cells in an indoor ambient-light environment.

SUMMARY: Without relying on cultivation, metagenomic sequencing greatly accelerated the novel RNA virus detection. However, it is not trivial to accurately identify RNA viral contigs from a mixture of species. The low content of RNA viruses in metagenomic data requires a highly specific detector, while new RNA viruses can exhibit high genetic diversity, posing a challenge for alignment-based tools. In this work, we developed VirBot, a simple yet effective RNA virus identification tool based on the protein families and the corresponding adaptive score cutoffs. We benchmarked it with seven popular tools for virus identification on both simulated and real sequencing data. VirBot shows its high specificity in metagenomic datasets and superior sensitivity in detecting novel RNA viruses. AVAILABILITY AND IMPLEMENTATION: https://github.com/GreyGuoweiChen/RNA_virus_detector. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This work presents the first solar cell (SC)-powered biofuel cell (BFC)-input biosensing system using 65 nm CMOS with pulse interval modulation (PIM) and pulse density modulation (PDM) LED driving capability for stand-alone RF-less continuous glucose monitoring (CGM) contact lenses, which notices diabetes patients of CGM level without any external devices. LED implementation can eliminate the necessity of wireless communication. Power supply from on-lens SCs can eliminate the necessity of wireless power delivery, enabling a fully stand-alone operation under office-room ambient light.

Reverse dictionary(RD) is to map a description or definition to a name of a concept. Most of the existing reverse dictionary methods are word-based, failing to handle the polysemy of words. In this paper, we propose Non-Ambiguous Reverse Dictionary(NARD), a synset-based model using synset embeddings to replace word embeddings. NARD provides different and effective embeddings for different senses in multi-sense words, allowing for a more convergent mapping of words and descriptive sentences at the semantic level. With a cross-dictionary sense tagging, NARD can utilize all word-based dictionaries for training. The pre-trained synset embeddings and the multi-task learning methods are adopted to improve the generalization performance of the model. NARD are trained on one-fifth of the synset-labeled English datasets, and achieves the new state-of-the-art results.

DrBox (detector using rotatable bounding box) achieves superior performance on detection of arbitrary rotated objects by applying rotatable bounding box that enables the network to learn rotational invariant property of objects. However, the current DrBox algorithm uses deep convolution architecture, which is redundant in model storage and inference. In this paper, we present a light weight detection model for remote sensing images. The proposed method, DrBoxLight, uses depthwise separable convolution to decrease model size. Remote sensing applications need strong generalization abilities of the model, which is usually a weak point of small models. We adopt knowledge distillation framework for training to strengthen the generalization ability of DrBoxLight. In the knowledge distillation process, a well-trained teacher network (DrBox) will provide supervision and guidance to student network (DrBoxLight) by optimizing the hint loss of feature extraction outputs and focal loss of soft labels from teacher network. The experiment results on our datasets demonstrate that DrBoxLight trained by knowledge distillation can achieve acceptable precision compared with a large model.

Objects in remote sensing images are difficult to detect due to arbitrarily rotated angles and the wide variance of scales. As bounding box plays an important role in object detection, we proposes a new bounding box type named rotated bounding box (rBox). With the application of rBox, we have proposed a series of detection techniques (DrBox, DrBoxLight, DrBoxSemi, DrBoxPro) to effectively handle the situation where the orientation angles of the objects are arbitrary. This article is a brief overview of these techniques. The original DrBox detector applies VGG-net as its main network framework, with image pyramid input to address multi-scale problem. DrBoxLight is a mini version of DrBox, which applies MobileNet and knowledge distillation to be deployed on embedded devices. DrBoxSemi is a semi-supervised version of DrBox, so annotation of all training samples is no longer necessary. DrBoxPro is the most important update for DrBox with professional designing of abundant prior-rBoxes on feature pyramid networks. In our experiments, we demonstrated how rBox helps to improve the performance of object detection compared with traditional bounding boxes. Besides, we evaluated the performance of our DrBox family on a series of object detection tasks.

Object detection is a challenging task in computer vision. Now many detection networks can get a good detection result when applying large training dataset. However, annotating sufficient amount of data for training is often time-consuming. To address this problem, a semi-supervised learning based method is proposed in this paper. Semi-supervised learning trains detection networks with few annotated data and massive amount of unannotated data. In the proposed method, Generative Adversarial Network is applied to extract data distribution from unannotated data. The extracted information is then applied to improve the performance of detection network. Experiment shows that the method in this paper greatly improves the detection performance compared with supervised learning using only few annotated data. The results prove that it is possible to achieve acceptable detection result when only few target object is annotated in the training dataset.

Inshore ship detection in SAR image faces difficulties on correctly identifying near-shore ships and onshore objects. This article proposes a multi-scale full convolutional network (MS-FCN) based sea-land segmentation method and applies a rotatable bounding box based object detection method (DR-Box) to solve the inshore ship detection problem. The sea region and land region are separated by MS-FCN then DR-Box is applied on sea region. The proposed method combines global information and local information of SAR image to achieve high accuracy. The networks are trained with Chinese Gaofen-3 satellite images. Experiments on the testing image show most inshore ships are successfully located by the proposed method.

This paper proposed a new portable measurement method for resistive leakage current of MOA (Metal Oxide Arrester). This method mainly utilizes electric field coupling theory to obtain the voltage phase information of MOA, and uses current transformer to get the effective value and phase information of full leakage current from lightning counter, then it obtains the resistance current value by calculation. With above principles, we designed a portable MOA performance detection circuit, and verified the validity. This method is simple, conveniconvenientent and secure.

In order to measure the mainstream news sites spread influence of mass media, an efficient algorithm has been proposed in this paper. By introduction PageRank algorithm and dynamics of human behavior, the algorithm with website attention factor archive the goal of quantitative analysis of mainstream news sites spread influence. For illustration, an example close to the actual situation is utilized to show the effect of the news sites rank algorithm in solving problem with website attention factor. Experimental results show that the algorithm based on PageRank algorithm effectively reflecting the spread influence of the news site.

Motivated by the requirement of processing large datahigh-resolution videos, a research on peer to peer (P2P) large data parallel computing is made. The proposed P2P system is unstructured, completely self-organized and load balanced, which make it very efficient in computing any kinds of dividable large data tasks. The system models and algorithms are described, and simulation results show that the proposed system has better performance than an existing grid system.

Routing in ad-hoc networks is challengeable due to the mobility of the nodes. Location-based routing protocols, unlike other protocols which rely on flooding, excel in network scalability. Further more, new location-based routing protocols, like BLR[1] , IGF[2], & CBF[3] have been proposed, with the feature of being beaconless, which improve more in terms of scalability and battery consumption. Such beaconless routing protocols can work efficiently in dense network area. However, these protocols' algorithms have no immunity from routing into sparse area. In this article, historical signal strength has been added as a factor into the BLR algorithm, which improves in avoidance of routing into sparse area, and consequently improves the global routing efficiency.