MicroRNAs (miRNAs) are a class of non-coding RNA molecules that have been shown to be closely associated with the sensitivity of chemotherapeutic drugs in cancer treatment. Given the high cost and extended duration of traditional biological experiments, there is an urgent need to develop computational models to predict the sensitivity scores between miRNAs and drugs. In this study, we proposed a dual-stream graph neural network method based on Zero-Shot Embeddings, named DSHGZS, to explore the potential sensitivity scores between miRNAs and drugs. DSHGZS first constructs two heterogeneous graphs with different isomorphic subgraphs based on zero-shot embeddings obtained from large language models (LLMs) and known miRNA-drug association data. It then utilized the enhanced LLM-derived node feature representations, embedding them into the layer feature learning process of the two heterogeneous graphs to generate high-quality vector representations of miRNAs and drugs. The learned high-quality feature embeddings are subsequently used in a segmented inner product decoder to evaluate the sensitivity association scores between miRNAs and drugs. To address the model’s excessive reliance on high-quality feature representations, we employed PCA to extract the core representations of the LLM-derived node features for data augmentation. Case studies demonstrated that DSHGZS is an effective tool for predicting potential sensitivity scores between miRNAs and drugs.

Neural Rendering representations have significantly contributed to the field of 3D computer vision. Given their potential, considerable efforts have been invested to improve their performance. Nonetheless, the essential question of selecting training views is yet to be thoroughly investigated. This key aspect plays a vital role in achieving high-quality results and aligns with the well-known tenet of deep learning: “garbage in, garbage out”. In this paper, we first illustrate the importance of view selection by demonstrating how a simple rotation of the test views within the most pervasive NeRF dataset can lead to consequential shifts in the performance rankings of state-of-the-art techniques. To address this challenge, we introduce a unified framework for view selection methods and devise a thorough benchmark to assess its impact. Significant improvements can be achieved without leveraging error or uncertainty estimation but focusing on uniform view coverage of the reconstructed object, resulting in a training-free approach. Using this technique, we show that high-quality renderings can be achieved faster by using fewer views. We conduct extensive experiments on both synthetic datasets and realistic data to demonstrate the effectiveness of our proposed method compared with random, conventional error-based, and uncertainty-guided view selection.

When the ambient temperature is low, a large amount of water mist and dust will inevitably appear around the zinc flotation cell, forming haze, which seriously affects the extraction of flotation froth image features. General defogging methods of natural image are difficult to obtain satisfactory results for such industrial haze image. Therefore, we propose a real-time defogging method based on ASM-VoFD (Atmospheric Scattering Model and the Variable-order Fractional Differential). First, the dark pixel ratio is used to detect fog in froth image, which solves the redundant calculation caused by unnecessary defogging operations. Second, the linear transformation of the atmospheric scattering model is used to calculate the initial transmission map, and the gaussian filter is used to optimize the initial transmittance, and the haze-free image is restored with atmospheric light estimation. Finally, a variable order fractional differential operator is used to enhance the edges and texture details of the restored froth image, which solves the problems of blurred edges and low contrast. The experiments show that the algorithm has a good defogging effect on the industrial images, enhance the edges of the image, and can be effectively implemented in O(N) time to meet the application requirements of real-time flotation monitoring.

The Internet of Vehicles is deployed in an open environment, and protecting its security and data privacy is the challenge. Carrying out the Internet of Vehicles secure authentication before interaction of information is an important part of ensuring the security foundation. Therefore, this article designs a safe and reliable Internet of Vehicles authentication and key agreement schemeassisted by blockchain. This article uses a multi-TA network model to improve the efficiency of authentication. Because of the rapid movement of vehicles, it will continue to appear cross-RSU and TA certification. Considering the disadvantages of most centralised authentication protocols using a single TA, this paper uses the multi-TA model to improve the efficiency of authentication. By usingblockchain technology to store the authentication information of vehicles, the cross-domain authentication of vehicles and the protection of user privacy information can be well realised. At the same time, in order to reduce the time of vehicle authentication, this scheme uses a lightweight calculation operation to complete the whole process of authentication. Through security analysis and results of Proverif simulation, the security of the solution is well proved, our scheme can resist various common attacks. Compared with some existing Internet of Vehicles security authentication protocols, the proposed scheme has a lower cost of computation, communication, and storage.

It is a research hotspot to restore decoded videos with existing bitstreams by applying deep neural network to improve compression efficiency at decoder-end. Existing research has verified that the utilization of redundancy at decoder-end, which is underused by the encoder, can bring an increase of compression efficiency. However, most existing research neglects the abundant multi-scale information among video frames as a typical type of such redundancy. It remains an interesting yet challenging topic how to build an effective, interpretable and fast deep neural network for the purpose of using the multi-scale similarity at decoder-end and further enhancing compression efficiency. To this end, this paper considers the use of underused inter multi-scale information and proposes the Fast Multi-Scale Deep Decoder (Fast MSDD) for the state-of-the-art video coding standard HEVC. The advantages of Fast MSDD are three-fold. First, it achieves a higher coding efficiency without modifying any encoding algorithm. Second, Fast MSDD is interpretable based on the framework of using the underused redundancy. Third, it guarantees the model's inference speed while fully using the multi-scale similarity among video frames. Extensive experimental results verify Fast MSDD's effectiveness, interpretability, and computational efficiency. Fast MSDD obtains averagely 14.3%, 10.8%, 8.5% and 7.6% BD gains for AI, LP, LB and RA respectively. Compared with our previous work MSDD, Fast MSDD achieves increases of 59.3%, 49.1%, 61.0% and 29.3%. Meanwhile, 16.9%, 11.2%, 9.2% and 8.3% BD gains are observed on videos with scale changes, which validate the interpretability of the proposed method. Furthermore, Fast MSDD can save at most 56.3% time compared to MSDD.

As we all know, there is strong multi-scale similarity among video frames. However, almost none of the current video coding standards takes this similarity into consideration. There exist two major problems when utilizing the multi-scale information at encoder-end: one is the extra motion models and the overheads brought by new motion parameters; the other is the extreme increment of the encoding algorithms' complexity. Is it possible to employ the multi-scale similarity only at the decoder-end to improve the decoded videos' quality, i.e., to further boost the coding efficiency? This paper mainly studies how to answer this question by proposing a novel Multi-Scale Deep Decoder (MSDD) for HEVC. Benefiting from the efficiency of deep learning technology (Convolutional Neural Network and Long Short-Term Memory network), MSDD achieves a higher coding efficiency only at the decoder-end without changing any encoding algorithms. Extensive experiments validate the feasibility and effectiveness of MSDD. MSDD leads to on averagely 6.5%, 8.0%, 6.4%, and 6.7% BD-rate reduction compared to HEVC anchor, for AI, LP, LB and RA coding configurations respectively. Especially for the videos with multi-scale similarity, the proposed approach obviously improves the coding efficiency indeed.

In the state-of-the-art H.265/HEVC video coding standard, the highest resolution of motion vectors is 1/4-pixel. However, the 1/8-pixel motion vector performs better in some specific scenes but brings more time cost. In this paper, we propose an optimally scalable and cost-effective algorithm for fractional-pixel motion estimation with 1/8-pixel motion vector resolution, which fits well to varying and constrained computing resources. In proposed method, each of the search points is assigned with a cost-effective priority, which determines the order in a refinement search. Also, we demonstrate a new search pattern, which makes it more likely to check the optimal motion vector earlier. Extensive experiments indicate that our method is capable of maximizing the R-D gains of each search point. As a side product, our approach can also serve as a fast algorithm. It can enhance time performance greatly with nearly no loss in R-D gains. Even compared with full fractional pixels search with 1/4-pixel resolution, the proposed method shows averagely 9.8% improvement in terms of time performance, with almost 1.3% increase in coding efficiency.