Intangible Cultural Heritage (ICH) faces challenges to survival and growth from globalization and economic pressures. Research has explored how Generative AI (GenAI) can capture, represent, and convey cultural meaning to help address these challenges. This paper bridges current GenAI ICH strategies and explores the forms of creativity that emerge when artisans use their embodied craft skills to create with GenAI fine-tuned on their own ICH. Collaborating in-situ with Huayao embroiderers in rural China produced eight novel hand-embroidered pieces from our custom GenAI workflows trained on Huayao ICH. Embroiderers noted that GenAI tools could enhance creativity and had the potential to support innovation within tradition, yet human creativity and manual refinement remained necessary to ensure cultural authenticity. We suggest that our combination of rapid iterative GenAI and in-situ slow making by hand created a space in which discourses on the meaning and value of ICH could be explored.

Transformer and its variants have shown great potential for various vision tasks in recent years, including image classification, object detection and segmentation. Meanwhile, recent studies also reveal that with proper architecture design, convolutional networks (ConvNets) also achieve competitive performance with transformers. However, no prior methods have explored to utilize pure convolution to build a Transformer-style Decoder module, which is essential for Encoder-Decoder architecture like Detection Transformer (DETR). To this end, in this paper we explore whether we could build query-based detection and segmentation framework with ConvNets instead of sophisticated transformer architecture. We propose a novel mechanism dubbed InterConv to perform interaction between object queries and image features via convolutional layers. Equipped with the proposed InterConv, we build Detection ConvNet (DECO), which is composed of a backbone and convolutional encoder-decoder architecture. We compare the proposed DECO against prior detectors on the challenging COCO benchmark. Despite its simplicity, our DECO achieves competitive performance in terms of detection accuracy and running speed. Specifically, with the ResNet-18 and ResNet-50 backbone, our DECO achieves $40.5\%$ and $47.8\%$ AP with $66$ and $34$ FPS, respectively. The proposed method is also evaluated on the segment anything task, demonstrating similar performance and higher efficiency. We hope the proposed method brings another perspective for designing architectures for vision tasks. Codes are available at \url{https://github.com/xinghaochen/DECO} and \url{https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/DECO}.

Driver genes play a crucial role in the growth of cancer cells. Accurate identification of cancer driver genes is essential for deepening our understanding of cancer pathogenesis and facilitating the development of cancer therapies and drug-targeted driver genes. However, the diversity and complexity of multi-omics data still make cancer driver identification highly challenging. In this study, we propose Transformer-Driver (Trans-Driver), a deep supervised learning method based on a novel transformer architecture, which integrates multi-omics data to learn the differences and associations between different omics modalities for cancer driver discovery. Trans-Driver introduces a kernel-based multi-head self-attention mechanism with gated residual connections, as well as a Dynamic Tanh (DyT) normalization function, to enhance the integration and modeling of heterogeneous multi-omics features. Compared with other state-of-the-art driver gene identification methods, Trans-Driver achieved excellent performance on TCGA, CGC, and PCAWG datasets. Among approximately 20,000 protein-coding genes, Trans-Driver reported 269 candidate driver genes, of which 132 genes (about 49.1%) were included in the gold standard CGC dataset. Feature contribution analysis further demonstrated that integrating multi-omics data improved performance compared to using somatic mutation data alone. Finally, detailed analysis revealed that the candidate drivers are clinically meaningful, demonstrating the practical value of Trans-Driver.

Inspired by the decision learning process of multilayer per-ceptron (MLP) and decision tree (DT), a new classification model, named the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, S0, by examining the discriminant power of each input feature. Then, it learns projections of features in S0to yield 1D subspaces and finds the optimal partition for each. A criterion is developed to choose the best q partitions that yield 2qpartitioned subspaces. The partitioning process is recursively applied at each child node to build an SLM tree. When the samples at a child node are sufficiently pure, the partitioning process stops, and each leaf node makes a prediction. The ensembles of SLM trees can yield a stronger predictor. Extensive experiments are conducted for performance benchmarking among SLM trees, ensembles and classical classifiers.

Cancer driver genes are critical in driving tumor cell growth, and precisely identifying these genes is crucial in advancing our understanding of cancer pathogenesis and developing targeted cancer drugs. Despite the current methods for discovering cancer driver genes that mainly rely on integrating multi-omics data, many existing models are overly complex, and it is difficult to interpret the results accurately. This study aims to address this issue by introducing InDEP, an interpretable machine learning framework based on cascade forests. InDEP is designed with easy-to-interpret features, cascade forests based on decision trees and a KernelSHAP module that enables fine-grained post-hoc interpretation. Integrating multi-omics data, InDEP can identify essential features of classified driver genes at both the gene and cancer-type levels. The framework accurately identifies driver genes, discovers new patterns that make genes as driver genes and refines the cancer driver gene catalog. In comparison with state-of-the-art methods, InDEP proved to be more accurate on the test set and identified reliable candidate driver genes. Mutational features were the primary drivers for InDEP's identifying driver genes, with other omics features also contributing. At the gene level, the framework concluded that substitution-type mutations were the main reason most genes were identified as driver genes. InDEP's ability to identify reliable candidate driver genes opens up new avenues for precision oncology and discovering new biomedical knowledge. This framework can help advance cancer research by providing an interpretable method for identifying cancer driver genes and their contribution to cancer pathogenesis, facilitating the development of targeted cancer drugs.

With the development of next-generation sequencing technology, massive genomic data has been generated, primarily encouraging research on cancer driver genes. Many bioinformatics methods were proposed to identify driver genes. However, the results of driver gene identification a mong these methods show considerable differences. It is still challenging to obtain a comprehensive catalog of cancer drivers. Although current methods have greatly promoted the development of driver genes, few methods can integrate the identification results of existing methods. To solve such problems in cancer driver genes research, we proposed a cascade forest model to discover cancer driver genes(CFC) that can integrate multi-omics data and annotation scores from different cancer driver gene identification algorithms. The proposed method got precise results for 33 cancer types and Pan-cancer. The CFC framework identified 275 driver genes in Pan-cancer, of which 179 were included in the Gold standard. The identified genes were enriched i n t he principal cancer signaling pathways.

An unsupervised nuclei segmentation method for histology images is proposed in this work. It consists of three modules applied to each of non-overlapping blocks: 1) data-driven color transform for dimension reduction, 2) fully-automated adaptive binarization, and 3) incorporation of geometric priors with morphological processing. The method is called CBM, which comes from the first letter of the three modules – "Color transform", "Binarization" and "Morphological processing". Experiments on the MoNuSeg dataset validate the effectiveness of the proposed CBM method. It outperforms all other unsupervised methods and offers a competitive standing among supervised models based on the Aggregated Jaccard Index (AJI) met-ric.

An ensemble method that fuses the output decision vectors of multiple feedforward-designed convolutional neural networks (FF-CNNs) to solve the image classification problem is proposed in this work. To enhance the performance of the ensemble system, it is critical to increase the diversity of FF-CNN models. To achieve this objective, we introduce diversities by adopting three strategies: 1) different parameter settings in convolutional layers, 2) flexible feature subsets fed into the Fully-connected (FC) layers, and 3) multiple image embeddings of the same input source. Furthermore, we partition input samples into easy and hard ones based on their decision confidence scores. As a result, we can develop a new ensemble system tailored to hard samples to further boost classification accuracy. Experiments are conducted on the MNIST and CIFAR-10 datasets to demonstrate the effectiveness of the ensemble method.

A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work. One unique property of FF-CNNs is that no backpropagation is used in model parameters determination. Since unlabeled data may not always enhance semi-supervised learning [1], we define an effective quality score and use it to select a subset of unlabeled data in the training process. We conduct experiments on the MNIST, SVHN, and CIFAR-10 datasets, and show that the proposed semi-supervised FF-CNN solution outperforms the CNN trained by backpropagation (BP-CNN) when the amount of labeled data is reduced. Furthermore, we develop an ensemble system that combines the output decision vectors of different semi-supervised FF-CNNs to boost classification accuracy. The ensemble systems can achieve further performance gains on all three benchmarking datasets.