Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors from speech data and transcripts are reviewed, which mainly contains acoustic features from speech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges.

Autonomous driving technology has advanced significantly with deep learning, but noise and attacks threaten its real-world deployment. While research has revealed vulnerabilities in individual intelligent tasks, a comprehensive evaluation of these impacts across complete end-to-end systems is still underexplored. To address this void, we thoroughly analyze the robustness of four end-to-end autonomous driving systems against various noise and build the RobustE2E Benchmark, including five traditional adversarial attacks and a newly proposed Module-Wise Attack specifically targeting end-to-end autonomous driving in white-box settings, as well as four major categories of natural corruptions (a total of 17 types, with five severity levels) in black-box settings. Additionally, we extend the robustness evaluation from the open-loop model level to the closed-loop case studies of autonomous driving system level. Our comprehensive evaluation and analysis provide valuable insights into the robustness of end-to-end autonomous driving, which may offer potential guidance for targeted improvements to models. For example, (1) even the most advanced end-to-end models suffer large planning failures under minor perturbations, with perception tasks showing the most substantial decline; (2) among adversarial attacks, our Module-Wise Attack poses the greatest threat to end-to-end autonomous driving models, while PGD-$l2$ is the weakest, and among four categories of natural corruptions, noise and weather are the most harmful, followed by blur and digital distortion being less severe; (3) the integrated, multitask approach results in significantly higher robustness and reliability compared with the simpler design, highlighting the critical role of collaborative multitask in autonomous driving; and (4) the autonomous driving systems amplify the model’s lack of robustness, etc. Our research contributes to developing more resilient autonomous driving models and their deployment in the real world.

Tibetan text recognition plays a key role in preserving the Tibetan language, religion, and traditions. While text recognition has made progress for high-resource languages, handwritten Tibetan character recognition remains difficult due to limited data and the lack of public large language models. Most existing datasets focus on printed or historical documents, as well as online handwriting data, but there are still few large offline handwritten Tibetan datasets. To solve this problem, we construct TibHCR, a large-scale offline handwritten character recognition dataset for the Tibetan language. To increase the diversity of the linguistic and font styles, more character categories and participants from 5 provinces in China are included. To collect and label the data efficiently, we introduce a grid sheet design, reducing manual annotation to just 1% of the samples. This design then allows for automatic data processing to extract each character sample and its corresponding label. The resulting TibHCR dataset contains 141,698 samples from 235 Tibetan writers, covering 47 character classes. We evaluate TibHCR using two recognition models: a convolutional recurrent neural network (CRNN) and a cross-lingual fine-tuning method, on a Chinese pretrained model using the PP-OCRv4 architecture to adapt Tibetan data. The results show that both models can recognize handwritten Tibetan characters efficiently, with an accuracy of 99.48% for CRNN and 99.70% for the fine-tuning method. The TibHCR dataset is publicly available at https://huggingface.co/datasets/qixiaoke/TibHCR.