The diagnosis and prognosis of cancer are typically based on multi-modal clinical data, including histology images and genomic data, due to the complex pathogenesis and high heterogeneity. Despite the advancements in digital pathology and high-throughput genome sequencing, establishing effective multi-modal fusion models for survival prediction and revealing the potential association between histopathology and transcriptomics remains challenging. In this paper, we propose Pathology-Genome Heterogeneous Graph (PGHG) that integrates whole slide images (WSI) and bulk RNA-Seq expression data with heterogeneous graph neural network for cancer survival analysis. The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph. The representation learning network utilizes the biological prior knowledge of intra-modal and inter-modal data associations to guide the feature extraction. The node features of each modality are updated through attention-based graph learning strategy. Unimodal features and bi-modal fused features are extracted via attention pooling module and then used for survival prediction. We evaluate the model on low-grade gliomas, glioblastoma, and kidney renal papillary cell carcinoma datasets from the Cancer Genome Atlas (TCGA) and the First Affiliated Hospital of Zhengzhou University (FAHZU). Extensive experimental results demonstrate that the proposed method outperforms both unimodal and other multi-modal fusion models. For demonstrating the model interpretability, we also visualize the attention heatmap of pathological images and utilize integrated gradient algorithm to identify important tissue structure, biological pathways and key genes.

Real-time railway rescheduling is an important technique to enable operational recovery in response to unexpected and dynamic conditions in a timely and flexible manner. Current research relies mostly on OD based data and model-based methods for estimating train passenger demands. These approaches primarily focus on averaged disruption patterns, often overlooking the immediate uneven distribution of demand over time. In reality, passenger demand deviates significantly from predictions, especially during a disaster. Disastrous situations such as flood in Zhengzhou, China in 2022 has created not only unprecedented effect on Zhengzhou railway station itself, which is a major railway hub in China, but also other major hubs connected to Zhengzhou, e.g., Xi'an, the closest hub west of Zhengzhou. In this study, we define a real-time demand-responsive (RTDR) railway rescheduling problem focusing two specific aspects, namely, volatility of the demand, and management of station crowdedness. For the first time, we propose a data-driven approach using real-time mobile data (MD) to deal with this RTDR problem. A hierarchical deep reinforcement learning (HDRL) framework is designed to perform real-time rescheduling in a demand-responsive manner. The use of MD has enabled the modelling of passenger dynamics in response to train delays and station crowdedness, and a real-time optimisation for rescheduling of train services in view of the change in demand as a result of passengers' behavioural response to disruption. Results show that the agent can steadily satisfy over 62% of the demand with only 61% of the original rolling stock, ensuring continuous operations without overcrowding. Moreover, the agent exhibits adaptability when transferred to a new environment with increased demand, highlighting its effectiveness in addressing unforeseen disruptions in real-time settings.

You've probably heard of Transitions lenses that can adapt to changing light conditions. Now, get ready for facial recognition lenses. Police officers in Zhengzhou, China have been spotted wearing sunglasses equipped with facial recognition software that allows them to identify individuals in a crowd. These surveillance sunglasses were actually rolled out last year, but a recent report from China's QQ published a series of photos of the glasses in action. China has consistently been ahead of the curve in terms of utilizing artificial intelligence (AI) for surveillance. The country's CCTV system tracked down a BBC reporter in just seven minutes during a demonstration in 2017.

Based on its core scientist team's top-level R&D strength, as well as technological innovation and breakthroughs, Squirrel AI Learning started holding four "man-machine competitions" in Zhengzhou, Chengdu and Dongying in October 2017 in a bid to identify any difference between its adaptive learning system and human teaching. Dr. Kalns demonstrated to the RE-WORK audience the results of the four competitions: surprisingly, machine teaching outperformed human teaching in all the four competitions. Taking the fourth competition, which unfolded in one hundred cities, as an example, students at the same intellectual level were divided into two groups and received human teaching and Squirrel AI Learning respectively. Every student in the machine teaching group learned 42 knowledge points on the average, while every student in the human teaching learned 28 knowledge points on the average; in terms of average scoring in the core part of the competition, the students in the AI teaching group had their scores increased by 5.4 on the average, while the students in the human teaching group just had their scores increased by 0.7 on the average, suggesting that machine teaching enabled students to take a firmer grasp of knowledge points than human teaching and improved the learning efficiency more significantly than human teaching. According to the results, Squirrel AI Learning is basically the same as or better than individualized human teaching.

ZHENGZHOU, China - In Zhengzhou, a police officer wearing facial recognition glasses spotted a heroin smuggler at a train station. In Qingdao, a city famous for its German colonial heritage, cameras powered by artificial intelligence helped police snatch two dozen criminal suspects in the midst of a big annual beer festival. In Wuhu, a fugitive murder suspect was identified by a camera as he bought food from a street vendor. With millions of cameras and billions of lines of code, China is building a high-tech authoritarian future. Beijing is embracing technologies like facial recognition and artificial intelligence to identify and track 1.4 billion people.

ZHENGZHOU: In the Chinese city of Zhengzhou, a police officer wearing facial recognition glasses spotted a heroin smuggler at a train station. In Qingdao, a city famous for its German colonial heritage, cameras powered by artificial intelligence helped police snatch two dozen criminal suspects in the midst of a big annual beer festival. In Wuhu, a fugitive murder suspect was identified by a camera as he bought food from a street vendor. With millions of cameras and billions of lines of code, China is building a high-tech authoritarian future. Beijing is embracing technologies like facial recognition and artificial intelligence to identify and track 1.4 billion people. It wants to assemble a vast and unprecedented national surveillance system, with crucial help from its thriving technology industry.

This photo taken on February 5, 2018 shows a police officer wearing a pair of smartglasses with a facial recognition system at Zhengzhou East Railway Station in Zhengzhou in China's central Henan province. Chinese police are sporting high-tech sunglasses that can spot suspects in a crowded train station, the newest use of facial recognition that has drawn concerns among human rights groups. We seem to be heading into a future where facial recognition technologies are going to be part of everyday life. Cities all over the world are now bristling with cameras, and in the case of China it is impossible to avoid being monitored either by CCTV or even by police wearing special glasses and then logged onto a database that checks on your habits, your social credit and even who your friends are. At the same time, cameras and facial recognition are increasingly being used in public and private buildings.

A police officer wears a pair of smart glasses with facial recognition at Zhengzhou East Railway Station in China's central Henan province. It doesn't link a system of killer robots ticked off at the human race (just yet). But Chinese police are expanding the use of futuristic facial recognition tech powered by a system dubbed "Skynet" to track a database of blacklisted individuals. Unlike in the "Terminator" franchise where Skynet is controlled by machines to connect genocide-minded bots, this version is a tool for law enforcement and security that's being tested out for added security at two sessions of China's parliament this year, according to Reuters. The technology is the same we saw Chinese police use last month to monitor travelers leading up to Chinese New Year.

For the past two months, cyborg police officers have screened travellers passing through Zhengzhou railway station in China. The officers, wearing smart glasses with built-in face recognition, have caught seven fugitives and 26 fake ID holders already. According to local media, some of the fugitives were wanted for alleged involvement in human trafficking cases.

For the past two months, cyborg police officers have screened travellers passing through Zhengzhou railway station in China. The officers, wearing smart glasses with built-in face recognition, have caught seven fugitives and 26 fake ID holders already. According to local media, some of the fugitives were wanted for alleged involvement in human trafficking cases.