Different scent delivery methods may influence how scents are perceived in interactive systems. This study presents PlayScent, a custom-built research prototype comprising our in-house multi-module driver (UniODriver) and an integrated 3D-printed apparatus. Using a single-outlet design, PlayScent enables controlled comparison of atomizer, air pump, heater, and fan-based delivery methods under shared spatial conditions. Through a mixed-methods study, we develop a preliminary perceptual mapping and an initial perception vocabulary for scent delivery methods, relating user descriptions to measured physical parameters and identifying distinct perceptual tendencies across methods. Exploratory expert feedback further reflects on the possible relevance of these findings for design practice. Building on the empirical results, we outline illustrative design scenarios for future exploration. Together, this work offers an early design-oriented account of how delivery methods may be considered alongside scent selection in olfactory interaction design.

Accurate medical image segmentation is essential for reliable diagnosis, surgical planning, and disease monitoring. Semi-supervised medical image segmentation offers great potential by exploiting abundant unlabeled data with limited annotations, but it is prone to confirmation bias. To overcome this, we propose Attention Inverted Feature Perturbation (AIFP), a novel method that adaptively inverts feature-level attention weights to generate perturbations. This strategy encourages diversity and maintains independence between networks within a co-training framework, thereby mitigating confirmation bias. Extensive experiments on four public benchmarks validate the effectiveness of AIFP. Specifically, our method achieves Dice scores of 89.90% on ACDC and 91.01% on LA using only 10% labeled data, and 84.58% on Pancreas-NIH and 82.58% on PROMISE12 using 20% labeled data. These results consistently outperform state-of-the-art semi-supervised approaches, highlighting the practical value of AIFP in advancing accurate and robust medical image segmentation. AIFP enables reliable segmentation with limited annotations, supporting critical tasks such as left atrium delineation, pancreas boundary identification, and prostate segmentation. By reducing annotation demands while ensuring robustness, it has the potential to accelerate the clinical adoption of artificial intelligence-driven imaging tools.

Although many countries have achieved rapid advancements in science and technology, they still rely on other nations in certain key technological fields. Effective prediction of technological risks is vital for national economic growth. Recently, countries have placed a strong emphasis on technological security. Technological risks arise from factors such as technology itself, its environment and management. Predicting these risks helps nations, enterprises and institutions achieve independent and controllable technology. It also enables effective management and control of potential risks. Risk prediction research encounters challenges such as technological diversity and the complexity of technological risks. To address the above issues, we propose a patent data meta-path-based technological risk prediction method. This method takes into account both technological diversity and the complexity of technological risks when predicting risks. Technological risk prediction involves analyzing potential risks and their degrees of severity in technological development. Compared to the baseline methods using our collected patent data, our method performs better in evaluation metrics, demonstrating its applicability in predicting technological risks.

Although China's scientific and technological strength and technical level have achieved rapid development, there is still dependence on other countries in some key technological fields.Effectively predicting technological risks is crucial to national economic development.In recent years, China has paid great attention to technological security.Technological risks refer to risks caused by factors such as the technology itself, the technological environment, and the technological management methods.Through technological risk prediction, we can facilitate the achievement of independent and controllable technology for our country, enterprises and institutions.Furthermore, we can effectively manage and control potential risks.Risk prediction research faces some challenges including: technological complexity, content diversity, and technological differences.To respond to the above issues, we propose a patent data meta-path based technological risk prediction method.In the method, technological complexity, content diversity, and technological differences are considered when we predict technological risk.Technological risk prediction is a process to analyze potential risks in technological development as well as their risk degrees.Our method is compared with the baseline method on the patent data we collected.The results show that the technological risk prediction method outperforms the baseline method in the evaluation measurements.Such method can be applied to predict technological risks.

Traditional visual question answering (VQA) often falls short as merely relying on image information is insufficient to answer given questions. Therefore, Knowledge-Based Visual Question Answering (KB-VQA) has emerged. Typically, KB-VQA involves first retrieving knowledge from external knowledge bases, then using the retrieved knowledge in conjunction with the understanding of visual content for joint reasoning to predict answers. However, current models often suffer from weak visual perception capabilities when processing image information. Additionally, due to the incompleteness of external knowledge bases, retrieved knowledge may contain noise or even irrelevant information. Moreover, the re-embedding of knowledge text features during the model's reasoning process may deviate from the original meanings in the knowledge base. To address these challenges, we propose a method for Knowledge-Based Visual Question Answering (KB-VQA) using GPT-3.5, leveraging image captions and in-context prompts. We utilize an advanced captioning model to convert images into accurate textual representations, enhancing the large language model's understanding of visual information. Moreover, we eliminate the need for additional knowledge bases by directly employing GPT-3.5 as a knowledge base for knowledge retrieval and generate logically consistent text during inference to predict answers. Furthermore, we enhance GPT-3.5's question-answering capability for VQA through in-context prompt learning. Experiments on the public OK-VQA dataset demonstrate the superior performance of our model.

Diffusion models have achieved remarkable success in diverse domains such as image synthesis, super-resolution, and 3D molecule generation. Surprisingly, the application of diffusion models in graph learning has garnered little attention. In this paper, we aim to bridge this gap by exploring the use of diffusion models for unsupervised graph representation learning. Our investigation commences with the identification of anisotropic structures within graphs and the recognition of a crucial limitation in the vanilla forward diffusion process when dealing with these anisotropic structures. The original forward diffusion process continually adds isotropic Gaussian noise to the data, which may excessively dilute anisotropic signals, leading to rapid signal-to-noise conversion. This rapid conversion poses challenges for training denoising neural networks and obstructs the acquisition of semantically meaningful representations during the reverse process. To overcome this challenge, we introduce a novel class of models termed {\it directional diffusion models}. These models adopt data-dependent, anisotropic, and directional noises in the forward diffusion process. In order to assess the effectiveness of our proposed models, we conduct extensive experiments on 12 publicly available datasets, with a particular focus on two distinct graph representation learning tasks. The experimental results unequivocally establish the superiority of our models over state-of-the-art baselines, underscoring their effectiveness in capturing meaningful graph representations. Our research not only sheds light on the intricacies of the forward process in diffusion models but also underscores the vast potential of these models in addressing a wide spectrum of graph-related tasks. Our code is available at \url{https://github.com/statsle/DDM}.

With the continuous improvement of supercomputer performance and the integration of artificial intelligence with traditional scientific computing, the scale of applications is gradually increasing, from millions to tens of millions of computing cores, which raises great challenges to achieve high scalability and efficiency of parallel applications on super-large-scale systems. Taking the Sunway exascale prototype system as an example, in this paper we first analyze the challenges of high scalability and high efficiency for parallel applications in the exascale era. To overcome these challenges, the optimization technologies used in the parallel supporting environment software on the Sunway exascale prototype system are highlighted, including the parallel operating system, input/output (I/O) optimization technology, ultra-large-scale parallel debugging technology, 10-million-core parallel algorithm, and mixed-precision method. Parallel operating systems and I/O optimization technology mainly support large-scale system scaling, while the ultra-large-scale parallel debugging technology, 10-million-core parallel algorithm, and mixed-precision method mainly enhance the efficiency of large-scale applications. Finally, the contributions to various applications running on the Sunway exascale prototype system are introduced, verifying the effectiveness of the parallel supporting environment design.

Quantum computational chemistry (QCC) is the use of quantum computers to solve problems in computational quantum chemistry. We develop a high performance variational quantum eigensolver (VQE) simulator for simulating quantum computational chemistry problems on a new Sunway supercomputer. The major innovations include: (1) a Matrix Product State (MPS) based VQE simulator to reduce the amount of memory needed and increase the simulation efficiency; (2) a combination of the Density Matrix Embedding Theory with the MPS-based VQE simulator to further extend the simulation range; (3) A three-level parallelization scheme to scale up to 20 million cores; (4) Usage of the Julia script language as the main programming language, which both makes the programming easier and enables cutting edge performance as native C or Fortran; (5) Study of real chemistry systems based on the VQE simulator, achieving nearly linearly strong and weak scaling. Our simulation demonstrates the power of VQE for large quantum chemistry systems, thus paves the way for large-scale VQE experiments on near-term quantum computers.

Classical simulation of quantum computation plays a critical role in numerical studies of quantum algorithms and the validation of quantum devices. Here, we introduce SW_Qsim, a tensor-network-based quantum simulator, which is designed with a two-level parallel structure for efficient implementation on the many-core New Sunway Supercomputer. We propose a minimize-memory contraction path algorithm for rectangular quantum grids to reduce the memory overhead, and provide the memory-limited simulation capacity of SW26010pro. Moreover, tensor operations are carefully optimized on the SW processor to achieve high performance. We design a fault tolerance mechanism to improve the extreme-scale parallel stability. We benchmark SW_Qsim's simulation of RQCs up to 400-qubits, achieving near-linear strong and weak scaling with up to 28.75 million cores, far beyond the previous state of the art. Our work sheds light on the development of efficient quantum algorithms for use in the physical, chemical, and engineering science fields.

We develop a high-performance tensor-based simulator for random quantum circuits(RQCs) on the new Sunway supercomputer. Our major innovations include: (1) a near-optimal slicing scheme, and a path-optimization strategy that considers both complexity and compute density; (2) a three-level parallelization scheme that scales to about 42 million cores; (3) a fused permutation and multiplication design that improves the compute efficiency for a wide range of tensor contraction scenarios; and (4) a mixed-precision scheme to further improve the performance. Our simulator effectively expands the scope of simulatable RQCs to include the 10X10(qubits)X(1+40+1)(depth) circuit, with a sustained performance of 1.2 Eflops (single-precision), or 4.4 Eflops (mixed-precision)as a new milestone for classical simulation of quantum circuits; and reduces the simulation sampling time of Google Sycamore to 304 seconds, from the previously claimed 10,000 years.