Hermite spectral method for the inelastic Boltzmann equation

We propose a Hermite spectral method for the inelastic Boltzmann equation, which makes two-dimensional periodic problem computation affordable by the hardware nowadays. The new algorithm is based on a Hermite expansion, where the expansion coefficients for the VHS model are reduced into several summations and can be derived exactly. Moreover, a new collision model is built with a combination of the quadratic collision operator and a linearized collision operator, which helps us to balance the computational cost and the accuracy. Various numerical experiments, including spatially two-dimensional simulations, demonstrate the accuracy and efficiency of this numerical scheme

Collaborative planning and optimization for electric-thermal-hydrogen-coupled energy systems with portfolio selection of the complete hydrogen energy chain

Under the global low-carbon target, the uneven spatiotemporal distribution of renewable energy resources exacerbates the uncertainty and seasonal power imbalance. Additionally, the issue of an incomplete hydrogen energy chain is widely overlooked in planning models, which hinders the complete analysis of the role of hydrogen in energy systems. Therefore, this paper proposes a high-resolution collaborative planning model for electricity-thermal-hydrogen-coupled energy systems considering both the spatiotemporal distribution characteristics of renewable energy resources and the multi-scale bottom-to-top investment strategy for the complete hydrogen energy chain. Considering the high-resolution system operation flexibility, this paper proposes a hydrogen chain-based fast clustering optimization method that can handle high-dimensional data and multi-time scale operation characteristics. The model optimizes the geographical distribution and capacity configuration of the Northeast China energy system in 2050, with hourly operational characteristics. The planning optimization covered single-energy devices, multi-energy-coupled conversion devices, and electric-hydrogen transmission networks. Last but not least, this paper thoroughly examines the optimal portfolio selection of different hydrogen technologies based on the differences in cost, flexibility, and efficiency. In the Pareto analysis, the proposed model reduces CO2 emissions by 60% with a competitive cost. This paper provides a zero-carbon pathway for multi-energy systems with a cost 4% less than the social cost of carbon $44.6/ton, and the integration of the complete hydrogen energy chain reduces the renewable energy curtailment by 97.0%. Besides, the portfolio selection results indicate that the system favors the SOEC with the highest energy efficiency and the PEMFC with the fastest dynamic response when achieving zero-carbon emissionsComment: 32 pages, 17 figure

π\pi-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation

Foundation models have achieved great advances in multi-task learning with a unified interface of unimodal and multimodal tasks. However, the potential of such multi-task learners has not been exploited during transfer learning. In this work, we present a universal parameter-efficient transfer learning method, termed Predict-Interpolate Tuning ($\pi$-Tuning), for vision, language, and vision-language tasks. It aggregates the parameters of lightweight task-specific experts learned from similar tasks to aid the target downstream task. The task similarities are predicted in a unified modality-independent space, yielding a scalable graph to demonstrate task relationships. $\pi$-Tuning has several appealing benefits. First, it flexibly explores both intra- and inter-modal transferability between similar tasks to improve the accuracy and robustness of transfer learning, especially in data-scarce scenarios. Second, it offers a systematical solution for transfer learning with multi-task prediction-and-then-interpolation, compatible with diverse types of parameter-efficient experts, such as prompt and adapter. Third, an extensive study of task-level mutual benefits on 14 unimodal and 6 multimodal datasets shows that $\pi$-Tuning surpasses fine-tuning and other parameter-efficient transfer learning methods both in full-shot and low-shot regimes. The task graph also enables an in-depth interpretable analysis of task transferability across modalities.Comment: To appear in ICML 202

Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploiting this structured information could potentially ease the detection of anomalies from radiography images. To this end, we propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID). We formulate anomaly detection as an image reconstruction task, consisting of a space-aware memory matrix and an in-painting block in the feature space. During the training, SimSID can taxonomize the ingrained anatomical structures into recurrent visual patterns, and in the inference, it can identify anomalies (unseen/modified visual patterns) from the test image. Our SimSID surpasses the state of the arts in unsupervised anomaly detection by +8.0%, +5.0%, and +9.9% AUC scores on ZhangLab, COVIDx, and CheXpert benchmark datasets, respectively. Code: https://github.com/MrGiovanni/SimSIDComment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). arXiv admin note: substantial text overlap with arXiv:2111.1349

Epidemiological features of tuberculosis infection in a rural prefecture of Japan from 2007 to 2018

This study aimed to investigate the epidemiological features of reported tuberculosis (TB) infections in a western prefecture (Nagasaki Prefecture) from 2007 to 2018, and to identify the high-risk group for TB infection. The characteristics of 12 years of reported TB infections from the Nagasaki Prefectural Informational Center of Infectious Diseases were summarized by median (interquartile range [IQR]) and proportion; the annual TB infections’ notification rate regarding sex/age was calculated accordingly. The diagnosis of TB infection was made according to clinic symptoms and laboratory examination. In total, 4364 TB infections were reported in 2007 and 2018, with a median age (IQR) of 74 (55–84) years. The majority of TB infections were male (52.6%, 2297/4364), > 65 years (65.8%, 2869/4364), and indigenous (98.1%, 4276/4364). Among active TB, 66.9% (1833/2740) had pulmonary TB, and 25.3% (694/2740) were diagnosed as extrapulmonary TB. The highest notification rate of TB infection was observed in the elderly male population (> 85 years). The annual notification rate of TB infections ranged between 19.4/and 34.0/100,000 for 12 years. The notification rates of TB infections were high in older people of both sexes, especially in men aged > 85. Therefore, appropriate interventions and health management are essential for TB control in (and with a focus on) the elderly population

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem of each dataset, as well as a limited investigation of diverse types of tumors, the resulting models are often limited to segmenting specific organs/tumors and ignore the semantics of anatomical structures, nor can they be extended to novel domains. To address these issues, we propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models. This CLIP-based label encoding captures anatomical relationships, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets. We rank first on the Medical Segmentation Decathlon (MSD) public leaderboard and achieve state-of-the-art results on Beyond The Cranial Vault (BTCV). Additionally, the Universal Model is computationally more efficient (6x faster) compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.Comment: Rank first in Medical Segmentation Decathlon (MSD) Competitio

Progress in electrolyte-free fuel cells

Solid oxide fuel cell (SOFC) represents a clean electrochemical energy conversion technology with characteristics of high conversion efficiency and low emissions. It is one of the most important new energy technologies in the future. However, the manufacture of SOFCs based on the structure of anode/electrolyte/cathode is complicated and time-consuming. Thus, the cost for the entire fabrication and technology is too high to be affordable, and challenges still hinder commercialization. Recently, a novel type of electrolyte-free fuel cell (EFFC) with single component was invented, which could be the potential candidate for the next generation of advanced fuel cells. This paper briefly introduces the EFFC, working principle, performance, and advantages with updated research progress. A number of key R&D issues about EFFCs have been addressed, and future opportunities and challenges are discussed