As a fundamental part of computational healthcare, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) provide volumetric data, making the development of algorithms for 3D image analysis a necessity. Despite being computationally cheap, 2D Convolutional Neural Networks can only extract spatial information. In contrast, 3D CNNs can extract three-dimensional features, but they have higher computational costs and latency, which is a limitation for clinical practice that requires fast and efficient models. Inspired by the field of video action recognition we propose a new 2D-based model dubbed Slice SHift UNet (SSH-UNet) which encodes three-dimensional features at 2D CNN's complexity. More precisely multi-view features are collaboratively learned by performing 2D convolutions along the three orthogonal planes of a volume and imposing a weights-sharing mechanism. The third dimension, which is neglected by the 2D convolution, is reincorporated by shifting a portion of the feature maps along the slices' axis. The effectiveness of our approach is validated in Multi-Modality Abdominal Multi-Organ Segmentation (AMOS) and Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) datasets, showing that SSH-UNet is more efficient while on par in performance with state-of-the-art architectures.
相關內容
Complex adaptive systems (CASs), from ecosystems to economies, are open systems and inherently dependent on external conditions. While a system can transition from one state to another based on the magnitude of change in external conditions, the rate of change -- irrespective of magnitude -- may also lead to system state changes due to a phenomenon known as a rate-induced transition (RIT). This study presents a novel framework that captures RITs in CASs through a local model and a network extension where each node contributes to the structural adaptability of others. Our findings reveal how RITs occur at a critical environmental change rate, with lower-degree nodes tipping first due to fewer connections and reduced adaptive capacity. High-degree nodes tip later as their adaptability sources (lower-degree nodes) collapse. This pattern persists across various network structures. Our study calls for an extended perspective when managing CASs, emphasizing the need to focus not only on thresholds of external conditions but also the rate at which those conditions change, particularly in the context of the collapse of surrounding systems that contribute to the focal system's resilience. Our analytical method opens a path to designing management policies that mitigate RIT impacts and enhance resilience in ecological, social, and socioecological systems. These policies could include controlling environmental change rates, fostering system adaptability, implementing adaptive management strategies, and building capacity and knowledge exchange. Our study contributes to the understanding of RIT dynamics and informs effective management strategies for complex adaptive systems in the face of rapid environmental change.
Geoscience foundation models represent a revolutionary approach in the field of Earth sciences by integrating massive cross-disciplinary data to simulate and understand the Earth systems dynamics. As a data-centric artificial intelligence (AI) paradigm, they uncover insights from petabytes of structured and unstructured data. Flexible task specification, diverse inputs and outputs and multi-modal knowledge representation enable comprehensive analysis infeasible with individual data sources. Critically, the scalability and generalizability of geoscience models allow for tackling diverse prediction, simulation, and decision challenges related to Earth systems interactions. Collaboration between domain experts and computer scientists leads to innovations in these invaluable tools for understanding the past, present, and future of our planet. However, challenges remain in validation and verification, scale, interpretability, knowledge representation, and social bias. Going forward, enhancing model integration, resolution, accuracy, and equity through cross-disciplinary teamwork is key. Despite current limitations, geoscience foundation models show promise for providing critical insights into pressing issues including climate change, natural hazards, and sustainability through their ability to probe scenarios and quantify uncertainties. Their continued evolution toward integrated, data-driven modeling holds paradigm-shifting potential for Earth science.
In this paper, a Robust Multi-branch Deep learning-based system for remaining useful life (RUL) prediction and condition operations (CO) identification of rotating machines is proposed. In particular, the proposed system comprises main components: (1) an LSTM-Autoencoder to denoise the vibration data; (2) a feature extraction to generate time-domain, frequency-domain, and time-frequency based features from the denoised data; (3) a novel and robust multi-branch deep learning network architecture to exploit the multiple features. The performance of our proposed system was evaluated and compared to the state-of-the-art systems on two benchmark datasets of XJTU-SY and PRONOSTIA. The experimental results prove that our proposed system outperforms the state-of-the-art systems and presents potential for real-life applications on bearing machines.
Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a systematic literature review on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We collect and analyze 229 research papers from 2017 to 2023 to answer four key research questions (RQs). In RQ1, we categorize different LLMs that have been employed in SE tasks, characterizing their distinctive features and uses. In RQ2, we analyze the methods used in data collection, preprocessing, and application highlighting the role of well-curated datasets for successful LLM for SE implementation. RQ3 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE. Finally, RQ4 examines the specific SE tasks where LLMs have shown success to date, illustrating their practical contributions to the field. From the answers to these RQs, we discuss the current state-of-the-art and trends, identifying gaps in existing research, and flagging promising areas for future study.
Hardware Reverse Engineering (HRE) is a technique for analyzing Integrated Circuits (ICs). Experts employ HRE for various security-critical tasks, such as design verification or the detection of intellectual property violations. However, HRE also enables threat actors to subvert the security of an IC. Previous studies have shown that analysts rely heavily on their cognitive abilities to perform HRE as no fully automated solutions exist. Therefore, conducting controlled experimental studies to assess the cognitive processes involved in HRE could open new avenues for hardware protection. However, researchers have faced the methodological challenge that HRE experts are largely unavailable for such empirical research. To address this scarcity, we have developed REVERSIM, a game-based simulation that mimics realistic HRE subprocesses and is specifically designed to require no prior knowledge. To support these claims, we conducted two empirical studies: First, we performed semi-structured interviews with 14 professionals and researchers from the HRE domain, who attested to the comparability of REVERSIM to real-world HRE problems. Second, we conducted a user study involving 89 non-expert participants, demonstrating that participants could engage in the simulation without prior knowledge in HRE or related domains. Finally, we outline several research directions for experiments with REVERSIM, highlighting its potential in advancing HRE research.
This paper presents a novel hybrid Automatic Speech Recognition (ASR) system designed specifically for resource-constrained robots. The proposed approach combines Hidden Markov Models (HMMs) with deep learning models and leverages socket programming to distribute processing tasks effectively. In this architecture, the HMM-based processing takes place within the robot, while a separate PC handles the deep learning model. This synergy between HMMs and deep learning enhances speech recognition accuracy significantly. We conducted experiments across various robotic platforms, demonstrating real-time and precise speech recognition capabilities. Notably, the system exhibits adaptability to changing acoustic conditions and compatibility with low-power hardware, making it highly effective in environments with limited computational resources. This hybrid ASR paradigm opens up promising possibilities for seamless human-robot interaction. In conclusion, our research introduces a pioneering dimension to ASR techniques tailored for robotics. By employing socket programming to distribute processing tasks across distinct devices and strategically combining HMMs with deep learning models, our hybrid ASR system showcases its potential to enable robots to comprehend and respond to spoken language adeptly, even in environments with restricted computational resources. This paradigm sets a innovative course for enhancing human-robot interaction across a wide range of real-world scenarios.
Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with complex structured text data and exploit global information. Many real text classification applications can be naturally cast into a graph, which captures words, documents, and corpus global features. In this survey, we bring the coverage of methods up to 2023, including corpus-level and document-level graph neural networks. We discuss each of these methods in detail, dealing with the graph construction mechanisms and the graph-based learning process. As well as the technological survey, we look at issues behind and future directions addressed in text classification using graph neural networks. We also cover datasets, evaluation metrics, and experiment design and present a summary of published performance on the publicly available benchmarks. Note that we present a comprehensive comparison between different techniques and identify the pros and cons of various evaluation metrics in this survey.
In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and the recognizability of the face image. Most previous works aim to estimate the sample-wise embedding uncertainty or pair-wise similarity as the quality score, which only considers the information from partial intra-class. However, these methods ignore the valuable information from the inter-class, which is for estimating to the recognizability of face image. In this work, we argue that a high-quality face image should be similar to its intra-class samples and dissimilar to its inter-class samples. Thus, we propose a novel unsupervised FIQA method that incorporates Similarity Distribution Distance for Face Image Quality Assessment (SDD-FIQA). Our method generates quality pseudo-labels by calculating the Wasserstein Distance (WD) between the intra-class similarity distributions and inter-class similarity distributions. With these quality pseudo-labels, we are capable of training a regression network for quality prediction. Extensive experiments on benchmark datasets demonstrate that the proposed SDD-FIQA surpasses the state-of-the-arts by an impressive margin. Meanwhile, our method shows good generalization across different recognition systems.
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes.
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose occupancy networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks.
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191