P2-type layered transition-metal oxides have garnered considerable attention as cathode materials for sodium-ion batteries (SIBs); however, they suffer from limited capacity and structural instability during (de)sodiation processes, posing significant challenges for practical applications. Here, we address these issues by synergistically tuning the transition-metal (TM) interlayer and intralayer distances through the substitution of Na with K, achieving a stable structure denoted as Na0.62K0.05Ni0.33Mn0.67O2 (NKNMO). The K pillars induce the expansion of the TM interlayer and the contraction of the TM intralayer, which facilitates the transport of sodium ions and stabilizes the structure. Theoretical calculation and electrochemical measurements demonstrate that this P2-type cathode shows superior rate capability and enhanced anion redox activity. Specifically, the NKNMO demonstrates alleviated detrimental phase transitions and significantly reduced lattice strains during cycling at various rates, as directly revealed via advanced high-time resolution two-dimensional X-ray diffraction. The stable Na-storage lattice structure can prevent the layered structure from collapsing especially for high current density charging, leading to exceptional cycling stability with an outstanding capacity retention of 95.99% after 500 cycles at 3C. This work offers a fundamental understanding of material structure and provides important clues for developing structure-stabilized, high-performance cathode materials for SIBs.
Regression errors of Deep Neural Network (DNN) models refer to the case that predictions were correct by the old-version model but wrong by the new-version model. They frequently occur when upgrading DNN models in production systems, causing disproportionate user experience degradation. In this paper, we propose a lightweight regression error reduction approach with two goals: 1) requiring no model retraining and even data, and 2) not sacrificing the accuracy. The proposed approach is built upon the key insight rooted in the unmanaged model uncertainty, which is intrinsic to DNN models, but has not been thoroughly explored especially in the context of quality assurance of DNN models. Specifically, we propose a simple yet effective ensemble strategy that estimates and aligns the two models' uncertainty. We show that a Pareto improvement that reduces the regression errors without compromising the overall accuracy can be guaranteed in theory and largely achieved in practice. Comprehensive experiments with various representative models and datasets confirm that our approaches significantly outperform the state-of-the-art alternatives.
Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.
Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks. Recent successes of Transformers inspire casting offline RL as sequence modeling, which performs well in long-horizon tasks. However, they are overly optimistic in stochastic environments with incorrect assumptions that the same goal can be consistently achieved by identical actions. In this paper, we introduce an UNcertainty-awaRE deciSion Transformer (UNREST) for planning in stochastic driving environments without introducing additional transition or complex generative models. Specifically, UNREST estimates state uncertainties by the conditional mutual information between transitions and returns, and segments sequences accordingly. Discovering the `uncertainty accumulation' and `temporal locality' properties of driving environments, UNREST replaces the global returns in decision transformers with less uncertain truncated returns, to learn from true outcomes of agent actions rather than environment transitions. We also dynamically evaluate environmental uncertainty during inference for cautious planning. Extensive experimental results demonstrate UNREST's superior performance in various driving scenarios and the power of our uncertainty estimation strategy.
Origami-inspired robots with multiple advantages, such as being lightweight, requiring less assembly, and exhibiting exceptional deformability, have received substantial and sustained attention. However, the existing origami-inspired robots are usually of limited functionalities and developing feature-rich robots is very challenging. Here, we report an origami-wheeled robot (OriWheelBot) with variable width and outstanding sand walking versatility. The OriWheelBot's ability to adjust wheel width over obstacles is achieved by origami wheels made of Miura origami. An improved version, called iOriWheelBot, is also developed to automatically judge the width of the obstacles. Three actions, namely direct pass, variable width pass, and direct return, will be carried out depending on the width of the channel between the obstacles. We have identified two motion mechanisms, i.e., sand-digging and sand-pushing, with the latter being more conducive to walking on the sand. We have systematically examined numerous sand walking characteristics, including carrying loads, climbing a slope, walking on a slope, and navigating sand pits, small rocks, and sand traps. The OriWheelBot can change its width by 40%, has a loading-carrying ratio of 66.7% on flat sand and can climb a 17-degree sand incline. The OriWheelBot can be useful for planetary subsurface exploration and disaster area rescue.
Abstract Origami-inspired robots with multiple advantages, such as being lightweight, requiring less assembly, and exhibiting exceptional deformability, have received substantial and sustained attention. However, the existing origami-inspired robots are usually of limited functionalities and developing feature-rich robots is very challenging. Here, we report an origami-wheeled robot (OriWheelBot) with variable width and outstanding sand walking versatility. The OriWheelBot’s ability to adjust wheel width over obstacles is achieved by origami wheels made of Miura origami. An improved version, called iOriWheelBot, is also developed to automatically judge the width of the obstacles. Three actions, namely direct pass, variable width pass, and direct return, will be carried out depending on the width of the channel between the obstacles. We have identified two motion mechanisms, i.e., sand-digging and sand-pushing, with the latter being more conducive to walking on the sand. We have systematically examined numerous sand walking characteristics, including carrying loads, climbing a slope, walking on a slope, and navigating sand pits, small rocks, and sand traps. The OriWheelBot can change its width by 40%, has a loading-carrying ratio of 66.7% on flat sand and can climb a 17-degree sand incline. The OriWheelBot can be useful for planetary subsurface exploration and disaster area rescue.
As the rapid development of computer music, the technique for recognizing the emotion of music also have many progresses. After the brief introduction of the history of computer music, this paper mainly discusses about the current existing machine learning models for the emotion recognition in music. The complexity of emotion has been emphasized in this paper for several reasons. In addition, by comparison different models, this paper summarizes common features, metrics and steps used in music emotion analyzation. Moreover, this study finds out the limitations and disadvantages of different classifications and feature extracting method for different models, pointing out the living problems, e.g., the difficulty of emotion recognition for experimental music. To sum up, this paper summarizes and analyzes the primary studying in the field of music emotion recognition, offering a guideline for implementations of different machine learning approaches in the field. These results shed light on paving a path for further exploration of emotion recognition in computer music.
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