Crystalline materials have atomic-scale fluctuations in their chemical composition that modulate various mesoscale *** chemistry–microstructure relationships in such materials requires proper characterization of thes...
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Crystalline materials have atomic-scale fluctuations in their chemical composition that modulate various mesoscale *** chemistry–microstructure relationships in such materials requires proper characterization of these chemical ***,current characterization approaches(e.g.,Warren–Cowley parameters)make only partial use of the complete chemical and structural information contained in local chemical *** we introduce a framework based on E(3)-equivariant graph neural networks that is capable of completely identifying chemical motifs in arbitrary crystalline structures with any number of chemical *** approach naturally leads to a proper information-theoretic measure for quantifying chemical short-range order(SRO)in chemically complex materials and a reduced representation of the chemical motif *** framework enables the correlation of any per-atom property with their corresponding local chemical motif,thereby enabling the exploration of structure–property relationships in chemically complex *** the MoTaNbTi high-entropy alloy as a test system,we demonstrate the versatility of this approach by evaluating the lattice strain associated with each chemical motif,and computing the temperature dependence of chemical-fluctuations length scale.
In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of acc...
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In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE
Customized keyword spotting needs to adapt quickly to small user *** methods primarily solve the problem under moderate noise *** work increases the level of difficulty in detecting keywords by introducing keyword ***...
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Customized keyword spotting needs to adapt quickly to small user *** methods primarily solve the problem under moderate noise *** work increases the level of difficulty in detecting keywords by introducing keyword ***,the current solution has been explored on large models with many parameters,making it unsuitable for deployment on small *** applying the current solution to lightweight models with minimal training data,the performance degrades compared to the baseline ***,we propose a light-weight multi-task architecture(<9.0×10^(4)parameters)created from integrating the triplet attention module in the ConvMixer networks and a new auxiliary mixed labeling encoding to address the *** results of our experiment show that the proposed model outperforms similar light-weight models for keyword spotting,with accuracy gains ranging from 0.73%to 2.95%for a clean set and from 2.01%to 3.37%for a mixed set under different scales of training ***,our model shows its robustness in different low-resource language datasets while converging faster.
XStorm, an FRP language for small-scale embedded systems, allows us to concisely describe state-dependent behaviors based on the state transition model. However, when we use different sets of peripheral devices depend...
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Accurate 3D hand pose estimation is a challenging computer vision problem primarily because of self-occlusion and viewpoint variations. Existing methods address viewpoint variations by applying data-centric transforma...
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Accurate 3D hand pose estimation is a challenging computer vision problem primarily because of self-occlusion and viewpoint variations. Existing methods address viewpoint variations by applying data-centric transformations, such as data alignments or generating multiple views, which are prone to data sensitivity, error propagation, and prohibitive computational requirements. We improve the estimation accuracy by mitigating the impact of self-occlusion and viewpoint variations from the network side and propose MH-Net, a novel multiheaded network for accurate 3D hand pose estimation from a depth image. MH-Net comprises three key components. First, a multiscale feature extraction backbone based on an improved multiscale vision transformer (MViTv2) is proposed to extract shift-invariant global features. Second, a 3D anchorset generator is proposed to generate three disjoint sets of 3D anchors that serve two purposes: formulating hand pose estimation as an anchor-to-joint offset estimation and defining three unique viewpoints from a single depth image. Third, three identical regression heads are proposed to regress 3D joint positions based on unique viewpoints defined by their respective anchorsets. Extensive ablation studies have been conducted to investigate the impact of anchorsets, regression heads, and feature extraction backbones. Experiments on three public datasets, ICVL, MSRA, and NYU, show significant improvements over the state-of-the-art. IEEE
Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-ti...
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Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.
Transformers have been widely studied in many natural language processing (NLP) tasks, which can capture the dependency from the whole sentence with a high parallelizability thanks to the multi-head attention and the ...
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Transformers have been widely studied in many natural language processing (NLP) tasks, which can capture the dependency from the whole sentence with a high parallelizability thanks to the multi-head attention and the position-wise feed-forward network. However, the above two components of transformers are position-independent, which causes transformers to be weak in modeling sentence structures. Existing studies commonly utilized positional encoding or mask strategies for capturing the structural information of sentences. In this paper, we aim at strengthening the ability of transformers on modeling the linear structure of sentences from three aspects, containing the absolute position of tokens, the relative distance, and the direction between tokens. We propose a novel bidirectional Transformer with absolute-position aware relative position encoding (BiAR-Transformer) that combines the positional encoding and the mask strategy together. We model the relative distance between tokens along with the absolute position of tokens by a novel absolute-position aware relative position encoding. Meanwhile, we apply a bidirectional mask strategy for modeling the direction between tokens. Experimental results on the natural language inference, paraphrase identification, sentiment classification and machine translation tasks show that BiAR-Transformer achieves superior performance than other strong baselines.
Slot filling,to extract entities for specific types of information(slot),is a vitally important modular of dialogue systems for automatic *** responses can be regarded as the weak supervision of patient *** this way,a...
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Slot filling,to extract entities for specific types of information(slot),is a vitally important modular of dialogue systems for automatic *** responses can be regarded as the weak supervision of patient *** this way,a large amount of weakly labeled data can be obtained from unlabeled diagnosis dialogue,alleviating the problem of costly and time-consuming data ***,weakly labeled data suffers from extremely noisy *** alleviate the problem,we propose a simple and effective Co-WeakTeaching *** method trains two slot filling models *** two models learn from two different weakly labeled data,ensuring learning from two ***,one model utilizes selected weakly labeled data generated by the other,*** model,obtained by the Co-WeakTeaching on weakly labeled data,can be directly tested on testing data or sequentially fine-tuned on a small amount of human-annotated *** results on these two settings illustrate the effectiveness of the method with an increase of 8.03%and 14.74%in micro and macro f1 scores,respectively.
Current motion detection and evaluation technologies face challenges such as limited scalability, imprecise feedback, and lack of personalized guidance. To address these challenges, this research integrated efficient ...
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In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems,...
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In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.
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