the brain tumor is a major human life concern. One of the main causes of mortality in individuals is brain tumors in recent years. It is tough to manually detect the tumor. Doctors may being confused to detect the tum...
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In this paper, we have proposed an algorithm for dynamic slicing of concurrent COPs that consist of multiple threads. In order to portray the concurrent COP effectively, an intermediate representation graph called con...
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Neural Radiance Fields (NeRF) learn a model for the high-quality 3D-view reconstruction of a single object. Category-specific representation makes it possible to generalize to the reconstruction and even generation of...
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ISBN:
(纸本)9783031301049;9783031301056
Neural Radiance Fields (NeRF) learn a model for the high-quality 3D-view reconstruction of a single object. Category-specific representation makes it possible to generalize to the reconstruction and even generation of multiple objects. Existing efforts mainly focus on the reconstruction performance including speed and quality. the steerability of generation processes has not been well studied while semantic attributes still exist in 3D neural representations. Inspired by interpreting underlying factors of GANs, this paper proposes a novel method named Eigen-GRF to disentangle the latent semantic subspace in an unsupervised manner. By learning a set of eigenbasis, we can readily control the process and the result of object synthesis accordingly. Concretely, our method brings a mapping network to NeRF by conditioning on a FiLM-SIREN layer. then we use a component analysis method for discovering steerable latent subspaces. Our experiments reveal that the proposed method is powerful for the 3D-aware generation with steerability by both synthetic and real-world datasets.
We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) withthe reasoning capabilities of probabilistic logic (L) programming. Besides standard latent subsymbolic variables, our ...
ISBN:
(纸本)9781713871088
We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) withthe reasoning capabilities of probabilistic logic (L) programming. Besides standard latent subsymbolic variables, our model exploits a probabilistic logic program to define a further structured representation, which is used for logical reasoning. the entire process is end-to-end differentiable. Once trained, VAEL can solve new unseen generation tasks by (i) leveraging the previously acquired knowledge encoded in the neural component and (ii) exploiting new logical programs on the structured latent space. Our experiments provide support on the benefits of this neuro-symbolic integration both in terms of task generalization and data efficiency. To the best of our knowledge, this work is the first to propose a general-purpose end-to-end framework integrating probabilistic logic programming into a deep generative model.
Artificial intelligence (AI) is a pivotal technology driving the next industrial revolution and shaping the future of education. this research focuses on developing a learner-centered educational environment that leve...
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Intelligent transportation systems (ITSs) provide a paradigm change in perceiving and interacting with transportation networks, leading to enhanced levels of safety, sustainability, and efficiency. Vehicular-to-everyt...
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ISBN:
(数字)9781737749769
ISBN:
(纸本)9798350371420
Intelligent transportation systems (ITSs) provide a paradigm change in perceiving and interacting with transportation networks, leading to enhanced levels of safety, sustainability, and efficiency. Vehicular-to-everything (V2X) communication is the core component in the ITSs. the proprioceptive and exteroceptive sensors allow these vehicles to be aware of the surrounding environment and respond to emergencies by utilizing their abilities to reach a high level of self-awareness. In this paper, we propose a self-awareness approach to learn a generative dynamic Bayesian network (G-DBN) from the real-time LiDAR perception. Without reducing the dimensionality, we perform offline training and online testing phases on the three-dimensional (3D) point clouds. In the offline training phase, initially, the raw point clouds are preprocessed using a joint probabilistic data association filter (JPDAF) to obtain the 3D tracks of the multiple vehicles in space. then, we perform an unsupervised clustering on all the generalized states (GSs) containing positions and velocities (a 6D vector) by considering the growing neural gas (GNG) technique, thus achieving a trained model from the 3D LiDAR point clouds. In the online testing phase, the high-dimensional Markov jump particle filter (HD-MJPF) utilizes the G-DBN’s probabilistic information to predict the positions of multiple vehicles and to detect the abnormalities at the discrete and continuous levels in normal and abnormal scenarios. Our proposed approach is useful for learning high-dimensional generative models and provides a way to meet the current curse of dimensionality challenges, that machine learning models are suffering.
Integrated satellite-ground network (ISGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage...
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ISBN:
(数字)9798350390643
ISBN:
(纸本)9798350390650
Integrated satellite-ground network (ISGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and reliable services. However, the inevitable existences of high-volume data, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient ISGN. Withthese focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication in ISGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate transmission. Based on our proposed framework, we formulate a total semantic rate maximization problem considering the impacts of jammer's channel state information (CSI) imperfection, while maintaining the semantic similarity requirement, semantic rate target, and MF-RIS's self-sustainability. then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.
this paper presents the development of a robot that can be controlled semi-autonomously and has the capability to detect toxic and combustible gases. the purpose of this research is to develop a semi-autonomous robot ...
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the brain tumor is a major human life concern. One of the main causes of mortality in individuals is brain tumors in recent years. It is tough to manually detect the tumor. Doctors may being confused to detect the tum...
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ISBN:
(纸本)9781665447539
the brain tumor is a major human life concern. One of the main causes of mortality in individuals is brain tumors in recent years. It is tough to manually detect the tumor. Doctors may being confused to detect the tumor since they have been utilized to do image operations in computer programming. It is very crucial to discover the brain tumor early on with its precise diagnosis. the interior architecture of the brain and the diagnosis, monitoring, and treatment of an illness is an essential component of medical research. In medical practice, X-ray is used to diagnose the body's human component via several types of imaging technologies, such as CT-scan and MRI. MRI is utilized in brain diagnosis or tumor location, tissue volume measurement, tumor size estimation. Because of the tumor variety the detection of the tumor is exceedingly challenging. For the identification and classification of brain tumors, several image processing and neural network algorithms are utilized. In this article, we will present comparisons of several approaches with brain tumor diagnosis. Our study identifies signs of Brain tumor by utilizing the Deep Neural Network (DNN) method based on these symptoms at the very first stage. the hybrid CNN model was utilized to identify brain tumor illness and was demonstrated to outperform it in comparison with standard styles such as InceptionV3, RestNet50, etc. We have built a web-based AI tool to identify Brain tumors using this model.
this paper proposes mathematical programming models for supply chain of perishable products using Possibilistic Linear programming (PLP) and Preemptive Possibilistic Linear programming (PPLP) models. the possible rang...
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ISBN:
(纸本)9781728108513
this paper proposes mathematical programming models for supply chain of perishable products using Possibilistic Linear programming (PLP) and Preemptive Possibilistic Linear programming (PPLP) models. the possible ranges of unit price and unit costs were used to increase flexibility of obtaining the better supply chain imprecise information. three crisp objective functions were generated, which are maximizing the most possible value of profit, minimizing the risk of obtaining the lower profit and maximizing the opportunity of obtaining the higher profit. the objective functions of the presented model looked for the maximum total profit with low risk in getting the lower profit and higher opportunity to get the better profit. Product shelf-life, time of all processes and quantity of products were determined. the numerical experiment was illustrated for an aromatic coconut supply chain. PPLP model is easier to select the preferred solution than PLP model. these models can find the effective solution for the problem.
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