Dates are one of the most popular and consumed fruits in North Africa, with millions of tons produced annually. Dates are an important part of diets because they are full of vital vitamins and minerals and have a high...
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Causation promotes the understanding of correlation to an advanced stage by elucidating its underlying mechanism. Although statisticians have specified the possible causal relations among correlations,inferring causal...
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Causation promotes the understanding of correlation to an advanced stage by elucidating its underlying mechanism. Although statisticians have specified the possible causal relations among correlations,inferring causal structures is impossible from only the observed correlations in the classical world. Quantum correlations encapsulating the most defining aspects of quantum physics have taken a new turn for the causal inference problem — the two-point spatial and temporal quantum correlations with observationally discernible characteristics correspond exactly to the two most basic causal structures. However, a direct causal interpretation for quantum correlations has only been established in very limited cases. Here, we explore to what extent quantum correlations promote causal inference. Theoretically, we have found that the distinguishable causal regime of two-point Pauli correlations can be expanded from a single value to an asymmetric interval, and the causal structures determining the quantum correlations can be interpreted by a simple distance criterion. Experimentally, we have devised and implemented a versatile non-unital quantum channel in an optical architecture to directly observe such an asymmetric interval. The setup enabled quantum causal inference without any requirement of active intervention, which is impossible in the classical realm. Our work facilitates the identification of causal links among quantum variables and provides insight into characterizing causation and spatial-temporal correlation in quantum mechanics.
Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been fully explore...
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Fractures occurring in the cervical spine are a severe medical condition that can have fatal consequences or result in permanent paralysis that lasts a lifetime. Rapid identification and location of vertebral fracture...
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This paper aims to investigate a non-degenerate Schrödinger equation with fractional integral type dynamic boundary control. We focus on establishing the well-posedness of the system by employing semigroup theory...
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This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based *** advances the convergence of the AEFA towards...
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This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based *** advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration *** new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based ***,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed ***-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural *** consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between ***,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such ***,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated *** proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy ***,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.
In this study, we introduce a novel hierarchical routing protocol known as the 'Energy-Efficient Hierarchical Routing Protocol' (noted EEHRP). The objective of EEHRP is to minimize energy consumption in WSNs w...
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Treemaps are a popular representation to show hierarchical as well as part-to-whole relationships in data. While most students are aware of node-link representations/network diagrams based on their K-12 education, tre...
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The task of aligning a score to corresponding audio is a well-studied problem of particular relevance for a number of applications. Having this information allows users to explore the materials in unique ways and buil...
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In this study, we introduce an experimental framework for Moroccan dialect speech recognition under various additive noise conditions using the open-source tool PocketSphinx. We curated a corpus comprising the ten mos...
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