Millimeter-wave(mmWave)Non-Orthogonal Multiple Access(NOMA)with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile *** this pap...
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Millimeter-wave(mmWave)Non-Orthogonal Multiple Access(NOMA)with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile *** this paper,we introduce a cost-effective and energy-efficient mmWave-NOMA system that exploits sparse antenna arrays in the *** analysis shows that utilizing low-weight and small-sized sparse antennas in the Base Station(BS)leads to better outage probability *** also introduce an optimum low complexity Equilibrium Optimization(EO)-based algorithm to further improve the outage *** simulation and analysis results show that the systems equipped with sparse antenna arrays making use of optimum beamforming vectors outperform the conventional systems with uniform linear arrays in terms of outage probability and sum rates.
A silicon solar cell with a power conversion efficiency (PCE)of 4% was born in Bell Lab in 1954, seven decades ago. Today,silicon solar cells have reached an efficiency above 25%and achieved pervasive commercial succe...
A silicon solar cell with a power conversion efficiency (PCE)of 4% was born in Bell Lab in 1954, seven decades ago. Today,silicon solar cells have reached an efficiency above 25%and achieved pervasive commercial success [1]. In spite of the steady improvement in efficiency, the interest and enthusiasm in search for new materials and innovative device architectures for newgeneration solar cells have never diminished or subsided;
Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb...
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Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb-Se-Te(GSST)rely on melt-quench and high temperature annealing processes to change the organization of the molecules in the materials’*** a reorganization leads to different optical,electrical,and thermal properties which can be exploited to implement photonic memory cells that are able to store the data at different resistance or optical transmission *** the great promise of conventional PCMs for realizing reconfigurable photonic memories,their slow and extremely power-hungry thermal mechanisms make scaling the systems based on such devices *** addition,such materials do not offer a stable multi-level response over a long period of *** address these shortcomings,the research carried out in this study shows the proof of concept to implement next-generation photonic memory cells based on two-dimensional(2D)birefringence PCMs such as SnSe,which offer anisotropic optical properties that can be switched *** demonstrate that by leveraging the ultrafast and low-power crystallographic direction change of the material,the optical polarization state of the input optical signal can be *** enables the implementation of next-generation high-speed polarization-encodable photonic memory cells for future photonic computing *** to the conventional PCMs,the proposed SnSe-based photonic memory cells offer an ultrafast switching and low-loss optical response relying on ferroelectric property of SnSe to encode the data on the polarization state of the input optical *** a polarization encoding scheme also reduces memory read-out errors and alleviates the scalability limitations due to the optical insertion loss often seen in optical transmission encoding.
Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions a...
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Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-toend trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.
Artificial intelligence(AI)is shifting the paradigm of two-phase heat transfer *** innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that ha...
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Artificial intelligence(AI)is shifting the paradigm of two-phase heat transfer *** innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past,for making their insights available to other domains,and for solving for physical quantities based on first principles for phasechange thermofluidic *** review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation *** technologies for meta-analysis,data extraction,and data stream analysis are described with their potential challenges,opportunities,and alternative ***,we offer outlooks and perspectives regarding physics-centered machine learning,sustainable cyberinfrastructures,and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer.
High Utility Itemset Mining (HUIM) and Frequent Itemset Mining (FIM) are two important branches in the data mining area, where Frequent Itemset Mining considers itemsets that occur in large numbers in the transaction ...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but th...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.
The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of *** authors model the system using an automaton and study the general notion of detectab...
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The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of *** authors model the system using an automaton and study the general notion of detectability where a given set of state pairs needs to be(eventually or periodically)distinguished in any estimate of the state of the *** authors adopt the ALTER sensor attack model from previous work and formulate four notions of CA-detectability in the context of this attack model based on the following attributes:strong or weak;eventual or *** authors present verification methods for strong CA-detectability and weak *** authors present definitions of strong and weak periodic CA-detectability that are based on the construction of a verifier automaton called the augmented *** development also resulted in relaxing assumptions in prior results on D-detectability,which is a special case of CA-detectability.
We evaluate different Neural Radiance Field(NeRF)techniques for the 3D reconstruction of plants in varied environments,from indoor settings to outdoor *** methods usually fail to capture the complex geometric details ...
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We evaluate different Neural Radiance Field(NeRF)techniques for the 3D reconstruction of plants in varied environments,from indoor settings to outdoor *** methods usually fail to capture the complex geometric details of plants,which is crucial for phenotyping and breeding *** evaluate the reconstruction fidelity of NeRFs in 3 scenarios with increasing complexity and compare the results with the point cloud obtained using light detection and ranging as ground *** the most realistic field scenario,the NeRF models achieve a 74.6％F1 score after 30 min of training on the graphics processing unit,highlighting the efficacy of NeRFs for 3D reconstruction in challenging ***,we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4％in the average F1 *** optimization process substantially enhances the speed and efficiency of 3D reconstruction using *** findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.
Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1...
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Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.
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