We calculate one loop scattering amplitudes for arbitrary number of positive helicity on-shell gluons and one off-shell gluon treated within the quasi-multi Regge kinematics. The result is fully gauge invariant and po...
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Sigma-phase Fe0.525Cr0.455Ni0.020alloy was studied by means of Mössbauer spectrometry in the temperature range of 5-293 K. The average center shift, , determined from the recorded Mössbauer spectra was shown...
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The paper deals with strong r-colorings of a random k-uniform hypergraph in the binomial model H(n, k, p). A vertex coloring is said to be strong for a hypergraph if any two vertices that share a common edge are color...
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By numerically investigating the synchronization of cascaded microresonator frequency combs, we find the optimal transmittance parameters and discover that partial injection from the leader is sufficient. We also pres...
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The Proca theory of the real massive vector field admits non-equilibrium solutions, where the asymptotic dynamics of the electric field is dominated by the periodically oscillating Coulomb component. We discuss how su...
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A very simple method for determining the center (isomer) shift, CS, and the spectral area, A, of a Mössbauer spectrum is outlined. Its applicability is demonstrated in two examples viz. pyrite and a ternary sigma...
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The main focus of this work is to perform a computer vision classification method for upper gastrointestinal tract image analysis with a pre-trained deep learning features extraction stage. An open-source dataset was ...
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Rarity meters are incorporated by industry and discursive by academia. Rarity, as an intuitive term, attracted numerous researchers to present their own view of it. While there is existing literature on comparing rari...
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ISBN:
(数字)9798350316742
ISBN:
(纸本)9798350316759
Rarity meters are incorporated by industry and discursive by academia. Rarity, as an intuitive term, attracted numerous researchers to present their own view of it. While there is existing literature on comparing rarity meters, it requires access to NFT collection data, which can be challenging for researchers without a background in blockchain technology. This has created a demand for an easily accessible rarity meter benchmark. In this paper, we introduce the Rating over all Rarities (ROAR) benchmark, which includes data from one hundred popular NFT collections from the Ethereum blockchain, implemented a weighted correlation-based performance measurement function, as well as four state-of-the-art rarity meters (***, Kramer, OpenRarity, and NFTGo), along with a new rarity meter called ROAR. Our experiments show that the ROAR rarity meter, an ensemble of the other four meters, outperforms its competitors, with *** and Kramer as runner-ups. The ROAR benchmark is a tool for examination and testing of rarity meter ideas, and we challenge readers to develop models that can outperform the ROAR rarity meter.
A very simple method for determining the center (isomer) shift, CS, of a Mössbauer spectrum is outlined. Its applicability is demonstrated on two examples viz. pyrite and a ternary sigma-phase Fe-Cr-Ni compound. ...
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In this paper, we propose novel methods to address the challenges of intelligent robotic arm operations in complex environments. Our approach includes a deep learning method for 6D object pose estimation and a deep re...
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In this paper, we propose novel methods to address the challenges of intelligent robotic arm operations in complex environments. Our approach includes a deep learning method for 6D object pose estimation and a deep reinforcement learning method for controlling complex tasks. For 6D object pose estimation, we introduce the Dense Fusion combined with Deep Fusion Transformer model (DFDFTr). This method integrates Dense Fusion and Deep Fusion Transformer, applies re-parameterization techniques such as Reparameterized Convolution and Batch Normalization Fusion, and incorporates specialized neural networks for each stage of the model. Experimental results on the LineMOD and Occlusion LineMOD datasets demonstrate that our method outperforms one-stage approaches like DenseFusion and MPF6D in accuracy and achieves results comparable to advanced two-stage methods such as PVN3D and DTr. Leveraging re-parameterization, our method maintains competitive inference speed with significantly fewer parameters than PVN3D and MPF6D, PoseCNN+ICP without sacrificing accuracy. Additionally, it efficiently processes multiple objects, handling 8 objects in 0.1 s compared to 0.07 s for a single object. For continuous task control, we propose a deep reinforcement learning approach using Realistic Actor-Critic combined with Decoupled Actor-Critic (RAC-DAC), integrated with techniques such as Relay Hindsight Experience Replay (RHER), Multi-step Hindsight Experience Replay (MHER), Curiosity-Driven Prioritization (CDP), and Projected Conflicting Gradients (PCGrad). Experimental results show that RAC-DAC-RHER achieves significantly higher success rates than DDPG-RHER and TD3-RHER, particularly in challenging tasks such as object grasping. It also ensures efficient learning and high accuracy. Furthermore, by refining hyperparameters and integrating MHER, CDP, PCGrad, MLP, and the Clipping Mechanism, we enhanced learning performance, mitigated instabilities, and enabled higher learning rates in spars
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