Wavefront shaping allows focusing light through or inside strongly scattering media, but the background intensity also increases which reduces the target’s contrast. By combining transmission or deposition matrices f...
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Wavefront shaping allows focusing light through or inside strongly scattering media, but the background intensity also increases which reduces the target’s contrast. By combining transmission or deposition matrices for different regions, we construct joint operators to achieve spatially resolved control of light in diffusive systems. The eigenmode of a contrast operator can maximize the power contrast between a target and its surrounding. A difference operator enhances the power delivery to a target while avoiding the background increase. This work opens the door to coherent control of nonlocal effects in wave transport for practical applications.
The topology selection plays a key role in minimizing the losses and improving the output waveform quality of an inverter. In addition, increasing the switching frequency of an inverter help to reduce the size of EMI ...
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Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (...
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In recent times, planar omnidirectional wireless power transfer has emerged as a prominent technology, especially in consumer electronics application. However, in such scenario, the presence of multiple receivers with...
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A key challenge in contrastive learning is to generate negative samples from a large sample set to contrast with positive samples, for learning better encoding of the data. These negative samples often follow a softma...
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A key challenge in contrastive learning is to generate negative samples from a large sample set to contrast with positive samples, for learning better encoding of the data. These negative samples often follow a softmax distribution which are dynamically updated during the training process. However, sampling from this distribution is non-trivial due to the high computational costs in computing the partition function. In this paper, we propose an Efficient Markov Chain Monte Carlo negative sampling method for Contrastive learning (EMC2). We follow the global contrastive learning loss as introduced in (Yuan et al., 2022), and propose EMC2 which utilizes an adaptive Metropolis-Hastings subroutine to generate hardness-aware negative samples in an online fashion during the optimization. We prove that EMC2 finds an O(1/√T)-stationary point of the global contrastive loss in T iterations. Compared to prior works, EMC2 is the first algorithm that exhibits global convergence (to stationarity) regardless of the choice of batch size while exhibiting low computation and memory cost. Numerical experiments validate that EMC2 is effective with small batch training and achieves comparable or better performance than baseline algorithms. We report the results for pre-training image encoders on STL-10 and Imagenet-100. Copyright 2024 by the author(s)
Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly thr...
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The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high responsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sens...
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Modern optical technologies encompass classical light phenomena and non-linear effects, crucial for biomedical imaging and therapies. Despite substantial interest and many experimental studies, non-linear optical effe...
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Infinite Gaussian mixture process is a model that computes the Gaus-sian mixture parameters with *** process is a probability density distribu-tion with adequate training data that can converge to the input density ***...
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Infinite Gaussian mixture process is a model that computes the Gaus-sian mixture parameters with *** process is a probability density distribu-tion with adequate training data that can converge to the input density *** this paper,we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling.A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data *** solution uses data mining techniques to link datum in groups by linking their *** learning techniques are novel presentations of Gaussian model-ling that use prior knowledge of the representation hyper-parameters and approx-imate them in a closed *** are performed on axial data modeling of Arabic Script classification and depict the effectiveness of the proposed method using a hand written benchmark dataset which contains complex handwritten Ara-bic *** are also performed on the application of facial expres-sion recognition and prove the accuracy of the proposed method using a benchmark dataset which contains eight different facial expressions.
This paper focuses on optimal voltage regulator(VR)planning to maximize the photovoltaic(PV)energy integration in distribution *** describe the amount of dynamic PV energy that can be integrated into the power system,...
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This paper focuses on optimal voltage regulator(VR)planning to maximize the photovoltaic(PV)energy integration in distribution *** describe the amount of dynamic PV energy that can be integrated into the power system,the concept of PV accommodation capability(PVAC)is introduced and modeled with *** proposed planning model is formulated as a Benders decomposition based bi-level stochastic optimization *** the upper-level problem,VR planning decisions and PVAC are determined via mixed integer linear programming(MILP)before considering *** in the lower-level problem,the feasibility of first-level results is checked by critical network constraints(*** magnitude constraints and line capacity constraints)under uncertainties considered by time-varying loads and PV *** this paper,these uncertainties are represented in the form of operational scenarios,which are generated by the Gaussian copula theory and reduced by a well-studied backward-reduction *** modified IEEE 33-node distribution grid is utilized to verify the effectiveness of the proposed *** results demonstrate that a PV energy integration can be significantly enhanced after optimal voltage regulator planning.
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