UMDA/S: An Effective Iterative Compilation Algorithm for Parameter Search

The search process is critical for iterative compilation because the large size of the search space and the cost of evaluating the candidate implementations make it infeasible to find the true optimal value of the optimization parameter by brute force. Considering it as a nonlinear global optimization problem, this paper introduces a new hybrid algorithm -- UMDA/S: Univariate Marginal Distribution Algorithm with Nelder-Mead Simplex Search, which utilizes the optimization space structure and parameter dependency to find the near optimal parameter. Elitist preservation, weighted estimation and mutation are proposed to improve the performance of UMDA/S. Experimental results show the ability of UMDA/S to locate more excellent parameters, as compared to existing static methods and search algorithms

Synthesis of Core-Shell @@ Microspheres and Their Application as Recyclable Photocatalysts

We report the fabrication of core-shell Fe3O4@SiO2@TiO2 microspheres through a wet-chemical approach. The Fe3O4@SiO2@TiO2 microspheres possess both ferromagnetic and photocatalytic properties. The TiO2 nanoparticles on the surfaces of microspheres can degrade organic dyes under the illumination of UV light. Furthermore, the microspheres are easily separated from the solution after the photocatalytic process due to the ferromagnetic Fe3O4 core. The photocatalysts can be recycled for further use with slightly lower photocatalytic efficiency

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards

Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i.e., playing games without extrinsic rewards but evaluated with extrinsic rewards. However, none of the existing intrinsic reward approaches can achieve human-level performance under this very challenging setting of intrinsically-motivated play. In this work, we propose a novel megalomania-driven intrinsic reward (called mega-reward), which, to our knowledge, is the first approach that achieves human-level performance in intrinsically-motivated play. Intuitively, mega-reward comes from the observation that infants' intelligence develops when they try to gain more control on entities in an environment; therefore, mega-reward aims to maximize the control capabilities of agents on given entities in a given environment. To formalize mega-reward, a relational transition model is proposed to bridge the gaps between direct and latent control. Experimental studies show that mega-reward (i) can greatly outperform all state-of-the-art intrinsic reward approaches, (ii) generally achieves the same level of performance as Ex-PPO and professional human-level scores, and (iii) has also a superior performance when it is incorporated with extrinsic rewards

Treatment of environmental contamination using sepiolite:current approaches and future potential

To evaluate the potential of sepiolite-based materials to resolve environmental pollution problems, a study is needed which looks at the whole life cycle of material application, including the residual value of material classified as waste from the exploitation of sepiolite deposits in the region or from its processing and purification. This would also maximize value from the exploitation process and provide new potential for local waste management. We review the geographical distribution of sepiolite, its application in the treatment of potentially toxic elements in soil and across the wider landscape, an assessment of modification and compositional variation of sepiolite-based applications within site remediation and wastewater treatment. The potential of sepiolite-based technologies is widespread and a number of processes utilize sepiolite-derived materials. Along with its intrinsic characteristics, both the long-term durability and the cost-effectiveness of the application need to be considered, making it possible to design ready-to-use products with good market acceptance. From a critical analysis of the literature, the most frequently associated terms associated with sepiolite powder are the use of lime and bentonite, while fly ash ranked in the top ten of the most frequently used material with sepiolite. These add improved performance for the inclusion as a soil or wastewater treatment options, alone or applied in combination with other treatment methods. This approach needs an integrated assessment to establish economic viability and environmental performance. Applications are not commonly evaluated from a cost–benefit perspective, in particular in relation to case studies within geographical regions hosting primary sepiolite deposits and wastes that have the potential for beneficial reuse

AMLP:Adaptive Masking Lesion Patches for Self-supervised Medical Image Segmentation

Self-supervised masked image modeling has shown promising results on natural images. However, directly applying such methods to medical images remains challenging. This difficulty stems from the complexity and distinct characteristics of lesions compared to natural images, which impedes effective representation learning. Additionally, conventional high fixed masking ratios restrict reconstructing fine lesion details, limiting the scope of learnable information. To tackle these limitations, we propose a novel self-supervised medical image segmentation framework, Adaptive Masking Lesion Patches (AMLP). Specifically, we design a Masked Patch Selection (MPS) strategy to identify and focus learning on patches containing lesions. Lesion regions are scarce yet critical, making their precise reconstruction vital. To reduce misclassification of lesion and background patches caused by unsupervised clustering in MPS, we introduce an Attention Reconstruction Loss (ARL) to focus on hard-to-reconstruct patches likely depicting lesions. We further propose a Category Consistency Loss (CCL) to refine patch categorization based on reconstruction difficulty, strengthening distinction between lesions and background. Moreover, we develop an Adaptive Masking Ratio (AMR) strategy that gradually increases the masking ratio to expand reconstructible information and improve learning. Extensive experiments on two medical segmentation datasets demonstrate AMLP's superior performance compared to existing self-supervised approaches. The proposed strategies effectively address limitations in applying masked modeling to medical images, tailored to capturing fine lesion details vital for segmentation tasks

Fast Charging of Lithium-Ion Batteries Using Deep Bayesian Optimization with Recurrent Neural Network

Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs. However, inappropriate charging strategies would cause severe degradation of batteries or even hazardous accidents. To optimize fast-charging strategies under various constraints, particularly safety limits, we propose a novel deep Bayesian optimization (BO) approach that utilizes Bayesian recurrent neural network (BRNN) as the surrogate model, given its capability in handling sequential data. In addition, a combined acquisition function of expected improvement (EI) and upper confidence bound (UCB) is developed to better balance the exploitation and exploration. The effectiveness of the proposed approach is demonstrated on the PETLION, a porous electrode theory-based battery simulator. Our method is also compared with the state-of-the-art BO methods that use Gaussian process (GP) and non-recurrent network as surrogate models. The results verify the superior performance of the proposed fast charging approaches, which mainly results from that: (i) the BRNN-based surrogate model provides a more precise prediction of battery lifetime than that based on GP or non-recurrent network; and (ii) the combined acquisition function outperforms traditional EI or UCB criteria in exploring the optimal charging protocol that maintains the longest battery lifetime