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Search for a new scalar resonance decaying to a pair of Z bosons in proton-proton collisions at √ s = 13 TeVThe CMS Collaboration * AbstractA search for a new scalar resonance decaying to a pair of Z bosons is performed in the mass range from 130 GeV to 3 TeV, and for various width scenarios. The analysis is based on proton-proton collisions recorded by the CMS experiment at the LHC in 2016, corresponding to an integrated luminosity of 35.9 fb −1 at a center-of-mass energy of 13 TeV. The Z boson pair decays are reconstructed using the 4 , 2 2q, and 2 2ν final states, where = e or µ. Both gluon fusion and electroweak production of the scalar resonance are considered, with a free parameter describing their relative cross sections. A dedicated categorization of events, based on the kinematic properties of associated jets, and matrix element techniques are employed for an optimal signal and background separation. A description of the interference between signal and background amplitudes for a resonance of an arbitrary width is included. No significant excess of events with respect to the standard model expectation is observed and limits are set on the product of the cross section for a new scalar boson and the branching fraction for its decay to ZZ for a large range of masses and widths.

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In this work, we propose a simple yet effective semisupervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data. To this end, we propose to align the empirical distributions of labeled and unlabeled data to alleviate the bias. On one hand, we adopt an adversarial training strategy to minimize the distribution distance between labeled and unlabeled data as inspired by domain adaptation works. On the other hand, to deal with the small sample size issue of labeled data, we also propose a simple interpolation strategy to generate pseudo training samples. Those two strategies can be easily implemented into existing deep neural networks. We demonstrate the effectiveness of our proposed approach on the benchmark SVHN and CIFAR10 datasets. Our code is available at https://github.com/qinenergy/adanet.

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Severe electrolyte decomposition under high voltage can easily lead to degradation of the performance of lithium-ion batteries, which has become a major obstacle to the practical application of high-energy-density batteries. To solve these problems, a dual-functional electrolyte additive comprising inorganic lithium difluorophosphate (LiDFP) and organic 1,3,6-hexanetrinitrile (HTN) was designed and employed to improve the performance of high-voltage Si@C/LiNi 0.5 Mn 1.5 O 4 full batteries. LiDFP with a lower LUMO energy than the solvent in the electrolyte takes priority in reduction, facilitating the formation of a dense and stable film on the anode, effectively suppressing side reactions of the electrolyte and aiding tolerance to the volume expansion of the Si@C electrode. Additionally, the lower HOMO energy of HTN can improve the oxidation resistance of the electrolyte, with the CN functional group of HTN helping to remove the trace water and the byproduct HF from the electrolyte. The Si@C/LiNi 0.5 Mn 1.5 O 4 full battery with 1 wt % LiDFP and 1 wt % HTN in 1.0 M LiPF 6 traditional electrolyte delivers high capacity retention of 91.57% after 150 cycles at 0.2C, compared to 34.58% capacity retention without any additives. Moreover, the Coulombic efficiency of batteries with electrolyte additives can reach 99.75% on average, compared to their counterparts at ∼96.54%. The synergistic effect of LiDFP and HTN provides a promising strategy for enhancing the performance of high-voltage batteries for practical industrialization.

Lipophilicity, as quantified by the decimal logarithm of the octanol–water partition coefficient (log KOW), is an essential environmental property. Deep neural networks (DNNs) based quantitative structure–property relationship (QSPR) studies have received more and more attention because of their excellent performance for prediction. However, the black‐box nature of DNNs limits the application range where interpretability is essential. Hence, this study aims to develop an accurate and interpretable deep neural network (AI‐DNN) model for log KOW prediction. A hybrid method of molecular representation was employed to guarantee the accuracy of the proposed AI‐DNN model. The hybrid molecular representations are able to integrate the directed message passing neural networks (D‐MPNNs) learned molecular representations and the fixed molecule‐level features of CDK descriptors, and can capture both the local and the global features of overall molecule. The performance analysis shows that the proposed QSPR model exhibits promising predictive accuracy and discriminative power in the structural isomers and stereoisomers. Moreover, the Monte Carlo Tree Search (MCTS) approach was used to interpret the proposed AI‐DNN model by identifying the molecular substructures contributed to the lipophilicity. This interpretability can be applied to critical fields where there is a high demand for interpretable deep networks, such as green solvent design and drug discovery.