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In the past decade, variational implicit solvation models (VISM) have achieved great success in solvation energy predictions. However, all existing VISMs in literature lack the uniqueness of an energy minimizing solute-solvent interface and thus prevent us from studying many important properties of the interface profile. To overcome this difficulty, we introduce a new constrained VISM and conduct a rigorous analysis of the model. Existence, uniqueness and regularity of the energy minimizing interface has been studied. A necessary condition for the formation of a sharp solute-solvent interface has been derived. Moreover, we develop a novel approach to the variational analysis of the constrained model, which provides a complete answer to a question in our previous work [55]. Model validation and numerical implementation have been demonstrated by using several common biomolecular modeling tasks. Numerical simulations show that the solvation energies calculated from our new model match the experimental data very well.

The DArk Matter Particle Explorer (DAMPE) is well suitable for searching for monochromatic and sharp $\gamma$-ray structures in the GeV$-$TeV range thanks to its unprecedented high energy resolution. In this work, we search for $\gamma$-ray line structures using five years of DAMPE data. To improve the sensitivity, we develop two types of dedicated data sets (including the BgoOnly data which is the first time to be used in the data analysis for the calorimeter-based gamma-ray observatories) and adopt the signal-to-noise ratio optimized regions of interest (ROIs) for different DM density profiles. No line signals or candidates are found between 10 and 300 GeV in the Galaxy. The constraints on the velocity-averaged cross section for $\chi\chi \to \gamma\gamma$ and the decay lifetime for $\chi \to \gamma\nu$, both at 95% confidence level, have been calculated and the systematic uncertainties have been taken into account. Comparing to the previous Fermi-LAT results, though DAMPE has an acceptance smaller by a factor of $\sim 10$, similar constraints on the DM parameters are achieved and below 100 GeV the lower limits on the decay lifetime are even stronger by a factor of a few. Our results demonstrate the potential of high-energy-resolution observations on dark matter detection., Comment: 14 pages, 8 figures. Update the content to keep up with the published version

In recent years, self-supervised representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods. The existing contrastive learning methods use normal augmentations to construct similar positive samples, which limits the ability to explore novel movement patterns. In this paper, to make better use of the movement patterns introduced by extreme augmentations, a Contrastive Learning framework utilizing Abundant Information Mining for self-supervised action Representation (AimCLR) is proposed. First, the extreme augmentations and the Energy-based Attention-guided Drop Module (EADM) are proposed to obtain diverse positive samples, which bring novel movement patterns to improve the universality of the learned representations. Second, since directly using extreme augmentations may not be able to boost the performance due to the drastic changes in original identity, the Dual Distributional Divergence Minimization Loss (D$^3$M Loss) is proposed to minimize the distribution divergence in a more gentle way. Third, the Nearest Neighbors Mining (NNM) is proposed to further expand positive samples to make the abundant information mining process more reasonable. Exhaustive experiments on NTU RGB+D 60, PKU-MMD, NTU RGB+D 120 datasets have verified that our AimCLR can significantly perform favorably against state-of-the-art methods under a variety of evaluation protocols with observed higher quality action representations. Our code is available at https://github.com/Levigty/AimCLR., Comment: Accepted by AAAI2022

The fast developments of radio astronomy open a new window to explore the properties of Dark Matter (DM). The recent direct imaging of the supermassive black hole (SMBH) at the center of M87 radio galaxy by the Event Horizon Telescope (EHT) collaboration is expected to be very useful to search for possible new physics. In this work, we illustrate that such results can be used to detect the possible synchrotron radiation signature produced by DM annihilation from the innermost region of the SMBH. Assuming the existence of a spike DM density profile, we obtain the flux density due to DM annihilation induced electrons and positrons, and derive new limits on the DM annihilation cross section via the comparison with the EHT integral flux density at 230 GHz. Our results show that the parameter space can be probed by the EHT observations is largely complementary to other experiments. For DM with typical mass regions of being weakly interacting massive particles, the annihilation cross section several orders of magnitude below the thermal production level can be excluded by the EHT observations under the density spike assumption. Future EHT observations may further improve the sensitivity on the DM searches, and may also provide a unique opportunity to test the interplay between DM and the SMBH., Comment: 15 pages, 4 figures, one appendix. Match the JHEP version

Using the multiconfiguration Dirac-Hartree-Fock (MCDHF) and the relativistic configuration interaction (RCI) methods, a consistent set of transition energies and radiative transition data for the lowest 150 states of the $3s^2 3p^3$, $3s 3p^4$, $3s^2 3p^2 3d$, $3s 3p^3 3d$, $3p^5$, and $3s^2 3p 3d^2$ configurations in P-like Ge XVIII is provided. To assess the accuracy of the MCDHF transition energies, we have also performed calculations using the many-body perturbation theory (MBPT). Comparisons are made between the present MCDHF and MBPT data sets, as well as with other available experimental and theoretical values. The resulting accurate and consistent MCDHF data set will be useful for line identification and modeling purposes. These data can be considered as a benchmark for other calculations.

The DArk Matter Particle Explorer (DAMPE) is a satellite-borne detector for high-energy cosmic rays and $\gamma$-rays. To fully understand the detector performance and obtain reliable physical results, extensive simulations of the detector are necessary. The simulations are particularly important for the data analysis of cosmic ray nuclei, which relies closely on the hadronic and nuclear interactions of particles in the detector material. Widely adopted simulation softwares include the GEANT4 and FLUKA, both of which have been implemented for the DAMPE simulation tool. Here we describe the simulation tool of DAMPE and compare the results of proton shower properties in the calorimeter from the two simulation softwares. Such a comparison gives an estimate of the most significant uncertainties of our proton spectral analysis., Comment: 7 pages, 7 figures, to be published in Chinese Physics Letters

When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction. Interaction between drugs may have a negative impact on patients or even cause death. Generally, drugs that conflict with a specific drug (or label drug) are usually described in its drug label or package insert. Since more and more new drug products come into the market, it is difficult to collect such information by manual. We take part in the Drug-Drug Interaction (DDI) Extraction from Drug Labels challenge of Text Analysis Conference (TAC) 2018, choosing task1 and task2 to automatically extract DDI related mentions and DDI relations respectively. Instead of regarding task1 as named entity recognition (NER) task and regarding task2 as relation extraction (RE) task then solving it in a pipeline, we propose a two step joint model to detect DDI and it's related mentions jointly. A sequence tagging system (CNN-GRU encoder-decoder) finds precipitants first and search its fine-grained Trigger and determine the DDI for each precipitant in the second step. Moreover, a rule based model is built to determine the sub-type for pharmacokinetic interation. Our system achieved best result in both task1 and task2. F-measure reaches 0.46 in task1 and 0.40 in task2., Comment: 8 pages, 6 figures