Complex dynamics on the one-dimensional quantum droplets via time piecewise PINNs

The dynamics of one-dimensional quantum droplets and the landing applications of deep learning are recent research hotspots. In this work, we propose a novel time piecewise physics-informed neural networks (PINNs) to study complex dynamics on the one-dimensional quantum droplets by solving the corresponding amended Gross-Pitaevskii equation. The training effect of this network model in the long time domain is far better than that of the conventional PINNs, and each of its subnetworks is independent and highly adjustable. By using time piecewise PINNs with scarce training points, we not only study intrinsic modulation of single droplet and collision between two droplets, but also excite the breathers on droplet background. Intriguingly, we obtain an interference pattern from training result of collision between two droplets, which is a significant feature of the interplay of coherent matter waves. The numerical results showcase that different parameters may lead to completely different dynamic behaviors under the same initial condition in a nonlinear non-integrable system. Our results provide the significant guidance for intrinsic modulation of single droplet, droplet collision and breathers excitation via deep learning technology

NASA Polynomial representation of molecular specific heats

So called NASA polynomials are widely used in plasma and combustion models to represent the specific heat of molecules as a function of temperature. In this work, we compute seven-term NASA polynomials for 464 molecules of which 44 are cations and 9 are anions; polynomials are not currently available for almost 200 of these species. Calculation of the NASA polynomials utilises data provided by the ExoMol database, the HITRAN database, the diatomic partition functions computed by Barklem and Collet, and the JANAF thermodynamic tables. Our results are compared against existing polynomial compilations where available, and for cases where there are multiple datasets the recommended polynomials are identified. As proposed in the original compilation, the seven-term polynomials are fitted separately for the temperature ranges 200 – 1000 K and 1000 – 6000 K. In general, different data sources give good agreement in the lower temperature range but there are significant discrepancies at higher temperatures, which can be attributed to the underlying assumptions made about highly excited rotation-vibration energy levels

PP-MobileSeg: Explore the Fast and Accurate Semantic Segmentation Model on Mobile Devices

The success of transformers in computer vision has led to several attempts to adapt them for mobile devices, but their performance remains unsatisfactory in some real-world applications. To address this issue, we propose PP-MobileSeg, a semantic segmentation model that achieves state-of-the-art performance on mobile devices. PP-MobileSeg comprises three novel parts: the StrideFormer backbone, the Aggregated Attention Module (AAM), and the Valid Interpolate Module (VIM). The four-stage StrideFormer backbone is built with MV3 blocks and strided SEA attention, and it is able to extract rich semantic and detailed features with minimal parameter overhead. The AAM first filters the detailed features through semantic feature ensemble voting and then combines them with semantic features to enhance the semantic information. Furthermore, we proposed VIM to upsample the downsampled feature to the resolution of the input image. It significantly reduces model latency by only interpolating classes present in the final prediction, which is the most significant contributor to overall model latency. Extensive experiments show that PP-MobileSeg achieves a superior tradeoff between accuracy, model size, and latency compared to other methods. On the ADE20K dataset, PP-MobileSeg achieves 1.57% higher accuracy in mIoU than SeaFormer-Base with 32.9% fewer parameters and 42.3% faster acceleration on Qualcomm Snapdragon 855. Source codes are available at https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.8.Comment: 8 pages, 3 figure

Higher-order Topological and Nodal Superconductors MS (M = Nb and Ta) Transition-metal Sulfides

Intrinsic topological superconducting materials are exotic and vital to develop the next-generation topological superconducting devices, topological quantum calculations, and quantum information technologies. Here, we predict the topological and nodal superconductivity of MS (M = Nb and Ta) transition-metal sulfides by using the density functional theory for superconductors combining with the symmetry indicators. We reveal their higher-order topology nature with an index of Z4 = 2. These materials have a higher Tc than the Nb or Ta metal superconductors due to their flat-band and strong electron-phonon coupling nature. Electron doping and lighter isotopes can effectively enhance the Tc. Our findings show that the MS (M = Nb and Ta) systems can be new platforms to study exotic physics in the higher-order topological superconductors, and provide a theoretical support to utilize them as the topological superconducting devices in the field of advanced topological quantum calculations and information technologies.Comment: 5 pages, 3 figure

Rescue of Retinal Degeneration in rd1 Mice by Intravitreally Injected Metformin

Retinitis pigmentosa (RP) is a progressive hereditary retinal degenerative disease in which photoreceptor cells undergo degeneration and apoptosis, eventually resulting in irreversible loss of visual function. Currently, no effective treatment exists for this disease. Neuroprotection and inflammation suppression have been reported to delay the development of RP. Metformin is a well-tested drug used to treat type 2 diabetes, and it has been reported to exert beneficial effects in neurodegenerative diseases, such as Parkinson’s disease and Alzheimer’s disease. In the present study, we used immunofluorescence staining, electroretinogram (ERG) recordings and RNA-Seq to explore the effects of metformin on photoreceptor degeneration and its mechanism in rd1 mice. We found that metformin significantly reduced apoptosis in photoreceptors and delayed the degeneration of photoreceptors and rod bipolar cells in rd1 mice, thus markedly improving the visual function of rd1 mice at P14, P18, and P22 when tested with a light/dark transition test and ERG. Microglial activation in the outer nuclear layer (ONL) of the retina of rd1 mice was significantly suppressed by metformin. RNA-Seq showed that metformin markedly downregulated inflammatory genes and upregulated the expression of crystallin proteins, which have been demonstrated to be important neuroprotective molecules in the retina, revealing the therapeutic potential of metformin for RP treatment. αA-crystallin proteins were further confirmed to be involved in the neuroprotective effects of metformin in a Ca2+ ionophore-damaged 661W photoreceptor-like cell line. These data suggest that metformin exerts a protective effect in rd1 mice via both immunoregulatory and new neuroprotective mechanisms