Neural Parametric Fokker-Planck Equations

In this paper, we develop and analyze numerical methods for high dimensional Fokker-Planck equations by leveraging generative models from deep learning. Our starting point is a formulation of the Fokker-Planck equation as a system of ordinary differential equations (ODEs) on finite-dimensional parameter space with the parameters inherited from generative models such as normalizing flows. We call such ODEs neural parametric Fokker-Planck equation. The fact that the Fokker-Planck equation can be viewed as the $L^2$-Wasserstein gradient flow of Kullback-Leibler (KL) divergence allows us to derive the ODEs as the constrained $L^2$-Wasserstein gradient flow of KL divergence on the set of probability densities generated by neural networks. For numerical computation, we design a variational semi-implicit scheme for the time discretization of the proposed ODE. Such an algorithm is sampling-based, which can readily handle Fokker-Planck equations in higher dimensional spaces. Moreover, we also establish bounds for the asymptotic convergence analysis of the neural parametric Fokker-Planck equation as well as its error analysis for both the continuous and discrete (forward-Euler time discretization) versions. Several numerical examples are provided to illustrate the performance of the proposed algorithms and analysis

A study of correlation between permeability and pore space based on dilation operation

CO2 and fracturing liquid injection into tight and shale gas reservoirs induces reactivity between minerals and injected materials, which results in porosity change and thus permeability change. In this paper, the dilation operation is used to simulate the change of the porosity and the corresponding change of permeability based on Lattice-Boltzmann is studied. Firstly we obtain digital images of a real core from CT experiment. Secondly the pore space of digital cores is expanded by dilation operation which is one of basic mathematical morphologies. Thirdly, the distribution of pore bodies and pore throats is obtained from the pore network modeling extracted by maximal ball method. Finally, the correlation between network modeling parameters and permeabilities is analyzed. The result is that the throat change leads to exponential change of permeability and that the big throats significantly influence permeability.Cited as: Zha, W., Yan, S., Li, D., et al. A study of correlation between permeability and pore space based on dilation operation. Advances in Geo-Energy Research, 2017, 1(2): 93-99, doi: 10.26804/ager.2017.02.0

Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models

Recently, pre-trained point cloud models have found extensive applications in downstream tasks like object classification. However, these tasks often require {full fine-tuning} of models and lead to storage-intensive procedures, thus limiting the real applications of pre-trained models. Inspired by the great success of visual prompt tuning (VPT) in vision, we attempt to explore prompt tuning, which serves as an efficient alternative to full fine-tuning for large-scale models, to point cloud pre-trained models to reduce storage costs. However, it is non-trivial to apply the traditional static VPT to point clouds, owing to the distribution diversity of point cloud data. For instance, the scanned point clouds exhibit various types of missing or noisy points. To address this issue, we propose an Instance-aware Dynamic Prompt Tuning (IDPT) for point cloud pre-trained models, which utilizes a prompt module to perceive the semantic prior features of each instance. This semantic prior facilitates the learning of unique prompts for each instance, thus enabling downstream tasks to robustly adapt to pre-trained point cloud models. Notably, extensive experiments conducted on downstream tasks demonstrate that IDPT outperforms full fine-tuning in most tasks with a mere 7\% of the trainable parameters, thus significantly reducing the storage pressure. Code is available at \url{https://github.com/zyh16143998882/IDPT}

Influence of Disturbance on Soil Respiration in Biologically Crusted Soil during the Dry Season

Soil respiration (Rs) is a major pathway for carbon cycling and is a complex process involving abiotic and biotic factors. Biological soil crusts (BSCs) are a key biotic component of desert ecosystems worldwide. In desert ecosystems, soils are protected from surface disturbance by BSCs, but it is unknown whether Rs is affected by disturbance of this crust layer. We measured Rs in three types of disturbed and undisturbed crusted soils (algae, lichen, and moss), as well as bare land from April to August, 2010, in Mu Us desert, northwest China. Rs was similar among undisturbed soils but increased significantly in disturbed moss and algae crusted soils. The variation of Rs in undisturbed and disturbed soil was related to soil bulk density. Disturbance also led to changes in soil organic carbon and fine particles contents, including declines of 60–70% in surface soil C and N, relative to predisturbance values. Once BSCs were disturbed, Q10 increased. Our findings indicate that a loss of BSCs cover will lead to greater soil C loss through respiration. Given these results, understanding the disturbance sensitivity impact on Rs could be helpful to modify soil management practices which promote carbon sequestration

Control of Intestinal Inflammation, Colitis-Associated Tumorigenesis, and Macrophage Polarization by Fibrinogen-Like Protein 2

Fibrinogen-like protein 2 (Fgl2) is critical for immune regulation in the inflammatory state. Elevated Fgl2 levels are observed in patients with inflammatory bowel disease (IBD), but little is known about its functional significance. In this study, we sought to investigate the role of Fgl2 in the development of intestinal inflammation and colitis-associated colorectal cancer (CAC). Here, we report that Fgl2 deficiency increased susceptibility to dextran sodium sulfate-induced colitis and CAC in a mouse model. During colitis development, the expression of the membrane-bound and secreted forms of Fgl2 (mFgl2 and sFgl2, respectively) in the colon were increased and predominantly expressed by colonic macrophages. In addition, using bone marrow chimeric mice, we determined that Fgl2 function in colitis is strictly related to its expression in the hematopoietic cells. Loss of Fgl2 induced the polarization of M1, but suppressed that of M2 both in vivo and in vitro, independent of intestinal inflammation. Thus, Fgl2 suppresses intestinal inflammation and CAC development through its role in macrophage polarization and may serve as a therapeutic target in inflammatory diseases, including IBD

Observation of nonrelativistic plaid-like spin splitting in a noncoplanar antiferromagnet

Spatial, momentum and energy separation of electronic spins in condensed matter systems guides the development of novel devices where spin-polarized current is generated and manipulated. Recent attention on a set of previously overlooked symmetry operations in magnetic materials leads to the emergence of a new type of spin splitting besides the well-studied Zeeman, Rashba and Dresselhaus effects, enabling giant and momentum dependent spin polarization of energy bands on selected antiferromagnets independent of relativistic spin-orbit interaction. Despite the ever-growing theoretical predictions, the direct spectroscopic proof of such spin splitting is still lacking. Here, we provide solid spectroscopic and computational evidence for the existence of such materials. In the noncoplanar antiferromagnet MnTe$_2$, the in-plane components of spin are found to be antisymmetric about the high-symmetry planes of the Brillouin zone, comprising a plaid-like spin texture in the antiferromagnetic ground state. Such an unconventional spin pattern, further found to diminish at the high-temperature paramagnetic state, stems from the intrinsic antiferromagnetic order instead of the relativistic spin-orbit coupling. Our finding demonstrates a new type of spin-momentum locking with a nonrelativistic origin, placing antiferromagnetic spintronics on a firm basis and paving the way for studying exotic quantum phenomena in related materials.Comment: Version 2, 30 pages, 4 main figures and 8 supporting figure

An Updated Search of Steady TeV γ−\gamma-Ray Point Sources in Northern Hemisphere Using the Tibet Air Shower Array

Using the data taken from Tibet II High Density (HD) Array (1997 February-1999 September) and Tibet-III array (1999 November-2005 November), our previous northern sky survey for TeV $\gamma-$ray point sources has now been updated by a factor of 2.8 improved statistics. From $0.0^{\circ}$ to $60.0^{\circ}$ in declination (Dec) range, no new TeV $\gamma-$ray point sources with sufficiently high significance were identified while the well-known Crab Nebula and Mrk421 remain to be the brightest TeV $\gamma-$ray sources within the field of view of the Tibet air shower array. Based on the currently available data and at the 90% confidence level (C.L.), the flux upper limits for different power law index assumption are re-derived, which are approximately improved by 1.7 times as compared with our previous reported limits.Comment: This paper has been accepted by hepn

Fundamentally manipulating the electronic structure of polar bifunctional catalysts for lithium-sulfur batteries: Heterojunction design versus doping engineering

Heterogeneous structures and doping strategies have been intensively used to manipulate the catalytic conversion of polysulfides to enhance reaction kinetics and suppress the shuttle effect in lithium-sulfur (Li-S) batteries. However, understanding how to select suitable strategies for engineering the electronic structure of polar catalysts is lacking. Here, a comparative investigation between heterogeneous structures and doping strategies is conducted to assess their impact on the modulation of the electronic structures and their effectiveness in catalyzing the conversion of polysulfides. These findings reveal that Co0.125Zn0.875Se, with metal-cation dopants, exhibits superior performance compared to CoSe2/ZnSe heterogeneous structures. The incorporation of low Co2+ dopants induces the subtle lattice strain in Co0.125Zn0.875Se, resulting in the increased exposure of active sites. As a result, Co0.125Zn0.875Se demonstrates enhanced electron accumulation on surface Se sites, improved charge carrier mobility, and optimized both p-band and d-band centers. The Li-S cells employing Co0.125Zn0.875Se catalyst demonstrate significantly improved capacity (1261.3 mAh g−1 at 0.5 C) and cycle stability (0.048% capacity delay rate within 1000 cycles at 2 C). This study provides valuable guidance for the modulation of the electronic structure of typical polar catalysts, serving as a design directive to tailor the catalytic activity of advanced Li-S catalysts