Correlation effects in the electronic structure of the Ni-based superconducting KNi2S2

The LDA plus Gutzwiller variational method is used to investigate the groundstate physical properties of the newly discovered superconducting KNi2S2. Five Ni-3d Wannier-orbital basis are constructed by the density-functional theory, to combine with local Coulomb interaction to describe the normal state electronic structure of Ni-based superconductor. The band structure and the mass enhanced are studied based on a multiorbital Hubbard model by using Gutzwiller approximation method. Our results indicate that the correlation effects lead to the mass enhancement of KNi2S2. Different from the band structure calculated from the LDA results, there are three energy bands across the Fermi level along the X-Z line due to the existence of the correlation effects, which induces a very complicated Fermi surface along the X-Z line. We have also investigated the variation of the quasi-particle weight factor with the hole or electron doping and found that the mass enhancement character has been maintained with the doping.Comment: 12 pages, 6 figure

PPT: Token Pruning and Pooling for Efficient Vision Transformers

Vision Transformers (ViTs) have emerged as powerful models in the field of computer vision, delivering superior performance across various vision tasks. However, the high computational complexity poses a significant barrier to their practical applications in real-world scenarios. Motivated by the fact that not all tokens contribute equally to the final predictions and fewer tokens bring less computational cost, reducing redundant tokens has become a prevailing paradigm for accelerating vision transformers. However, we argue that it is not optimal to either only reduce inattentive redundancy by token pruning, or only reduce duplicative redundancy by token merging. To this end, in this paper we propose a novel acceleration framework, namely token Pruning & Pooling Transformers (PPT), to adaptively tackle these two types of redundancy in different layers. By heuristically integrating both token pruning and token pooling techniques in ViTs without additional trainable parameters, PPT effectively reduces the model complexity while maintaining its predictive accuracy. For example, PPT reduces over 37% FLOPs and improves the throughput by over 45% for DeiT-S without any accuracy drop on the ImageNet dataset. The code is available at https://github.com/xjwu1024/PPT and https://github.com/mindspore-lab/models

Lateral Stiffness and Damping Coefficient of Soils for Seismic Analysis of Buried Pipelines

The stiffness and damping coefficient of soil are critical parameters in the Winkler\u27s model for the seismic analysis of buried pipelines. This paper presents an analytical study in calculating the lateral dynamic stiffness (elastic and inelastic) and damping coefficient of soils for the seismic analysis of buried pipelines. The effects of the depth of the buried pipeline and the variation of Poisson\u27s ratio of soils are cons1dered. In the analysts, first, by using the elastic half-space theory, the elastic stiffness and geometrical damping coefficient of soils are obtained. From the numerical results, the elastic stiffness and geometrical damping coefficient by using best fitted formulas are presented. Secondly, empirical inelastic characteristics of soils are considered. The empirical data includes relationships among dynamic shear modulus, material damping ratio, and dynamic shear strain amplitude. The total damping coefficient of the system can be obtained by adding the material damping to the geometrical damping coefficient

Game Model for “Shortage of Logistics” in Online Shopping in Service Engineering

AbstractThis paper analyzes the imbalance between e-commerce and logistics service by using factor sub-game perfect Nash equilibrium as an analytical tool from the view of system and links up the bargaining process between sellers and express enterprise involved in service engineering during online shopping with discount factor. The change of interests between sellers and express enterprise is systematically analyzed from the perspective of discount factor on the equilibrium solution through the application of model towards service engineering during holidays online shopping. Finally it is concluded that discount factor is a key factor influencing the express fee between sells and express enterprise in logistic system, and some recommendations are put forward accordingly

An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts

In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less intelligible and tractable to humans as the model complexity increases. In this paper, we target a similarity learning task in the context of image retrieval, with a focus on the model interpretability issue. An effective similarity neural network (SNN) is proposed not only to seek robust retrieval performance but also to achieve satisfactory post-hoc interpretability. The network is designed by linking the neuron architecture with the organization of a concept tree and by formulating neuron operations to pass similarity information between concepts. Various ways of understanding and visualizing what is learned by the SNN neurons are proposed. We also exhaustively evaluate the proposed approach using a number of relevant datasets against a number of state-of-the-art approaches to demonstrate the effectiveness of the proposed network. Our results show that the proposed approach can offer superior performance when compared against state-of-the-art approaches. Neuron visualization results are demonstrated to support the understanding of the trained neurons

A method for extracting the preseismic gravity anomalies over the Tibetan Plateau based on the maximum shear strain using GRACE data

The occurrence of earthquakes is closely related to the crustal geotectonic movement and the migration of mass, which consequently cause changes in gravity. The Gravity Recovery And Climate Experiment (GRACE) satellite data can be used to detect gravity changes associated with large earthquakes. However, previous GRACE satellite-based seismic gravity-change studies have focused more on coseismic gravity changes than on preseismic gravity changes. Moreover, the noise of the north–south stripe in GRACE data is difficult to eliminate, thereby resulting in the loss of some gravity information related to tectonic activities. To explore the preseismic gravity anomalies in a more refined way, we first propose a method of characterizing gravity variation based on the maximum shear strain of gravity, inspired by the concept of crustal strain. The offset index method is then adopted to describe the gravity anomalies, and the spatial and temporal characteristics of gravity anomalies before earthquakes are analyzed at the scales of the fault zone and plate, respectively. In this work, experiments are carried out on the Tibetan Plateau and its surrounding areas, and the following findings are obtained: First, from the observation scale of the fault zone, we detect the occurrence of large-area gravity anomalies near the epicenter, oftentimes about half a year before an earthquake, and these anomalies were distributed along the fault zone. Second, from the observation scale of the plate, we find that when an earthquake occurred on the Tibetan Plateau, a large number of gravity anomalies also occurred at the boundary of the Tibetan Plateau and the Indian Plate. Moreover, the aforementioned experiments confirm that the proposed method can successfully capture the preseismic gravity anomalies of large earthquakes with a magnitude of less than 8, which suggests a new idea for the application of gravity satellite data to earthquake research

Analysis of Multiple Intracranial Aneurysms with Different Outcomes in the Same Patient After Endovascular Treatment

BACKGROUND: Aneurysm recanalization after coiling, with or without stent assistance, is a major issue in the endovascular management of intracranial aneurysms. Multiple intracranial aneurysms with different outcomes after endovascular treatment may represent a useful disease model in which patient-specific risk factors can be balanced to investigate possible features linked to aneurysm recanalization. In the present study, we evaluated the impact of aneurysm-specific, treatment-related, and hemodynamics-related factors on multiple aneurysms and to explore the reason why one aneurysm recanalized and the other did not. METHODS: Between 2010 and 2015, 763 multiple intracranial aneurysms in 326 patients were diagnosed by digital subtraction angiography. We retrospectively collected and analyzed 13 pairs of multiple aneurysms with different outcomes (recanalized or stable) in the same patient. Patient-specific models were constructed and analyzed by a computational fluid dynamics method. The virtual stent deployment method was used, and the coils were simulated by a porous medium model. Factors were evaluated for significance with respect to recanalization. RESULTS: Aneurysm size (P = 0.021), neck width (P = 0.027), ruptured aneurysms (P = 0.002), reduction ratio of averaged velocity (P = 0.008), and wall shear stress (P = 0.024) were significantly associated with aneurysm recanalization. By contrast, the aneurysm location, all of treatment-related factors (packing density, duration of follow-up, stent use, initial angiographic result) and the reduction ratio of averaged pressure were not significantly associated (P > 0.05). CONCLUSIONS: Small aneurysm size and neck width, unruptured aneurysm, and perianeurysmal hemodynamics with marked reduction may be important factors associated with the midterm durability of aneurysm embolization