Target tracking based on a multi-sensor covariance intersection fusion Kalman filter

In a multi-sensor target tracking system, the correlation of the sensors is unknown, and the cross-covariance between the local sensors can not be calculated. To solve the problem, the multisensor covariance intersection fusion steady-state Kalman filter is proposed. The advantage of the proposed method is that the identification and computation of cross-covariance is avoided, thus the computational burden is significantly reduced. The new algorithm gives an upper bound of the covariance intersection fused variance matrix based on the convex combination of local estimations, therefore, ensures the convergence of the fusion filter. The accuracy of the covariance intersection (CI) fusion filter is lower than and close to that of the optimal distributed fusion steady-state Kalman filter, and is far higher than that of each local estimator. A numerical example shows that the covariance intersection fusion Kalman filter has enough fused accuracy without computing the cross-covariance

Leaching and freeze-thaw events contribute to litter decomposition - a review

Litter decomposition is vital for carbon and nutrient turnover in terrestrial ecosystems, and this process has now been thoroughly demonstrated to be regulated by various mechanisms. The total environment has been continuously changing in recent decades, especially in high-latitude regions; these alterations, however, profoundly contribute to the decomposition process, but a comprehensive recognition has not available. Here we reviewed the empirical observations and current knowledge regarding how hydrological leaching and freeze-thaw events modulate early decomposition of plant litter. Leaching contributes a considerable percentage of mass loss and carbon and nutrient release in early stage of decomposition, but the magnitudes are different between species levels depending on the chemical traits. Frequent freezing and thawing events could positively influence decomposition rate in cold biomes but also hamper soil decomposer and there is no general and predictable pattern has been emerged. Further experiments should be manipulated to estimate how the altered freezing and thawing effect on carbon and nutrient release from plant litter to better understanding the changing environment on litter decomposition

Force: Making 4PC > 4 × PC in Privacy Preserving Machine Learning on GPU

Tremendous efforts have been made to improve the efficiency of secure Multi-Party Computation (MPC), which allows n ≥ 2 parties to jointly evaluate a target function without leaking their own private inputs. It has been confirmed by previous researchers that 3-Party Computation (3PC) and outsourcing computations to GPUs can lead to huge performance improvement of MPC in computationally intensive tasks such as Privacy-Preserving Machine Learning (PPML). A natural question to ask is whether super-linear performance gain is possible for a linear increase in resources. In this paper, we give an affirmative answer to this question. We propose Force, an extremely efficient 4PC system for PPML. To the best of our knowledge, each party in Force enjoys the least number of local computations and lowest data exchanges between parties. This is achieved by introducing a new sharing type X -share along with MPC protocols in privacy-preserving training and inference that are semi-honest secure with an honest-majority. Our contribution does not stop at theory. We also propose engineering optimizations and verify the high performance of the protocols with implementation and experiments. By comparing the results with state-of-the-art researches such as Cheetah, Piranha, CryptGPU and CrypTen, we showcase that Force is sound and extremely efficient, as it can improve the PPML performance by a factor of 2 to 1200 compared with other latest 2PC, 3PC and 4PC syste

A novel dilated contextual attention module for breast cancer mitosis cell detection

Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity.Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells.Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model’s ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Attention Module (DiCoA) that utilizes sparse global attention during the detection. For reclassifying mitotic cell areas localized in the detection stage, we integrate the EfficientNet-B7 and VGG16 pre-trained models (InPreMo) in the classification step.Results: Based on the canine mammary carcinoma (CMC) mitosis dataset, DilCasNet demonstrates superior overall performance compared to the benchmark model. The specific metrics of the model’s performance are as follows: F1 score of 82.9%, Precision of 82.6%, and Recall of 83.2%. With the incorporation of the DiCoA attention module, the model exhibited an improvement of over 3.5% in the F1 during the detection stage.Conclusion: The DilCasNet achieved a favorable detection performance of mitotic cells in breast cancer and provides a solution for detecting mitotic cells in pathological images of other cancers

Analysis of the Strategic Emission-Based Energy Policies of Developing and Developed Economies with Twin Prediction Model

Upholding sustainability in the use of energies for the increasing global industrial activity has been one of the priority agendas of the global leaders of the West and East. The projection of different GHGs has thus been the important policy agenda of the economies to justify the positions of their own as well as of others. Methane is one of the important components of GHGs, and its main sources of generation are the agriculture and livestock activities. Global diplomacy regarding the curtailment of the GHGs has set the target of reducing the levels of GHGs time to time, but the ground reality regarding the reduction is far away from the targets. Sometimes, the targets are fixed without the application of scientific methods. The aim of the present study is to examine sustainability of energy systems through the forecasting of the methane emission and agricultural output of the world’s different income groups up to 2030 using the data for the period 1981–2012. The work is novel in two senses: the existing studies did not use both the Box–Jenkins and artificial neural network methods, and the present study covers all the major economic groups in the world which is unlike to any existing studies. Two methods are used for forecasting of the two. One is the Box–Jenkins method, where linear nature of the two variables is considered and the other is artificial neural network methods where nonlinear nature of the variables is also considered. The results show that, except the OECD group, all the remaining groups display increasing trends of methane emission, but unquestionably, all the groups display increasing trends of agricultural output, where middle- and upper middle-income groups hold the upper berths. The forecasted emission is justified to be sustainable in major groups under both methods of estimations since overall growth of agricultural output is greater than that of methane emission

Weighted-elite-memory mechanism enhances cooperation in social dilemmas

The issue of how to enhance cooperation has been a hot topic of research in evolutionary games for a long time. A mechanism is proposed to facilitate the cooperation behavior of evolutionary groups on networks in three game models, including prisoner's dilemma, snowdrift game, and stag hunt game. The core of the mechanism lies in: 1) Each player has a length of memory and uses the information of the elite in the memory span to update its strategy. 2) Each player has the chance to game with a certain neighbor more than once in each round. 3) The accumulative payoff of a player consists of two parts, one from playing with elites in memory length and another from playing with current neighbors, and a weight is introduced to adjust these two parts. The findings of the simulation demonstrate that a small weight can significantly enhance cooperation in three typical social dilemmas. Furthermore, the level of cooperation increases at first and then stays stable as the memory length increases

Profit Allocation Problem and Algorithm of Network Freight Platform under Carbon Trading Background

With the gradual popularization of carbon trading, individual carbon emission behavior will come with carbon costs, which will have a significant impact on the network freight platform carrier drivers. Therefore, in order to improve the stability within the network freight platform, this paper considers the fairness of benefit distribution among network freight carriers and aims to offset the impact of carbon cost to a greater extent by reducing the relative deprivation of the network freight platform carrier group, so as to finally realize the benign operation of network freight. This paper introduces a number of indicators such as contribution value, expectation realization degree, and relative deprivation feeling, and establishes a dynamic benefit distribution optimization model oriented by relative deprivation feeling. Based on the basic characteristics of the problem, the ant colony labor division model is extended, and the corresponding algorithm is designed to solve the problem. By introducing the contribution value, contribution rate and expected return to reset the stimulus value of the environment and the response threshold of agent, the relative deprivation sense is used to quantify the degree of unfair benefit distribution, which is used as a benchmark to dynamically coordinate the benefit distribution of the carrier group and optimize the benefit distribution scheme. The experimental results show that the extended model and algorithm designed in this paper can significantly reduce the relative deprivation perception of the carrier group in the online freight platform at a low cost