Coal Subsided Area Land Harmonious Governance and Suitability Evaluation Methods

AbstractThe soil is the foundation of human survival, to realize the sustainable development and utilize of the soil resources in coal subsided area, to speed up constructing a conservation-minded society, the land reclamation as a practical and effective measures to protect the soil has been paid more and more recognition and attention by the government and society. This paper combined the Coal subsided area land reclamation planning, adapted coal subsided area land suitability evaluation, land structure optimization method, the evaluation method of combining cost and benefit impact factors quantitatively and qualitatively to research the design of land reclamation plan. Discussed some related content about the land reclamation technology and ecological reconstruction suit for the local environment

SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning

SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to protect data privacy in vertical federated learning setting. It is widely used in fields such as finance and healthcare due to its interpretability, effectiveness, and privacy-preserving capability. However, SecureBoost suffers from high computational complexity and risk of label leakage. To harness the full potential of SecureBoost, hyperparameters of SecureBoost should be carefully chosen to strike an optimal balance between utility, efficiency, and privacy. Existing methods either set hyperparameters empirically or heuristically, which are far from optimal. To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage. We design measurements of the three objectives. In particular, the privacy leakage is measured using our proposed instance clustering attack. Experimental results demonstrate that the CMOSB yields not only hyperparameters superior to the baseline but also optimal sets of hyperparameters that can support the flexible requirements of FL participants.Comment: FL-ICAI'2

1-(2-Methyl­benz­yl)-1H-indole-3-carbaldehyde

In the title compound, C17H15NO, the benzene ring and the indole system are almost perpendicular, making a dihedral angle of 87.82 (6)°. The crystal packing is stabilized by C—H⋯O and π–π stacking inter­actions with centroid–centroid distances of 3.592 (4) Å between the pyrrole and the benzene rings in the indole systems of neighboring mol­ecules

Soil C:N:P stoichiometry in tropical forests on Hainan Island of China: Spatial and vertical variations

Soil carbon (C), nitrogen (N), and phosphorus (P) are three important elements. The study of stoichiometric relationships of soil C, N, and P in tropical forests on Hainan Island, China could improve our understanding of nutrient cycling and provide valuable information for forest management. Soil samples were collected at five different depths from 0 to 100 cm at 100 sites among four different forest types on Hainan Island, and total C, N, and P concentrations were measured. Soil C and N concentrations and soil C:P and N:P ratios declined from the surface soil layer to the deeper soil layers and soil P and C:N ratio had relatively small variations among different depths, due to that soil C and N were mostly controlled by biological processes such as photosynthesis and N2-fixation, while P was more influenced by bedrock. Large spatial variations were found for soil C, N, P concentrations and their ratios. Soil C and N concentrations were significantly influenced by longitude and vegetation cover, while soil P concentration and C:P and N:P ratios were significantly controlled by latitude. This study produced a comprehensive data set of soil C, N, and P stoichiometry, and their variation patterns and controls in the tropical forests. The information generated here could help improve ecosystem models for better understanding of forest element stoichiometry, ecosystem productivity, and plant-environment relationships

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model

Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL entails exposing the model to various participants. This poses a risk of unauthorized model distribution or resale by the malicious client, compromising the intellectual property rights of the FL group. To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants. In this paper, we present FedTracker, the first FL model protection framework that provides both ownership verification and traceability. FedTracker adopts a bi-level protection scheme consisting of global watermark mechanism and local fingerprint mechanism. The former authenticates the ownership of the global model, while the latter identifies which client the model is derived from. FedTracker leverages Continual Learning (CL) principles to embedding the watermark in a way that preserves the utility of the FL model on both primitive task and watermark task. FedTracker also devises a novel metric to better discriminate different fingerprints. Experimental results show FedTracker is effective in ownership verification, traceability, and maintains good fidelity and robustness against various watermark removal attacks

Stacking Group Structure of Fermionic Symmetry-Protected Topological Phases

In the past decade, there has been a systematic investigation of symmetry-protected topological (SPT) phases in interacting fermion systems. Specifically, by utilizing the concept of equivalence classes of finite-depth fermionic symmetric local unitary (FSLU) transformations and the decorating symmetry domain wall picture, a large class of fixed-point wave functions have been constructed for fermionic SPT (FSPT) phases. Remarkably, this construction coincides with the Atiyah-Hirzebruch spectral sequence, enabling a complete classification of FSPT phases. However, unlike bosonic SPT phases, the stacking group structure in fermion systems proves to be much more intricate. The construction of fixed-point wave functions does not explicitly provide this information. In this paper, we employ FSLU transformations to investigate the stacking group structure of FSPT phases. Specifically, we demonstrate how to compute stacking FSPT data from the input FSPT data in each layer, considering both unitary and anti-unitary symmetry, up to 2+1 dimensions. As concrete examples, we explictly compute the stacking group structure for crystalline FSPT phases in all 17 wallpaper groups using the fermionic crystalline equivalence principle. Importantly, our approach can be readily extended to higher dimensions, offering a versatile method for exploring the stacking group structure of FSPT phases