3,138 research outputs found
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-MethylÂbenzÂyl)-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 interÂactions with centroid–centroid distances of 3.592 (4) Å between the pyrrole and the benzene rings in the indole systems of neighboring molÂecules
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
Comparative transcriptomic analysis revealed adaptation mechanism of Phrynocephalus erythrurus, the highest altitude Lizard living in the Qinghai-Tibet Plateau
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
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