DATA RECOVERY Web Results

IRTF: A new tensor factorization for irregular multidimensional data ... By integrating the proposed IRTF and TV-CST, we establish an irregular multidimensional data recovery model. From (d), we can observe that our method preserves the color and the edge of the spatial-irregular multidimensional data as compared with traditional tensor factorization with preprocessing.

Distributed neural tensor completion for network monitoring data recovery Abstract Network monitoring data is usually incomplete, accurate and fast recovery of missing data is of great significance for practical applications. The tensor-based nonlinear methods have attracted recent attentions with their capability of capturing complex interactions among data for more accurate recovery.

Waste heat recoveries in data centers: A review - ScienceDirect Waste heat recovery technology is considered as a promising approach to improve energy efficiency, achieve energy and energy cost savings, and mitigate environmental impacts (caused by both carbon emission and waste heat discharge) at the same time.

Analysis of false lock in Mueller-Muller clock and data recovery system ... Another view suggests that data correlation is the key contributor [ [9], [10]]. In this work, we provide a comprehensive analysis of MMPD false lock and introduce an enhanced mitigation strategy, validated via simulations. Section 2 investigates the false-lock mechanism and presents an improved phase detection strategy.

A neural tensor decomposition model for high-order sparse data recovery When faced with high missing ratios or sparse observed sets, the recovery results become less ideal [20]. More importantly, the nonlinear information in the data may obscure the low rankness and the model performance may be hindered by the multi-linear hypothesis in the decomposition, making it fail to capture the nonlinear features [21].

Tensor network decomposition for data recovery: Recent advancements and ... Tensor network (TN) decomposition stands as a pivotal technique for characterizing the essential features of high-dimensional data, attracting significant interest and achieving notable success in high-dimensional data recovery.

Advances in direct liquid cooling technology and waste heat recovery ... The escalating demand for computational power and data storage has led to a substantial increase in the number and scale of data centers (DCs) worldwide. Due to characteristics of high heat flux and reliable operational security, there is an urgent need for innovative solutions to solve the problem of DC cooling and waste heat recovery.

Trustworthy data recovery for incomplete multi-view learning Therefore, in this paper, we aim to develop a trustworthy data recovery framework for incomplete multi-view learning (TDR-IML), which ensures reliable imputed data with an uncertainty-aware scheme.

Urban recovery patterns after the 2023 Turkey-Syria ... - ScienceDirect This study made use of time series SDGSAT-1 NTL data to measure the recovery trajectories and patterns after the Turkey-Syria earthquake, revealing several important insights.

Minimum FSI: Genetics requirements for publishing data on DNA transfer ... • Mandates standardised protocols for the publication of DNA transfer and recovery data to ensure consistency • Requires open-sharing of non-allelic experimental and casework data in accessible formats to build a databank. •