319 research outputs found
An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning
Unsupervised sentence embeddings learning has been recently dominated by
contrastive learning methods (e.g., SimCSE), which keep positive pairs similar
and push negative pairs apart. The contrast operation aims to keep as much
information as possible by maximizing the mutual information between positive
instances, which leads to redundant information in sentence embedding. To
address this problem, we present an information minimization based contrastive
learning (InforMin-CL) model to retain the useful information and discard the
redundant information by maximizing the mutual information and minimizing the
information entropy between positive instances meanwhile for unsupervised
sentence representation learning. Specifically, we find that information
minimization can be achieved by simple contrast and reconstruction objectives.
The reconstruction operation reconstitutes the positive instance via the other
positive instance to minimize the information entropy between positive
instances. We evaluate our model on fourteen downstream tasks, including both
supervised and unsupervised (semantic textual similarity) tasks. Extensive
experimental results show that our InforMin-CL obtains a state-of-the-art
performance.Comment: 11 pages, 3 figures, published to COLING 202
Triticeae crop genome biology: an endless frontier
Triticeae, the wheatgrass tribe, includes several major cereal crops and their wild relatives. Major crops within the Triticeae are wheat, barley, rye, and oat, which are important for human consumption, animal feed, and rangeland protection. Species within this tribe are known for their large genomes and complex genetic histories. Powered by recent advances in sequencing technology, researchers worldwide have made progress in elucidating the genomes of Triticeae crops. In addition to assemblies of high-quality reference genomes, pan-genome studies have just started to capture the genomic diversities of these species, shedding light on our understanding of the genetic basis of domestication and environmental adaptation of Triticeae crops. In this review, we focus on recent signs of progress in genome sequencing, pan-genome analyses, and resequencing analysis of Triticeae crops. We also propose future research avenues in Triticeae crop genomes, including identifying genome structure variations, the association of genomic regions with desired traits, mining functions of the non-coding area, introgression of high-quality genes from wild Triticeae resources, genome editing, and integration of genomic resources
Research on singular spectrum decomposition and its application to rotor failure detection
As an important part of rotating machinery, a healthy rotor is critical to ensuring optimal working conditions of the entire system. Considering that the vibration signal of rotor consists of different frequency components when the failure arises, a novel rotor failure detection method based on singular spectrum decomposition (SSD) is presented. The original vibration signal is adaptively decomposed into a number of singular spectrum components (SSCs) by the SSD method. Then, energy separation algorithm (ESA) is adopted to demodulate each singular spectrum component. Finally, the SSD-ESA time-frequency spectrum can be obtained and the fault features contained in the SSD-ESA time-frequency spectrum can be identified to determine the fault types. The effectiveness of SSD for harmonic separation was assessed through tones separation analyses, the results show that SSD is able to separate more harmonic pairs of different amplitude ratios than empirical mode decomposition (EMD). Furthermore, three simulations of multi-component signals were designed to investigate the use of SSD for signal decomposition. The SSD method was then applied to detect signatures caused by rotor oil film whirl in experimental signals and compared to both EMD and ensemble EMD (EEMD). The simulated analysis results reflect that SSD shows superiority to EMD and EEMD in inhibiting mode mixing and extracting the time-varying frequency components. The experimental analysis results demonstrate that the SSD based rotor failure detection method is an alternative method under both constant and variable speed conditions
Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis
A time-frequency analysis method based on improved variational mode decomposition and Teager energy operator (IVMD-TEO) is proposed for fault diagnosis of turbine rotor. Variational mode decomposition (VMD) can decompose a multicomponent signal into a number of band-limited monocomponent signals and can effectively avoid model mixing. To determine the number of monocomponents adaptively, VMD is improved using the correlation coefficient criterion. Firstly, IVMD algorithm is used to decompose a multicomponent signal into a number of monocompositions adaptively. Second, all the monocomponent signalsâ instantaneous amplitude and instantaneous frequency are demodulated via TEO, respectively, because TEO has fast and high precision demodulation advantages to monocomponent signal. Finally, the time-frequency representation of original signal is exhibited by superposing the time-frequency representations of all the monocomponents. The analysis results of simulation signal and experimental turbine rotor in rising speed condition demonstrate that the proposed method has perfect multicomponent signal decomposition capacity and good time-frequency expression. It is a promising time-frequency analysis method for rotor fault diagnosis
Intracellular redox-responsive nanocarrier for plasmid delivery: in vitro characterization and in vivo studies in mice
Performance Entitlement by Using Novel High Strength Electrical Steels and Copper Alloys for High-Speed Laminated Rotor Induction Machines
The laminated rotor Induction Machine (IM), with its simple construction and manufacturing, robustness, ease of control and comparatively lower cost remains by far the most utilized electromechanical energy converter. At very high speeds, traditionally its use is considered to be limited to the previously established operational limits of 2.5 Ă 105 rpmâkW, beyond which the surface Permanent Magnet (PM) Machine and the solid rotor Induction Machine become the machines available for consideration. The aforesaid limits are derived from the use of classic materials. This paper reviews the recent developments in electrical steels and copper alloys and translates these into the resulting performance entitlement and operational limits through a case study involving a marine application, for which an existing rare-earth PM machine is in use. It is concluded that with novel materials, laminated rotor induction machines can be operated up to 6 Ă 105 rpmâkW, thus opening the use of the rare-earth free Induction Machine for a wider application range previously limited to PM machines
Global systematic review with meta-analysis shows that warming effects on terrestrial plant biomass allocation are influenced by precipitation and mycorrhizal association
Biomass allocation in plants is fundamental for understanding and predicting
terrestrial carbon storage. Yet, our knowledge regarding warming effects on root: shoot ratio (R/S) remains limited. Here, we present a meta-analysis encompassing more than 300 studies and including angiosperms and gymnosperms as well as different biomes (cropland, desert, forest, grassland, tundra, and wetland). The meta-analysis shows that average warming of 2.50 °C (median = 2 °C) significantly increases biomass allocation to roots with a mean increase of 8.1% in R/S. Two factors associate significantly with this response to warming: mean annual precipitation and the type of mycorrhizal fungi associated with plants. Warming-induced allocation to roots is greater in drier habitats when compared to shoots (+15.1% in R/S), while lower in wetter habitats (+4.9% in R/S). This R/S pattern is more frequent in plants associated with arbuscular mycorrhizal fungi, compared to ectomycorrhizal fungi. These results show that precipitation variability and mycorrhizal association can affect terrestrial carbon dynamics by influencing biomass allocation strategies in a warmer world, suggesting that climate change could influence belowground C sequestration
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Amorphous-Like Ultralow Thermal Transport in Crystalline Argyrodite Cu7PS6
Due to their amorphous-like ultralow lattice thermal conductivity both below and above the superionic phase transition, crystalline Cu- and Ag-based superionic argyrodites have garnered widespread attention as promising thermoelectric materials. However, despite their intriguing properties, quantifying their lattice thermal conductivities and a comprehensive understanding of the microscopic dynamics that drive these extraordinary properties are still lacking. Here, an integrated experimental and theoretical approach is adopted to reveal the presence of Cu-dominated low-energy optical phonons in the Cu-based argyrodite Cu7PS6. These phonons yield strong acoustic-optical phonon scattering through avoided crossing, enabling ultralow lattice thermal conductivity. The Unified Theory of thermal transport is employed to analyze heat conduction and successfully reproduce the experimental amorphous-like ultralow lattice thermal conductivities, ranging from 0.43 to 0.58Â WÂ mâ1 Kâ1, in the temperature range of 100â400 K. The study reveals that the amorphous-like ultralow thermal conductivity of Cu7PS6 stems from a significantly dominant wave-like conduction mechanism. Moreover, the simulations elucidate the wave-like thermal transport mainly results from the contribution of Cu-associated low-energy overlapping optical phonons. This study highlights the crucial role of low-energy and overlapping optical modes in facilitating amorphous-like ultralow thermal transport, providing a thorough understanding of the underlying complex dynamics of argyrodites
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