As a rich country in genetic resources, Indonesia has the potential for strengthening food security. Maize is one of the commodities that greatly affect food security in Indonesia. Maize is also widely used as animal ...
As a rich country in genetic resources, Indonesia has the potential for strengthening food security. Maize is one of the commodities that greatly affect food security in Indonesia. Maize is also widely used as animal feed. The high demand for maize requires efforts to increase the productivity of sustainable cultivation. Various research efforts that have been carried out require the support of a comprehensive information system. This study aimed to build a research model for maize genetic resource information systems that can help search for superior varieties. The data collection method used was collecting genotype, phenotype, climate data and interviewing the maize breeders or researchers. As for the design of the model, this study used the Object-Oriented Analysis & Design (OOAD) method with notation from Unified Modelling Language (UML). The result of this research was a web-based model of information systems for maize research.
In the quest for novel quantum states driven by topology and correlation, kagome lattice materials have garnered significant interest due to their distinctive electronic band structures, featuring flat bands (FBs) ari...
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There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodolog...
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Because of their natural one-dimensional (1D) structure combined with intricate chiral variations, carbon nanotubes (CNTs) exhibit various exceptional physical properties, such as ultrahigh electrical and thermal cond...
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Nonlinear spectral mapping-based models based on supervised learning have successfully applied for speech enhancement. However, as supervised learning approaches, a large amount of labelled data (noisy-clean speech pa...
ISBN:
(数字)9781509066315
ISBN:
(纸本)9781509066322
Nonlinear spectral mapping-based models based on supervised learning have successfully applied for speech enhancement. However, as supervised learning approaches, a large amount of labelled data (noisy-clean speech pairs) should be provided to train those models. In addition, their performances for unseen noisy conditions are not guaranteed, which is a common weak point of supervised learning approaches. In this study, we proposed an unsupervised learning approach for speech enhancement, i.e., denoising autoencoder with linear regression decoder (DAELD) model for speech enhancement. The DAELD is trained with noisy speech as both input and target output in a self-supervised learning manner. In addition, with properly setting a shrinkage threshold for internal hidden representations, noise could be removed during the reconstruction from the hidden representations via the linear regression decoder. Speech enhancement experiments were carried out to test the proposed model. Results confirmed that the proposed DAELD could achieve comparable and sometimes even better enhancement performance as compared to the conventional supervised speech enhancement approaches, in both seen and unseen noise environments. Moreover, we observe that higher performances tend to achieve by DAELD when the training data cover more diverse noise types and signal-tonoise-ratio (SNR) levels.
We study how efficiently a k-element set S ⊆ [n] can be learned from a uniform superposition |Si of its elements. One can think of |Si = Pi∈S |ii /√|S| as the quantum version of a uniformly random sample over S, as ...
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Understanding the emergent electronic structure in twisted atomically thin layers has led to the exciting field of twistronics. However, practical applications of such systems are challenging since the specific angula...
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The performance of superconducting qubits is often limited by dissipation and two-level systems (TLS) losses. The dominant sources of these losses are believed to originate from amorphous materials and defects at inte...
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We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac...
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ISBN:
(纸本)9781957171258
We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vacuum filtration and chiral stacking of aligned carbon nanotubes.
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