360 research outputs found
Multi-level structured self-attentions for distantly supervised relation extraction
Attention mechanisms are often used in deep
neural networks for distantly supervised relation extraction (DS-RE) to distinguish valid
from noisy instances. However, traditional 1-
D vector attention models are insufficient for
the learning of different contexts in the selection of valid instances to predict the relationship for an entity pair. To alleviate
this issue, we propose a novel multi-level
structured (2-D matrix) self-attention mechanism for DS-RE in a multi-instance learning
(MIL) framework using bidirectional recurrent
neural networks. In the proposed method,
a structured word-level self-attention mechanism learns a 2-D matrix where each row vector represents a weight distribution for different aspects of an instance regarding two entities. Targeting the MIL issue, the structured
sentence-level attention learns a 2-D matrix
where each row vector represents a weight
distribution on selection of different valid instances. Experiments conducted on two publicly available DS-RE datasets show that the
proposed framework with a multi-level structured self-attention mechanism significantly
outperform state-of-the-art baselines in terms
of PR curves, P@N and F1 measures
NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation
Most of the current anti money laundering (AML) systems, using handcrafted
rules, are heavily reliant on existing structured databases, which are not capable
of effectively and efficiently identifying
hidden and complex ML activities, especially those with dynamic and timevarying characteristics, resulting in a high
percentage of false positives. Therefore,
analysts1
are engaged for further investigation which significantly increases human capital cost and processing time. To
alleviate these issues, this paper presents
a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language
processing (NLP) technologies in a distributed and scalable manner to augment
AML monitoring and investigation. The
proposed distributed framework performs
news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and
tweets) to provide additional evidence to
human investigators for final decisionmaking. Each NLP module is evaluated
on a task-specific data set, and the overall experiments are performed on synthetic
and real-world datasets. Feedback from
AML practitioners suggests that our system can reduce approximately 30% time
and cost compared to their previous manual approaches of AML investigation
Dummy Molecularly Imprinted Polymers-Capped CdTe Quantum Dots for the Fluorescent Sensing of 2,4,6-Trinitrotoluene
Molecularly imprinted polymers (MIPs) with trinitrophenol (TNP) as a dummy template molecule capped with CdTe quantum dots (QDs) were prepared using 3-aminopropyltriethoxy silane (APTES) as the functional monomer and tetraethoxysilane (TEOS) as the cross linker through a seedgrowth method via a sol gel process (i.e., DMIP@QDs) for the sensing of 2,4,6-trinitrotoluene (TNT) on the basis of electron-transfer-induced fluorescence quenching. With the presence and increase of TNT in sample solutions, a Meisenheimer complex was formed between TNT and the primary amino groups on the surface of the QDs. The energy of the QDs was transferred to the complex, resulting in the quenching of the QDs and thus decreasing the fluorescence intensity, which allowed the TNT to be sensed optically. DMIP@QDs generated a significantly reduced fluorescent intensity within less than 10 min upon binding TNT. The fluorescence-quenching fractions of the sensor presented a satisfactory linearity with TNT concentrations in the range of 0.8-30 mu M, and its limit of detection could reach 0.28 mu M. The sensor exhibited distinguished selectivity and a high binding affinity to TNT over its possibly competing molecules of 2,4-dinitrophenol (DNP), 4-nitrophenol (4-NP), phenol, and dinitrotoluene (DNT) because there are more nitro groups in TNT and therefore a stronger electron-withdrawing ability and because it has a high similarity in shape and volume to TNP. The sensor was successfully applied to determine the amount of TNT in soil samples, and the average recoveries of TNT at three spiking levels ranged from 90.3 to 97.8% with relative standard deviations below 5.12%. The results provided an effective way to develop sensors for the rapid recognition and determination of hazardous materials from complex matrices.Molecularly imprinted polymers (MIPs) with trinitrophenol (TNP) as a dummy template molecule capped with CdTe quantum dots (QDs) were prepared using 3-aminopropyltriethoxy silane (APTES) as the functional monomer and tetraethoxysilane (TEOS) as the cross linker through a seedgrowth method via a sol gel process (i.e., DMIP@QDs) for the sensing of 2,4,6-trinitrotoluene (TNT) on the basis of electron-transfer-induced fluorescence quenching. With the presence and increase of TNT in sample solutions, a Meisenheimer complex was formed between TNT and the primary amino groups on the surface of the QDs. The energy of the QDs was transferred to the complex, resulting in the quenching of the QDs and thus decreasing the fluorescence intensity, which allowed the TNT to be sensed optically. DMIP@QDs generated a significantly reduced fluorescent intensity within less than 10 min upon binding TNT. The fluorescence-quenching fractions of the sensor presented a satisfactory linearity with TNT concentrations in the range of 0.8-30 mu M, and its limit of detection could reach 0.28 mu M. The sensor exhibited distinguished selectivity and a high binding affinity to TNT over its possibly competing molecules of 2,4-dinitrophenol (DNP), 4-nitrophenol (4-NP), phenol, and dinitrotoluene (DNT) because there are more nitro groups in TNT and therefore a stronger electron-withdrawing ability and because it has a high similarity in shape and volume to TNP. The sensor was successfully applied to determine the amount of TNT in soil samples, and the average recoveries of TNT at three spiking levels ranged from 90.3 to 97.8% with relative standard deviations below 5.12%. The results provided an effective way to develop sensors for the rapid recognition and determination of hazardous materials from complex matrices
Analysis of crucial molecules involved in herniated discs and degenerative disc disease
OBJECTIVES: Herniated discs and degenerative disc disease are major health problems worldwide. However, their pathogenesis remains obscure. This study aimed to explore the molecular mechanisms of these ailments and to identify underlying therapeutic targets. MATERIAL AND METHODS: Using the GSE23130 microarray datasets downloaded from the Gene Expression Omnibus database, differentially co-expressed genes and links were identified using the differentially co-expressed gene and link method with a false discovery rate ,0.25 as a significant threshold. Subsequently, the underlying molecular mechanisms of the differential co-expression of these genes were investigated using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. In addition, the transcriptional regulatory relationship was also investigated. RESULTS: Through the analysis of the gene expression profiles of different specimens from patients with these diseases, 539 differentially co-expressed genes were identified for these ailments. The ten most significant signaling pathways involving the differentially co-expressed genes were identified by enrichment analysis. Among these pathways, apoptosis and extracellular matrix-receptor interaction pathways have been reported to be related to these diseases. A total of 62 pairs of regulatory relationships between transcription factors and their target genes were identified as critical for the pathogenesis of these diseases. CONCLUSION: The results of our study will help to identify the mechanisms responsible for herniated discs and degenerative disc disease and provides a theoretical basis for further therapeutic study
Retinex-qDPC: automatic background rectified quantitative differential phase contrast imaging
The quality of quantitative differential phase contrast reconstruction (qDPC)
can be severely degenerated by the mismatch of the background of two oblique
illuminated images, yielding problematic phase recovery results. These
background mismatches may result from illumination patterns, inhomogeneous
media distribution, or other defocusing layers. In previous reports, the
background is manually calibrated which is time-consuming, and unstable, since
new calibrations are needed if any modification to the optical system was made.
It is also impossible to calibrate the background from the defocusing layers,
or for high dynamic observation as the background changes over time. To tackle
the mismatch of background and increases the experimental robustness, we
propose the Retinex-qDPC in which we use the images edge features as data
fidelity term yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high
background-robustness qDPC reconstruction. The split Bregman method is used to
solve the L1-Retinex DPC. We compare both Retinex-qDPC models against
state-of-the-art DPC reconstruction algorithms including total-variation
regularized qDPC, and isotropic-qDPC using both simulated and experimental
data. Results show that the Retinex qDPC can significantly improve the phase
recovery quality by suppressing the impact of mismatch background. Within, the
L1-Retinex-qDPC is better than L2-Retinex and other state-of-the-art DPC
algorithms. In general, the Retinex-qDPC increases the experimental robustness
against background illumination without any modification of the optical system,
which will benefit all qDPC applications
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