1,743 research outputs found
Number of Repetitions in Re-randomization Tests
In covariate-adaptive or response-adaptive randomization, the treatment
assignment and outcome can be correlated. Under this situation,
re-randomization tests are a straightforward and attractive method to provide
valid statistical inference. In this paper, we investigate the number of
repetitions in the re-randomization tests. This is motivated by the group
sequential design in clinical trials, where the nominal significance bound can
be very small at an interim analysis. Accordingly, re-randomization tests lead
to a very large number of required repetitions, which may be computationally
intractable. To reduce the number of repetitions, we propose an adaptive
procedure and compare it with multiple approaches under pre-defined criteria.
Monte Carlo simulations are conducted to show the performance of different
approaches in a limited sample size. We also suggest strategies to reduce total
computation time and provide practical guidance in preparing, executing and
reporting before and after data are unblinded at an interim analysis, so one
can complete the computation within a reasonable time frame
Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images
Automatic table detection in PDF documents has achieved a great success but
tabular data extraction are still challenging due to the integrity and noise
issues in detected table areas. The accurate data extraction is extremely
crucial in finance area. Inspired by this, the aim of this research is
proposing an automated table detection and tabular data extraction from
financial PDF documents. We proposed a method that consists of three main
processes, which are detecting table areas with a Faster R-CNN (Region-based
Convolutional Neural Network) model with Feature Pyramid Network (FPN) on each
page image, extracting contents and structures by a compounded layout
segmentation technique based on optical character recognition (OCR) and
formulating regular expression rules for table header separation. The tabular
data extraction feature is embedded with rule-based filtering and restructuring
functions that are highly scalable. We annotate a new Financial Documents
dataset with table regions for the experiment. The excellent table detection
performance of the detection model is obtained from our customized dataset. The
main contributions of this paper are proposing the Financial Documents dataset
with table-area annotations, the superior detection model and the rule-based
layout segmentation technique for the tabular data extraction from PDF files
FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications
Real-time object detection on Unmanned Aerial Vehicles (UAVs) is a
challenging issue due to the limited computing resources of edge GPU devices as
Internet of Things (IoT) nodes. To solve this problem, in this paper, we
propose a novel lightweight deep learning architectures named FasterX based on
YOLOX model for real-time object detection on edge GPU. First, we design an
effective and lightweight PixSF head to replace the original head of YOLOX to
better detect small objects, which can be further embedded in the depthwise
separable convolution (DS Conv) to achieve a lighter head. Then, a slimmer
structure in the Neck layer termed as SlimFPN is developed to reduce parameters
of the network, which is a trade-off between accuracy and speed. Furthermore,
we embed attention module in the Head layer to improve the feature extraction
effect of the prediction head. Meanwhile, we also improve the label assignment
strategy and loss function to alleviate category imbalance and box optimization
problems of the UAV dataset. Finally, auxiliary heads are presented for online
distillation to improve the ability of position embedding and feature
extraction in PixSF head. The performance of our lightweight models are
validated experimentally on the NVIDIA Jetson NX and Jetson Nano GPU embedded
platforms.Extensive experiments show that FasterX models achieve better
trade-off between accuracy and latency on VisDrone2021 dataset compared to
state-of-the-art models.Comment: 12 pages, 7 figure
Multiband superconductivity and a deep gap minimum evidenced by specific heat in KCa(FeNi)AsF
Specific heat can explore low-energy quasiparticle excitations of
superconductors, so it is a powerful tool for bulk measurement on the
superconducting gap structure and pairing symmetry. Here, we report an in-depth
investigation on the specific heat of the multiband superconductors
KCa(FeNi)AsF ( = 0, 0.05, 0.13) single crystals
and the overdoped non-superconducting one with = 0.17. For the samples with
= 0 and = 0.05, the magnetic field induced specific heat coefficient
in the low temperature limit increases rapidly below 2 T,
then it rises slowly above 2 T. Using the non-superconducting sample with =
0.17 as a reference, and applying a mixed model that combines Debye and
Einstein modes, the specific heat of phonon background for various
superconducting samples can be fitted and the detailed information of the
electronic specific heat is obtained. Through comparative analyses, it is found
that the energy gap structure including two -wave gaps and an extended
-wave gap with large anisotropy can reasonably describe the electronic
specific heat data. According to these results, we suggest that at least one
anisotropic superconducting gap with a deep gap minimum should exist in this
multiband system. With the doping of Ni, the of the sample decreases
along with the decrease of the large -wave gap, but the extended -wave
gap increases due to the enlarged electron pockets via adding more electrons.
Despite these changes, the general properties of the gap structure remain
unchanged versus doping Ni. In addition, the calculation of condensation energy
of the parent and doped samples shows the rough consistency with the
correlation of with = 3-4, which is beyond the
understanding of the BCS theory
Research on low frequency ripple suppression technology of inverter based on model prediction
The low frequency ripple of the input side current of the single-phase inverter will reduce the efficiency of the power generation system and affect the overall performance of the system. Aiming at this problem, this paper proposes a two-modal modulation method and its MPC multi-loop composite control strategy on the circuit topology of a single-stage boost inverter with a buffer unit. The control strategy achieves the balance of active power on both sides of AC and DC by controlling the stable average value of the buffer capacitor voltage, and provides a current reference for inductance current of the DC input side. At the same time, the MPC controller uses the minimum inductor current error as the cost function to control inductor current to track its reference to achieve low frequency ripple suppression of the input current. In principle, it is expounded that the inverter using the proposed control strategy has better low frequency ripple suppression effect than the multi-loop PI control strategy, and the conclusion is proved by the simulation data. Finally, an experimental device of a single-stage boost inverter using MPC multi-loop composite control strategy is designed and fabricated, and the experimental results show that the proposed research scheme has good low frequency ripple suppression effect and strong adaptability to different types of loads
How is Vaping Framed on Online Knowledge Dissemination Platforms?
We analyze 1,888 articles and 1,119,453 vaping posts to study how vaping is
framed across multiple knowledge dissemination platforms (Wikipedia, Quora,
Medium, Reddit, Stack Exchange, wikiHow). We use various NLP techniques to
understand these differences. For example, n-grams, emotion recognition, and
question answering results indicate that Medium, Quora, and Stack Exchange are
appropriate venues for those looking to transition from smoking to vaping.
Other platforms (Reddit, wikiHow) are more for vaping hobbyists and may not
sufficiently dissuade youth vaping. Conversely, Wikipedia may exaggerate vaping
harms, dissuading smokers from transitioning. A strength of our work is how the
different techniques we have applied validate each other. Based on our results,
we provide several recommendations. Stakeholders may utilize our findings to
design informational tools to reinforce or mitigate vaping (mis)perceptions
online.Comment: arXiv admin note: text overlap with arXiv:2206.07765,
arXiv:2206.0902
CNTF Induces Regeneration of Cone Outer Segments in a Rat Model of Retinal Degeneration
Cone photoreceptors are responsible for color and central vision. In the late stage of retinitis pigmentosa and in geographic atrophy associated with age-related macular degeneration, cone degeneration eventually causes loss of central vision. In the present work, we investigated cone degeneration secondary to rod loss in the S334ter-3 transgenic rats carrying the rhodopsin mutation S334ter.Recombinant human ciliary neurotrophic factor (CNTF) was delivered by intravitreal injection to the left eye of an animal, and vehicle to the right eye. Eyes were harvested 10 days after injection. Cone outer segments (COS), and cell bodies were identified by staining with peanut agglutinin and cone arrestin antibodies in whole-mount retinas. For long-term treatment with CNTF, CNTF secreting microdevices were implanted into the left eyes at postnatal day (PD) 20 and control devices into the right eyes. Cone ERG was recorded at PD 160 from implanted animals. Our results demonstrate that an early sign of cone degeneration is the loss of COS, which concentrated in many small areas throughout the retina and is progressive with age. Treatment with CNTF induces regeneration of COS and thus reverses the degeneration process in early stages of cone degeneration. Sustained delivery of CNTF prevents cones from degeneration and helps them to maintain COS and light-sensing function.Loss of COS is an early sign of secondary cone degeneration whereas cell death occurs much later. At early stages, degenerating cones are capable of regenerating outer segments, indicating the reversal of the degenerative process. Sustained delivery of CNTF preserves cone cells and their function. Long-term treatment with CNTF starting at early stages of degeneration could be a viable strategy for preservation of central vision for patients with retinal degenerations
Development and Validation of a Nomogram for Predicting Pulmonary Infection in Patients Receiving Immunosuppressive Drugs
Objective: Pulmonary infection (PI), a severe complication of immunosuppressive therapy, affects patients\u27 prognosis. As part of this study, we aimed to construct a pulmonary infection prediction (PIP) model and validate it in patients receiving immunosuppressive drugs (ISDs). Methods: Totally, 7,977 patients being treated with ISDs were randomised 7:3 to the developing (n = 5,583) versus validation datasets (n = 2,394). Our predictive nomogram was established using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. With the use of the concordance index (C-index) and calibration curve, the prediction performance of the final model was evaluated. Results: Among the patients taking immunosuppressive medication, PI was observed in 548 (6.9%). The median time of PI occurrence after immunosuppressive therapy was 123.0 (interquartile range: 63.0, 436.0) days. Thirteen statistically significant independent predictors (sex, age, hypertension, DM, malignant tumour, use of biologics, use of CNIs, use of methylprednisolone at 500 mg, use of methylprednisolone at 40 mg, use of methylprednisolone at 40 mg total dose, use of oral glucocorticoids, albumin level, and haemoglobin level) were screened using the LASSO algorithm and multivariate COX regression analysis. The PIP model built on these features performed reasonably well, with the developing C-index of 0.87 (sensitivity: 85.4%; specificity: 81.0%) and validation C-indices of 0.837, 0.829, 0.832 and 0.830 for predicting 90-, 180-, 270- and 360-day PI probability, respectively. The decision curve analysis (DCA) and calibration curves displayed excellent clinical utility and calibration performance of the nomogram. Conclusion: The PIP model presented herein could aid in the prediction of PI risk in individual patients who receive immunosuppressive treatment and help personalise clinical decision-making
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