79 research outputs found
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
The point pair feature (PPF) is widely used for 6D pose estimation. In this
paper, we propose an efficient 6D pose estimation method based on the PPF
framework. We introduce a well-targeted down-sampling strategy that focuses
more on edge area for efficient feature extraction of complex geometry. A pose
hypothesis validation approach is proposed to resolve the symmetric ambiguity
by calculating edge matching degree. We perform evaluations on two challenging
datasets and one real-world collected dataset, demonstrating the superiority of
our method on pose estimation of geometrically complex, occluded, symmetrical
objects. We further validate our method by applying it to simulated punctures.Comment: 16 pages,20 figure
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Multivariate time series forecasting with hierarchical structure is pervasive
in real-world applications, demanding not only predicting each level of the
hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the
forecasts should satisfy the hierarchical aggregation constraints. Moreover,
the disparities of statistical characteristics between levels can be huge,
worsened by non-Gaussian distributions and non-linear correlations. To this
extent, we propose a novel end-to-end hierarchical time series forecasting
model, based on conditioned normalizing flow-based autoregressive transformer
reconciliation, to represent complex data distribution while simultaneously
reconciling the forecasts to ensure coherency. Unlike other state-of-the-art
methods, we achieve the forecasting and reconciliation simultaneously without
requiring any explicit post-processing step. In addition, by harnessing the
power of deep model, we do not rely on any assumption such as unbiased
estimates or Gaussian distribution. Our evaluation experiments are conducted on
four real-world hierarchical datasets from different industrial domains (three
public ones and a dataset from the application servers of Alipay's data center)
and the preliminary results demonstrate efficacy of our proposed method
Effectiveness of Educational Interventions for Health Workers on Antibiotic Prescribing in Outpatient Settings in China: A Systematic Review and Meta-Analysis
Educational interventions are considered an important component of antibiotic stewardship, but their effect has not been systematically evaluated in outpatient settings in China. This research aims to evaluate the effectiveness of educational interventions for health workers on antibiotic prescribing rates in Chinese outpatient settings. Eight databases were searched for relevant randomized clinical trials, non-randomized trials, controlled before-after studies and interrupted time-series studies from January 2001 to July 2021. A total of 16 studies were included in the systematic review and 12 in the meta-analysis. The results showed that educational interventions overall reduced the antibiotic prescription rate significantly (relative risk, RR 0.72, 95% confidence interval, CI 0.61 to 0.84). Subgroup analysis demonstrated that certain features of education interventions had a significant effect on antibiotic prescription rate reduction: (1) combined with compulsory administrative regulations (RR With: 0.65 vs. Without: 0.78); (2) combined with financial incentives (RR With: 0.51 vs. Without: 0.77). Educational interventions can also significantly reduce antibiotic injection rates (RR 0.83, 95% CI 0.74 to 0.94) and the inappropriate use of antibiotics (RR 0.61, 95% CI 0.51 to 0.73). The limited number of high-quality studies limits the validity and reliability of the results. More high-quality educational interventions targeting the reduction of antibiotic prescribing rates are needed
Large-scale single-photon imaging
Benefiting from its single-photon sensitivity, single-photon avalanche diode
(SPAD) array has been widely applied in various fields such as fluorescence
lifetime imaging and quantum computing. However, large-scale high-fidelity
single-photon imaging remains a big challenge, due to the complex hardware
manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we
introduce deep learning into SPAD, enabling super-resolution single-photon
imaging over an order of magnitude, with significant enhancement of bit depth
and imaging quality. We first studied the complex photon flow model of SPAD
electronics to accurately characterize multiple physical noise sources, and
collected a real SPAD image dataset (64 32 pixels, 90 scenes, 10
different bit depth, 3 different illumination flux, 2790 images in total) to
calibrate noise model parameters. With this real-world physical noise model, we
for the first time synthesized a large-scale realistic single-photon image
dataset (image pairs of 5 different resolutions with maximum megapixels, 17250
scenes, 10 different bit depth, 3 different illumination flux, 2.6 million
images in total) for subsequent network training. To tackle the severe
super-resolution challenge of SPAD inputs with low bit depth, low resolution,
and heavy noise, we further built a deep transformer network with a
content-adaptive self-attention mechanism and gated fusion modules, which can
dig global contextual features to remove multi-source noise and extract
full-frequency details. We applied the technique on a series of experiments
including macroscopic and microscopic imaging, microfluidic inspection, and
Fourier ptychography. The experiments validate the technique's state-of-the-art
super-resolution SPAD imaging performance, with more than 5 dB superiority on
PSNR compared to the existing methods
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Multivariate time series forecasting with hierarchical structure is widely
used in real-world applications, e.g., sales predictions for the geographical
hierarchy formed by cities, states, and countries. The hierarchical time series
(HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation.
In the previous works, hierarchical information is only integrated in the
reconciliation step to maintain coherency, but not in forecasting step for
accuracy improvement. In this paper, we propose two novel tree-based feature
integration mechanisms, i.e., top-down convolution and bottom-up attention to
leverage the information of the hierarchical structure to improve the
forecasting performance. Moreover, unlike most previous reconciliation methods
which either rely on strong assumptions or focus on coherent constraints
only,we utilize deep neural optimization networks, which not only achieve
coherency without any assumptions, but also allow more flexible and realistic
constraints to achieve task-based targets, e.g., lower under-estimation penalty
and meaningful decision-making loss to facilitate the subsequent downstream
tasks. Experiments on real-world datasets demonstrate that our tree-based
feature integration mechanism achieves superior performances on hierarchical
forecasting tasks compared to the state-of-the-art methods, and our neural
optimization networks can be applied to real-world tasks effectively without
any additional effort under coherence and task-based constraint
Synaptic Neurotransmission Depression in Ventral Tegmental Dopamine Neurons and Cannabinoid-Associated Addictive Learning
Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP) and long-term depression (LTD). Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses) of the midbrain ventral tegmental area (VTA) following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids), the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction
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