363 research outputs found
A New 95 GHz Methanol Maser Catalog: I. Data
The Purple Mountain Observatory 13.7 m radio telescope has been used to
search for 95 GHz (8--7A) class I methanol masers towards 1020
Bolocam Galactic Plane Survey (BGPS) sources, leading to 213 detections. We
have compared the line width of the methanol and HCO thermal emission in
all of the methanol detections and on that basis we find 205 of the 213
detections are very likely to be masers. This corresponds to an overall
detection rate of 95 GHz methanol masers towards our BGPS sample of 20%. Of the
205 detected masers 144 (70%) are new discoveries. Combining our results with
those of previous 95 GHz methanol masers searches, a total of four hundred and
eighty-one 95 GHz methanol masers are now known, we have compiled a catalog
listing the locations and properties of all known 95 GHz methanol masers.Comment: 18 pages, 7 figures, 8 tables, accepted for publication in ApJ
Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning
Parameter regularization or allocation methods are effective in overcoming
catastrophic forgetting in lifelong learning. However, they solve all tasks in
a sequence uniformly and ignore the differences in the learning difficulty of
different tasks. So parameter regularization methods face significant
forgetting when learning a new task very different from learned tasks, and
parameter allocation methods face unnecessary parameter overhead when learning
simple tasks. In this paper, we propose the Parameter Allocation &
Regularization (PAR), which adaptively select an appropriate strategy for each
task from parameter allocation and regularization based on its learning
difficulty. A task is easy for a model that has learned tasks related to it and
vice versa. We propose a divergence estimation method based on the
Nearest-Prototype distance to measure the task relatedness using only features
of the new task. Moreover, we propose a time-efficient relatedness-aware
sampling-based architecture search strategy to reduce the parameter overhead
for allocation. Experimental results on multiple benchmarks demonstrate that,
compared with SOTAs, our method is scalable and significantly reduces the
model's redundancy while improving the model's performance. Further qualitative
analysis indicates that PAR obtains reasonable task-relatedness.Comment: Accepted by CVPR2023. Code is available at
https://github.com/WenjinW/PA
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Horizontal animation deformity as unusual complication of neurotoxin modulation of the gummy smile
Injections of botulinum toxin type A represent the most common nonsurgical cosmetic treatment worldwide. The authors report a case of dynamic horizontal wrinkling in the upper lip that appeared after botulinum toxin type A injections to treat gummy smile associated with nasal alar base reduction, in a 28-year-old woman. The anatomic features and pathogenic mechanism underlying this unusual complication are analyzed and discussed
Development of high performance catalysts for CO oxidation using data-based modeling
Abstract This paper presents a model-aided approach to the development of catalysts for CO oxidation. This is in contrast to the traditional methodology whereby experiments are guided based on experience and intuition of chemists. The proposed approach operates in two stages. To screen a promising combination of active phase, promoter and support material, a powerful "space-filling" experimental design (specifically, Hammersley sequence sampling) was adopted. The screening stage identified Au-ZnO/Al 2 O 3 as a promising recipe for further optimization. In the second stage, the loadings of Au and ZnO were adjusted to optimize the conversion of CO through the integration of a Gaussian process regression (GPR) model and the technique of maximizing expected improvement. Considering that Au constitutes the main cost of the catalyst, we further attempted to reduce the loading of Au with the aid of GPR, while keeping the low-temperature conversion to a high level. Finally we obtained 2.3%Au-5.0%ZnO/Al 2 O 3 with 21 experiments. Infrared reflection absorption spectroscopy and hydrogen temperature-programmed reduction confirmed that ZnO significantly promotes the catalytic activity of Au
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Evidence for the contribution of COMT gene Val158/108Met polymorphism (rs4680) to working memory training-related prefrontal plasticity.
BackgroundGenetic factors have been suggested to affect the efficacy of working memory training. However, few studies have attempted to identify the relevant genes.MethodsIn this study, we first performed a randomized controlled trial (RCT) to identify brain regions that were specifically affected by working memory training. Sixty undergraduate students were randomly assigned to either the adaptive training group (N = 30) or the active control group (N = 30). Both groups were trained for 20 sessions during 4 weeks and received fMRI scans before and after the training. Afterward, we combined the data from the 30 participants in the RCT study who received adaptive training with data from 71 additional participants who also received the same adaptive training but were not part of the RCT study (total N = 101) to test the contribution of the COMT Val158/108Met polymorphism to the interindividual difference in the training effect within the identified brain regions.ResultsIn the RCT study, we found that the adaptive training significantly decreased brain activation in the left prefrontal cortex (TFCE-FWE corrected p = .030). In the genetic study, we found that compared with the Val allele homozygotes, the Met allele carriers' brain activation decreased more after the training at the left prefrontal cortex (TFCE-FWE corrected p = .025).ConclusionsThis study provided evidence for the neural effect of a visual-spatial span training and suggested that genetic factors such as the COMT Val158/108Met polymorphism may have to be considered in future studies of such training
Learning with Free Object Segments for Long-Tailed Instance Segmentation
One fundamental challenge in building an instance segmentation model for a
large number of classes in complex scenes is the lack of training examples,
especially for rare objects. In this paper, we explore the possibility to
increase the training examples without laborious data collection and
annotation. We find that an abundance of instance segments can potentially be
obtained freely from object-centric images, according to two insights: (i) an
object-centric image usually contains one salient object in a simple
background; (ii) objects from the same class often share similar appearances or
similar contrasts to the background. Motivated by these insights, we propose a
simple and scalable framework FreeSeg for extracting and leveraging these
"free" object foreground segments to facilitate model training in long-tailed
instance segmentation. Concretely, we investigate the similarity among
object-centric images of the same class to propose candidate segments of
foreground instances, followed by a novel ranking of segment quality. The
resulting high-quality object segments can then be used to augment the existing
long-tailed datasets, e.g., by copying and pasting the segments onto the
original training images. Extensive experiments show that FreeSeg yields
substantial improvements on top of strong baselines and achieves
state-of-the-art accuracy for segmenting rare object categories
Real-Time Assembly Operation Recognition with Fog Computing and Transfer Learning for Human-Centered Intelligent Manufacturing
In a human-centered intelligent manufacturing system, every element is to assist the operator in achieving the optimal operational performance. The primary task of developing such a human-centered system is to accurately understand human behavior. In this paper, we propose a fog computing framework for assembly operation recognition, which brings computing power close to the data source in order to achieve real-time recognition. For data collection, the operator\u27s activity is captured using visual cameras from different perspectives. For operation recognition, instead of directly building and training a deep learning model from scratch, which needs a huge amount of data, transfer learning is applied to transfer the learning abilities to our application. A worker assembly operation dataset is established, which at present contains 10 sequential operations in an assembly task of installing a desktop CNC machine. The developed transfer learning model is evaluated on this dataset and achieves a recognition accuracy of 95% in the testing experiments
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