487 research outputs found
A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images
In the past decade, using Street View images and machine learning to measure
human perception has become a mainstream research approach in urban science.
However, this approach using only image-shallow information makes it difficult
to comprehensively understand the deep semantic features of human perception of
a scene. In this study, we proposed a new framework based on a pre-train
natural language model to understand the relationship between human perception
and the sense of a scene. Firstly, Place Pulse 2.0 was used as our base
dataset, which contains a variety of human-perceived labels, namely, beautiful,
safe, wealthy, depressing, boring, and lively. An image captioning network was
used to extract the description information of each street view image.
Secondly, a pre-trained BERT model was finetuning and added a regression
function for six human perceptual dimensions. Furthermore, we compared the
performance of five traditional regression methods with our approach and
conducted a migration experiment in Hong Kong. Our results show that human
perception scoring by deep semantic features performed better than previous
studies by machine learning methods with shallow features. The use of deep
scene semantic features provides new ideas for subsequent human perception
research, as well as better explanatory power in the face of spatial
heterogeneity.Comment: 11 pages, 8 figure
A super-Eddington wind scenario for the progenitors of type Ia supernovae: binary population synthesis calculations
The super-Eddington wind scenario has been proposed as an alternative way for
producing type Ia supernovae (SNe Ia). The super-Eddington wind can naturally
prevent the carbon--oxygen white dwarfs (CO WDs) with high mass-accretion rates
from becoming red-giant-like stars. Furthermore, it works in low-metallicity
environments, which may explain SNe Ia observed at high redshifts. In this
article, we systematically investigated the most prominent single-degenerate
WD+MS channel based on the super-Eddington wind scenario. We combined the
Eggleton stellar evolution code with a rapid binary population synthesis (BPS)
approach to predict SN Ia birthrates for the WD+MS channel by adopting the
super-Eddington wind scenario and detailed mass-accumulation efficiencies of
H-shell flashes on the WDs. Our BPS calculations found that the estimated SN Ia
birthrates for the WD+MS channel are ~0.009-0.315*10^{-3}{yr}^{-1} if we adopt
the Eddington accretion rate as the critical accretion rate, which are much
lower than that of the observations (<10% of the observed SN Ia birthrates).
This indicates that the WD+MS channel only contributes a small proportion of
all SNe Ia. The birthrates in this simulation are lower than previous studies,
the main reason of which is that new mass-accumulation efficiencies of H-shell
flashes are adopted. We also found that the critical mass-accretion rate has a
significant influence on the birthrates of SNe Ia. Meanwhile, the results of
our BPS calculations are sensitive to the values of the common-envelope
ejection efficiency.Comment: 14 pages, 9 figures, 1 table, accepted for publication in Astronomy
and Astrophysic
Experimental Study on Unidirectional Pedestrian Descending and Ascending Stair With a Fixed Obstacle
Staircase is one of the most essential vertical passageway for pedestrians’ timely evacuation, and has distinct constraint on pedestrians’ movement characteristics when compared with corridors and hallways. During evacuation, temporary obstacles can be observed on stairs, e.g., the abruptly stopped pedestrians or the luggage of pedestrians discarded. It is noticed that studies on the effect of obstacles on pedestrian dynamics mainly focused on planar locomotion, the impact of obstacle on the movement characteristics of pedestrians ascending and descending stairs have not been systematically studied yet. Therefore, in this study, a series of unidirectional pedestrian avoid obstacle movement experiments on staircase under controlled laboratory conditions were performed. The avoidance characteristic of pedestrians is observed from trajectory diagram. Target drift angle towards left and right is further calculated and analyzed. The study found that target drift angle curve occur to relatively large fluctuations to avoid obstacle of a pedestrian rather than not appear to obvious variety to avoid obstacle of a suitcase. Meanwhile, the change trend of target drift angle towards left and right for scenarios S3 and S4 is consistent with results of scenarios S1 and S2. Then, an interesting discovery indicates that the pedestrians will accelerate after passing obstacles whether it is ascending process or descending process. Finally, the obstacle of a pedestrian will accelerate the movement efficiency in ascending process from results of flow rates, but the result is contrary to that of descending process. The systematic experimental data can not only be used for the verification and validation of pedestrian models but also can provide a benchmark for the design of related facilities aiming at improving traffic efficiency
Higher Auslander's defect and classifying substructures of n-exangulated categories
Herschend-Liu-Nakaoka introduced the notion of -exangulated categories. It
is not only a higher dimensional analogue of extriangulated categories defined
by Nakaoka-Palu, but also gives a simultaneous generalization of -exact
categories and -angulated categories. In this article, we give an
-exangulated version of Auslander's defect and Auslander-Reiten duality
formula. Moreover, we also give a classification of substructures (=closed
subbifunctors) of a given skeletally small -exangulated category by using
the category of defects.Comment: 24 pages. arXiv admin note: text overlap with arXiv:1709.06689 by
other author
On the Pareto Front of Multilingual Neural Machine Translation
In this work, we study how the performance of a given direction changes with
its sampling ratio in Multilingual Neural Machine Translation (MNMT). By
training over 200 multilingual models with various model sizes, data sizes, and
language directions, we find it interesting that the performance of certain
translation direction does not always improve with the increase of its weight
in the multi-task optimization objective. Accordingly, scalarization method
leads to a multitask trade-off front that deviates from the traditional Pareto
front when there exists data imbalance in the training corpus, which poses a
great challenge to improve the overall performance of all directions. Based on
our observations, we propose the Double Power Law to predict the unique
performance trade-off front in MNMT, which is robust across various languages,
data adequacy, and the number of tasks. Finally, we formulate the sample ratio
selection problem in MNMT as an optimization problem based on the Double Power
Law. In our experiments, it achieves better performance than temperature
searching and gradient manipulation methods with only 1/5 to 1/2 of the total
training budget. We release the code at
https://github.com/pkunlp-icler/ParetoMNMT for reproduction.Comment: NeurIPS 202
A spatiotemporal identification method for deformation characteristics of expansive soil canal slope based on spectral clustering
Performance of a new Candida anti-mannan IgM and IgG assays in the diagnosis of candidemia
Candida is one of the most frequent pathogens of bloodstream infections, which is associated with high morbidity and mortality rates. Rapid immunological detection methods are essential in the early diagnosis of candidemia. Anti-mannan is one of host-derived biomarkers against cell wall components of Candida. We conducted this study to evaluate the diagnostic performance of two anti-mannan assays (IgM, IgG) for candidemia through the analysis of 40 candidemia patients, 48 participants with Candida colonization and 213 participants with neither Candida colonization nor Candida infections (13 patients with other bloodstream infections, 145 hospitalized patients and 55 healthy controls). The performance of the two assays were evaluated by calculating their sensitivity and specificity. The sensitivity ranged from 0.78 to 0.80 for the IgM assay and 0.68 to 0.75 for the IgG assay. The specificity ranged from 0.97 to 0.98 for the IgM assay and 0.91 to 0.94 for the IgG assay. The diagnostic performance of the anti-mannan IgM assay was better than that of IgG, with higher sensitivity and specificity. Combining the two assays (positive results of single or both assays are both considered as positive) could improve the sensitivity up to 0.93 (0.79-0.98) and only slightly reduce the specificity (0.93(0.89-0.95)). The anti-mannan IgM, IgG assays are rapid and cost-effective assays that may be probably useful in the diagnosis of candidemia
HLT-MT: High-resource Language-specific Training for Multilingual Neural Machine Translation
Multilingual neural machine translation (MNMT) trained in multiple language
pairs has attracted considerable attention due to fewer model parameters and
lower training costs by sharing knowledge among multiple languages.
Nonetheless, multilingual training is plagued by language interference
degeneration in shared parameters because of the negative interference among
different translation directions, especially on high-resource languages. In
this paper, we propose the multilingual translation model with the
high-resource language-specific training (HLT-MT) to alleviate the negative
interference, which adopts the two-stage training with the language-specific
selection mechanism. Specifically, we first train the multilingual model only
with the high-resource pairs and select the language-specific modules at the
top of the decoder to enhance the translation quality of high-resource
directions. Next, the model is further trained on all available corpora to
transfer knowledge from high-resource languages (HRLs) to low-resource
languages (LRLs). Experimental results show that HLT-MT outperforms various
strong baselines on WMT-10 and OPUS-100 benchmarks. Furthermore, the analytic
experiments validate the effectiveness of our method in mitigating the negative
interference in multilingual training.Comment: 7 pages, 7 figures, IJCAI-ECAI 202
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