107 research outputs found
Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance
Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, âAP+DPâ and âDP+APâ as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. âAP+DPâ method generally performs better than the âDP+APâ method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method âAP+DPâ improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that âAP+DPâ outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy
GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network
With the rapid development of 3D vision, point cloud has become an
increasingly popular 3D visual media content. Due to the irregular structure,
point cloud has posed novel challenges to the related research, such as
compression, transmission, rendering and quality assessment. In these latest
researches, point cloud quality assessment (PCQA) has attracted wide attention
due to its significant role in guiding practical applications, especially in
many cases where the reference point cloud is unavailable. However, current
no-reference metrics which based on prevalent deep neural network have apparent
disadvantages. For example, to adapt to the irregular structure of point cloud,
they require preprocessing such as voxelization and projection that introduce
extra distortions, and the applied grid-kernel networks, such as Convolutional
Neural Networks, fail to extract effective distortion-related features.
Besides, they rarely consider the various distortion patterns and the
philosophy that PCQA should exhibit shifting, scaling, and rotational
invariance. In this paper, we propose a novel no-reference PCQA metric named
the Graph convolutional PCQA network (GPA-Net). To extract effective features
for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which
attentively captures the perturbation of structure and texture. Then, we
propose the multi-task framework consisting of one main task (quality
regression) and two auxiliary tasks (distortion type and degree predictions).
Finally, we propose a coordinate normalization module to stabilize the results
of GPAConv under shift, scale and rotation transformations. Experimental
results on two independent databases show that GPA-Net achieves the best
performance compared to the state-of-the-art no-reference PCQA metrics, even
better than some full-reference metrics in some cases
Point Cloud Quality Assessment using 3D Saliency Maps
Point cloud quality assessment (PCQA) has become an appealing research field
in recent days. Considering the importance of saliency detection in quality
assessment, we propose an effective full-reference PCQA metric which makes the
first attempt to utilize the saliency information to facilitate quality
prediction, called point cloud quality assessment using 3D saliency maps
(PQSM). Specifically, we first propose a projection-based point cloud saliency
map generation method, in which depth information is introduced to better
reflect the geometric characteristics of point clouds. Then, we construct point
cloud local neighborhoods to derive three structural descriptors to indicate
the geometry, color and saliency discrepancies. Finally, a saliency-based
pooling strategy is proposed to generate the final quality score. Extensive
experiments are performed on four independent PCQA databases. The results
demonstrate that the proposed PQSM shows competitive performances compared to
multiple state-of-the-art PCQA metrics
Incomplete Wood-Ljungdahl pathway facilitates one-carbon metabolism in organohalide-respiring Dehalococcoides mccartyi.
The acetyl-CoA "Wood-Ljungdahl" pathway couples the folate-mediated one-carbon (C1) metabolism to either CO2 reduction or acetate oxidation via acetyl-CoA. This pathway is distributed in diverse anaerobes and is used for both energy conservation and assimilation of C1 compounds. Genome annotations for all sequenced strains of Dehalococcoides mccartyi, an important bacterium involved in the bioremediation of chlorinated solvents, reveal homologous genes encoding an incomplete Wood-Ljungdahl pathway. Because this pathway lacks key enzymes for both C1 metabolism and CO2 reduction, its cellular functions remain elusive. Here we used D. mccartyi strain 195 as a model organism to investigate the metabolic function of this pathway and its impacts on the growth of strain 195. Surprisingly, this pathway cleaves acetyl-CoA to donate a methyl group for production of methyl-tetrahydrofolate (CH3-THF) for methionine biosynthesis, representing an unconventional strategy for generating CH3-THF in organisms without methylene-tetrahydrofolate reductase. Carbon monoxide (CO) was found to accumulate as an obligate by-product from the acetyl-CoA cleavage because of the lack of a CO dehydrogenase in strain 195. CO accumulation inhibits the sustainable growth and dechlorination of strain 195 maintained in pure cultures, but can be prevented by CO-metabolizing anaerobes that coexist with D. mccartyi, resulting in an unusual syntrophic association. We also found that this pathway incorporates exogenous formate to support serine biosynthesis. This study of the incomplete Wood-Ljungdahl pathway in D. mccartyi indicates a unique bacterial C1 metabolism that is critical for D. mccartyi growth and interactions in dechlorinating communities and may play a role in other anaerobic communities
Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition
Speech emotion recognition (SER) plays a vital role in improving the
interactions between humans and machines by inferring human emotion and
affective states from speech signals. Whereas recent works primarily focus on
mining spatiotemporal information from hand-crafted features, we explore how to
model the temporal patterns of speech emotions from dynamic temporal scales.
Towards that goal, we introduce a novel temporal emotional modeling approach
for SER, termed Temporal-aware bI-direction Multi-scale Network (TIM-Net),
which learns multi-scale contextual affective representations from various time
scales. Specifically, TIM-Net first employs temporal-aware blocks to learn
temporal affective representation, then integrates complementary information
from the past and the future to enrich contextual representations, and finally,
fuses multiple time scale features for better adaptation to the emotional
variation. Extensive experimental results on six benchmark SER datasets
demonstrate the superior performance of TIM-Net, gaining 2.34% and 2.61%
improvements of the average UAR and WAR over the second-best on each corpus.
The source code is available at https://github.com/Jiaxin-Ye/TIM-Net_SER.Comment: Accepted by ICASSP 202
Temporal Action Localization with Enhanced Instant Discriminability
Temporal action detection (TAD) aims to detect all action boundaries and
their corresponding categories in an untrimmed video. The unclear boundaries of
actions in videos often result in imprecise predictions of action boundaries by
existing methods. To resolve this issue, we propose a one-stage framework named
TriDet. First, we propose a Trident-head to model the action boundary via an
estimated relative probability distribution around the boundary. Then, we
analyze the rank-loss problem (i.e. instant discriminability deterioration) in
transformer-based methods and propose an efficient scalable-granularity
perception (SGP) layer to mitigate this issue. To further push the limit of
instant discriminability in the video backbone, we leverage the strong
representation capability of pretrained large models and investigate their
performance on TAD. Last, considering the adequate spatial-temporal context for
classification, we design a decoupled feature pyramid network with separate
feature pyramids to incorporate rich spatial context from the large model for
localization. Experimental results demonstrate the robustness of TriDet and its
state-of-the-art performance on multiple TAD datasets, including hierarchical
(multilabel) TAD datasets.Comment: An extended version of the CVPR paper arXiv:2303.07347, submitted to
IJC
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
Customized generation using diffusion models has made impressive progress in
image generation, but remains unsatisfactory in the challenging video
generation task, as it requires the controllability of both subjects and
motions. To that end, we present DreamVideo, a novel approach to generating
personalized videos from a few static images of the desired subject and a few
videos of target motion. DreamVideo decouples this task into two stages,
subject learning and motion learning, by leveraging a pre-trained video
diffusion model. The subject learning aims to accurately capture the fine
appearance of the subject from provided images, which is achieved by combining
textual inversion and fine-tuning of our carefully designed identity adapter.
In motion learning, we architect a motion adapter and fine-tune it on the given
videos to effectively model the target motion pattern. Combining these two
lightweight and efficient adapters allows for flexible customization of any
subject with any motion. Extensive experimental results demonstrate the
superior performance of our DreamVideo over the state-of-the-art methods for
customized video generation. Our project page is at
https://dreamvideo-t2v.github.io
Intra-annual carbon fluxes and resource use efficiency of subtropical urban forests: insights from Chongming Island ecological observatory
Understanding the carbon budget within cities is crucial in the context of carbon peaking and carbon neutrality. This study investigates the carbon source-sink dynamics of urban forest ecosystems using carbon flux observations from the Chongming Island Ecological Observatory in Shanghai. The study aims to reveal the intra-annual variations of carbon fluxes and explore the changes in resource use efficiency of urban forest ecosystems within the framework of the big-leaf model. The results reveal distinct patterns in temperature (Tair), relative humidity (RH), radiation, and vapor pressure deficit (VPD). Diurnal cycles of net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (Reco) exhibit seasonal variations, with higher amplitudes observed from April to September. The observed forest ecosystem acts as a moderate carbon sink (318.47 gC mâ2 yearâ1), with the highest carbon uptake occurring in May and the highest carbon emission in February. During the growing season, the total carbon sink was 225.37 gC mâ2, composed of GPP 1337.01 gC mâ2 and Reco 1111.64 gC mâ2. Water-use efficiency (WUE) and light-use efficiency (LUE) exhibit seasonal variations, while carbon-use efficiency (CUE) declines after May. These findings contribute to our understanding of urban forest carbon dynamics and their potential role in carbon management strategies
Characterization of cellulase production by carbon sources in two Bacillus species
The induction of cellulase production in two Bacillus spp. was studied by means of measuring cellulase activities under the condition of different carbon sources. The results indicate that cellulase could not be induced by cellulose material as a sole carbon source. Instead, they could be induced by monosaccharide or disaccharide with reducing group. Moreover, the expression of cellulase components was synergistic. When cell wall/envelope enzyme and endoenzyme from two Bacillus spp. acted on these inducers, analysis of reaction products by high performance liquid chromatography (HPLC) revealed that cell wall/envelope enzyme and endoenzyme from two Bacillus spp. were inactive on these inducers. It also indicated that these inducers entered cells directly and served function of induction.Keywords: Bacillus, cellulase, induction, carbon source
Exceptional capture of methane at low pressure by an ironâbased metalâorganic framework
The selective capture of methane (CH4) at low concentrations and its separation from N2 are extremely challenging owing to the weak host-guest interactions between CH4 molecules and any sorbent material. Here, we report the exceptional adsorption of CH4 at low pressure and efficient separation of CH4/N2 by MFM-300(Fe). MFM-300(Fe) shows a very high uptake for CH4 of 0.85 mmol gâ1 at 1 mbar and 298 K and a record CH4/N2 selectivity of 45 for porous solids, representing a new benchmark for CH4 capture and CH4/N2 separation. The excellent separation of CH4/N2 by MFM-300(Fe) has been confirmed by dynamic breakthrough experiments. In situ neutron powder diffraction, and solid-state nuclear magnetic resonance and diffuse reflectance infrared Fourier transform spectroscopies, coupled with modelling, reveal a unique and strong binding of CH4 molecules involving Fe-OH···CH4 and C···phenyl ring interactions within the pores of MFM-300(Fe), thus promoting the exceptional adsorption of CH4 at low pressure
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