311 research outputs found
Generative Noisy-Label Learning by Implicit Dicriminative Approximation with Partial Label Prior
The learning with noisy labels has been addressed with both discriminative
and generative models. Although discriminative models have dominated the field
due to their simpler modeling and more efficient computational training
processes, generative models offer a more effective means of disentangling
clean and noisy labels and improving the estimation of the label transition
matrix. However, generative approaches maximize the joint likelihood of noisy
labels and data using a complex formulation that only indirectly optimizes the
model of interest associating data and clean labels. Additionally, these
approaches rely on generative models that are challenging to train and tend to
use uninformative clean label priors. In this paper, we propose a new
generative noisy-label learning approach that addresses these three issues.
First, we propose a new model optimisation that directly associates data and
clean labels. Second, the generative model is implicitly estimated using a
discriminative model, eliminating the inefficient training of a generative
model. Third, we propose a new informative label prior inspired by partial
label learning as supervision signal for noisy label learning. Extensive
experiments on several noisy-label benchmarks demonstrate that our generative
model provides state-of-the-art results while maintaining a similar
computational complexity as discriminative models
A Closer Look at Audio-Visual Semantic Segmentation
Audio-visual segmentation (AVS) is a complex task that involves accurately
segmenting the corresponding sounding object based on audio-visual queries.
Successful audio-visual learning requires two essential components: 1) an
unbiased dataset with high-quality pixel-level multi-class labels, and 2) a
model capable of effectively linking audio information with its corresponding
visual object. However, these two requirements are only partially addressed by
current methods, with training sets containing biased audio-visual data, and
models that generalise poorly beyond this biased training set. In this work, we
propose a new strategy to build cost-effective and relatively unbiased
audio-visual semantic segmentation benchmarks. Our strategy, called Visual
Post-production (VPO), explores the observation that it is not necessary to
have explicit audio-visual pairs extracted from single video sources to build
such benchmarks. We also refine the previously proposed AVSBench to transform
it into the audio-visual semantic segmentation benchmark AVSBench-Single+.
Furthermore, this paper introduces a new pixel-wise audio-visual contrastive
learning method to enable a better generalisation of the model beyond the
training set. We verify the validity of the VPO strategy by showing that
state-of-the-art (SOTA) models trained with datasets built by matching audio
and visual data from different sources or with datasets containing audio and
visual data from the same video source produce almost the same accuracy. Then,
using the proposed VPO benchmarks and AVSBench-Single+, we show that our method
produces more accurate audio-visual semantic segmentation than SOTA models.
Code and dataset will be available
Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques
In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark
Nanoscale pore characteristics of the Upper Permian mudrocks from a transitional environment in and around eastern Sichuan Basin, China
Atmospheric nitrogen deposition in the Yangtze River basin: spatial pattern and source attribution
The Yangtze River basin is one of the world's hotspots for nitrogen (N) deposition and likely plays an important role in China's riverine N output. Here we constructed a basin-scale total dissolved inorganic N (DIN) deposition (bulk plus dry) pattern based on published data at 100 observational sites between 2000 and 2014, and assessed the relative contributions of different reactive N (Nr) emission sectors to total DIN deposition using the GEOS-Chem model. Our results show a significant spatial variation in total DIN deposition across the Yangtze River basin (33.2 kg N ha−1 yr−1 on average), with the highest fluxes occurring mainly in the central basin (e.g., Sichuan, Hubei and Hunan provinces, and Chongqing municipality). This indicates that controlling N deposition should build on mitigation strategies according to local conditions, namely, implementation of stricter control of Nr emissions in N deposition hotspots but moderate control in the areas with low N deposition levels. Total DIN deposition in approximately 82% of the basin area exceeded the critical load of N deposition for semi-natural ecosystems along the basin. On the basin scale, the dominant source of DIN deposition is fertilizer use (40%) relative to livestock (11%), industry (13%), power plant (9%), transportation (9%), and others (18%, which is the sum of contributions from human waste, residential activities, soil, lighting and biomass burning), suggesting that reducing NH3 emissions from improper fertilizer (including chemical and organic fertilizer) application should be a priority in curbing N deposition. This, together with distinct spatial variations in emission sector contributions to total DIN deposition also suggest that, in addition to fertilizer, major emission sectors in different regions of the basin should be considered when developing synergistic control measures
Learning Support and Trivial Prototypes for Interpretable Image Classification
Prototypical part network (ProtoPNet) methods have been designed to achieve
interpretable classification by associating predictions with a set of training
prototypes, which we refer to as trivial prototypes because they are trained to
lie far from the classification boundary in the feature space. Note that it is
possible to make an analogy between ProtoPNet and support vector machine (SVM)
given that the classification from both methods relies on computing similarity
with a set of training points (i.e., trivial prototypes in ProtoPNet, and
support vectors in SVM). However, while trivial prototypes are located far from
the classification boundary, support vectors are located close to this
boundary, and we argue that this discrepancy with the well-established SVM
theory can result in ProtoPNet models with inferior classification accuracy. In
this paper, we aim to improve the classification of ProtoPNet with a new method
to learn support prototypes that lie near the classification boundary in the
feature space, as suggested by the SVM theory. In addition, we target the
improvement of classification results with a new model, named ST-ProtoPNet,
which exploits our support prototypes and the trivial prototypes to provide
more effective classification. Experimental results on CUB-200-2011, Stanford
Cars, and Stanford Dogs datasets demonstrate that ST-ProtoPNet achieves
state-of-the-art classification accuracy and interpretability results. We also
show that the proposed support prototypes tend to be better localised in the
object of interest rather than in the background region
A Deep Q-Network based optimized modulation scheme for Dual-Active-Bridge converter to reduce the RMS current
Transcription of AAT•ATT Triplet Repeats in Escherichia coli Is Silenced by H-NS and IS1E Transposition
The trinucleotide repeats AAT•ATT are simple DNA sequences that potentially form different types of non-B DNA secondary structures and cause genomic instabilities in vivo.The molecular mechanism underlying the maintenance of a 24-triplet AAT•ATT repeat was examined in E. coli by cloning the repeats into the EcoRI site in plasmid pUC18 and into the attB site on the E. coli genome. Either the AAT or the ATT strand acted as lagging strand template in a replication fork. Propagations of the repeats in either orientation on plasmids did not affect colony morphology when triplet repeat transcription using the lacZ promoter was repressed either by supplementing LacI(Q)in trans or by adding glucose into the medium. In contrast, transparent colonies were formed by inducing transcription of the repeats, suggesting that transcription of AAT•ATT repeats was toxic to cell growth. Meanwhile, significant IS1E transposition events were observed both into the triplet repeats region proximal to the promoter side, the promoter region of the lacZ gene, and into the AAT•ATT region itself. Transposition reversed the transparent colony phenotype back into healthy, convex colonies. In contrast, transcription of an 8-triplet AAT•ATT repeat in either orientation on plasmids did not produce significant changes in cell morphology and did not promote IS1E transposition events. We further found that a role of IS1E transposition into plasmids was to inhibit transcription through the repeats, which was influenced by the presence of the H-NS protein, but not of its paralogue StpA.Our findings thus suggest that the longer AAT•ATT triplet repeats in E. coli become vulnerable after transcription. H-NS and its facilitated IS1E transposition can silence long triplet repeats transcription and preserve cell growth and survival
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