245 research outputs found
Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors
This work proposes a frame-wise online/streaming end-to-end neural
diarization (FS-EEND) method in a frame-in-frame-out fashion. To frame-wisely
detect a flexible number of speakers and extract/update their corresponding
attractors, we propose to leverage a causal speaker embedding encoder and an
online non-autoregressive self-attention-based attractor decoder. A look-ahead
mechanism is adopted to allow leveraging some future frames for effectively
detecting new speakers in real time and adaptively updating speaker attractors.
The proposed method processes the audio stream frame by frame, and has a low
inference latency caused by the look-ahead frames. Experiments show that,
compared with the recently proposed block-wise online methods, our method
FS-EEND achieves state-of-the-art diarization results, with a low inference
latency and computational cost
Local Feature Discriminant Projection
In this paper, we propose a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) for supervised dimensionality reduction of local features. LFDP is able to efficiently seek a subspace to improve the discriminability of local features for classification. We make three novel contributions. First, the proposed LFDP is a general supervised subspace learning algorithm which provides an efficient way for dimensionality reduction of large-scale local feature descriptors. Second, we introduce the Differential Scatter Discriminant Criterion (DSDC) to the subspace learning of local feature descriptors which avoids the matrix singularity problem. Third, we propose a generalized orthogonalization method to impose on projections, leading to a more compact and less redundant subspace. Extensive experimental validation on three benchmark datasets including UIUC-Sports, Scene-15 and MIT Indoor demonstrates that the proposed LFDP outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification
Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs
The emerging Graph Convolutional Network (GCN) has now been widely used in
many domains, and it is challenging to improve the efficiencies of applications
by accelerating the GCN trainings. For the sparsity nature and exploding scales
of input real-world graphs, state-of-the-art GCN training systems (e.g.,
GNNAdvisor) employ graph processing techniques to accelerate the message
exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these
systems treat both the aggregation stages of forward and backward propagation
phases as all-active graph processing procedures that indiscriminately conduct
computation on all vertices of an input graph.
In this paper, we first point out that in a GCN training problem with a given
training set, the aggregation stages of its backward propagation phase (called
as backward aggregations in this paper) can be converted to partially-active
graph processing procedures, which conduct computation on only partial vertices
of the input graph. By leveraging such a finding, we propose an execution path
preparing method that collects and coalesces the data used during backward
propagations of GCN training conducted on GPUs. The experimental results show
that compared with GNNAdvisor, our approach improves the performance of the
backward aggregation of GCN trainings on typical real-world graphs by
1.48x~5.65x. Moreover, the execution path preparing can be conducted either
before the training (during preprocessing) or on-the-fly with the training.
When used during preprocessing, our approach improves the overall GCN training
by 1.05x~1.37x. And when used on-the-fly, our approach improves the overall GCN
training by 1.03x~1.35x
A genetic toolbox for metabolic engineering of Issatchenkia orientalis
The nonconventional yeast Issatchenkia orientalis can grow under highly acidic conditions and has been explored for production of various organic acids. However, its broader application is hampered by the lack of efficient genetic tools to enable sophisticated metabolic manipulations. We recently constructed an episomal plasmid based on the autonomously replicating sequence (ARS) from Saccharomyces cerevisiae (ScARS) in I. orientalis and developed a CRISPR/Cas9 system for multiplex gene deletions. Here we report three additional genetic tools including: (1) identification of a 0.8 kb centromere-like (CEN-L) sequence from the I. orientalis genome by using bioinformatics and functional screening; (2) discovery and characterization of a set of constitutive promoters and terminators under different culture conditions by using RNA-Seq analysis and a fluorescent reporter; and (3) development of a rapid and efficient in vivo DNA assembly method in I. orientalis, which exhibited ∼100% fidelity when assembling a 7 kb-plasmid from seven DNA fragments ranging from 0.7 kb to 1.7 kb. As proof of concept, we used these genetic tools to rapidly construct a functional xylose utilization pathway in I. orientalis
Correlation of visceral adiposity index with chronic kidney disease in the People’s Republic of China: to rediscover the new clinical potential of an old indicator for visceral obesity
An Initiation Kinetics Prediction Model Enables Rational Design of Ruthenium Olefin Metathesis Catalysts Bearing Modified Chelating Benzylidenes
Rational design of second-generation ruthenium olefin metathesis catalysts with desired initiation rates can be enabled by a computational model that is dependent on a single thermodynamic parameter. Using a computational model with no assumption about the specific initiation mechanism, the initiation kinetics of a spectrum of second-generation ruthenium olefin metathesis catalysts bearing modified chelating ortho-alkoxy benzylidenes were predicted in this work. Experimental tests of the validity of the computational model were achieved by the synthesis of a series of ruthenium olefin metathesis catalysts and investigation of initiation rates by ultraviolet–visible light (UV-vis) kinetics, nuclear magnetic resonance (NMR) spectroscopy, and structural characterization by X-ray crystallography. Included in this series of catalysts were 13 catalysts bearing alkoxy groups with varied steric bulk on the chelating benzylidene, ranging from ethoxy to dicyclohexylmethoxy groups. The experimentally observed initiation kinetics of the synthesized catalysts were in good accordance with computational predictions. Notably, the fast initiation rate of the dicyclohexylmethoxy catalyst was successfully predicted by the model, and this complex is believed to be among the fastest initiating Hoveyda–Grubbs-type catalysts reported to date. The compatibility of the predictive model with other catalyst families, including those bearing alternative N-heterocyclic carbene (NHC) ligands or disubstituted alkoxy benzylidenes, was also examined
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