255 research outputs found
Explore the Power of Dropout on Few-shot Learning
The generalization power of the pre-trained model is the key for few-shot
deep learning. Dropout is a regularization technique used in traditional deep
learning methods. In this paper, we explore the power of dropout on few-shot
learning and provide some insights about how to use it. Extensive experiments
on the few-shot object detection and few-shot image classification datasets,
i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness
of our method.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0640
Isospin-dependent pairing interaction from nuclear matter calculations
The isospin dependence of the effective pairing interaction is discussed on the basis of the Bardeen, Cooper, and Schrieffer theory of superfluid asymmetric nuclear matter. It is shown that the energy gap, calculated within the mean field approximation in the range from symmetric nuclear matter to pure neutron matter, is not linearly dependent on the symmetry parameter owing to the nonlinear structure of the gap equation. Moreover, the construction of a zero-range effective pairing interaction compatible with the neutron and proton gaps in homogeneous matter is investigated, along with some recent proposals of isospin dependence tested on the nuclear data table
Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation
Existing neural methods have shown great potentials towards generating
informative text from structured tabular data as well as maintaining high
content fidelity. However, few of them shed light on generating personalized
expressions, which often requires well-aligned persona-table-text datasets that
are difficult to obtain. To overcome these obstacles, we explore personalized
table-to-text generation under a zero-shot setting, by assuming no well-aligned
persona-table-text triples are required during training. To this end, we
firstly collect a set of unpaired persona information and then propose a
semi-supervised approach with contrastive persona distillation (S2P-CPD) to
generate personalized context. Specifically, tabular data and persona
information are firstly represented as latent variables separately. Then, we
devise a latent space fusion technique to distill persona information into the
table representation. Besides, a contrastive-based discriminator is employed to
guarantee the style consistency between the generated context and its
corresponding persona. Experimental results on two benchmarks demonstrate
S2P-CPD's ability on keeping both content fidelity and personalized
expressions.Comment: Accepted by ICASSP 202
Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks
Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency
Transient analysis of arm locking controller
Arm locking is one of the key technologies to suppress the laser phase noise
in spaced-based gravitational waves observatories. Since arm locking was
proposed, phase margin criterion was always used as the fundamental design
strategy for the controller development. In this paper, we find that this
empirical method from engineering actually cannot guarantee the arm locking
stability. Therefore, most of the advanced arm locking controllers reported so
far may have instable problems. After comprehensive analysis of the single arm
locking's transient responses, strict analytical stability criterions are
summarized for the first time. These criterions are then generalized to dual
arm locking, modified-dual arm locking and common arm locking, and special
considerations for the design of arm locking controllers in different
architectures are also discussed. It is found that PI controllers can easily
meet our stability criterions in most of the arm locking systems. Using a
simple high gain PI controller, it is possible to suppress the laser phase
noise by 5 orders of magnitude within the science band. Our stability
criterions can also be used in other feedback systems, where several modules
with different delays are connected in parallel.Comment: 24 pages, 24 figure
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders
Pre-trained sentence representations are crucial for identifying significant
sentences in unsupervised document extractive summarization. However, the
traditional two-step paradigm of pre-training and sentence-ranking, creates a
gap due to differing optimization objectives. To address this issue, we argue
that utilizing pre-trained embeddings derived from a process specifically
designed to optimize cohensive and distinctive sentence representations helps
rank significant sentences. To do so, we propose a novel graph pre-training
auto-encoder to obtain sentence embeddings by explicitly modelling
intra-sentential distinctive features and inter-sentential cohesive features
through sentence-word bipartite graphs. These pre-trained sentence
representations are then utilized in a graph-based ranking algorithm for
unsupervised summarization. Our method produces predominant performance for
unsupervised summarization frameworks by providing summary-worthy sentence
representations. It surpasses heavy BERT- or RoBERTa-based sentence
representations in downstream tasks.Comment: Accepted by the 2023 Conference on Empirical Methods in Natural
Language Processing (EMNLP 2023
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