281 research outputs found
Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning
Underwater Sound Speed Profile (SSP) distribution has great influence on the
propagation mode of acoustic signal, thus the fast and accurate estimation of
SSP is of great importance in building underwater observation systems. The
state-of-the-art SSP inversion methods include frameworks of matched field
processing (MFP), compressive sensing (CS), and feedforeward neural networks
(FNN), among which the FNN shows better real-time performance while maintain
the same level of accuracy. However, the training of FNN needs quite a lot
historical SSP samples, which is diffcult to be satisfied in many ocean areas.
This situation is called few-shot learning. To tackle this issue, we propose a
multi-task learning (MTL) model with partial parameter sharing among different
traning tasks. By MTL, common features could be extracted, thus accelerating
the learning process on given tasks, and reducing the demand for reference
samples, so as to enhance the generalization ability in few-shot learning. To
verify the feasibility and effectiveness of MTL, a deep-ocean experiment was
held in April 2023 at the South China Sea. Results shows that MTL outperforms
the state-of-the-art methods in terms of accuracy for SSP inversion, while
inherits the real-time advantage of FNN during the inversion stage
Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems
Modern neural collaborative filtering techniques are critical to the success
of e-commerce, social media, and content-sharing platforms. However, despite
technical advances -- for every new application domain, we need to train an NCF
model from scratch. In contrast, pre-trained vision and language models are
routinely applied to diverse applications directly (zero-shot) or with limited
fine-tuning. Inspired by the impact of pre-trained models, we explore the
possibility of pre-trained recommender models that support building recommender
systems in new domains, with minimal or no retraining, without the use of any
auxiliary user or item information. Zero-shot recommendation without auxiliary
information is challenging because we cannot form associations between users
and items across datasets when there are no overlapping users or items. Our
fundamental insight is that the statistical characteristics of the user-item
interaction matrix are universally available across different domains and
datasets. Thus, we use the statistical characteristics of the user-item
interaction matrix to identify dataset-independent representations for users
and items. We show how to learn universal (i.e., supporting zero-shot
adaptation without user or item auxiliary information) representations for
nodes and edges from the bipartite user-item interaction graph. We learn
representations by exploiting the statistical properties of the interaction
data, including user and item marginals, and the size and density distributions
of their clusters
Jahn-Teller distortion driven ferromagnetism in a perovskite fluoride monolayer
The Jahn-Teller distortion and the resulting orbital order usually cause some
fascinating correlated electronic behaviors, and generally lead to
antiferromagnetism in perovskite bulks. Here we demonstrate that the
Jahn-Teller distortion present in the perovskite fluoride KCrF bulk can be
retained to the two-dimensional limit, resulting in a staggered orbital order
and ferromagnetism in the perovskite monolayer. Octahedral tilt and rotation
distortion also appear in the ground-state structure of the perovskite
monolayer, which have minor effects on the electronic and magnetic properties
with respect to the Jahn-Teller distortion. In addition, in the prototype phase
without structural distortion, the partial occupation of the orbitals
leads to a ferromagnetic metallic state. This work facilitates the design of
two-dimensional ferromagnets and functional properties based on Jahn-Teller
distortion and orbital orderComment: 8 pages, 5 figures, 1 tabl
Causal conditional hidden Markov model for multimodal traffic prediction
Multimodal traffic flow can reflect the health of the transportation system,
and its prediction is crucial to urban traffic management. Recent works
overemphasize spatio-temporal correlations of traffic flow, ignoring the
physical concepts that lead to the generation of observations and their causal
relationship. Spatio-temporal correlations are considered unstable under the
influence of different conditions, and spurious correlations may exist in
observations. In this paper, we analyze the physical concepts affecting the
generation of multimode traffic flow from the perspective of the observation
generation principle and propose a Causal Conditional Hidden Markov Model
(CCHMM) to predict multimodal traffic flow. In the latent variables inference
stage, a posterior network disentangles the causal representations of the
concepts of interest from conditional information and observations, and a
causal propagation module mines their causal relationship. In the data
generation stage, a prior network samples the causal latent variables from the
prior distribution and feeds them into the generator to generate multimodal
traffic flow. We use a mutually supervised training method for the prior and
posterior to enhance the identifiability of the model. Experiments on
real-world datasets show that CCHMM can effectively disentangle causal
representations of concepts of interest and identify causality, and accurately
predict multimodal traffic flow.Comment: 8 pages, 5 figure
Gene expression profile indicates involvement of NO in Camellia sinensis pollen tube growth at low temperature
DEGs identified from the comparison between control (CsPT-CK) and 4 °C-treated (CsPT-LT) pollen tbues. All of the samples were replicated three times. CK and LT FPKM: fragments per kb per million reads for each unigene in the CK and LT libraries, respectively. The log2Ratio (LT/CK): ratio between the FPKM of LT and CK. The absolute values of log2Ratio > 1 and probability > 0.7 were used as threshold for assigning significance. Annotation of DEGs against NR, NT, Swiss-Prot protein, KEGG, COG and GO were all reported in the tables. “-”: no hit. (XLS 381 kb
Online search for UAV relay placement for free-space optical communication under shadowing
Unmanned aerial vehicle (UAV) relaying is promising to overcome the challenge of signal blockage in free-space optical (FSO) communications for users in dense urban area. Existing works on UAV relay placement are mostly based on simplified line-of-sight (LOS) channel models or probabilistic channel models, and thus fail to capture the actual LOS status of the optical communication link. By contrast, this paper studies three-dimensional (3D) online placement for a UAV to construct relay links to two ground users in deep shadow with LOS guarantees. By analyzing the properties of the UAV relay placement problem, it is found that searching on a plane that approximates the equipotential surface can achieve a good performance and complexity trade-off for a good placement of the UAV relay in 3D. Based on these insights, a two-stage online search algorithm on an equipotential plane (TOSEP) is developed for a special case where the equipotential surface turns out to be an equipotential plane. For the general case, a strategy called gradient projected online search algorithm on an approximated equipotential plane (GOSAEP) is developed, which approximates the equipotential surface with a perpendicular plane using the gradient projection method. Numerical experiments are conducted over a real-world city topology, and it is shown that the GOSAEP achieves over 95% of the performance of the exhaustive 3D search scheme within a 300-m search length
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