17,856 research outputs found
A multiresolution approach for tensor completion from coarse and partial observations
Existing tensor completion formulation mostly relies on partial observations from a single tensor. However, tensors extracted from real-world data often are more complex due to: (i) Partial observation: Only a small subset of tensor elements are available. (ii) Coarse observation: Some tensor modes only present coarse and aggregated patterns (e.g., monthly summary instead of daily reports). In this paper, we are given a subset of the tensor and some aggregated/coarse observations (along with one or more modes) and seek to recover the original fine-granular tensor with low-rank factorization. We formulate a coupled tensor completion problem and propose an efficient Multi-resolution Tensor Completion model (MTC) to solve the problem. Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares. MTC ensures low computational and space complexity. We evaluate our model on two COVID-19 related spatio-temporal tensors. The experiments show that MTC could provide 65.20% and 75.79% percentage of fitness (PoF) in tensor completion with only 5% fine granular observations, which is a 27.96% relative improvement over the best baseline. To evaluate the learned low-rank factors, we also design a tensor prediction task for daily and cumulative disease case predictions, where MTC achieves 50% in PoF and 30% relative improvements over the best baseline.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste
Nonconvex Tensor Relative Total Variation for Image Completion
Image completion, which falls to a special type of inverse problems, is an important but challenging task. The difficulties lie in that (i) the datasets usually appear to be multi-dimensional; (ii) the unavailable or corrupted data entries are randomly distributed. Recently, low-rank priors have gained importance in matrix completion problems and signal separation; however, due to the complexity of multi-dimensional data, using a low-rank prior by itself is often insufficient to achieve desirable completion, which requires a more comprehensive approach. In this paper, different from current available approaches, we develop a new approach, called relative total variation (TRTV), under the tensor framework, to effectively integrate the local and global image information for data processing. Based on our proposed framework, a completion model embedded with TRTV and tensor p-shrinkage nuclear norm minimization with suitable regularization is established. An alternating direction method of multiplier (ADMM)-based algorithm under our framework is presented. Extensive experiments in terms of denoising and completion tasks demonstrate our proposed method are not only effective but also superior to existing approaches in the literature
Thread-safe lattice Boltzmann for high-performance computing on GPUs
We present thread-safe, highly-optimized lattice Boltzmann implementations,
specifically aimed at exploiting the high memory bandwidth of GPU-based
architectures. At variance with standard approaches to LB coding, the proposed
strategy, based on the reconstruction of the post-collision distribution via
Hermite projection, enforces data locality and avoids the onset of memory
dependencies, which may arise during the propagation step, with no need to
resort to more complex streaming strategies. The thread-safe lattice Boltzmann
achieves peak performances, both in two and three dimensions and it allows to
sensibly reduce the allocated memory ( tens of GigaBytes for order billions
lattice nodes simulations) by retaining the algorithmic simplicity of standard
LB computing. Our findings open attractive prospects for high-performance
simulations of complex flows on GPU-based architectures
Differentiable programming tensor networks for Kitaev magnets
We present a general computational framework to investigate ground state
properties of quantum spin models on infinite two-dimensional lattices using
automatic differentiation-based gradient optimization of infinite projected
entangled-pair states. The approach exploits the variational uniform matrix
product states to contract infinite tensor networks with unit-cell structure
and incorporates automatic differentiation to optimize the local tensors. We
applied this framework to the Kitaev-type model, which involves complex
interactions and competing ground states. To evaluate the accuracy of this
method, we compared the results with exact solutions for the Kitaev model and
found that it has a better agreement for various observables compared to
previous tensor network calculations based on imaginary-time projection.
Additionally, by finding out the ground state with lower variational energy
compared to previous studies, we provided convincing evidence for the existence
of nematic paramagnetic phases and 18-site configuration in the phase diagram
of the - model. Furthermore, in the case of the realistic
--- model for the Kitaev material -RuCl, we
discovered a non-colinear zigzag ground state. Lastly, we also find that the
strength of the critical out-of-plane magnetic field that suppresses such a
zigzag state has a lower transition field value than the previous
finite-cylinder calculations. The framework is versatile and will be useful for
a quick scan of phase diagrams for a broad class of quantum spin models
The signs of computer tomography combined with artificial intelligence can indicate the correlation between status of consciousness and primary brainstem hemorrhage of patients
BackgroundFor patients of primary brainstem hemorrhage (PBH), it is crucial to find a method that can quickly and accurately predict the correlation between status of consciousness and PBH.ObjectiveTo analyze the value of computer tomography (CT) signs in combination with artificial intelligence (AI) technique in predicting the correlation between status of consciousness and PBH.MethodsA total of 120 patients with PBH were enrolled from August 2011 to March 2021 according to the criteria. Patients were divided into three groups [consciousness, minimally conscious state (MCS) and coma] based on the status of consciousness. Then, first, Mann–Whitney U test and Spearman rank correlation test were used on the factors: gender, age, stages of intracerebral hemorrhage, CT signs with AI or radiology physicians, hemorrhage involving the midbrain or ventricular system. We collected hemorrhage volumes and mean CT values with AI. Second, those significant factors were screened out by the Mann–Whitney U test and those highly or moderately correlated by Spearman’s rank correlation test, and a further ordinal multinomial logistic regression analysis was performed to find independent predictors of the status of consciousness. At last, receiver operating characteristic (ROC) curves were drawn to calculate the hemorrhage volume for predictively assessing the status of consciousness.ResultsPreliminary meaningful variables include hemorrhage involving the midbrain or ventricular system, hemorrhage volume, grade of hematoma shape and density, and CT value from Mann–Whitney U test and Spearman rank correlation test. It is further shown by ordinal multinomial logistic regression analysis that hemorrhage volume and hemorrhage involving the ventricular system are two major predictors of the status of consciousness. It showed from ROC that the hemorrhage volumes of <3.040 mL, 3.040 ~ 6.225 mL and >6.225 mL correspond to consciousness, MCS or coma, respectively. If the hemorrhage volume is the same, hemorrhage involving the ventricular system should be correlated with more severe disorders of consciousness (DOC).ConclusionCT signs combined with AI can predict the correlation between status of consciousness and PBH. Hemorrhage volume and hemorrhage involving the ventricular system are two independent factors, with hemorrhage volume in particular reaching quantitative predictions
mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds
Connectomics has emerged as a powerful tool in neuroimaging and has spurred
recent advancements in statistical and machine learning methods for
connectivity data. Despite connectomes inhabiting a matrix manifold, most
analytical frameworks ignore the underlying data geometry. This is largely
because simple operations, such as mean estimation, do not have easily
computable closed-form solutions. We propose a geometrically aware neural
framework for connectomes, i.e., the mSPD-NN, designed to estimate the geodesic
mean of a collections of symmetric positive definite (SPD) matrices. The
mSPD-NN is comprised of bilinear fully connected layers with tied weights and
utilizes a novel loss function to optimize the matrix-normal equation arising
from Fr\'echet mean estimation. Via experiments on synthetic data, we
demonstrate the efficacy of our mSPD-NN against common alternatives for SPD
mean estimation, providing competitive performance in terms of scalability and
robustness to noise. We illustrate the real-world flexibility of the mSPD-NN in
multiple experiments on rs-fMRI data and demonstrate that it uncovers stable
biomarkers associated with subtle network differences among patients with
ADHD-ASD comorbidities and healthy controls.Comment: Accepted into IPMI 202
GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering
In this work, we present an end-to-end Knowledge Graph Question Answering
(KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text
pre-trained language model. The model takes a question in natural language as
input and produces a simpler form of the intended SPARQL query. In the simpler
form, the model does not directly produce entity and relation IDs. Instead, it
produces corresponding entity and relation labels. The labels are grounded to
KG entity and relation IDs in a subsequent step. To further improve the
results, we instruct the model to produce a truncated version of the KG
embedding for each entity. The truncated KG embedding enables a finer search
for disambiguation purposes. We find that T5 is able to learn the truncated KG
embeddings without any change of loss function, improving KGQA performance. As
a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata
datasets on end-to-end KGQA over Wikidata.Comment: 16 pages single column format accepted at ESWC 2023 research trac
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Natural videos captured by consumer cameras often suffer from low framerate
and motion blur due to the combination of dynamic scene complexity, lens and
sensor imperfection, and less than ideal exposure setting. As a result,
computational methods that jointly perform video frame interpolation and
deblurring begin to emerge with the unrealistic assumption that the exposure
time is known and fixed. In this work, we aim ambitiously for a more realistic
and challenging task - joint video multi-frame interpolation and deblurring
under unknown exposure time. Toward this goal, we first adopt a variant of
supervised contrastive learning to construct an exposure-aware representation
from input blurred frames. We then train two U-Nets for intra-motion and
inter-motion analysis, respectively, adapting to the learned exposure
representation via gain tuning. We finally build our video reconstruction
network upon the exposure and motion representation by progressive
exposure-adaptive convolution and motion refinement. Extensive experiments on
both simulated and real-world datasets show that our optimized method achieves
notable performance gains over the state-of-the-art on the joint video x8
interpolation and deblurring task. Moreover, on the seemingly implausible x16
interpolation task, our method outperforms existing methods by more than 1.5 dB
in terms of PSNR.Comment: Accepted by CVPR 2023, available at
https://github.com/shangwei5/VIDU
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