127 research outputs found
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Although Deep Learning (DL) has achieved success in complex Artificial
Intelligence (AI) tasks, it suffers from various notorious problems (e.g.,
feature redundancy, and vanishing or exploding gradients), since updating
parameters in Euclidean space cannot fully exploit the geometric structure of
the solution space. As a promising alternative solution, Riemannian-based DL
uses geometric optimization to update parameters on Riemannian manifolds and
can leverage the underlying geometric information. Accordingly, this article
presents a comprehensive survey of applying geometric optimization in DL. At
first, this article introduces the basic procedure of the geometric
optimization, including various geometric optimizers and some concepts of
Riemannian manifold. Subsequently, this article investigates the application of
geometric optimization in different DL networks in various AI tasks, e.g.,
convolution neural network, recurrent neural network, transfer learning, and
optimal transport. Additionally, typical public toolboxes that implement
optimization on manifold are also discussed. Finally, this article makes a
performance comparison between different deep geometric optimization methods
under image recognition scenarios.Comment: 41 page
DSAM-GN:Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification
In recent years, vehicle re-identification (Re-ID) has gained increasing
importance in various applications such as assisted driving systems, traffic
flow management, and vehicle tracking, due to the growth of intelligent
transportation systems. However, the presence of extraneous background
information and occlusions can interfere with the learning of discriminative
features, leading to significant variations in the same vehicle image across
different scenarios. This paper proposes a method, named graph network based on
dynamic similarity adjacency matrices (DSAM-GN), which incorporates a novel
approach for constructing adjacency matrices to capture spatial relationships
of local features and reduce background noise. Specifically, the proposed
method divides the extracted vehicle features into different patches as nodes
within the graph network. A spatial attention-based similarity adjacency matrix
generation (SASAMG) module is employed to compute similarity matrices of nodes,
and a dynamic erasure operation is applied to disconnect nodes with low
similarity, resulting in similarity adjacency matrices. Finally, the nodes and
similarity adjacency matrices are fed into graph networks to extract more
discriminative features for vehicle Re-ID. Experimental results on public
datasets VeRi-776 and VehicleID demonstrate the effectiveness of the proposed
method compared with recent works.Comment: This paper has been accepted by the 20th Pacific Rim International
Conference on Artificial Intelligence in 202
Targeted splicing therapy: new strategies for colorectal cancer
RNA splicing is the process of forming mature mRNA, which is an essential phase necessary for gene expression and controls many aspects of cell proliferation, survival, and differentiation. Abnormal gene-splicing events are closely related to the development of tumors, and the generation of oncogenic isoform in splicing can promote tumor progression. As a main process of tumor-specific splicing variants, alternative splicing (AS) can promote tumor progression by increasing the production of oncogenic splicing isoforms and/or reducing the production of normal splicing isoforms. This is the focus of current research on the regulation of aberrant tumor splicing. So far, AS has been found to be associated with various aspects of tumor biology, including cell proliferation and invasion, resistance to apoptosis, and sensitivity to different chemotherapeutic drugs. This article will review the abnormal splicing events in colorectal cancer (CRC), especially the tumor-associated splicing variants arising from AS, aiming to offer an insight into CRC-targeted splicing therapy
Research progress of Ustekinumab in the treatment of inflammatory bowel disease
Inflammatory bowel disease (IBD) is a chronic, recurrent gastrointestinal disorder with elusive etiology. Interleukin-12 (IL-12) and IL-23 have emerged as key proinflammatory mediators/cytokines in IBD pathogenesis. Ustekinumab (UST), targeting IL-12 and IL-23, has demonstrated promising efficacy and safety in the treatment of IBD. Recently, UST has become increasingly favored as a potential first-line treatment option. This review delineates USTās mechanism of action, its clinical applications in IBD, including the response rates, strategies for dose optimization for case of partial or lost response, and potential adverse events. This review aims to offer a comprehensive understanding of USTās role as a therapeutic option in IBD management
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Spatial patterns of climate change across the PaleoceneāEocene Thermal Maximum
This study was supported by HeisingāSimons Founda-tion Grants 2016-015 (to J.E.T.), 2016-011 (to M.L. and L.R.K.), 2016-013 (toA.R.), 2016-014 (to G.J.H.), and 2016-012 (to C.J.P.). R.D.M.W. and J.W.B.R. acknowledge funding from the European Research Council under the European Unionās Horizon 2020 Research and Innovation Program Grant 805246. This material is based on work supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by NSF Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (https://arc.ucar.edu/knowledgebase/70549542), were provided by the Computational and Information Systems Laboratory at NCAR.The PaleoceneāEocene Thermal Maximum (PETM; 56 Ma) is one of our best geological analogs for understanding climate dynamics in a āgreenhouseā world. However, proxy data representing the event are only available from select marine and terrestrial sedimentary sequences that are unevenly distributed across Earthās surface, limiting our view of the spatial patterns of climate change. Here, we use paleoclimate data assimilation (DA) to combine climate model and proxy information and create a spatially complete reconstruction of the PETM and the climate state that precedes it (āPETM-DAā). Our data-constrained results support strong polar amplification, which in the absence of an extensive cryosphere, is related to temperature feedbacks and loss of seasonal snow on land. The response of the hydrological cycle to PETM warming consists of a narrowing of the Intertropical Convergence Zone, off-equatorial drying, and an intensification of seasonal monsoons and winter storm tracks. Many of these features are also seen in simulations of future climate change under increasing anthropogenic emissions. Since the PETM-DA yields a spatially complete estimate of surface air temperature, it yields a rigorous estimate of global mean temperature change (5.6 āC; 5.4 āC to 5.9 āC, 95% CI) that can be used to calculate equilibrium climate sensitivity (ECS). We find that PETM ECS was 6.5 āC (5.7 āC to 7.4 āC, 95% CI), which is much higher than the present-day range. This supports the view that climate sensitivity increases substantially when greenhouse gas concentrations are high.Publisher PDFPeer reviewe
Profile of immunoglobulin G N-glycome in COVID-19 patients: A case-control study
Coronavirus disease 2019 (COVID-19) remains a major health challenge globally. Previous studies have suggested that changes in the glycosylation of IgG are closely associated with the severity of COVID-19. This study aimed to compare the profiles of IgG N-glycome between COVID-19 patients and healthy controls. A case-control study was conducted, in which 104 COVID-19 patients and 104 age- and sex-matched healthy individuals were recruited. Serum IgG N-glycome composition was analyzed by hydrophilic interaction liquid chromatography with the ultra-high-performance liquid chromatography (HILIC-UPLC) approach. COVID-19 patients have a decreased level of IgG fucosylation, which upregulates antibody-dependent cell cytotoxicity (ADCC) in acute immune responses. In severe cases, a low level of IgG sialylation contributes to the ADCC-regulated enhancement of inflammatory cytokines. The decreases in sialylation and galactosylation play a role in COVID-19 pathogenesis via the activation of the lectin-initiated alternative complement pathway. IgG N-glycosylation underlines the complex clinical phenotypes of SARS-CoV-2 infection
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A Soy Protein-Based Composite Film with a Hierarchical Structure Inspired by Nacre
This article develops a strategy to fabricate a novel bio-based film with excellent toughness and high strength
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