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

A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b)

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 UST’s 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 UST’s role as a therapeutic option in IBD management

Study on overlying strata containing primary fractures migration and spatial-temporal characteristics of water gushing (leaching) caused by mining field disturbance

The super-thick, high-pressure, medium-strong water-rich Luohe Formation aquifer is overlying in the Binchang mining area of Shanxi Province, and the fractures in the overlying rock are developed, it makes the water channel easier to communicate with the aquifer and stope of Luohe Formation, resulting in the increase of water inflow and area in the stope. In order to study the morphological characteristics of water inrush induced by the network of water-conducting channels formed by primary fractures communicating with the aquifer of the thick Luohe Formation under the influence of mining, the solid-flow coupling similar material simulation test was carried out based on the similar simulation physical experiment system of water-sand inrush in overburden rock. The results show that when the working face is advanced to 140 m, the lower strata of the bed separation are broken in advance due to the influence of the primary fractures. The left incomplete bed separation space and the triangular space formed by the right cantilever beam support form the “Z” bed separation space. When the working face is advanced to 160 m, two “Z-type” bed separation spaces are developed in the overlying strata, which are interconnected with the primary fractures and mining-induced fractures to form a water channel network. The form of gushing (leaching) water in the stope changed from ‘ drip-drip and flow-flow-multi-state ’, and the overall gushing (leaching) water volume increased first and then decreased. The water pressure of overlying strata and the advancing distance of the working face show a segmented evolution characteristic of decreasing first and then increasing. The minimum interval and the position of the inflection point of the segmentation increase with the increase of the distance between the monitoring point and the open-off cut. The final water pressure values near the central area of the goaf are greater than the two boundary monitoring points. The analysis results show that the existence of primary fractures promotes the development of water-conducting fracture channel network, accelerates the process of water transport, and induces the formation and development of water gushing (leaching) in the stope. The research results clarify the influence of primary fractures on the distribution characteristics of water conduction channel network and the evolution law of water gushing (leaching) form morphology, and explain the conduction mechanism of thick and high confined aquifer water to stope water inrush

Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum

This study was supported by Heising–Simons 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 Union’s 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 Paleocene–Eocene Thermal Maximum (PETM; 56 Ma) is one of our best geological analogs for understanding climate dynamics in a “greenhouse” world. However, proxy data representing the event are only available from select marine and terrestrial sedimentary sequences that are unevenly distributed across Earth’s 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 (“PETM-DA”). 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 ∘C; 5.4 ∘C to 5.9 ∘C, 95% CI) that can be used to calculate equilibrium climate sensitivity (ECS). We find that PETM ECS was 6.5 ∘C (5.7 ∘C to 7.4 ∘C, 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