Integrating Social Circles and Network Representation Learning for Item Recommendation

With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users’ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms

Assessing the potential capability of reconstructing glacial Atlantic water masses and AMOC using multiple proxies in CESM

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Gu, S., Liu, Z., Oppo, D. W., Lynch-Stieglitz, J., Jahn, A., Zhang, J., & Wu, L. Assessing the potential capability of reconstructing glacial Atlantic water masses and AMOC using multiple proxies in CESM. Earth and Planetary Science Letters, 541, (2020): 11629, doi:10.1016/j.epsl.2020.116294.Reconstructing the Atlantic Meridional Overturning Circulation (AMOC) during the Last Glacial Maximum (LGM) is essential for understanding glacial-interglacial climate change and the carbon cycle. However, despite many previous studies, uncertainties remain regarding the glacial water mass distributions in the Atlantic and the AMOC intensity. Here we use an isotope enabled ocean model with multiple geotracers (δ 13 C,E Νd,231 Pa/ 230Th,δ 18 Ο and Δ 14 C) and idealized water tracers to study the potential constraints on LGM ocean circulation from multiple proxies. Our model suggests that the glacial Atlantic water mass distribution can be accurately constrained by the air-sea gas exchange signature of water masses (δ13 C AS), but E Nd might overestimate the North Atlantic Deep Water (NADW) percentage in the deep Atlantic probably because of the boundary source of Nd. A sensitivity experiment with an AMOC of similar geometry but much weaker strength suggests that the correct AMOC geometry is more important than the AMOC strength for simulating the observed glacial δ13 C AS and E Nd and distributions. The kinematic tracer 231Pa/230Th is sensitive to AMOC intensity, but the interpretation might be complicated by the AMOC geometry and AABW transport changes during the LGM. δ 18 Ο in the benthic foraminifera (δ 18 Οc) from the Florida Straits provides a consistent measure of the upper ocean boundary current in the model, which potentially provides an unambiguous method to reconstruct glacial AMOC intensity. Finally, we propose that the moderate difference between AMOC intensity at LGM and PD, if any, is caused by the competition of the responses to CO2 forcing and continental ice sheet forcing.We thank two anonymous reviewers for their useful and constructive comments. We also thank Editor Dr Laura F. Robinson for handling the manuscript. This work is supported by National Science Foundation of China No. 41630527, US National Science Foundation (NSF) P2C2 projects (1401778, 1401802, and 1566432). We would like to acknowledge the high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) and Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation and from Center for High Performance Computing and System Simulation, Pilot National Laboratory for Marine Science and Technology (Qingdao). Data used to produce the results in this study can be obtained from HPSS at CISL: /home/sgu28/CTRACE_decadal or by contacting the authors

Remineralization dominating the δ13 C decrease in the mid-depth Atlantic during the last deglaciation

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Gu, S., Liu, Z., Oppo, D. W., Lynch-Stieglitz, J., Jahn, A., Zhang, J., Lindsay, K., & Wu, L. Remineralization dominating the δ13 C decrease in the mid-depth Atlantic during the last deglaciation. Earth and Planetary Science Letters, 571, (2021): 117106, https://doi.org/10.1016/j.epsl.2021.117106.δ 13 C records from the mid-depth Atlantic show a pronounced decrease during the Heinrich Stadial 1 (HS1), a deglacial episode of dramatically weakened Atlantic Meridional Ocean Circulation (AMOC). Proposed explanations for this mid-depth decrease include a greater fraction of δ 13 C -depleted southern sourced water (SSW), a δ 13 C decrease in the North Atlantic Deep Water (NADW) end-member, and accumulation of the respired organic carbon. However, the relative importance of these proposed mechanisms cannot be quantitatively constrained from current available observations alone. Here we diagnose the individual contributions to the deglacial Atlantic mid-depth δ 13 C change from these mechanisms using a transient simulation with carbon isotopes and idealized tracers. We find that although the fraction of the low- δ 13 C SSW increases in response to a weaker AMOC during HS1, the water mass mixture change only plays a minor role in the mid-depth Atlantic δ 13 C decrease. Instead, increased remineralization due to the AMOC-induced mid-depth ocean ventilation decrease is the dominant cause. In this study, we differentiate between the deep end-members, which are assigned to deep water regions used in previous paleoceanography studies, and the surface end-members, which are from the near-surface water defined from the physical origin of deep water masses. We find that the deep NADW end-member includes additional remineralized material accumulated when sinking from the surface (surface NADW end-member). Therefore, the surface end-members should be used in diagnosing mechanisms of changes. Furthermore, our results suggest that remineralization in the surface end-member is more critical than the remineralization along the transport pathway from the near-surface formation region to the deep ocean, especially during the early deglaciation.This work is supported by US National Science Foundation (NSF) P2C2 projects (1401778, 1401802, and 1566432), and the National Science Foundation of China No. 41630527. S.G. is supported by Shanghai Pujiang program

SARS-CoV-2 N protein induced acute kidney injury in diabetic db/db mice is associated with a Mincle-dependent M1 macrophage activation

“Cytokine storm” is common in critically ill COVID-19 patients, however, mechanisms remain largely unknown. Here, we reported that overexpression of SARS-CoV-2 N protein in diabetic db/db mice significantly increased tubular death and the release of HMGB1, one of the damage-associated molecular patterns (DAMPs), to trigger M1 proinflammatory macrophage activation and production of IL-6, TNF-α, and MCP-1 via a Mincle-Syk/NF-κB-dependent mechanism. This was further confirmed in vitro that overexpression of SARS-CoV-2 N protein caused the release of HMGB1 from injured tubular cells under high AGE conditions, which resulted in M1 macrophage activation and production of proinflammatory cytokines via a Mincle-Syk/NF-κB-dependent mechanism. This was further evidenced by specifically silencing macrophage Mincle to block HMGB1-induced M1 macrophage activation and production of IL-6, TNF-α, and MCP-1 in vitro. Importantly, we also uncovered that treatment with quercetin largely improved SARS-CoV-2 N protein-induced AKI in db/db mice. Mechanistically, we found that quercetin treatment significantly inhibited the release of a DAMP molecule HMGB1 and inactivated M1 pro-inflammatory macrophage while promoting reparative M2 macrophage responses by suppressing Mincle-Syk/NF-κB signaling in vivo and in vitro. In conclusion, SARS-CoV-2 N protein-induced AKI in db/db mice is associated with Mincle-dependent M1 macrophage activation. Inhibition of this pathway may be a mechanism through which quercetin inhibits COVID-19-associated AKI

HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception

Model pre-training is essential in human-centric perception. In this paper, we first introduce masked image modeling (MIM) as a pre-training approach for this task. Upon revisiting the MIM training strategy, we reveal that human structure priors offer significant potential. Motivated by this insight, we further incorporate an intuitive human structure prior - human parts - into pre-training. Specifically, we employ this prior to guide the mask sampling process. Image patches, corresponding to human part regions, have high priority to be masked out. This encourages the model to concentrate more on body structure information during pre-training, yielding substantial benefits across a range of human-centric perception tasks. To further capture human characteristics, we propose a structure-invariant alignment loss that enforces different masked views, guided by the human part prior, to be closely aligned for the same image. We term the entire method as HAP. HAP simply uses a plain ViT as the encoder yet establishes new state-of-the-art performance on 11 human-centric benchmarks, and on-par result on one dataset. For example, HAP achieves 78.1% mAP on MSMT17 for person re-identification, 86.54% mA on PA-100K for pedestrian attribute recognition, 78.2% AP on MS COCO for 2D pose estimation, and 56.0 PA-MPJPE on 3DPW for 3D pose and shape estimation.Comment: Accepted by NeurIPS 202

Minimizing the programming power of phase change memory by using graphene nanoribbon edge-contact

Nonvolatile phase change random access memory (PCRAM) is regarded as one of promising candidates for emerging mass storage in the era of Big Data. However, relatively high programming energy hurdles the further reduction of power consumption in PCRAM. Utilizing narrow edge-contact of graphene can effectively reduce the active volume of phase change material in each cell, and therefore realize low-power operation. Here, we demonstrate that a write energy can be reduced to about ~53.7 fJ in a cell with ~3 nm-wide graphene nanoribbon (GNR) as edge-contact, whose cross-sectional area is only ~1 nm2. It is found that the cycle endurance exhibits an obvious dependence on the bias polarity in the cell with structure asymmetry. If a positive bias was applied to graphene electrode, the endurance can be extended at least one order longer than the case with reversal of polarity. The work represents a great technological advance for the low power PCRAM and could benefit for in-memory computing in future.Comment: 14 pages, 4 figure

Influence of radiation on Hemarthria compressa's genetic variations

Using the material of Hemarthria compressa (L.F.) R.Br. cv. YA’AN, we carried out this research to study the influence of radiation on the genetic variation of plants. Genetic difference was analyzed with expressed sequence tag-simple sequence repeat (EST-SSR) molecular marker through the comparison of 60Co-γ radiation on H. compressa seed stems and original variety. By using 20 primer pairs, 176 polymerase chain reaction (PCR)-amplifications with clear and consistent bands were obtained. The results showed that 155 of 176 bands were polymorphic, which indicating an 88.07% polymorphism rate, and each pair of primers had 8.8 amplified bands on average; the amplitude of polymorphism information content was 0.4709–0.6952 with an average value 0.6081. The genetic similarity coefficient of H. compressa and its mutants ranged from 0.4318 to 0.8239 with an average of 0.6671. As a consequence, existence of genetic differences between the mutants and the basic material was proved.We gratefully acknowledge financial support from the Modern Agro-industry Technology Research System (CARS-34) and the Sichuan Province Breeding Research grant (2016NZ0098-11).Peer reviewe

Mobile Robot Path Planning Based on a Generalized Wavefront Algorithm

This study develops a generalized wavefront algorithm for conducting mobile robot path planning. The algorithm combines multiple target point sets, multilevel grid costs, logarithmic expansion around obstacles, and subsequent path optimization. The planning performances obtained with the proposed algorithm, the A∗ algorithm, and the rapidly exploring random tree (RRT) algorithm optimized using a Bézier curve are compared using simulations with different grid map environments comprising different numbers of obstacles with varying shapes. The results demonstrate that the generalized wavefront algorithm generates smooth and safe paths around obstacles that meet the required kinematic conditions associated with the actual maneuverability of mobile robots and significantly reduces the planned path length compared with the results obtained with the A∗ algorithm and the optimized RRT algorithm with a computation time acceptable for real-time applications. Therefore, the generated path is not only smooth and effective but also conforms to actual robot maneuverability in practical applications