Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search

In e-commerce search, personalized retrieval is a crucial technique for improving user shopping experience. Recent works in this domain have achieved significant improvements by the representation learning paradigm, e.g., embedding-based retrieval (EBR) and collaborative filtering (CF). EBR methods do not sufficiently exploit the useful collaborative signal and are difficult to learn the representations of long-tail item well. Graph-based CF methods improve personalization by modeling collaborative signal within the user click graph. However, existing Graph-based methods ignore user's multiple behaviours, such as click/purchase and the relevance constraint between user behaviours and items.In this paper, we propose a Graph Contrastive Learning with Multi-Objective (GCL-MO) collaborative filtering model, which solves the problems of weak relevance and incomplete personalization in e-commerce search. Specifically, GCL-MO builds a homogeneous graph of items and then optimizes a multi-objective function of personalization and relevance. Moreover, we propose a modified contrastive loss for multi-objectives graph learning, which avoids the mutual suppression among positive samples and thus improves the generalization and robustness of long-tail item representations. These learned item embeddings are then used for personalized retrieval by constructing an efficient offline-to-online inverted table. GCL-MO outperforms the online collaborative filtering baseline in both offline/online experimental metrics and shows a significant improvement in the online A/B testing of Taobao search

Digital Twin Brain: a simulation and assimilation platform for whole human brain

In this work, we present a computing platform named digital twin brain (DTB) that can simulate spiking neuronal networks of the whole human brain scale and more importantly, a personalized biological brain structure. In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive. We utilize a number of optimization techniques to balance and integrate the computation loads and communication traffics from the heterogeneous biological structure to the general GPU-based HPC and achieve leading simulation performance for the whole human brain-scaled spiking neuronal networks. On the other hand, the biological structure, equipped with a mesoscopic data assimilation, enables the DTB to investigate brain cognitive function by a reverse-engineering method, which is demonstrated by a digital experiment of visual evaluation on the DTB. Furthermore, we believe that the developing DTB will be a promising powerful platform for a large of research orients including brain-inspiredintelligence, rain disease medicine and brain-machine interface.Comment: 12 pages, 11 figure

Evaluating biochar and its modifications for the removal of ammonium, nitrate, and phosphate in water

Removal of nitrogen (N) and phosphorus (P) from water through the use of various sorbents is often considered an economically viable way for supplementing conventional methods. Biochar has been widely studied for its potential adsorption capabilities for soluble N and P, but the performance of different types of biochars can vary widely. In this review, we summarized the adsorption capacities of biochars in removing N (NH4-N and NO3-N) and P (PO4-P) based on the reported data, and discussed the possible mechanisms and influencing factors. In general, the NH4-N adsorption capacity of unmodified biochars is relatively low, at levels of less than 20 mg/g. This adsorption is mainly via ion exchange and/or interactions with oxygen-containing functional groups on biochar surfaces. The affinity is even lower for NO3-N, because of electrostatic repulsion by negatively charged biochar surfaces. Precipitation of PO4-P by metals/metal oxides in biochar is the primary mechanism for PO4-P removal. Biochars modified by metals have a significantly higher capacity to remove NH4-N, NO3-N, and PO4-P than unmodified biochar, due to the change in surface charge and the increase in metal oxides on the biochar surface. Ambient conditions in the aqueous phase, including temperature, pH, and co-existing ions, can significantly alter the adsorption of N and P by biochars, indicating the importance of optimal processing parameters for N and P removal. However, the release of endogenous N and P from biochar to water can impede its performance, and the presence of competing ions in water poses practical challenges for the use of biochar for nutrient removal. This review demonstrates that progress is needed to improve the performance of biochars and overcome challenges before the widespread field application of biochar for N and P removal is realized

Fine-Tuning Bi2Te3-Copper Selenide Alloys Enables an Efficient n-Type Thermoelectric Conversion

Bismuth tellurides is one of the most promising thermoelectric (TE) material candidates in low-temperature application circumstances, but the n-type thermoelectric property is relatively low compared to the p-type counterpart and still needs to be improved. Herein, we incorporated different copper selenides (CuSe, Cu3Se2 and Cu2−xSe) into a Bi2Te3 matrix to create the alloy by grinding and successive sintering to enable higher thermoelectric performance. The results demonstrated that all alloys achieved n-type TE characteristics and Bi2Te3-CuSe exhibited the best Seebeck coefficient and power factor among them. Along with the low thermal conductivity, the maximum dimensionless TE figure of merit (ZT) value of 1.64 at 573 K was delivered for Bi2Te3-CuSe alloy, which is among the best reported results in the n-type Bi2Te3-based TE materials to the best of our knowledge. The improved TE properties should be related to the co-doping process of Se and Cu. Our investigation shows a new method to enhance the performance of n-type TE materials by appropriate co-doping or alloying

Development and characterisation of an AI-in-the-loop testing platform for floating wind turbines PART I : construction, validation, and benchmark testing

Model testing is an inevitable means to verify design optimization because it is more economical than prototype testing and more reliable than numerical simulation. However, in the floating wind turbine experiment, the hydrodynamic Froude number and the aerodynamic Reynolds number cannot satisfy similar rules simultaneously, making the scale effect problem a major difficulty in the experiment. Therefore, this paper innovatively introduces AI-prediction-in-the-loop experimental technologies. The Froude similarity criterion is applied to model production and physical set-up. A Froude-similar wind turbine model (except for the blades) is placed in the wave flume and the floating platform moves. The response measurement data is input into the AI prediction module to perform real-time prediction of aerodynamic loads such as rotor thrust, output the calculation results and control the simulated load of the actuator, thereby realizing aerodynamic-hydrodynamic-structural coupling experiments under Froude's rules. Characterization benchmark and tank tests are carried out to validate the AI-in-the-loop testing methodology, and the results show good agreement between measured and predicted rotor thrust values across both high and low frequencies. Moreover, the time delay and systematic uncertainty of the proposed testing platform are identified for the first time

DTBVis: An interactive visual comparison system for digital twin brain and human brain

The digital twin brain (DTB) computing model from brain-inspired computing research is an emerging artificial intelligence technique, which is realized by a computational modeling approach of hardware and software. It can achieve various cognitive abilities and their synergistic mechanisms in a manner similar to the human brain. Given that the task of the DTB is to simulate the functions of the human brain, comparing the similarities and differences between the two is crucial. However, the visualization study of the DTB is still under-researched. Moreover, the complexity of the datasets (multilevel spatiotemporal granularity and different types of comparison tasks) presents new challenges to the analysis and exploration of visualization. Therefore, in this study, we proposed DTBVis, a visual analytics system that supports comparison tasks for the DTB. DTBVis supports iterative explorations from different levels and at different granularities. Combined with automatic similarity recommendation, and high-dimensional exploration, DTBVis can assist experts in understanding the similarities and differences between the DTB and the human brain, thus helping them adjust their model and enhance its functionality. The highest level of DTBVis shows an overview of the datasets from the brain, which is used for comparison and exploration of the function and structure of the DTB and the human brain. The medium level is used for the comparison and exploration of a designated brain region. The low level can analyze a designated brain voxel. We worked closely with experts of brain science and held regular seminars with them. Feedback from the experts indicates that our approach helps them conduct comparative studies of the DTB and human brain and make modeling adjustments of the DTB through intuitive visual comparisons and interactive explorations

Development of a ferroptosis-based molecular markers for predicting RFS in prostate cancer patients

Abstract The goal of this study was to develop a ferroptosis-based molecular signature that can predict recurrence-free survival (RFS) in patients with prostate cancer (PCa). In this study, we obtained ferroptosis-related genes (FRGs) in FerrDb database and clinical transcriptome data in TCGA database and GEO database. Consensus cluster analysis was used to identify three molecular markers of ferroptosis in PCa with differential expression of 40 FRGs, including PD-L1 expression levels. We conducted a new ferroptosis-related signature for PCa RFS using four FRGs identified through univariate and multivariate Cox regression analyses. The signature was validated in the training, testing, and validation cohorts, and it demonstrated remarkable results in the area under the time-dependent receiver operating characteristic (ROC) curve of 0.757, 0.715, and 0.732, respectively. Additionally, we observed that younger patients, those with stage T III and stage T IV, stage N0, cluster 1, and cluster 2 PCa were more accurately predicted by the signature as independent predictors of RFS. DU-145 and RWPE-1 cells were successfully analyzed by qRT-PCR and Western blot for ASNS, GPT2, RRM2, and NFE2L2. In summary, we developed a novel ferroptosis-based signature for RFS in PC, utilizing four FRGs identified through univariate and multivariate Cox regression analyses. This signature was rigorously validated across training, testing, and validation cohorts, demonstrating exceptional performance as evidenced by its ROC curves. Notably, our findings indicate that this signature is particularly effective as an independent predictor of RFS in younger patients or those with stage T III and T IV, stage N0, and in clusters 1 and 2. Finally, we confirmed the expression of these four FRGs in DU-145 and RWPE-1 cell lines

Interleukin-27 as a Novel Biomarker for Early Cardiopulmonary Failure in Enterovirus 71-Infected Children with Central Nervous System Involvement

Enterovirus 71 (EV71) is a major pathogen for severe hand, foot, and mouth disease (HFMD), which leads to severe neurological complications and has high morbidity and mortality. Reliable biomarker for the prediction of deterioration in EV71-infected children with central nervous system (CNS) involvement may reduce the cardiopulmonary failure and mortality. Here, we found that serum IL-27 levels were significantly higher in stage III EV71-infected HFMD patients with early cardiopulmonary failure and strong correlation with CRP levels. IL27p28 polymorphisms (rs153109, rs17855750, and rs181206) did not influence IL-27 production, and these three SNPs were not associated with EV71 infection risk and clinical stage. IL-27 can be used as an prediction indicator for early cardiopulmonary failure in EV71-infected children with CNS involvement

Interleukin-27 as a Novel Biomarker for Early Cardiopulmonary Failure in Enterovirus 71-Infected Children with Central Nervous System Involvement

Enterovirus 71 (EV71) is a major pathogen for severe hand, foot, and mouth disease (HFMD), which leads to severe neurological complications and has high morbidity and mortality. Reliable biomarker for the prediction of deterioration in EV71-infected children with central nervous system (CNS) involvement may reduce the cardiopulmonary failure and mortality. Here, we found that serum IL-27 levels were significantly higher in stage III EV71-infected HFMD patients with early cardiopulmonary failure and strong correlation with CRP levels. IL27p28 polymorphisms (rs153109, rs17855750, and rs181206) did not influence IL-27 production, and these three SNPs were not associated with EV71 infection risk and clinical stage. IL-27 can be used as an prediction indicator for early cardiopulmonary failure in EV71-infected children with CNS involvement