Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations

Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, challenge still remains in balancing the trade-off of various tasks since model performance is sensitive to the relationships between them. Less correlated or even conflict tasks will deteriorate the performance by introducing unhelpful or negative information. Therefore, it is important to efficiently exploit and learn fine-grained feature representation corresponding to each task. In this paper, we propose an Adaptive Pattern Extraction Multi-task (APEM) framework, which is adaptive and flexible for large-scale industrial application. APEM is able to fully utilize the feature information by learning the interactions between the input feature fields and extracted corresponding tasks-specific information. We first introduce a DeepAuto Group Transformer module to automatically and efficiently enhance the feature expressivity with a modified set attention mechanism and a Squeeze-and-Excitation operation. Second, explicit Pattern Selector is introduced to further enable selectively feature representation learning by adaptive task-indicator vectors. Empirical evaluations show that APEM outperforms the state-of-the-art MTL methods on public and real-world financial services datasets. More importantly, we explore the online performance of APEM in a real industrial-level recommendation scenario.Comment: 18 pages, 9 figure

Linking Corporate Social Responsibility with Reputation and Brand of the Firm

There are no agreements among scientists in terms of relationships between Corporate Social Responsibility (CSR), Corporate Identity and Corporate Image (CI), Corporate Reputation (CR), and Corporate Brand (CB), and their mutual impacts as well the definition of these constructs cause a lot of scientific debates among scholars. The paper analyses the relationship between CSR, Corporate Image, Corporate Reputation and Corporate Brand and their outcomes linked to the financial and other benefits of the firm and society. The main studies that are dealing with this subject were analysed, and the theoretical model that is linking these constructs was developed. The proposed model can be used for carrying empirical studies in specific industries and branches of the economy in order to assess the main measures to increase Corporate Reputation and maximise its outcome

Information strategy failure:personal interaction success, in urban residential food waste segregation

AbstractDirect measurements were taken of residential food waste sorting in a sample from over 5000 communities (5 million households) assigned to a pilot program delivered by government branches in Shanghai which relied on an information strategy for implementation. The results are compared to a population of N = 36 similar communities (36,000 households) assigned to a different program which involved considerable personal interaction. The results show that the information–based program communities did not noticeably sort their waste, whereas those given personal interaction approaches were very successful, with purity rates of 95%(8) and extra costs of about 50 RMB (8 USD) per household. This is a rare direct comparison of two different programs at such large scales, 6–36 months after launch, and suggests that personal interaction approaches should be considered by policy makers. Qualitative key informant interviews yielded data on each program's activities, which provide suggestions for further studies of the underlying behaviour change determinants involved

Proteomic responses to ocean acidification of the marine diazotroph Trichodesmium under iron-replete and iron-limited conditions

Abstract(#br)Growth and dinitrogen (N 2 ) fixation of the globally important diazotrophic cyanobacteria Trichodesmium are often limited by iron (Fe) availability in surface seawaters. To systematically examine the combined effects of Fe limitation and ocean acidification (OA), T. erythraeum strain IMS101 was acclimated to both Fe-replete and Fe-limited concentrations under ambient and acidified conditions. Proteomic analysis showed that OA affected a wider range of proteins under Fe-limited conditions compared to Fe-replete conditions. OA also led to an intensification of Fe deficiency in key cellular processes (e.g., photosystem I and chlorophyll a synthesis) in already Fe-limited T. erythraeum . This is a result of reallocating Fe from these processes to Fe-rich nitrogenase to..

Nutrient regulation of biological nitrogen fixation across the tropical western North Pacific

Nitrogen fixation is critical for the biological productivity of the ocean, but clear mechanistic controls on this process remain elusive. Here, we investigate the abundance, activity, and drivers of nitrogen-fixing diazotrophs across the tropical western North Pacific. We find a basin-scale coherence of diazotroph abundances and N 2 fixation rates with the supply ratio of iron:nitrogen to the upper ocean. Across a threshold of increasing supply ratios, the abundance of nifH genes and N 2 fixation rates increased, phosphate concentrations decreased, and bioassay experiments demonstrated evidence for N 2 fixation switching from iron to phosphate limitation. In the northern South China Sea, supply ratios were hypothesized to fall around this critical threshold and bioassay experiments suggested colimitation by both iron and phosphate. Our results provide evidence for iron:nitrogen supply ratios being the most important factor in regulating the distribution of N 2 fixation across the tropical ocean

Segmental Membranous Glomerulopathy in Adults

Introduction: The clinicopathological features of segmental membranous glomerulopathy (SMGN) have not been well characterized. The aim of this study was to investigate the prevalence and clinicopathological features of SMGN in adults. Methods: Adult patients with biopsy-confirmed SMGN in the native kidney at our center between January 2017 to September 2020 were identified. The clinicopathological features of SMGN were collected. The glomerular deposition of IgG subclasses, M-type phospholipase A2 receptor 1 (PLA2R), thrombospondin type 1 domain-containing 7A (THSD7A) and neural epidermal growth factor-like 1 protein (NELL1) were tested. Clinical and pathologic features were comparable between NELL1-positive and NELL1-negative SMGN. Results: A total of 167 patients with biopsy-proven SMGN were enrolled. During the same period, 32,640 (33.0%) out of 98,939 renal biopsies were diagnosed with membranous nephropathy (MN) in adults. SMGN accounted for 0.17% of total kidney biopsies and 0.51% of MN in adults. One hundred and fifty (89.8%) cases were isolated SMGN and 17 (10.2%) cases were complicated with other kidney disease. Clinically, the median age of isolated SMGN patients was 41.5 years, with female (74%) predominance, and 33.1% had full nephrotic syndrome. Pathologically, IgG1 was the dominant subclass (92.5%), followed by IgG4 (45.0%). PLA2R and THSD7A staining were done in 142 and 136 isolated SMGN cases, respectively. In which, all the cases showed negative. NELL1 staining was done in 135 isolated SMGN cases, 58 cases (43.0%) showed positive. Fifty-eight patients (41.1%) had diffuse (≥90%) foot process effacement, 119 patients (83.8%) had either stage I (38.0%) or stage II (45.8%) membranous alterations in patients with SMGN. Most patients with NELL1-positive SMGN were female. Patients with NELL1-positive SMGN were more likely with lower prevalence of full nephrotic syndrome than NELL1-negative SMGN. Conclusions: SMGN is a relatively rare pathological type. Majority of patients with isolated SMGN were female, with a median age of 41.5 years, 33.1% had full nephrotic syndrome, absence of PLA2R and THSD7A, 43.0% with NELL1-positive, and mainly stage I or II MN (83.8%). NELL1 is the major target antigen of SMGN in adults

Increasing stomatal conductance inresponse to rising atmospheric CO2

Background and Aims: Studies have indicated that plant stomatal conductance (gs) decreases in response to elevated atmospheric CO2, a phenomenon of significance for the global hydrological cycle. However, gs increases across certain CO2 ranges have been predicted by optimisation models. The aim of this work was to demonstrate that under certain environmental condition, gs can increase in response to elevated CO2. Methods: When using (i) an extensive, up-to-date, synthesis of gs responses in FACE experiments, (ii) in situ measurements across four biomes showing dynamic gs responses to a CO2 rise of ~50ppm (characterising the change in this greenhouse gas over the past three decades) and (iii) a photosynthesis-stomatal conductance model, it is demonstrated that gs can in some cases increase in response to increasing atmospheric CO2. Key Results: Field observations are corroborated by an extensive synthesis of gs responses in FACE experiments showing that 11.8% of gs responses under experimentally elevated CO2 are positive. They are further supported by a strong data-model fit (r2=0.607) using a stomatal optimization model applied to the field gs dataset. A parameter space identified in the Farquhar-Ball-Berry photosynthesis-stomatal conductance model confirms field observations of increasing gs under elevated CO2 in hot dry conditions. It was shown that contrary to the general assumption, positive gs responses to elevated CO2, although relatively rare, are a feature of woody taxa adapted to warm, low-humidity conditions, and that this response is also demonstrated in global simulations using the Community Land Model (CLM4). Conclusions: The results contradict the over-simplistic notion that global vegetation always responds with decreasing gs to elevated CO2, a finding that has important implications for predicting future vegetation feedbacks on the hydrological cycle at the regional level.Irish Research CouncilScience Foundation Irelan