Using Bourdieu's trilogy of habitus, capital and field to explore the employment experiences of the highly qualified 1,5th generation of Chinese immigrants in Portugal

Oriented by Pierre Bourdieu’ theory of field, habitus, and capital, this dissertation examines the highly educated 1.5th Generation of Chinese immigrants’ life and professional trace in Portugal. In reviewing the literature on status and stereotypes associated with immigration, this dissertation took the initiative to study the process and the reasons for the successful integration of 1.5th Generation in its day to day and in the labor market. Empirical research encompasses participants’ life and work experiences, including the cultivation of diverse competencies that play a decisive role in their professional course. In the spirit of discovery, a model was developed that reflects the transition from school to work in the 1.5th generation of Chinese immigrants and found that they have no obstacles to access the Portuguese labor market. The dissertation provides recommendations in both theoretical and organizational terms on the strategy of immigrants’ integration, as well as the recruitment and retention of bicultural talents.Orientada pela teoria do "field, habitus e capital" de Pierre Bourdieu, esta dissertação examina o percurso profissional e de vida da geração 1.5 de imigrantes chineses em Portugal. Ao rever a literatura sobre "status" e estereótipo associados à imigração, esta dissertação tomou a iniciativa de estudar o processo e os motivos da integração bem-sucedida da geração 1.5 no seu dia a dia e no mercado de trabalho. A pesquisa empírica abrange as experiências de vida e de trabalho dos participantes, incluindo o cultivo de diversas competências com papel decisivo no seu percurso profissional. Em espírito de descoberta, foi desenvolvido um modelo que reflete a rota de transição de escola para trabalho na geração 1.5 dos imigrantes chineses e constatou-se que estes não têm obstáculos para aceder ao mercado de trabalho português. A dissertação proporciona recomendações tanto em termos teóricos quanto organizacionais sobre a estratégia de integração dos imigrantes, bem como o recrutamento e retenção de talentos biculturais

On the optimality of Kalman Filter for Fault Detection

Kalman filter is widely used for residual generation in fault detection. It leads to optimality in fault detection using some performance indices and also leads to statistically sound residual evaluation and threshold setting. This paper shows that these nice features do not necessarily imply an optimal fault detection performance. Based on a performance index related to fault detection rate and false alarm rate, several occasions where Kalman filter should not be used are pointed out; further the residual evaluation and threshold setting are discussed, in which it is pointed out that in stochastic setting an optimal statistical test of Kamlan filter is not related to optimality of commonly used detection performance indicators. The theoretical analysis is verified through Monte Carlo simulations

Robust Fully-Asynchronous Methods for Distributed Training over General Architecture

Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers. We propose a Robust Fully-Asynchronous Stochastic Gradient Tracking method (R-FAST), where each device performs local computation and communication at its own pace without any form of synchronization. Different from existing asynchronous distributed algorithms, R-FAST can eliminate the impact of data heterogeneity across devices and allow for packet losses by employing a robust gradient tracking strategy that relies on properly designed auxiliary variables for tracking and buffering the overall gradient vector. More importantly, the proposed method utilizes two spanning-tree graphs for communication so long as both share at least one common root, enabling flexible designs in communication architectures. We show that R-FAST converges in expectation to a neighborhood of the optimum with a geometric rate for smooth and strongly convex objectives; and to a stationary point with a sublinear rate for general non-convex settings. Extensive experiments demonstrate that R-FAST runs 1.5-2 times faster than synchronous benchmark algorithms, such as Ring-AllReduce and D-PSGD, while still achieving comparable accuracy, and outperforms existing asynchronous SOTA algorithms, such as AD-PSGD and OSGP, especially in the presence of stragglers

Evaluation of Epigallocatechin-3-Gallate as a Radioprotective Agent During Radiotherapy of Lung Cancer Patients: A 5-Year Survival Analysis of a Phase 2 Study

BackgroundPrevious analysis of the study (NCT02577393) had demonstrated the application of epigallocatechin-3-gallate (EGCG) could be safe and effective in the prevention and treatment of acute radiation esophagitis in patients with advanced lung cancer. EGCG seemed to improve the response rate of small cell lung cancer (SCLC) to radiotherapy in a subgroup analysis. This research continued to analyze the impact of EGCG application on cancer-radiation efficacy and patient survival.MethodsAll patients with SCLC in the NCT02577393 study were included. Patients were randomized into EGCG group or conventional therapy group as protocol. The primary endpoints of the study were radiation response rate and progression-free survival (PFS). Overall survival (OS) and the efficacy of EGCG in the treatment of esophagitis were assessed as secondary endpoints.ResultsA total of 83 patients with lung cancer in the NCT02577393 study were screened, and all 38 patients with SCLC were eligible for analysis. No significant differences with regard to baseline demographic and clinical characteristics were observed between the two groups. The objective response rate (ORR) was higher than that of conventionally treated patients (84.6 vs 50%, P = 0.045), while the median PFS and OS were not significantly prolonged. At data cut-off (1 January 2021), 5-year PFS was 33% with EGCG versus 9.3% with conventional treatment, and 5-year OS was 30.3% versus 33.3%, respectively. The mean adjusted esophagitis index and pain index of patients with EGCG application were lower than conventional treatment (5.15 ± 2.75 vs 7.17 ± 1.99, P = 0.030; 8.62 ± 5.04 vs 15.42 ± 5.04, P < 0.001).ConclusionThe study indicates EGCG may alleviate some esophagitis-related indexes in SCLC patients exposed to ionizing radiation without reducing survival. However, this conclusion should be confirmed by further studies with large sample size

Microbial mediated arsenic biotransformation in wetlands

Arsenic (As) is a pervasive environmental toxin and carcinogenic metalloid. It ranks at the top of the US priority List of Hazardous Substances and causes worldwide human health problems. Wetlands, including natural and artificial ecosystems (i.e. paddy soils) are highly susceptible to As enrichment; acting not only as repositories for water but a host of other elemental/chemical moieties. While macro-scale processes (physical and geological) supply As to wetlands, it is the micro-scale biogeochemistry that regulates the fluxes of As and other trace elements from the semi-terrestrial to neighboring plant/aquatic/atmospheric compartments. Among these fine-scale events, microbial mediated As biotransformations contribute most to the element’s changing forms, acting as the ‘switch’ in defining a wetland as either a source or sink of As. Much of our understanding of these important microbial catalyzed reactions follows relatively recent scientific discoveries. Here we document some of these key advances, with focuses on the implications that wetlands and their microbial mediated transformation pathways have on the global As cycle, the chemistries of microbial mediated As oxidation, reduction and methylation, and future research priorities areas

Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion

User post-click conversion prediction is of high interest to researchers and developers. Recent studies employ multi-task learning to tackle the selection bias and data sparsity problem, two severe challenges in post-click behavior prediction, by incorporating click data. However, prior works mainly focused on pointwise learning and the orders of labels (i.e., click and post-click) are not well explored, which naturally poses a listwise learning problem. Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach. Specifically, we define an aggregation operator to combine predicted outputs of different tasks to a unified score, then we use the computed scores to model the label relations via differentiable sorting. Extensive experiments on public and industrial datasets show the superiority of our proposed model against competitive baselines.Comment: The paper is accepted as a short research paper by SIGIR 202

One at A Time: Multi-step Volumetric Probability Distribution Diffusion for Depth Estimation

Recent works have explored the fundamental role of depth estimation in multi-view stereo (MVS) and semantic scene completion (SSC). They generally construct 3D cost volumes to explore geometric correspondence in depth, and estimate such volumes in a single step relying directly on the ground truth approximation. However, such problem cannot be thoroughly handled in one step due to complex empirical distributions, especially in challenging regions like occlusions, reflections, etc. In this paper, we formulate the depth estimation task as a multi-step distribution approximation process, and introduce a new paradigm of modeling the Volumetric Probability Distribution progressively (step-by-step) following a Markov chain with Diffusion models (VPDD). Specifically, to constrain the multi-step generation of volume in VPDD, we construct a meta volume guidance and a confidence-aware contextual guidance as conditional geometry priors to facilitate the distribution approximation. For the sampling process, we further investigate an online filtering strategy to maintain consistency in volume representations for stable training. Experiments demonstrate that our plug-and-play VPDD outperforms the state-of-the-arts for tasks of MVS and SSC, and can also be easily extended to different baselines to get improvement. It is worth mentioning that we are the first camera-based work that surpasses LiDAR-based methods on the SemanticKITTI dataset