Identifying and managing interorganisational work related psychosocial risks in New Zealand : a thesis presented in fulfilment of the requirements for the degree of Master of Business Studies, Massey University, Albany Campus, New Zealand

Appendices A-E are not available online but may be supplied by the author upon request to the Library.Current research studies about workplace psychosocial risks focus more on organisational work instead of interorganisational (IO) work. It shows limited studies in relation to IO work related psychosocial risks. IO work can be defined as collaboration. It is done by more than two organizations and is organized to achieve better outcome, having more effective results and significant impact. This research study refers to the type of IO work that is carried out by more than one organization other than National Emergency Management Agency and Civil Defence. Based on workers’ experiences, IO work environment is dynamic. It may cause different psychosocial risks compare to organisational work. Furthermore, IO work does not simply involve getting tasks completed with multiple organizations, but also requires dealing with different organisational cultures, structures and people who have been trained to response, communicate and report in various ways. This research study identifies IO work related psychosocial risks and explains the differences of psychosocial risks in IO work versus organisational work. Most importantly, it outlines possible strategies that could be used in managing these risks. Giving the significant impact of the pandemic, this research study also analyses the influence of COVID-19 responses to IO work related psychosocial risks. The findings and discussions are based on responses from 24 participants who have had at least three months’ IO work experience. Some of the participants are interviewed twice to gain in depth understanding about their IO work experiences. The first interview is designed as a semi-interview and guided by 26 interview questions, which are combined with 20 Copenhagen Psychosocial Questionnaire III (COPSOQ III) and 6 questions to help understand the differences of psychosocial risks and impact of COVID-19 responses. The 3 second interviews are designed to allow participants to share as much information draw from their IO work experiences, understanding of IO work and associated issues, their understanding of IO work in comparison with organisational work. The literature review summaries scholarships related to workplace psychosocial risks and highlights the gaps and limitations. The recommendations and future studies emphasise the importance of understanding psychosocial risks in IO work and encourage future research to study IO work from various lens including gender, age, work experiences, human reward system and functions of dopamine. Overall, this research aims to increase researchers’ awareness about IO work related psychosocial risks. As more and more IO work happening, future of work will involve frequent and continuous collaboration between multiple organizations. There is a strong need to conduct more academic and non-academic research and studies in this area. The studies will contribute to enhance workers’ health and wellbeing and improve workplace health and safety risk management and harm prevention, in turn it reduces costs of organizations in managing workers’ physical and psychological health, increase workers’ productivity and engagement

Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models

Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspective of the bias-variance tradeoff, we propose regularized deep generative model (Reg-DGM), which leverages a nontransferable pre-trained model to reduce the variance of generative modeling with limited data. Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the expectation of an energy function, where the divergence is between the data and the model distributions, and the energy function is defined by the pre-trained model w.r.t. the model distribution. We analyze a simple yet representative Gaussian-fitting case to demonstrate how the weighting hyperparameter trades off the bias and the variance. Theoretically, we characterize the existence and the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and prove its convergence with neural networks trained by gradient-based methods. Empirically, with various pre-trained feature extractors and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs with limited data and achieves competitive results to the state-of-the-art methods

Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding

Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embedding (SNE), whose goal is to preserve pairwise distances. From the perspective of preserving neighboring information, SSCL can be viewed as a special case of SNE with the input space pairwise similarities specified by data augmentation. The established correspondence facilitates deeper theoretical understanding of learned features of SSCL, as well as methodological guidelines for practical improvement. Specifically, through the lens of SNE, we provide novel analysis on domain-agnostic augmentations, implicit bias and robustness of learned features. To illustrate the practical advantage, we demonstrate that the modifications from SNE to $t$-SNE can also be adopted in the SSCL setting, achieving significant improvement in both in-distribution and out-of-distribution generalization.Comment: Accepted by ICLR 202

Intertwining Order Preserving Encryption and Differential Privacy

Ciphertexts of an order-preserving encryption (OPE) scheme preserve the order of their corresponding plaintexts. However, OPEs are vulnerable to inference attacks that exploit this preserved order. At another end, differential privacy has become the de-facto standard for achieving data privacy. One of the most attractive properties of DP is that any post-processing (inferential) computation performed on the noisy output of a DP algorithm does not degrade its privacy guarantee. In this paper, we intertwine the two approaches and propose a novel differentially private order preserving encryption scheme, OP$\epsilon$. Under OP$\epsilon$, the leakage of order from the ciphertexts is differentially private. As a result, in the least, OP$\epsilon$ ensures a formal guarantee (specifically, a relaxed DP guarantee) even in the face of inference attacks. To the best of our knowledge, this is the first work to intertwine DP with a property-preserving encryption scheme. We demonstrate OP$\epsilon$'s practical utility in answering range queries via extensive empirical evaluation on four real-world datasets. For instance, OP$\epsilon$ misses only around $4$ in every $10K$ correct records on average for a dataset of size $\sim732K$ with an attribute of domain size $\sim18K$ and $\epsilon= 1$

SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task-specific algorithms make it difficult to integrate them into a single neural network architecture, and large amounts of parameters make it difficult to achieve real-time inference. To tackle these problems, we propose a novel network, SYENet, with only $~$6K parameters, to handle multiple low-level vision tasks on mobile devices in a real-time manner. The SYENet consists of two asymmetrical branches with simple building blocks. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, to improve performance, a new Outlier-Aware Loss is proposed to process the image. The proposed method proves its superior performance with the best PSNR as compared with other networks in real-time applications such as Image Signal Processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge

rAAV immunogenicity, toxicity, and durability in 255 clinical trials: A meta-analysis

Recombinant Adeno-associated virus (rAAV) is one of the main delivery vectors for gene therapy. To assess immunogenicity, toxicity, and features of AAV gene therapy in clinical settings, a meta-analysis of 255 clinical trials was performed. A total of 7,289 patients are planned to be dosed. AAV2 was the most dominantly used serotype (29.8%, n=72), and 8.3% (n=20) of trials used engineered capsids. 38.7% (n=91) of trials employed neutralizing antibody assays for patient enrollment, while 15.3% (n=36) used ELISA-based total antibody assays. However, there was high variability in the eligibility criteria with cut-off tiers ranging from 1:1 to 1:1,600. To address potential immunogenicity, 46.3% (n=118) of trials applied immunosuppressants (prophylactic or reactive), while 32.7% (n=18) of CNS and 37.5% (n=24) of ocular-directed trials employed immunosuppressants, possibly due to the immune-privileged status of CNS and retina. There were a total of 11 patient deaths across 8 trials, and 18 out of 30 clinical holds were due to toxicity findings in clinical studies. 30.6% (n=78) of trials had treatment-emergent serious adverse events (TESAEs), with hepatotoxicity and thrombotic microangiopathy (systemic delivery) and neurotoxicity (CNS delivery) being the most prominent. Additionally, the durability of gene therapy may be impacted by two distinct decline mechanisms: 1) rapid decline presumably due to immune responses; or 2) gradual decline due to vector dilution. The durability varied significantly depending on disease indication, dose, serotypes, and patient individuals. Most CNS (90.0%) and muscle trials (73.3%) achieved durable transgene expression, while only 43.6% of ocular trials had sustained clinical outcomes. The rAAV production system can affect rAAV quality and thus immunogenicity and toxicity. Out of 186 trials that have disclosed production system information, 63.0% (n=126) of trials used the transient transfection of the HEK293/HEK293T system, while 18.0% (n=36) applied the baculovirus/Sf9 (rBac/Sf9) system. There were no significant differences in TESAEs and durability between AAV generated by rBac/Sf9 and HEK293/HEK293T systems. In summary, rAAV immunogenicity and toxicity poses significant challenges for clinical development of rAAV gene therapies, and it warrants collaborative efforts to standardize monitoring/measurement methods, design novel strategies to overcome immune responses, and openly share relevant information