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

Multiplayer General Lotto game

In this paper, we explore the multiplayer General Lotto Blotto game over a single battlefield, a notable variant of the Colonel Blotto game. In this version, each player employs a probability distribution for resource allocation, ensuring that the expected expenditure does not surpass their budget. We first establish the existence of a Nash equilibrium for a modified version of this game, in which there is a common threshold that no player's bid can exceed. We next extend our findings to demonstrate the existence of a Nash equilibrium in the original game, which does not incorporate this threshold. Moreover, we provide detailed characterizations of the Nash equilibrium for both the original game and its modified version. In the Nash equilibrium of the unmodified game, we observe that the upper endpoints of the supports of players' equilibrium strategies coincide, and the minimum value of a player's support above zero inversely correlates with their budget. Specifically, we present closed-form solutions for the Nash equilibrium with threshold for two players

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

Enhancing the Robustness of QMIX against State-adversarial Attacks

Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent's observation. Most recent research has concentrated on robust single-agent reinforcement learning (SARL) algorithms against state-adversarial attacks. Still, there has yet to be much work on robust multi-agent reinforcement learning. Using QMIX, one of the popular cooperative multi-agent reinforcement algorithms, as an example, we discuss four techniques to improve the robustness of SARL algorithms and extend them to multi-agent scenarios. To increase the robustness of multi-agent reinforcement learning (MARL) algorithms, we train models using a variety of attacks in this research. We then test the models taught using the other attacks by subjecting them to the corresponding attacks throughout the training phase. In this way, we organize and summarize techniques for enhancing robustness when used with MARL

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 $\epsilon= 1$

Effect of sacubitril–valsartan on left ventricular remodeling in patients with acute myocardial infarction after primary percutaneous coronary intervention: a systematic review and meta-analysis

BackgroundSacubitril–valsartan has been widely reported for reducing the risk of cardiovascular death and improving left ventricular remodeling in patients with heart failure (HF). However, the effect of sacubitril–valsartan in patients with acute myocardial infarction (AMI) remains controversial. Therefore, we conducted this meta-analysis to investigate whether sacubitril–valsartan could reverse left ventricular remodeling and reduce cardiovascular adverse events in AMI patients after primary percutaneous coronary intervention (PPCI).Materials and methodsTwo researchers independently retrieved the relevant literature from PubMed, Embase, The Cochrane Library, China National Knowledge Infrastructure (CNKI), and the Wanfang database. The retrieval time was limited from inception to 1 June 2023. Randomized controlled trials (RCTs) meeting the inclusion criteria were included and analyzed.ResultsIn total, 21 RCTs involving 2442 AMI patients who underwent PPCI for revascularization were included in this meta-analysis. The meta-analysis showed that compared with the angiotensin-converting enzyme inhibitors (ACEI)/angiotensin receptor blockers (ARB), sacubitril–valsartan treatment in AMI patients after PPCI significantly reduced left ventricular end-diastolic dimension (LVEDD) (weighted mean difference (WMD) −3.11, 95%CI: −4.05∼−2.16, p < 0.001), left ventricular end-diastolic volume (LVEDV) (WMD −7.76, 95%CI: −12.24∼−3.27, p = 0.001), left ventricular end-systolic volume (LVESV) (WMD −6.80, 95%CI: −9.45∼−4.15, p < 0.001) and left ventricular end-systolic dimension (LVESD) (WMD −2.53, 95%CI: −5.30–0.24, p < 0.001). Subgroup analysis according to the dose of sacubitril–valsartan yielded a similar result. Meanwhile, PPCI patients using sacubitril–valsartan therapy showed lower risk of major adverse cardiac events (MACE) (OR = 0.36, 95%CI: 0.28–0.46, p < 0.001), myocardial reinfarction (OR = 0.54, 95%CI: 0.30–0.98, p = 0.041) and HF (OR = 0.35, 95%CI: 0.26–0.47, p < 0.001) without increasing the risk of renal insufficiency, hyperkalemia, or symptomatic hypotension. At the same time, the change of LV ejection fraction (LVEF) (WMD 3.91, 95%CI: 3.41–4.41, p < 0.001), 6 min walk test (6MWT) (WMD 43.56, 95%CI: 29.37–57.76, p < 0.001) and NT-proBNP level (WMD −130.27, 95%CI: −159.14∼−101.40, p < 0.001) were statistically significant.ConclusionIn conclusion, our meta-analysis indicates that compared with ACEI/ARB, sacubitril–valsartan may be superior to reverse left ventricular remodeling, improve cardiac function, and effectively reduce the risk of MACE, myocardial reinfarction, and HF in AMI patients after PPCI during follow-up without increasing the risk of adverse reactions including renal insufficiency, hyperkalemia, and symptomatic hypotension