Learning Data Quality Analytics for Financial Services

Financial institutions put tremendous efforts on the data analytics work associated with the risk data in recent years. Their analytical reports are yet to be accepted by regulators in financial services industry till early 2019. In particular, the enhancement needs to meet the regulatory requirement the APRA CPG 235. To improve data quality, we assist in the data quality analytics by developing a machine learning model to identify current issues and predict future issues. This helps to remediate data as early as possible for the mitigation of risk of re-occurrence. The analytical dimensions are customer related risks (market, credit, operational & liquidity risks) and business segments (private, wholesale & retail banks). The model is implemented with multiple Long Short-Term Memory ( LSTM ) Recurrent Neural Network ( RNNs ) to find the best one for the quality & prediction analytics. They are evaluated by divergent algorithms and cross-validation techniques

Impact of the COVID-19 pandemic on young people from black and mixed ethnic groups’ mental health in West London: a qualitative study

OBJECTIVE: The COVID-19 pandemic has disproportionately impacted vulnerable groups’ physical and mental health, especially young people and minority ethnic groups, yet little is known about the crux of their experiences and what support they would like. To address this gap, this qualitative study aims to uncover the effect of the COVID-19 outbreak on young people with ethnic minority backgrounds’ mental health, how this changed since the end of lockdown and what support they need to cope with these issues. DESIGN: The study utilised semi-structured interviews to conduct a phenomenological analysis. SETTING: Community centre in West London, England. PARTICIPANTS: Ten 15 min in-person semistructured interviews were conducted with young people aged 12–17 years old from black and mixed ethnic groups who regularly attend the community centre. RESULTS: Through Interpretative Phenomenological Analysis, results indicated that the participants’ mental health was negatively impacted by the COVID-19 pandemic, with feelings of loneliness being the most common experience. However, positive effects were concurrently observed including improved well-being and better coping strategies post lockdown, which is a testament to the young people’s resilience. That said, it is clear that young people from minority ethnic backgrounds lacked support during the COVID-19 pandemic and would now need psychological, practical and relational assistance to cope with these challenges. CONCLUSIONS: While future studies would benefit from a larger ethnically diverse sample, this is a start. Study findings have the potential to inform future government policies around mental health support and access for young people from ethnic minority groups, notably prioritising support for grassroots initiatives during times of crisis

于省吾《雙劍誃尚書新證》研究

于省吾（1896-1984)，中國著名古文字學家、訓詁學家，著作成果頗豐，其古籍《新證》系列，以古文字及出土器物作為考訂及訓釋的材料，被視為開啟山林之作。當中《尚書新證》是《新證》系列中最早出版的著作，可反映于氏訓詁方法的早期面貌。在乾嘉考據學派的訓詁方法和王國維「二重證據法」的影響下，于氏《尚書新證》對《尚書》今文28篇的字詞、句義多有新見，其訓釋的價值實不容忽視。然而，近人研究于氏《新證》系列，卻沒有以《尚書新證》作研究對象，因此，本文試以《尚書新證》為研究對象，並探討其價值。 論文共分三章。第一章交代《尚書新證》的撰作背景、動機，及其版本、體例。第二章會從《尚書新證》内文及于氏發表的論文，歸納並討論于省吾的《尚書》學觀。第三章為《尚書新證》之内容考述及特色，介紹此書有別於其他《尚書》注釋著作之連類訓釋方法，討論此書運用古文字資料為證及紀錄異文的情況，並分別歸納于氏解釋字義、詞義，以至句義之方法，亦會兼及《尚書新證》的内容特色。最後總結全書，討論《尚書新證》對後世學者的影響，並對此書作出評債

從古文字看巫與醫之關係

在現代社會中，〝巫〞與〝醫〞完全扯不上關係，前者被標籤為迷信，而後者則是一門科學，但在遠古時期，兩者的關係其實是密不可分的。 原始社會的巫師大多是綜合性人才，他們能通天地、事鬼神，負責占卜、祭祀，同時又掌管人類當時所有知識，上至天文，下至地理，是社會上最有能力的人。先民對於未知的事物如疾病、自然災害，會習慣將之歸咎於鬼神作祟，〝巫〞作為人與天地鬼神之間的溝通橋樑，有能力平定禍事。而作為全能的人，關乎人類生死的治病救傷，自然也是〝巫〞的本領之一，因此古代是巫醫不分的。 古時候，巫術與醫療的關係密切，除了能從史料引證，先民在創造文字時也留下了線索。〝巫〞與〝醫〞二字連用的情況能在史籍文獻中找到，用以表示〝巫師作醫師〞之義，而古代的醫字又可寫作〝毉〞，从巫。由於〝巫〞先於〝醫〞，因此本論文會先從巫字的字義入手，解釋巫字的構形，再連繫到〝巫〞與〝醫〞之間的同源關係。再述醫字的字義，將醫字分拆成三個部件作仔細分析。最後論述巫醫分立的原因。 由於文字與文化之間相輔相成，因此本論文除了會從文字學的角度作分析，亦會從文獻資料入手，希望能較全面地展示出古文字中〝巫〞與〝醫〞之關係。 而在研究的過程中發現，〝醫〞字的上半部的構形對於整體字義的闡釋有著重要的影響。其中的部件〝医〞，大部份學者均將其解釋為〝象盛載（匸）箭矢利器（矢）的器物〞。翻查資料，〝匸〞義為〝匿藏的地方〞，與〝匸〞寫法相似的〝匚〞才是〝象盛物之器〞。另外，〝医〞中的〝矢〞或許不是代表箭矢，而是因字形相近而混淆，在〝匸〞中的應為〝人〞，因此本論文將展示〝醫〞字新的詮釋

從「僧」到「仙」的濟公 : 《濟公全傳》的濟公信仰及其形象

本文主要研究濟公信仰核心和其真正形象。過往論文中，學者普遍把濟公信仰簡單說明如何配合民眾的心理，並將濟公歸入佛門之內。本文主要目的正是再深入地分析濟公信仰的體系，梳理濟公對不同方面的價值觀。正文當中，會詳細分辨出民間信仰的特色，與濟公信仰互通的地方。本文以清代郭小亭的《濟公全傳》為研究中心，探討濟公信仰的民間屬性、意義和意涵。本文將會分為五部份：第一章為引言。第二章主要分析民間信仰與《濟公全傳》呈現的濟公信仰，從而梳理出來濟公信仰的特點和體系。第三章濟公僧人和神仙色彩的成份，剖析濟公出現由僧轉仙的轉化原因。第四章是全文總結

Learning regulatory compliance data for data governance in financial services industry by machine learning models

While regulatory compliance data has been governed in the financial services industry for a long time to identify, assess, remediate and prevent risks, improving data governance (“DG”) has emerged as a new paradigm that uses machine learning models to enhance the level of data management. In the literature, there is a research gap. Machine learning models have not been extensively applied to DG processes by a) predicting data quality (“DQ”) in supervised learning and taking temporal sequences and correlations of data noise into account in DQ prediction; b) predicting DQ in unsupervised learning and learning the importance of data noise jointly with temporal sequences and correlations of data noise in DQ prediction; c) analyzing DQ prediction at a granular level; d) measuring network run-time saving in DQ prediction; and e) predicting information security compliance levels. Our main research focus is whether our ML models accurately predict DQ and information security compliance levels during DG processes of financial institutions by learning regulatory compliance data from both theoretical and experimental perspectives. We propose five machine learning models including a) a DQ prediction sequential learning model in supervised learning; b) a DQ prediction sequential learning model with an attention mechanism in unsupervised learning; c) a DQ prediction analytical model; d) a DQ prediction network efficiency improvement model; and e) an information security compliance prediction model. Experimental results demonstrate the effectiveness of these models by accurately predicting DQ in supervised learning, precisely predicting DQ in unsupervised learning, analyzing DQ prediction by divergent dimensions such as risk types and business segments, saving significant network run-time in DQ prediction for improving the network efficiency, and accurately predicting information security compliance levels. Our models strengthen DG capabilities of financial institutions by improving DQ, data risk management, bank-wide risk management, and information security based on regulatory requirements in the financial services industry including Basel Committee on Banking Supervision Standard Number 239, Australia Prudential Regulation Authority (“APRA”) Standard Number CPG 235 and APRA Standard Number CPG 234. These models are part of DG programs under the DG framework of financial institutions

Is it good to be bad? An evolutionary analysis of the adaptive potential of psychopathic traits

Although psychopathy is widely conceptualised as a mental disorder, some researchers question the maladaptive nature of psychopathy, and argue that it might be advantageous from an evolutionary point of view. According to this view psychopathy can be seen as an evolutionary adaptative strategy that relies on deception and manipulation to gain short-term reproductive benefits. Psychopathy is also identified as a fast life strategy in response to early life stress and an adaptation to harsh environments. This paper investigates the evidence that psychopathic traits are adaptive, while also addressing the limitations of current evolutionary models of psychopathy based on frequency-dependent selection and life-history theory. We review recent studies on the fitness correlates of psychopathy and find that psychopathic traits present potential adaptive trade-offs between fertility and mortality, and offspring quantity and quality. On a proximate level, individual differences in stress reactivity and environmental risk factors in early development predispose to psychopathy through gene-environment interactions. We propose that environmental, developmental, social and cultural factors can mediate the relationship between psychopathic traits and fitness and therefore should be considered to make accurate predictions on the adaptive potential of psychopathy. We end by outlining gaps in the literature and making recommendations for future evolutionary research on psychopathy

A three-timepoint network analysis of Covid-19's impact on schizotypal traits, paranoia and mental health through loneliness

The 2019 coronavirus (Covid-19) pandemic has impacted people's mental wellbeing. Studies to date have examined the prevalence of mental health symptoms (anxiety and depression), yet fewer longitudinal studies have compared across background factors and other psychological variables to identify vulnerable subgroups in the general population. This study tests to what extent higher levels of schizotypal traits and paranoia are associated with mental health variables 6- and 12-months since April 2020. Over 2300 adult volunteers (18-89 years, female = 74.9%) with access to the study link online were recruited from the UK, the USA, Greece and Italy. Self-reported levels of schizotypy, paranoia, anxiety, depression, aggression, loneliness and stress from three timepoints (17 April to 13 July 2020, N1 = 1599; 17 October to 31 January 2021, N2 = 774; and 17 April to 31 July 2021, N3 = 586) were mapped using network analysis and compared across time and background variables (sex, age, income, country). Schizotypal traits and paranoia were positively associated with poorer mental health through loneliness, with no effect of age, sex, income levels, countries and timepoints. Loneliness was the most influential variable across all networks, despite overall reductions in levels of loneliness, schizotypy, paranoia and aggression during the easing of lockdown (time 3). Individuals with higher levels of schizotypal traits/paranoia reported poorer mental health outcomes than individuals in the low-trait groups. Schizotypal traits and paranoia are associated with poor mental health outcomes through self-perceived feelings of loneliness, suggesting that increasing social/community cohesion may improve individuals' mental wellbeing in the long run

On the structure of the Figueroa unital and the existence of O’Nan configurations

AbstractThe finite Figueroa planes are non-Desarguesian projective planes of order q3 for all prime powers q>2, constructed algebraically in 1982 by Figueroa, and Hering and Schaeffer, and synthetically in 1986 by Grundhöfer. All Figueroa planes of finite square order are shown to possess a unitary polarity by de Resmini and Hamilton in 1998, and hence admit unitals. Hui and Wong (2012) have shown that these polar unitals do not satisfy a necessary condition, introduced by Wilbrink in 1983, for a unital to be classical, and hence they are not classical. In this article we introduce and make use of a new alternative synthetic description of the Figueroa plane and unital to demonstrate the existence of O’Nan configurations, thus providing support to Piper’s conjecture (1981)