Fund family tournament and performance consequences: evidence from the UK fund industry

By applying tournament analysis to the UK Unit Trusts data, the results support significant risk shifting in the family tournament; i.e. interim winning managers tend to increase their level of risk exposure more than losing managers. It also shows that the risk-adjusted returns of the winners outperform those of the losers following the risk taking, which implies that risk altering can be regarded as an indication of managers’ superior ability. However, the tournament behaviour can still be a costly strategy for investors, since winners can be seen to beat losers in the observed returns due to the deterioration in the performance of their major portfolio holdings

Harmonious Concordance of Men, Women and Nature: A Study of Lawrence’s Ecological Philosophy in His Lady Chatterley’s Lover

D. H. Lawrence stands as a talented and unconventional writer in the twentieth century English Literature. Lady Chatterley’s Lover is his last novel which embodies his mature thought. The novel earns him both great fame and strong criticism. In spite of the controversies over Lawrence’s daring description of sexuality, the novel stands the test of time and becomes a classic of literature. The paper intends to reveal Lawrence’s ecological philosophy in Lady Chatterley’s Lover. By depicting harmonious nature and harmonious sex relationship, Lawrence presents his ecological philosophy. In the novel, harmonious nature is a silent protest against industrial civilization reflected by the contrast between Wragby and the wood. The harmonious sex relationship in nature is a great liberation of suppressed human nature. The disharmonious relationship between Clifford and Connie is like the deadwood lacking vitality, while the harmonious sex relationship between Mellors and Connie is like intertwining shoots which give mutual supports and vigor. Lady Chatterley’s Lover reflects Lawrence’s far-reaching ecological views and his concern about the whole ecosphere which embodies his strong social responsibility.

Understanding How Management Control Affects the Triangle Relationship between Management, Sales Agent and Client: A Case Study from a Chinese Life Insurance Company

There has been growing literature on management control in the context of front-line service work, including sales work. Given the nature of front-line work, service recipients – customers and clients – play an important role in management control and in the social relations in the workplace. Moreover, the triangle relationship has become a research focus for decades. However, most of the research has been done in western countries. In addition, little attention has been paid to sales work mixed with service elements. Furthermore, the complexity of the triangle relationship between three parties has not been sufficiently evaluated. This thesis investigates management control in a Chinese life insurance company and the effects of management on individual sales agents and the triangle relationship between management, sales agents and clients. In order to address the research target, this study employs a qualitative approach and chooses a single-case study research design. The qualitative data has been collected from a documentary analysis, observations and semi-structured interviews. The empirical findings suggest that: a. The management controls in the studied case can be categorised into two main types: formally coercive control that consists of output control, bureaucratic control, direct control and attendant control; and informally normative control that consists of concertive control in the sales teams and normative control in morning meetings. b. The controls for contradictory logic that interact with one another to have an impact on individual sales agents and the triangle relationship. c. Sales agents who have a greater family responsibility and the capability of meeting sales targets set by the company are more motivated by financial incentives, identified with organisational and occupational value and self-disciplined. d. Sales agents who have a poor performance and lack the capability to improve their performance are more likely to resist management control. e. The relationship between management and the sales agents is not always conflictual. f. Co-worker assistance and informal coping are found in the relationships among sales agents, although their sales work is highly individualised and competitive. g. The relationship between sales agents and clients is instrumental in its nature, but when there is an ongoing relationship and a high level of trust between sales agents and clients, this relationship appears to be less instrumental. h. Improving the quality of service and enhancing the myth of customer sovereignty may sacrifice efficiency in the short term, but it can potentially increase sales efficiency in the long run

Exploration of visual pedagogy to make Mandarin learnable : a teacher action research project

Visual pedagogy plays a crucial role in foreign language teaching and the majority of students are mainly visual learners, especially public-school students. As a result, the teacher-researcher adopted various visual materials in this research for students from an Australian Public school in the Year Five and Six class. Based on those visual materials, the teacher-researcher developed visual teaching strategies aimed to fully explore visual pedagogy that can bring substantial benefits to young learners. This thesis is composed of 6 chapters. In Chapter 1, the thesis introduces a general background of the study, and in Chapter 2, it examines the research literature on which this study is based. The Chapter 3 explains the method and describes who the participants are. The Chapter 4 provides the data from the use of chosen visual materials. Chapter 5 extends the data by examining a range of visual strategies. In the final chapter, the thesis discusses the implications of this research for the teaching of Mandarin language in Australian schools. In the course of the research, the teacher-researcher found that a significant amount of content can be presented through visual materials. Considering students’ interest and curiosity, the teacher-researcher believes that the choice of visual materials should be student-centered. Additionally, a variety of visual materials based on strategies the teacher-researcher used can resulted in higher levels of students’ engagement in classroom than what would have been achieved with visual materials that only presente words. For an example, students can be more easily distracted by watching a lenthy video, but are better engaged with short videos. Basically, students are interested in visualizing and guessing, however, the teacher-researcher found that the same strategy may not work equally well for both boys and girls. Cultural issues also have impacts on the implementation of visual teaching strategies. Therefore, students’ age, interest, gender and cultural background should be considered when choosing visual materials and developing visual strategies

Learning from imperfect data : incremental learning and Few-shot Learning

In recent years, artificial intelligence (AI) has achieved great success in many fields, e.g., computer vision, speech recognition, recommendation engines, and neural language processing. Although impressive advances have been made, AI algorithms still suffer from an important limitation: they rely on large-scale datasets. In contrast, human beings naturally possess the ability to learn novel knowledge from real-world and imperfect data such as a small number of samples or a non-static continual data stream. Attaining such an ability is particularly appealing. Specifically, an ideal AI system with human-level intelligence should work with the following imperfect data scenarios. 1)~The training data distribution changes while learning. In many real scenarios, data are streaming, might disappear after a given period of time, or even can not be stored at all due to storage constraints or privacy issues. As a consequence, the old knowledge is over-written, a phenomenon called catastrophic forgetting. 2)~The annotations of the training data are sparse. There are also many scenarios where we do not have access to the specific large-scale data of interest due to privacy and security reasons. As a consequence, the deep models overfit the training data distribution and are very likely to make wrong decisions when they encounter rare cases. Therefore, the goal of this thesis is to tackle the challenges and develop AI algorithms that can be trained with imperfect data. To achieve the above goal, we study three topics in this thesis. 1)~Learning with continual data without forgetting (i.e., incremental learning). 2)~Learning with limited data without overfitting (i.e., few-shot learning). 3)~Learning with imperfect data in real-world applications (e.g., incremental object detection). Our key idea is learning to learn/optimize. Specifically, we use advanced learning and optimization techniques to design data-driven methods to dynamically adapt the key elements in AI algorithms, e.g., selection of data, memory allocation, network architecture, essential hyperparameters, and control of knowledge transfer. We believe that the adaptive and dynamic design of system elements will significantly improve the capability of deep learning systems under limited data or continual streams, compared to the systems with fixed and non-optimized elements. More specifically, we first study how to overcome the catastrophic forgetting problem by learning to optimize exemplar data, allocate memory, aggregate neural networks, and optimize key hyperparameters. Then, we study how to improve the generalization ability of the model and tackle the overfitting problem by learning to transfer knowledge and ensemble deep models. Finally, we study how to apply incremental learning techniques to the recent top-performance transformer-based architecture for a more challenging and realistic vision, incremental object detection.Künstliche Intelligenz (KI) hat in den letzten Jahren in vielen Bereichen große Erfolge erzielt, z. B. Computer Vision, Spracherkennung, Empfehlungsmaschinen und neuronale Sprachverarbeitung. Obwohl beeindruckende Fortschritte erzielt wurden, leiden KI-Algorithmen immer noch an einer wichtigen Einschränkung: Sie sind auf umfangreiche Datensätze angewiesen. Im Gegensatz dazu besitzen Menschen von Natur aus die Fähigkeit, neuartiges Wissen aus realen und unvollkommenen Daten wie einer kleinen Anzahl von Proben oder einem nicht statischen kontinuierlichen Datenstrom zu lernen. Das Erlangen einer solchen Fähigkeit ist besonders reizvoll. Insbesondere sollte ein ideales KI-System mit Intelligenz auf menschlicher Ebene mit den folgenden unvollkommenen Datenszenarien arbeiten. 1)~Die Verteilung der Trainingsdaten ändert sich während des Lernens. In vielen realen Szenarien werden Daten gestreamt, können nach einer bestimmten Zeit verschwinden oder können aufgrund von Speicherbeschränkungen oder Datenschutzproblemen überhaupt nicht gespeichert werden. Infolgedessen wird das alte Wissen überschrieben, ein Phänomen, das als katastrophales Vergessen bezeichnet wird. 2)~Die Anmerkungen der Trainingsdaten sind spärlich. Es gibt auch viele Szenarien, in denen wir aus Datenschutz- und Sicherheitsgründen keinen Zugriff auf die spezifischen großen Daten haben, die von Interesse sind. Infolgedessen passen die tiefen Modelle zu stark an die Verteilung der Trainingsdaten an und treffen sehr wahrscheinlich falsche Entscheidungen, wenn sie auf seltene Fälle stoßen. Daher ist das Ziel dieser Arbeit, die Herausforderungen anzugehen und KI-Algorithmen zu entwickeln, die mit unvollkommenen Daten trainiert werden können. Um das obige Ziel zu erreichen, untersuchen wir in dieser Arbeit drei Themen. 1)~Lernen mit kontinuierlichen Daten ohne Vergessen (d. h. inkrementelles Lernen). 2) ~ Lernen mit begrenzten Daten ohne Überanpassung (d. h. Lernen mit wenigen Schüssen). 3) ~ Lernen mit unvollkommenen Daten in realen Anwendungen (z. B. inkrementelle Objekterkennung). Unser Leitgedanke ist Lernen lernen/optimieren. Insbesondere verwenden wir fortschrittliche Lern- und Optimierungstechniken, um datengesteuerte Methoden zu entwerfen, um die Schlüsselelemente in KI-Algorithmen dynamisch anzupassen, z. B. Auswahl von Daten, Speicherzuweisung, Netzwerkarchitektur, wesentliche Hyperparameter und Steuerung des Wissenstransfers. Wir glauben, dass das adaptive und dynamische Design von Systemelementen die Leistungsfähigkeit von Deep-Learning-Systemen bei begrenzten Daten oder kontinuierlichen Streams im Vergleich zu Systemen mit festen und nicht optimierten Elementen erheblich verbessern wird. Genauer gesagt untersuchen wir zunächst, wie das katastrophale Vergessensproblem überwunden werden kann, indem wir lernen, Beispieldaten zu optimieren, Speicher zuzuweisen, neuronale Netze zu aggregieren und wichtige Hyperparameter zu optimieren. Dann untersuchen wir, wie die Verallgemeinerungsfähigkeit des Modells verbessert und das Overfitting-Problem angegangen werden kann, indem wir lernen, Wissen zu übertragen und tiefe Modelle in Ensembles zusammenzufassen. Schließlich untersuchen wir, wie man inkrementelle Lerntechniken auf die jüngste transformatorbasierte Hochleistungsarchitektur für eine anspruchsvollere und realistischere Vision, inkrementelle Objekterkennung, anwendet

A Comparative Analysis of English Education Between Chinese and Japanese Universities

With the strengthening of the trend of global integration, the world needs more and more bilingual talents. English, as the first common language in the world, is undoubtedly important. Both China and Japan are in Asia, so there are many similarities in education that can be borrowed from each other. This article makes a comparative analysis in Chinese and Japanese college English education from the following three perspectives: educator, educatee and educational influence. In this paper, documentation method, comparison analytic method and logic reasoning are used to study Chinese and Japanese college English education which includes faculty, student source, students’ attitude, instructional objectives and educational evaluation. The result makes a reference to the college English education reform in the future. It also helps to improve the English teaching method and the quality of teaching

Effects of spatial resolution on radar-based precipitation estimation using sub-kilometer X-band radar measurements

Known for the ability to observe precipitation at spatial resolution higher than rain gauge networks and satellite products, weather radars allow us to measure precipitation at spatial resolutions of 1 kilometer (typical resolution for operational radars) and a few hundred meters (often used in research activities). In principle, we can operate a weather radar at resolution higher than 100m and the expectation is that radar data at higher spatial resolution can provide more information. However, there is no systematic research about whether the additional information is noise or useful data contributing to the quantitative precipitation estimation. In order to quantitatively investigate the changes, as either benefits or drawbacks, caused by increasing the spatial resolution of radar measurements, we set up an X-band radar field experiment from May to October in 2017 in the Stuttgart metropolitan region. The scan strategy consists of two quasi-simultaneous scans with a 75-m and a 250-m radial resolution respectively. They are named as the fine scan and the coarse scan, respectively. Both scans are compared to each other in terms of the radar data quality and their radar-based precipitation estimates. The primary results from these comparisons between the radar data of these two scans show that, in contrast to the coarse scan, the fine scan data are characterized with losses of weak echoes, are more subjected to external signals and second-trip echoes (drawback), are more effective in removing non-meteorological echoes (benefit), are more skillful in delineating convective storms (benefit), and show a better agreement with the external reference data (benefit)

Motivation To Play Esports: Case of League of Legends

The population of playing electronic sports has increased recently, and the most popular one is League of Legends (LoL). As a multiplayer online battle arena video game, it’s not only a game, but also a competitive electronic sport. The purpose of this study was to assess the motivations of playing League of Legends and to relate them by genders, age groups and frequency groups. The final sample comprised 111 LoL players. The study categorized 12 items into three factors: achievement, socialization and immersion. Results indicated that achievement factors were stronger motives for men than women. For different age groups, there was no significant difference on socialization factors. The immersion factors for players who spent different times on LoL were not very influential

MODELING OF STRAIN EFFECT ON THERMAL AND ELECTRICAL TRANSPORT PROPERTIES OF SI/GE NANOCOMPOSITES AND ITS APPLICATIONS

Nanocomposites are composite materials which incorporate nanosized particles, platelets or fibers. The addition of nanosized phases into the bulk matrix can lead to significantly different material properties compared to their macrocomposite counterparts. Due to their extraordinary properties, nanocomposites promise new applications in many fields such as ultra-high strength and ultra-light automotive parts, non-linear optics, biomedical applications, sensors and actuators, and thermoelectric devices. The design and fabrication of nanocomposite structures, devices and systems can be accelerated by developing accurate and efficient computational tools that can describe the properties and behavior of the nanocomposites. However, the development of such tools is challenging due to the multiscale nature of the materials. In addition, many devices where nanocomposites are employed are multiphysics systems with interactions of the mechanical, thermal and electrical energy domains. In such systems, while mechanical deformation is dependent on the temperature change, the thermal and electrical transport properties are functions of mechanical strain. In this work, we develop theoretical and computational models to address these issues and investigate the strain effect on the thermal and electrical transport properties in Si/Ge nanocomposites. We model strain effect on the phonon thermal conductivities in the Si/Ge nanocomposite materials by combining the strain dependent lattice dynamics and the ballistic phonon Boltzmann transport equation (BTE). The Seebeck coefficient and electrical conductivity of the Si/Ge nanocomposites are calculated by using an analytical model derived from the BTE under the relaxation-time approximation. The effect of strain is incorporated into the analytical model through strain induced energy shift and effective mass variation calculated from the deformation potential theory and a degenerate kp method at the zone-boundary X point. By using the models, strain effect on the thermoelectric figure of merit is investigated for n-type Si/Ge nanocomposite materials. Our calculations reveal that in the 300 − 800 K temperature range, uniaxial tensile strain along \u3c 100 \u3e direction increases dimensionless figure of merit parallel to the tension, and biaxial tensile strain along [100] and [010] directions decreases it at low temperatures and increases it at high temperatures in the tension directions. Shear strain and compressive uniaxial and biaxial strains decrease the figure of merit. At 800K with an electron concentration of 10^19/cm^3, 1% uniaxial tensile strain can increase the figure of merit of Si(0.8)Ge(0.2) nanocomposites by as much as 14%. In light of nanocomposites\u27 high electrical to thermal conductivity ratio, we propose to use Si/Ge nanocomposite materials to improve the performance of micro thermal actuators. The high electrical to thermal conductivity ratio of Si/Ge nanocomposites is utilized to facilitate a rapid temperature change within a short distance, enabling a high temperature increase in a large region of the actuator beams. The total structural thermal expansion and consequently the actuation distance can be increased significantly. A top-down quasicontinuum multiscale model is presented for computational analysis of the nanocomposite based thermal actuators. Numerical results indicate that incorporating Si/Ge nanocomposites in thermal actuators can significantly increase their energy efficiency and mechanical performance. In addition, parametric studies show that the size of the nanocomposite region and atomic percentage of the material components have significant effects on the overall performance of the actuators