Optimal Reinsurance-Investment Strategy for a Monotone Mean-Variance Insurer in the Cram\'er-Lundberg Model

As classical mean-variance preferences have the shortcoming of non-monotonicity, portfolio selection theory based on monotone mean-variance preferences is becoming an important research topic recently. In continuous-time Cram\'er-Lundberg insurance and Black-Scholes financial market model, we solve the optimal reinsurance-investment strategies of insurers under mean-variance preferences and monotone mean-variance preferences by the HJB equation and the HJBI equation, respectively. We prove the validity of verification theorems and find that the optimal strategies under the two preferences are the same. This illustrates that neither the continuity nor the completeness of the market is necessary for the consistency of two optimal strategies. We make detailed explanations for this result. Thus, we develop the existing theory of portfolio selection problems under the monotone mean-variance criterion

Preliminary design and optimization of toroidally-wound limited angle servo motor based on a generalized magnetic circuit model

This paper proposes a new generalized equivalent magnetic circuit model for the preliminary design of a toroidally-wound limited angle servo motor (LASM). In the model, the magnetic networks are formulated as a function of the pole number and geometric dimensions. Nonlinear saturation effect of the ferromagnetic material is also taken into consideration. A multi-objective optimization function involving the torque requirement, the mass, the time constant, and magnetic saturations of ferromagnetic material is introduced. Based on the proposed model, six design cases with different objectives have been carried by the particle swarm optimization (PSO) method. The comparisons of different optimization cases demonstrate the effectiveness and computation efficiency of the proposed method, and hence its suitability in preliminary design. Moreover, the generalized model can be readily applied in the other electromagnetic modelling

SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models

A common limitation of diagnostic tests for detecting social biases in NLP models is that they may only detect stereotypic associations that are pre-specified by the designer of the test. Since enumerating all possible problematic associations is infeasible, it is likely these tests fail to detect biases that are present in a model but not pre-specified by the designer. To address this limitation, we propose SODAPOP (SOcial bias Discovery from Answers about PeOPle) in social commonsense question-answering. Our pipeline generates modified instances from the Social IQa dataset (Sap et al., 2019) by (1) substituting names associated with different demographic groups, and (2) generating many distractor answers from a masked language model. By using a social commonsense model to score the generated distractors, we are able to uncover the model's stereotypic associations between demographic groups and an open set of words. We also test SODAPOP on debiased models and show the limitations of multiple state-of-the-art debiasing algorithms.Comment: EACL 202

Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps

We investigate the role of various demonstration components in the in-context learning (ICL) performance of large language models (LLMs). Specifically, we explore the impacts of ground-truth labels, input distribution, and complementary explanations, particularly when these are altered or perturbed. We build on previous work, which offers mixed findings on how these elements influence ICL. To probe these questions, we employ explainable NLP (XNLP) methods and utilize saliency maps of contrastive demonstrations for both qualitative and quantitative analysis. Our findings reveal that flipping ground-truth labels significantly affects the saliency, though it's more noticeable in larger LLMs. Our analysis of the input distribution at a granular level reveals that changing sentiment-indicative terms in a sentiment analysis task to neutral ones does not have as substantial an impact as altering ground-truth labels. Finally, we find that the effectiveness of complementary explanations in boosting ICL performance is task-dependent, with limited benefits seen in sentiment analysis tasks compared to symbolic reasoning tasks. These insights are critical for understanding the functionality of LLMs and guiding the development of effective demonstrations, which is increasingly relevant in light of the growing use of LLMs in applications such as ChatGPT. Our research code is publicly available at https://github.com/paihengxu/XICL.Comment: 10 pages, 5 figure

Enhanced bandwidth nonlinear resonance electromagnetic human motion energy harvester using magnetic-springs and ferrofluid

An enhanced bandwidth nonlinear resonant electromagnetic energy harvester has been designed to harness low frequency energy from basic human motion. Some vertical stacked cylindrical permanent magnets (PMs) constitute the inertial mass of the proposed harvester, which is suspended axially by two magnetic-springs and circumferentially by ferrofluid within a carbon fiber tube. In order to widen the frequency band and improve harvesting efficiency, two PMs are respectively fixed on the two end caps of the carbon fiber tube, so as to form two magnetic-springs with variable stiffness by cooperating with the PM stack. The self-assembled ferrofluid around the PM stack acts as its bearing system to minimize any friction during its movement. Copper wire are wrapped outside the tube to form the armature winding. The stiffness characteristic of the magnetic-springs and the optimum equilibrium position and number of windings have been determined by finite element method (FEM) analysis. As a proof of concept, a portable prototype of the proposed energy harvester that weighs 110g and with a volume of only 37.7cm $^3$ is fabricated. A series of experiments are carried out and the results show that the frequency band of the harvester becomes wider as the external vibration intensity increases. In addition, the effectiveness of ferrofluid in reducing friction is demonstrated under walking and running conditions. Without ferrofluid, the maximum average outputs are 10.15 mW and 32.53 mW respectively for walking and running. With ferrofluid, the maximum outputs are 17.72 mW and 54.61 mW, representing an increase of 74.58% and 67.88%, respectively. Furthermore, the prototype exhibits an average power density of 1.45 mW/cm $^3$ during running motions, which compares favorably with existing harvesters used in low power wearable devices

An electromagnetic wearable 3-DoF resonance human body motion energy harvester using ferrofluid as a lubricant

Wearable energy harvester offers clean and continuous power for wearable sensors or devices, and plays an important role in a wide range of applications such as the health monitoring and motion track. In this study, we investigate a small electromagnetic resonance wearable kinetic energy harvester. It consists of a permanent magnet (PM) supported by two elastic strings within a rectangular box form a 3-degree-of-freedom (3-DoF) vibrator. Copper windings are attached to the outer surface of the box to generate electrical energy when the PM is forced to vibrate. To minimize any frictional losses, ferrofluid is used such that the poles of PM are cushioned by the ferrofluid, to the effect that the PM will not touch the inner of the box. Simulation results show that the ferrofluid can keep the PM ‘contactless’ from the box even subject to 10 times gravity acceleration. A prototype is built and tested under different loading conditions. Resistance load experimental results indicate the proposed harvester can generate 1.11.1 mW in walking condition and 2.282.28 mW in running condition. An energy storage circuit is employed and the energy storage experimental results show that the average storage power during walking and running conditions are 0.0140.014 mW and 0.1490.149 mW respectively. It is shown that the developed harvester can be readily attached on a shoe to offer continuous power supply for wearable sensors and devices

CloudVO: building a secure virtual organization for multiple clouds collaboration

Cloud computing has become a popular computing paradigm in which virtualized and scalable resources are consolidated to provide services over Internet. However, the resource capability of a single cloud is generally limited, and some applications often require various cloud centers over Internet to deliver services together. Therefore, a Virtual Organization (VO) will be a promising approach to integrate services and users across multiple autonomous clouds. However, how to build a secure virtual organization to achieve the collaboration goals is a critical problem, and some issues such as membership agreement, policy conflict and trust management should be adequately addressed. In this paper, we present a framework CloudVO which based on security policies and trust management techniques to provide some flexible and dynamic VO management protocols for clouds. Therefore, CloudVO can achieve inter-cloud collaboration without destroying a cloud's local policies. Based on previous VO security management experiences, we have conducted some preliminary simulations to verify the effectiveness our approaches for cloud computing environments