An Optimization Approach for Pricing of Sherpa Target Redemption Notes

Based on the one-factor CIR interest rate model, the pricing of Sherpa Target Redemption Notes (STARN) with early-excise features is investigated in this paper. Firstly, the characteristics of Sherpa target redemption notes were described and the partial differential equation was proposed. Secondly, both non-arbitrage jump conditions on the coupon date and early-excise policy on the redemption date were provided; furthermore, the boundary conditions of partial differential equations were also discussed. Thirdly, a numerical method for solving the partial differential equation was obtained based on the control volume in the theory of finite volume by making use of the upwind weighting scheme to avoid the numerical oscillation phenomenon. Finally, the sensitivity of the model parameters was analyzed. The results show that the STARN value decreases rapidly with the increase in short-term interest rates, furthermore, when short-term interest rates reached a turning point the rate of decline slowed. As volatility increases, the value of the Notes is increased; increasingly as the proportion redeemed is large, STARN value increases

Unified nonequilibrium dynamical theory for exchange bias and training effects

We investigate the exchange bias and training effects in the FM/AF heterostructures using a unified Monte Carlo dynamical approach. This real dynamical method has been proved reliable and effective in simulating dynamical magnetization of nanoscale magnetic systems. The magnetization of the uncompensated AF layer is still open after the first field cycling is finished. Our simulated results show obvious shift of hysteresis loops (exchange bias) and cycling dependence of exchange bias (training effect) when the temperature is below 45 K. The exchange bias fields decrease with decreasing the cooling rate or increasing the temperature and the number of the field cycling. With the simulations, we show the exchange bias can be manipulated by controlling the cooling rate, the distributive width of the anisotropy energy, or the magnetic coupling constants. Essentially, these two effects can be explained on the basis of the microscopical coexistence of both reversible and irreversible moment reversals of the AF domains. Our simulated results are useful to really understand the magnetization dynamics of such magnetic heterostructures. This unified nonequilibrium dynamical method should be applicable to other exchange bias systems.Comment: Chin. Phys. B, in pres

A Lévy Option Pricing model of FFT-Based High-order Multinomial Tree

This paper studies the method of constructing high order recombined multinomial tree based on fast Fourier transform (FFT), and applies multinomial tree option pricing under the Lévy process. First, the Lévy option pricing model and Fourier transform are introduced. Then, the network model based on FFT (Markov chain) is presented. After that, a method of constructing a recombined multinomial tree based on FFT is given. It is proved that the discrete random variables corresponding to the multinomial tree converge to the Lévy distributed continuous random variable. Next, we obtain the European option pricing formula of FFT multinomial tree pricing, and apply the reverse iteration method to the American option pricing. Finally, under the Jump-diffuse process, the difference between the computational accuracy and computational efficiency of the Semi-analytical solution of European Option and Merton European Call Option which are priced under FFT is compared. The results show that the method of constructing a high-order recombined multinomial tree based on FFT has very high calculation precision and calculation speed, which can solve the problem of traditional risk-neutral multinomial tree construction and it is a promising pricing method for derivative products

Multi-dimensional vibration sensing and simultaneous self-homodyne optical transmission of single wavelength net 5.36 Tb/s signal using telecom 7-core fiber

We present a high-capacity self-homodyne optical transmission system that enables simultaneously multidimensional vibration sensing based on a weakly-coupled 7-core fiber. To our knowledge, we demonstrate for the first-time detection of fiber vibration direction along with strength, frequency, and location of the vibration source, while transmitting in the meantime single-carrier 16 QAM signal reaching a net date rate of 5.36 Tb/s over 41.4 km of telecom 7-core fiber.Comment: 5 pages, 4 figure

UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized Multimodal Framework

In the current landscape of artificial intelligence, foundation models serve as the bedrock for advancements in both language and vision domains. OpenAI GPT-4 has emerged as the pinnacle in large language models (LLMs), while the computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models such as Meta's SAM and DINO, and YOLOS. However, the financial and computational burdens of training new models from scratch remain a significant barrier to progress. In response to this challenge, we introduce UnifiedVisionGPT, a novel framework designed to consolidate and automate the integration of SOTA vision models, thereby facilitating the development of vision-oriented AI. UnifiedVisionGPT distinguishes itself through four key features: (1) provides a versatile multimodal framework adaptable to a wide range of applications, building upon the strengths of multimodal foundation models; (2) seamlessly integrates various SOTA vision models to create a comprehensive multimodal platform, capitalizing on the best components of each model; (3) prioritizes vision-oriented AI, ensuring a more rapid progression in the CV domain compared to the current trajectory of LLMs; and (4) introduces automation in the selection of SOTA vision models, generating optimal results based on diverse multimodal inputs such as text prompts and images. This paper outlines the architecture and capabilities of UnifiedVisionGPT, demonstrating its potential to revolutionize the field of computer vision through enhanced efficiency, versatility, generalization, and performance. Our implementation, along with the unified multimodal framework and comprehensive dataset, is made publicly available at https://github.com/LHBuilder/SA-Segment-Anything.Comment: 9 pages, 29 figure

Experimental study of THGEM detector with mini-rim

The gas gain and energy resolution of single and double THGEM detectors (5{\times}5cm2 effective area) with mini-rims (rim is less than 10{\mu}m) were studied. The maximum gain can reach 5{\times}103 and 2{\times}105 for single and double THGEM respectively, while the energy resolution of 5.9 keV X-ray varied from 18% to 28% for both single and double THGEM detectors of different hole sizes and thicknesses.All the experiments were investigated in mixture of noble gases(argon,neon) and small content of other gases(iso-butane,methane) at atmospheric pressure.Comment: 4pages,6figures, it has been submitted to Chinese Physics

Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models

Large language models (LLMs) have achieved remarkable advancements in the field of natural language processing. However, the sheer scale and computational demands of these models present formidable challenges when considering their practical deployment in resource-constrained contexts. While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still carry over flawed reasoning or hallucinations inherited from their LLM counterparts. To address these issues, we propose a twofold methodology: First, we introduce a novel method for distilling the self-evaluation capability inherent in LLMs into SLMs, which aims to mitigate the adverse effects of erroneous reasoning and reduce hallucinations. Second, we advocate for a comprehensive distillation process that incorporates multiple distinct chain-of-thought and self-evaluation paradigms and ensures a more holistic and robust knowledge transfer into SLMs. Experiments on three NLP benchmarks demonstrate that our method significantly improves the performance of distilled SLMs and sheds light on the path towards developing smaller models closely aligned with human cognition.Comment: 13 pages, 5 figure

A hybrid Si@FeSiy/SiOx anode structure for high performance lithium-ion batteries via ammonia-assisted one-pot synthesis

Synthesised via planetary ball-milling of Si and Fe powders in an ammonia (NH3) environment, a hybrid Si@FeSiy/SiOx structure shows exceptional electrochemical properties for lithium-ion battery anodes, exhibiting a high initial capacity of 1150 mA h g−1 and a retention capacity of 880 mA h g−1 after 150 cycles at 100 mA g−1; and a capacity of 560 mA h g−1 at 4000 mA g−1. These are considerably high for carbon-free micro-/submicro-Si-based anodes. NH3 gradually turns into N2 and H2 during the synthesis, which facilitates the formation of highly conductive FeSiy (y = 1, 2) phases, whereas such phases were not formed in an Ar atmosphere. Milling for 20–40 h leads to partial decomposition of NH3 in the atmosphere, and a hybrid structure of a Si core of mixed nanocrystalline and amorphous Si domains, shelled by a relatively thick SiOx layer with embedded FeSi nanocrystallites. Milling for 60–100 h results in full decomposition of NH3 and a hybrid structure of a much-refined Si-rich core surrounded by a mantle of a relatively low level of SiOx and a higher level of FeSi2. The formation mechanisms of the SiOx and FeSiy phases are explored. The latter structure offers an optimum combination of the high capacity of a nanostructural Si core, relatively high electric conductivity of the FeSiy phase and high structural stability of a SiOx shell accommodating the volume change for high performance electrodes. The synthesis method is new and indispensable for the large-scale production of high-performance Si-based anode materials