Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction

In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, financial conditions, such as the absence of arbitrage, the boundaries and the asymptotic slope, are embedded into the loss function. This is one of the very first studies which discuss a methodological framework that incorporates prior financial domain knowledge into neural network architecture design and model training. The proposed model outperforms the benchmarked models with the option data on the S&P 500 index over 20 years. More importantly, the domain knowledge is satisfied empirically, showing the model is consistent with the existing financial theories and conditions related to implied volatility surface.Comment: 8 pages, SIGKDD 202

Machine learning for fiber nonlinearity mitigation in long-haul coherent optical transmission systems

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation

Smartphone data usage : downlink and uplink asymmetry

Mobile phone usage has changed significantly over the past few years and smartphone data usage is still not well understood on a statistically significant scale. This Letter analyses 2.1 million smartphone usage data values and explore the current wireless downlink–uplink demand asymmetry for different time periods and across different radio access networks. The current data demand over 2G networks remains largely symmetric with strong temporal variations, whereas the demand over 3G networks is asymmetric with surprisingly weak temporal variations is shown here

Mutually Guided Few-shot Learning for Relational Triple Extraction

Knowledge graphs (KGs), containing many entity-relation-entity triples, provide rich information for downstream applications. Although extracting triples from unstructured texts has been widely explored, most of them require a large number of labeled instances. The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations. To draw the connection between entity and relation, we design a proto-level fusion module to boost the performance of both entity extraction and relation classification. Moreover, a new cross-domain few-shot triple extraction task is introduced. Extensive experiments show that our method outperforms many state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single-domain) and 20.5 F1 score on FewRel 2.0 (cross-domain).Comment: Accepted by ICASSP 202

Seismic retrofit design and risk assessment of an irregular thermal power plant building

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154642/1/tal1719_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154642/2/tal1719.pd

Experimental study and mechanism analysis of high-pressure water jet for mud cake cutting during shield tunneling

When the opening rate of the cutter head of tunnel boring machines is insufficient for the removal of excavated soil in a timely manner, the soil tends to accumulate in front of the cutter head and inside the earth or slurry chamber, leading to mud caking. High-pressure water jetting is an effective method for removing mud cakes. This study explored the influence of high-pressure water jet parameters on the efficiency of mud cake cleaning by using highly weathered argillaceous siltstone as experimental materials. Mud cake compaction equipment and high-pressure water jetting devices were developed. In addition, the impact of jetting parameters such as jet pressure and flow rate on the mud cake cutting performance was investigated. The results indicated that with an increase in the erosion distance, the cutting width of the mud cake first increased and then gradually decreased to zero, while the cutting depth progressively diminished. Under the same jet pressure, when the flow rate increased from 49.1 L/min to 110 L/min, the cutting width of the high-pressure water jet increased. With further increase in the flow rate from 110 L/min to 202.8 L/min, the cutting width decreased and the cutting depth increased. Under the same jet flow, the increase in water pressure resulted in greater cutting width and depth

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models

Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. JARVIS-1 is the existing most general agent in Minecraft, capable of completing over 200 different tasks using control and observation space similar to humans. These tasks range from short-horizon tasks, e.g., "chopping trees" to long-horizon tasks, e.g., "obtaining a diamond pickaxe". JARVIS-1 performs exceptionally well in short-horizon tasks, achieving nearly perfect performance. In the classic long-term task of $\texttt{ObtainDiamondPickaxe}$, JARVIS-1 surpasses the reliability of current state-of-the-art agents by 5 times and can successfully complete longer-horizon and more challenging tasks. The project page is available at https://craftjarvis.org/JARVIS-1Comment: update project pag