Market Making of Options via Reinforcement Learning

Market making of options with different maturities and strikes is a challenging problem due to its high dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes. When the arrival of market orders is linearly inverse to the spreads, the optimal policy is normally distributed

Plasma-Assisted Combustion

As a promising technology, plasma-assisted combustion (PAC) has attracted many researchers to explore the effect of PAC on improving the combustion in propulsion devices, such as scramjet, detonation engines, internal engines, and so on. In this chapter, we aim to exhibit the influence of quasi-DC discharge plasma on the operating performance of scramjet combustor and find the internal mechanisms, which may contribute to the development of PAC technology in supersonic combustion. For case one, a plasma filament is generated upstream of fuel jet through quasi-DC discharge in a scramjet combustor; for case two, the plasma is formed across the backward facing step of a flame holding cavity to improve the flame stabilization of the cavity in the scramjet combustor. The two cases are investigated in detail through three-dimensional numerical simulation based on the dominant thermal blocking mechanism. Important parameters including temperature distribution, separation zone, water production, stagnation pressure loss, combustion efficiency, cavity drag, mass exchange rate, and cavity oscillating characteristics are obtained and analyzed. It shows that the quasi-DC discharge plasma does benefit for the improvement of the combustion in a scramjet combustor

Arginyltransferase, Its Specificity, Putative Substrates, Bidirectional Promoter, and Splicing-derived Isoforms

Substrates of the N-end rule pathway include proteins with destabilizing N-terminal residues. Three of them, Asp, Glu, and (oxidized) Cys, function through their conjugation to Arg, one of destabilizing N-terminal residues that are recognized directly by the pathway's ubiquitin ligases. The conjugation of Arg is mediated by arginyltransferase, encoded by ATE1. Through its regulated degradation of specific proteins, the arginylation branch of the N-end rule pathway mediates, in particular, the cardiovascular development, the fidelity of chromosome segregation, and the control of signaling by nitric oxide. We show that mouse ATE1 specifies at least six mRNA isoforms, which are produced through alternative splicing, encode enzymatically active arginyltransferases, and are expressed at varying levels in mouse tissues. We also show that the ATE1 promoter is bidirectional, mediating the expression of both ATE1 and an oppositely oriented, previously uncharacterized gene. In addition, we identified GRP78 (glucose-regulated protein 78) and protein-disulfide isomerase as putative physiological substrates of arginyltransferase. Purified isoforms of arginyltransferase that contain the alternative first exons differentially arginylate these proteins in extract from ATE1-/- embryos, suggesting that specific isoforms may have distinct functions. Although the N-end rule pathway is apparently confined to the cytosol and the nucleus, and although GRP78 and protein-disulfide isomerase are located largely in the endoplasmic reticulum, recent evidence suggests that these proteins are also present in the cytosol and other compartments in vivo, where they may become N-end rule substrates

Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation

Existing neural methods have shown great potentials towards generating informative text from structured tabular data as well as maintaining high content fidelity. However, few of them shed light on generating personalized expressions, which often requires well-aligned persona-table-text datasets that are difficult to obtain. To overcome these obstacles, we explore personalized table-to-text generation under a zero-shot setting, by assuming no well-aligned persona-table-text triples are required during training. To this end, we firstly collect a set of unpaired persona information and then propose a semi-supervised approach with contrastive persona distillation (S2P-CPD) to generate personalized context. Specifically, tabular data and persona information are firstly represented as latent variables separately. Then, we devise a latent space fusion technique to distill persona information into the table representation. Besides, a contrastive-based discriminator is employed to guarantee the style consistency between the generated context and its corresponding persona. Experimental results on two benchmarks demonstrate S2P-CPD's ability on keeping both content fidelity and personalized expressions.Comment: Accepted by ICASSP 202

AliCHI: A Large-scale Multi-modal Dataset and Automated Evaluation Tool for Human-like Dialogue Systems

A well-designed interactive human-like dialogue system is expected to take actions (e.g. smiling) and respond in a pattern similar to humans. However, due to the limitation of single-modality (only speech) or small volume of currently public datasets, most dialogue systems can only respond in speech and cannot take human-like actions. In this work, we build a large-scale multi-modal dataset of human-to-human conversation in a face-to-face fashion, with fine-grained annotations. The raw data in video format contains 635 dialogue sessions, being collected from 200 participants on designed topics and lasting 52 hours in total. Moreover, we manually annotated the verbal and non-verbal behaviors in each dialogue session on their start/end timestamp. Furthermore, we developed a corresponding evaluation tool for human-like dialogue systems to automatically evaluates the accuracy of two basic tasks, turn-taking prediction, and backchannel prediction, on both time and content. We have opened the data, the tools will be released at the conference