arXiv subjects

Fei Ren

Publications and source records attributed to Fei Ren.

At least 37 records · Page 2Linked to original sources

arXiv2024

Duality for Hodge-Witt cohomology with modulus

Given an effective Cartier divisor D with simple normal crossing support on a smooth and proper scheme X over a perfect field of positive characteristic p, there is a natural notion of de Rham-Witt sheaves on X with zeros along D. We show that these sheaves correspond under Grothendieck duality for coherent sheaves to de Rham-Witt sheaves on X with modulus (X,D), as defined in the theory of cube invariant modulus sheaves with transfers developed by Kahn-Miyazaki-Saito-Yamazaki. From this we deduce refined versions of Ekedahl - and Poincaré duality for crystalline cohomology generalizing results of Mokrane and Nakkajima for reduced D, and a modulus version of Milne-Kato duality for étale motivic cohomology with p-primary torsion coefficients, which refines a result of Jannsen-Saito-Zhao. We furthermore get new integral models for rigid cohomology with compact supports on the complement of D and a modulus version of Milne's perfect Brauer group pairing for smooth projective surfaces over finite fields.

math.AG↗

arXiv2023

Bloch's cycle complex and coherent dualizing complexes in positive characteristic

Let $X$ be a separated scheme of dimension $d$ of finite type over a perfect field $k$ of positive characteristic $p$. In this work, we show that Bloch's cycle complex $\mathbb{Z}^c_X$ of zero cycles mod $p^n$ is quasi-isomorphic to the Cartier operator fixed part of a certain dualizing complex from coherent duality theory. From this we obtain new vanishing results for the higher Chow groups of zero cycles with mod $p^n$ coefficients for singular varieties.

math.AG↗

arXiv2022

New volatility evolution model after extreme events

In this paper, we propose a new dynamical model to study the two-stage volatility evolution of stock market index after extreme events, and find that the volatility after extreme events follows a stretched exponential decay in the initial stage and becomes a power law decay at later times by using high frequency minute data. Empirical study of the evolutionary behaviors of volatility after endogenous and exogenous events further demonstrates the descriptive power of our new model. To further explore the underlying mechanisms of volatility evolution, we introduce the sequential arrival of information hypothesis (SAIH) and the mixture of distribution hypothesis (MDH) to test the two-stage assumption, and find that investors transform from the uninformed state to the informed state in the first stage and informed investors subsequently dominate in the second stage. The testing results offer a supporting explanation for the validity of our new model and the fitted values of relevant parameters.

q-fin.ST↗

arXiv2020

Evolutionary dynamics in financial markets with heterogeneities in strategies and risk tolerance

In nature and human societies, the effects of homogeneous and heterogeneous characteristics on the evolution of collective behaviors are quite different from each other. It is of great importance to understand the underlying mechanisms of the occurrence of such differences. By incorporating pair pattern strategies and reference point strategies into an agent-based model, we have investigated the coupled effects of heterogeneous investment strategies and heterogeneous risk tolerance on price fluctuations. In the market flooded with the investors with homogeneous investment strategies or homogeneous risk tolerance, large price fluctuations are easy to occur. In the market flooded with the investors with heterogeneous investment strategies or heterogeneous risk tolerance, the price fluctuations are suppressed. For a heterogeneous population, the coexistence of investors with pair pattern strategies and reference point strategies causes the price to have a slow fluctuation around a typical equilibrium point and both a large price fluctuation and a no-trading state are avoided, in which the pair pattern strategies push the system far away from the equilibrium while the reference point strategies pull the system back to the equilibrium. A theoretical analysis indicates that the evolutionary dynamics in the present model is governed by the competition between different strategies. The strategy that causes large price fluctuations loses more while the strategy that pulls the system back to the equilibrium gains more. Overfrequent trading does harm to one's pursuit for more wealth.

q-fin.GN↗

arXiv2020

Coupled effects of epidemic information and risk awareness on contagion

By incorporating delayed epidemic information and self-restricted travel behavior into the SIS model, we have investigated the coupled effects of timely and accurate epidemic information and people's sensitivity to the epidemic information on contagion. In the population with only local random movement, whether the epidemic information is delayed or not has no effect on the spread of the epidemic. People's high sensitivity to the epidemic information leads to their risk aversion behavior and the spread of the epidemic is suppressed. In the population with only global person-to-person movement, timely and accurate epidemic information helps an individual cut off the connections with the infected in time and the epidemic is brought under control in no time. A delay in the epidemic information leads to an individual's misjudgment of who has been infected and who has not, which in turn leads to rapid progress and a higher peak of the epidemic. In the population with coexistence of local and global movement, timely and accurate epidemic information and people's high sensitivity to the epidemic information play an important role in curbing the epidemic. A theoretical analysis indicates that people's misjudgment caused by the delayed epidemic information leads to a higher encounter probability between the susceptible and the infected and people's self-restricted travel behavior helps reduce such an encounter probability. A functional relation between the ratio of infected individuals and the susceptible-infected encounter probability has been found.

physics.soc-ph↗

arXiv2019

Multiple learning mechanisms promote cooperation in public goods games with project selection

How evolution favors cooperation is a fundamental issue in social and economic systems. In the business world, actively selecting a suitable project is usually helpful for a businessman to be in an advantageous position. By incorporating project selection mechanism into the threshold public goods game, we have investigated the coupling effect of mutation and imitation in updating one's preferred project scale on the evolution of cooperation. Compared with the situation where there is no project selection mechanism, the existence of project selection may suppress or promote cooperation depending upon different updating rules. There exists a critical ratio of the imitators in the population, below which cooperation is suppressed while above which cooperation is promoted. With the coevolving of individual strategies and preferred project scales, a higher level of cooperation corresponds to a larger average value of the preferred project scales. A theoretical analysis indicates that, as most of the individuals are mutants, the coevolving process is governed by the mutation process, which leads to a smaller average value of the preferred project scales and a lower level of cooperation. As most of the individuals are imitators, the coevolving process is governed by the coupling of the mutation and imitation processes, which leads to a larger average value of the preferred project scales and a higher level of cooperation. As all the individuals are imitators, the coevolving process is governed by the imitation process, which leads to an intermediate average value of the preferred project scales and an intermediate level of cooperation.

physics.soc-ph↗

arXiv2019

Quantifying Robotic Swarm Coverage

In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. The proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. The first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. The second benchmark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.

eess.SY↗

arXiv2019

Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.

cs.CL↗

arXiv2018

Quantitative Assessment of Robotic Swarm Coverage

This paper studies a generally applicable, sensitive, and intuitive error metric for the assessment of robotic swarm density controller performance. Inspired by vortex blob numerical methods, it overcomes the shortcomings of a common strategy based on discretization, and unifies other continuous notions of coverage. We present two benchmarks against which to compare the error metric value of a given swarm configuration: non-trivial bounds on the error metric, and the probability density function of the error metric when robot positions are sampled at random from the target swarm distribution. We give rigorous results that this probability density function of the error metric obeys a central limit theorem, allowing for more efficient numerical approximation. For both of these benchmarks, we present supporting theory, computation methodology, examples, and MATLAB implementation code.

math.OC↗

arXiv2018

Modelling stock correlations with expected returns from investors

Stock correlations is crucial to asset pricing, investor decision-making, and financial risk regulations. However, microscopic explanation based on agent-based modeling is still lacking. We here propose a model derived from minority game for modeling stock correlations, in which an agent's expected return for one stock is influenced by the historical return of the other stock. Each agent makes a decision based on his expected return with reference to information dissemination and the historical return of the stock. We find that the returns of the stocks are positively (negatively) correlated when agents' expected returns for one stock are positively (negatively) correlated with the historical return of the other. We provide both numerical simulations and analytical studies and give explanations to stock correlations for cases with agents having either homogeneous or heterogeneous expected returns. The result still holds when other factors such as holding decisions and external events are included which broadens the practicability of the model.

q-fin.CP↗

arXiv2018

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system. The embeddings are trained with no explicit labels, yet learn to model a large range of acoustic expressiveness. GSTs lead to a rich set of significant results. The soft interpretable "labels" they generate can be used to control synthesis in novel ways, such as varying speed and speaking style - independently of the text content. They can also be used for style transfer, replicating the speaking style of a single audio clip across an entire long-form text corpus. When trained on noisy, unlabeled found data, GSTs learn to factorize noise and speaker identity, providing a path towards highly scalable but robust speech synthesis.

cs.CL↗

arXiv2017

Self-reinforcing feedback loop in financial markets with coupling of market impact and momentum traders

By incorporating market impact and momentum traders into an agent-based model, we investigate the conditions for the occurrence of self-reinforcing feedback loops and the coevolutionary mechanism of prices and strategies. For low market impact, the price fluctuations are originally large. The existence of momentum traders has little impact on the change of price fluctuations but destroys the equilibrium between the trend-following and trend-rejecting strategies. The trend-following herd behaviors become dominant. A self-reinforcing feedback loop exists. For high market impact, the existence of momentum traders leads to an increase in price fluctuations. The trend-following strategies of rational individuals are suppressed while the trend-following strategies of momentum traders are promoted. The crowd-anticrowd behaviors become dominant. A negative feedback loop exists. A theoretical analysis indicates that, for low market impact, the majority effect is beneficial for the trend-followers to earn more, which in turn promotes the trend-following strategies. For high market impact, the minority effect causes the trend-followers to suffer great losses, which in turn suppresses the trend-following strategies.

physics.soc-ph↗

arXiv2017

A generalized public goods game with coupling of individual ability and project benefit

Facing a heavy task, any single person can only make a limited contribution and team cooperation is needed. As one enjoys the benefit of the public goods, the potential benefits of the project are not always maximized and may be partly wasted. By incorporating individual ability and project benefit into the original public goods game, we study the coupling effect of the four parameters, the upper limit of individual contribution, the upper limit of individual benefit, the needed project cost and the upper limit of project benefit on the evolution of cooperation. Coevolving with the individual-level group size preferences, an increase in the upper limit of individual benefit promotes cooperation while an increase in the upper limit of individual contribution inhibits cooperation. The coupling of the upper limit of individual contribution and the needed project cost determines the critical point of the upper limit of project benefit, where the equilibrium frequency of cooperators reaches its highest level. Above the critical point, an increase in the upper limit of project benefit inhibits cooperation. The evolution of cooperation is closely related to the preferred group-size distribution. A functional relation between the frequency of cooperators and the dominant group size is found.

physics.soc-ph↗

arXiv2016

Dynamic structure of stock communities: A comparative study between stock returns and turnover rates

The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PMFG and infomap methods based on a distance matrix. We find that a few of the largest communities are composed of certain specific industry or conceptional sectors and the correlation inside a sector is generally larger than the correlation between different sectors. In comparison with returns, the community structure for turnover rates is more complex and the sector effect is relatively weaker. The financial dynamics is further studied by analyzing the community structures over five sub-periods. Sectors like banks, real estate, health care and New Shanghai take turns to compose a few of the largest communities for both returns and turnover rates in different sub-periods. Several specific sectors appear in the communities with different rank orders for the two time series even in the same sub-period. A comparison between the evolution of prices and turnover rates of stocks from these sectors is conducted to better understand their differences. We find that stock prices only had large changes around some important events while turnover rates surged after each of these events relevant to specific sectors, which may offer a possible explanation for the complexity of stock communities for turnover rates.

q-fin.ST↗

arXiv2016

Dynamic portfolio strategy using clustering approach

The problem of portfolio optimization is one of the most important issues in asset management. This paper proposes a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for investment. A portfolio strategy comprises two stages: selecting the portfolios by choosing central and peripheral stocks in the selection horizon using five topological parameters, i.e., degree, betweenness centrality, distance on degree criterion, distance on correlation criterion and distance on distance criterion, then using the portfolios for investment in the investment horizon. The optimal portfolio is chosen by comparing central and peripheral portfolios under different combinations of market conditions in the selection and investment horizons. Market conditions in our paper are identified by the ratios of the number of trading days with rising index or the sum of the amplitudes of the trading days with rising index to the total number of trading days. We find that central portfolios outperform peripheral portfolios when the market is under a drawup condition, or when the market is stable or drawup in the selection horizon and is under a stable condition in the investment horizon. We also find that the peripheral portfolios gain more than central portfolios when the market is stable in the selection horizon and is drawdown in the investment horizon. Empirical tests are carried out based on the optimal portfolio strategy. Among all the possible optimal portfolio strategy based on different parameters to select portfolios and different criteria to identify market conditions, $65\%$ of our optimal portfolio strategies outperform the random strategy for the Shanghai A-Share market and the proportion is $70\%$ for the Shenzhen A-Share market.

q-fin.ST↗

arXiv2015

High thermal conductivity of hexagonal boron nitride laminates

Two-dimensional materials are characterised by a number of unique physical properties which can potentially make them useful to a wide diversity of applications. In particular, the large thermal conductivity of graphene and hexagonal boron nitride has already been acknowledged and these materials have been suggested as novel core materials for thermal management in electronics. However, it was not clear if mass produced flakes of hexagonal boron nitride would allow one to achieve an industrially-relevant value of thermal conductivity. Here we demonstrate that laminates of hexagonal boron nitride exhibit thermal conductivity of up to 20 W/mK, which is significantly larger than that currently used in thermal management. We also show that the thermal conductivity of laminates increases with the increasing volumetric mass density, which creates a way of fine-tuning its thermal properties.

cond-mat.mtrl-sci↗

arXiv2013

Dynamic evolution of cross-correlations in the Chinese stock market

We study the dynamic evolution of cross-correlations in the Chinese stock market mainly based on the random matrix theory (RMT). The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different moving windows, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes.

q-fin.ST↗

arXiv2013

The position profiles of order cancellations in an emerging stock market

Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price level in the limit-order book (LOB) and the position in the queue at each price level. We study the profiles of these two order cancellation positions through rebuilding the limit-order book using the order flow data of 23 liquid stocks traded on the Shenzhen Stock Exchange in the year 2003. We find that the profiles of relative price levels where cancellations occur obey a log-normal distribution. After normalizing the relative price level by removing the factor of order numbers stored at the price level, we find that the profiles exhibit a power-law scaling behavior on the right tails for both buy and sell orders. When focusing on the order cancellation positions in the queue at each price level, we find that the profiles increase rapidly in the front of the queue, and then fluctuate around a constant value till the end of the queue. These profiles are similar for different stocks. In addition, the profiles of cancellation positions can be fitted by an exponent function for both buy and sell orders. These two kinds of cancellation profiles seem universal for different stocks investigated and exhibit minor asymmetry between buy and sell orders. Our empirical findings shed new light on the order cancellation dynamics and pose constraints on the construction of order-driven stock market models.

q-fin.TR↗