pwc-archive/papers-with-abstracts huggingface dataset:
Is response priming based on surface color? Response to Skrzypulec (2021)
Authors: Thomas Schmidt
Date: 2021-05-09 00:00:00

Skrzypulec (2021) raises the question whether motor activation by masked color primes is based on the same type of color representation as conscious vision. He postulates that the literature on color processing without awareness makes an implicit assumption that "conscious" and "unconscious" color representations have the same properties, in which case priming by masked color stimuli would indeed indicate that the same complex representation of surface color can be conscious as well as unconscious. I review some evidence from response priming by lightness stimuli in the context of a visual illusion that alters the perceived lightness of the primes (Schmidt et al., 2010). Those results clearly show that response priming is not driven by color-constant information but instead by local image contrast, making it unlikely that rapid response activation by color primes is based on a color-constant representation of surface color.
Conditions for the existence of zero-determinant strategies under observation errors in repeated games
Authors: Azumi Mamiya, Daiki Miyagawa, Genki Ichinose
Date: 2021-05-09 00:00:00

Repeated games are useful models to analyze long term interactions of living species and complex social phenomena. Zero-determinant (ZD) strategies in repeated games discovered by Press and Dyson in 2012 enforce a linear payoff relationship between a focal player and the opponent. This linear relationship can be set arbitrarily by a ZD player. Hence, a subclass of ZD strategies can fix the opponent's expected payoff and another subclass of the strategies can exceed the opponent for the expected payoff. Since this discovery, theories for ZD strategies are extended to cope with various natural situations. It is especially important to consider the theory of ZD strategies for repeated games with a discount factor and observation errors because it allows the theory to be applicable in the real world. Recent studies revealed their existence of ZD strategies even in repeated games with both factors. However, the conditions for the existence has not been sufficiently analyzed. Here, we mathematically analyzed the conditions in repeated games with both factors. First, we derived the thresholds of a discount factor and observation errors which ensure the existence of Equalizer and positively correlated ZD (pcZD) strategies, which are well-known subclasses of ZD strategies. We found that ZD strategies exist only when a discount factor remains high as the error rates increase. Next, we derived the conditions for the expected payoff of the opponent enforced by Equalizer as well as the conditions for the slope and base line payoff of linear lines enforced by pcZD. As a result, we found that, as error rates increase or a discount factor decreases, the conditions for the linear line that Equalizer or pcZD can enforce become strict.
Episome partitioning and symmetric cell divisions: quantifying the role of random events in the persistence of HPV infections
Authors: Thomas Beneteau, Christian Selinger, Mircea T. Sofonea, Samuel Alizon
Date: 2021-05-08 00:00:00

Human Papillomaviruses (HPV) are one of the most prevalent sexually transmitted infections (STI) and the most oncogenic viruses known to humans. The vast majority of HPV infections clear in less than 3 years, but the underlying mechanisms, especially the involvement of the immune response, are still poorly known. Building on earlier work stressing the potential importance of stochasticity in HPV infection clearance, we develop a stochastic mathematical model of HPV dynamics with an explicit description of the intracellular level. We show that the random partitioning of virus episomes upon stem cell division and the occurrence of symmetric divisions greatly affect viral persistence. These results call for more detailed within-host studies to better understand the relative importance of stochasticity and immunity in HPV infection clearance.
A modified two-leaf light use efficiency model for improving the simulation of GPP using a radiation scalar
Authors: Xiaobin Guan, Jing M. Chen, Huanfeng Shen, Xinyao Xie
Date: 2021-05-09 00:00:00

A TL-LUE model modified with a radiation scalar (RTL-LUE) is developed in this paper. The same maximum LUE is used for both sunlit and shaded leaves, and the difference in LUE between sunlit and shaded leaf groups is determined by the same radiation scalar. The RTL-LUE model was calibrated and validated at global 169 FLUXNET eddy covariance (EC) sites. Results indicate that although GPP simulations from the TL-LUE model match well with the EC GPP, the RTL-LUE model can further improve the simulation, for half-hour, 8-day, and yearly time scales. The TL-LUE model tends to overestimate GPP under conditions of high incoming photosynthetically active radiation (PAR), because the radiation-independent LUE values for both sunlit and shaded leaves are only suitable for low-medium (e.g. average) incoming PAR conditions. The errors in the RTL-LUE model show lower sensitivity to PAR, and its GPP simulations can better track the diurnal and seasonal variations of EC GPP by alleviating the overestimation at noon and growing seasons associated with the TL-LUE model. This study demonstrates the necessity of considering a radiation scalar in GPP simulation in LUE models even if the first-order effect of radiation is already considered through differentiating sunlit and shaded leaves. The simple RTL-LUE developed in this study would be a useful alternative to complex process-based models for global carbon cycle research.
Rough Set Microbiome Characterisation
Authors: Benjamin Wingfield, Sonya Coleman, T. M. McGinnity, Anthony J. Bjourson
Date: 2021-05-09 00:00:00

Microbiota profiles measure the structure of microbial communities in a defined environment (known as microbiomes). In the past decade, microbiome research has focused on health applications as a result of which the gut microbiome has been implicated in the development of a broad range of diseases such as obesity, inflammatory bowel disease, and major depressive disorder. A key goal of many microbiome experiments is to characterise or describe the microbial community. High-throughput sequencing is used to generate microbiota profiles, but data gathered via this method are extremely challenging to analyse, as the data violate multiple strong assumptions of standard models. Rough Set Theory (RST) has weak assumptions that are less likely to be violated, and offers a range of attractive tools for extracting knowledge from complex data. In this paper we present the first application of RST for characterising microbiomes. We begin with a demonstrative benchmark microbiota profile and extend the approach to gut microbiomes gathered from depressed subjects to enable knowledge discovery. We find that RST is capable of excellent characterisation of the gut microbiomes in depressed subjects and identifying previously undescribed alterations to the microbiome-gut-brain axis. An important aspect of the application of RST is that it provides a possible solution to an open research question regarding the search for an optimal normalisation approach for microbiome census data, as one does not currently exist.
Detection of acoustic events in Lavender for measuring the xylem vulnerability to embolism and cellular damages
Authors: Lia Lamacque, Florian Sabin, Thierry Améglio, Stéphane Herbette, Guillaume Charrier
Date: 2021-05-09 00:00:00

Acoustic emission analysis is a promising technique to investigate the physiological events leading to drought-induced injuries and mortality. However, the nature and the source of the acoustic emissions are not fully understood and make the use of this technique difficult as a direct measure of the loss of xylem hydraulic conductance. In this study, acoustic emissions were recorded during severe dehydration in lavender plants and compared to the dynamics of embolism development and cell lysis. The timing and characteristics of acoustic signals from two independent recording systems were compared by principal component analysis. In parallel, changes in water potential, branch diameter, loss of hydraulic conductance and electrolyte leakage were measured to quantify drought-induced damages. Two distinct phases of acoustic emissions were observed during dehydration: the first one associated with a rapid loss of diameter and a significant increase in loss of xylem conductance (90%) and the second one with a significant increase in electrolyte leakage and slower diameter changes. This second phase corresponds to a complete loss of recovery capacity. The acoustic signals of both phases were discriminated by the third and fourth principal components. The loss of hydraulic conductance during the first acoustic phase suggests the hydraulic origin of these signals (i.e. cavitation events). For the second phase, the signals showed much higher variability between plants and acoustic systems suggesting that the sources of these signals may be plural, although likely including cellular damage. A simple algorithm was developed to discriminate hydraulic-related acoustic signals from other sources, allowing the reconstruction of dynamic hydraulic vulnerability curves. However, hydraulic failure precedes cellular damage and lack of whole plant recovery is associated to these latter.
Least squares Monte Carlo methods in stochastic Volterra rough volatility models
Authors: Henrique Guerreiro, João Guerra
Date: 2021-05-10 00:00:00
Tasks: valid

In stochastic Volterra rough volatility models, the volatility follows a truncated Brownian semi-stationary process with stochastic vol-of-vol. Recently, efficient VIX pricing Monte Carlo methods have been proposed for the case where the vol-of-vol is Markovian and independent of the volatility. Following recent empirical data, we discuss the VIX option pricing problem for a generalized framework of these models, where the vol-of-vol may depend on the volatility and/or not be Markovian. In such a setting, the aforementioned Monte Carlo methods are not valid. Moreover, the classical least squares Monte Carlo faces exponentially increasing complexity with the number of grid time steps, whilst the nested Monte Carlo method requires a prohibitive number of simulations. By exploring the infinite dimensional Markovian representation of these models, we device a scalable least squares Monte Carlo for VIX option pricing. We apply our method firstly under the independence assumption for benchmarks, and then to the generalized framework. We also discuss the rough vol-of-vol setting, where Markovianity of the vol-of-vol is not present. We present simulations and benchmarks to establish the efficiency of our method.
Global Index on Financial Losses due to Crime in the United States
Authors: Thilini Mahanama, Abootaleb Shirvani, Svetlozar Rachev
Date: 2021-05-07 00:00:00

Crime can have a volatile impact on investments. Despite the potential importance of crime rates in investments, there are no indices dedicated to evaluating the financial impact of crime in the United States. As such, this paper presents an index-based insurance portfolio for crime in the United States by utilizing the financial losses reported by the Federal Bureau of Investigation for property crimes and cybercrimes. Our research intends to help investors envision risk exposure in our portfolio, gauge investment risk based on their desired risk level, and hedge strategies for potential losses due to economic crashes. Underlying the index, we hedge the investments by issuing marketable European call and put options and providing risk budgets (diversifying risk to each type of crime). We find that real estate, ransomware, and government impersonation are the main risk contributors. We then evaluate the performance of our index to determine its resilience to economic crisis. The unemployment rate potentially demonstrates a high systemic risk on the portfolio compared to the economic factors used in this study. In conclusion, we provide a basis for the securitization of insurance risk from certain crimes that could forewarn investors to transfer their risk to capital market investors.
Hedging under rough volatility
Authors: Masaaki Fukasawa, Blanka Horvath, Peter Tankov
Date: 2021-05-10 00:00:00

In this chapter we first briefly review the existing approaches to hedging in rough volatility models. Next, we present a simple but general result which shows that in a one-factor rough stochastic volatility model, any option may be perfectly hedged with a dynamic portfolio containing the underlying and one other asset such as a variance swap. In the final section we report the results of a back-test experiment using real data, where VIX options are hedged with a forward variance swap. In this experiment, using a rough volatility model allows to almost completely remove the bias and reduce the overall hedging error by a factor of 27% compared to traditional diffusion-based models.
On the Time-Inconsistent Deterministic Linear-Quadratic Control
Authors: Hongyan Cai, Danhong Chen, Yunfei Peng, Wei Wei
Date: 2021-05-08 00:00:00

A fundamental theory of deterministic linear-quadratic (LQ) control is the equivalent relationship between control problems, two-point boundary value problems and Riccati equations. In this paper, we extend the equivalence to a general time-inconsistent deterministic LQ problem, where the inconsistency arises from non-exponential discount functions. By studying the solvability of the Riccati equation, we show the existence and uniqueness of the linear equilibrium for the time-inconsistent LQ problem.
Bayesian inference and superstatistics to describe long memory processes of financial time series
Authors: Geoffrey Ducournau
Date: 2021-05-10 00:00:00
Tasks: Bayesian Inference, Time Series, Time Series Analysis

One of the standardized features of financial data is that log-returns are uncorrelated, but absolute log-returns or their squares namely the fluctuating volatility are correlated and is characterized by heavy tailed in the sense that some moment of the absolute log-returns is infinite and typically non-Gaussian [20]. And this last characteristic change accordantly to different timescales. We propose to model this long-memory phenomenon by superstatistical dynamics and provide a Bayesian Inference methodology drawing on Metropolis-Hasting random walk sampling to determine which superstatistics among inverse-Gamma and log-Normal describe the best log-returns complexity on different timescales, from high to low frequency. We show that on smaller timescales (minutes) even though the Inverse-Gamma superstatistics works the best, the log-Normal model remains very reliable and suitable to fit the absolute log-returns probability density distribution with strong capacity of describing heavy tails and power law decays. On larger timescales (daily), we show in terms of Bayes factor that the inverse-Gamma superstatistics is preferred to the log-Normal model. We also show evidence of a transition of statistics from power law decay on small timescales to exponential decay on large scale with less heavy tails meaning that on larger time scales the fluctuating volatility tend to be memoryless, consequently superstatistics becomes less relevant.
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature
Authors: Yihao Luo, Ailing Yang, Fupeng Sun, Huafei Sun
Date: 2021-04-26 00:00:00
Tasks: Denoising

In this paper, we introduce the adaptive Wasserstein curvature denoising (AWCD), an original processing approach for point cloud data. By collecting curvatures information from Wasserstein distance, AWCD consider more precise structures of data and preserves stability and effectiveness even for data with noise in high density. This paper contains some theoretical analysis about the Wasserstein curvature and the complete algorithm of AWCD. In addition, we design digital experiments to show the denoising effect of AWCD. According to comparison results, we present the advantages of AWCD against traditional algorithms.
U-BERT: Pre-training User Representations for Improved Recommendation
Authors: zhaopengqiu
Date: 2021-01-01 00:00:00

Learning user representation is a critical task for recommen-
dation systems as it can encode user preference for person-
alized services. User representation is generally learned from
behavior data, such as clicking interactions and review com-
ments. However, for less popular domains, the behavior data
is insufficient to learn precise user representations. To deal
with this problem, a natural thought is to leverage content-
rich domains to complement user representations. Inspired by
the recent success of BERT in NLP, we propose a novel pre-
training and fine-tuning based approach U-BERT. Different
from typical BERT applications, U-BERT is customized for
recommendation and utilizes different frameworks in pre-
training and fine-tuning. In pre-training, U-BERT focuses on
content-rich domains and introduces a user encoder and a
review encoder to model users’ behaviors. Two pre-training
strategies are proposed to learn the general user representa-
tions; In fine-tuning, U-BERT focuses on the target content-
insufficient domains. In addition to the user and review en-
coders inherited from the pre-training stage, U-BERT further
introduces an item encoder to model item representations.
Besides, a review co-matching layer is proposed to capture
more semantic interactions between the reviews of the user
and item. Finally, U-BERT combines user representations,
item representations and review interaction information to
improve recommendation performance. Experiments on six
benchmark datasets from different domains demonstrate the
state-of-the-art performance of U-BERT.
Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data
Authors: Efthymios Tzinis, Jonah Casebeer, Zhepei Wang, Paris Smaragdis
Date: 2021-05-11 00:00:00
Tasks: Federated Learning, Speech Enhancement, Transfer Learning

We propose FEDENHANCE, an unsupervised federated learning (FL) approach for speech enhancement and separation with non-IID distributed data across multiple clients. We simulate a real-world scenario where each client only has access to a few noisy recordings from a limited and disjoint number of speakers (hence non-IID). Each client trains their model in isolation using mixture invariant training while periodically providing updates to a central server. Our experiments show that our approach achieves competitive enhancement performance compared to IID training on a single device and that we can further facilitate the convergence speed and the overall performance using transfer learning on the server-side. Moreover, we show that we can effectively combine updates from clients trained locally with supervised and unsupervised losses. We also release a new dataset LibriFSD50K and its creation recipe in order to facilitate FL research for source separation problems.
Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus
Authors: Jack Bandy, Nicholas Vincent
Date: 2021-05-11 00:00:00
Tasks: BIG-bench Machine Learning

Recent literature has underscored the importance of dataset documentation work for machine learning, and part of this work involves addressing "documentation debt" for datasets that have been used widely but documented sparsely. This paper aims to help address documentation debt for BookCorpus, a popular text dataset for training large language models. Notably, researchers have used BookCorpus to train OpenAI's GPT-N models and Google's BERT models, even though little to no documentation exists about the dataset's motivation, composition, collection process, etc. We offer a preliminary datasheet that provides key context and information about BookCorpus, highlighting several notable deficiencies. In particular, we find evidence that (1) BookCorpus likely violates copyright restrictions for many books, (2) BookCorpus contains thousands of duplicated books, and (3) BookCorpus exhibits significant skews in genre representation. We also find hints of other potential deficiencies that call for future research, including problematic content, potential skews in religious representation, and lopsided author contributions. While more work remains, this initial effort to provide a datasheet for BookCorpus adds to growing literature that urges more careful and systematic documentation for machine learning datasets.
Towards Discovery and Attribution of Open-world GAN Generated Images
Authors: Sharath Girish, Saksham Suri, Saketh Rambhatla, Abhinav Shrivastava
Date: 2021-05-10 00:00:00
Proceeding: ICCV 2021 10
Tasks: Attribute, Clustering, Out-of-Distribution Detection

With the recent progress in Generative Adversarial Networks (GANs), it is imperative for media and visual forensics to develop detectors which can identify and attribute images to the model generating them. Existing works have shown to attribute images to their corresponding GAN sources with high accuracy. However, these works are limited to a closed set scenario, failing to generalize to GANs unseen during train time and are therefore, not scalable with a steady influx of new GANs. We present an iterative algorithm for discovering images generated from previously unseen GANs by exploiting the fact that all GANs leave distinct fingerprints on their generated images. Our algorithm consists of multiple components including network training, out-of-distribution detection, clustering, merge and refine steps. Through extensive experiments, we show that our algorithm discovers unseen GANs with high accuracy and also generalizes to GANs trained on unseen real datasets. We additionally apply our algorithm to attribution and discovery of GANs in an online fashion as well as to the more standard task of real/fake detection. Our experiments demonstrate the effectiveness of our approach to discover new GANs and can be used in an open-world setup.
Enhancing Photorealism Enhancement
Authors: Stephan R. Richter, Hassan Abu Alhaija, Vladlen Koltun
Date: 2021-05-10 00:00:00
Tasks: Image-to-Image Translation, Translation

We present an approach to enhancing the realism of synthetic images. The images are enhanced by a convolutional network that leverages intermediate representations produced by conventional rendering pipelines. The network is trained via a novel adversarial objective, which provides strong supervision at multiple perceptual levels. We analyze scene layout distributions in commonly used datasets and find that they differ in important ways. We hypothesize that this is one of the causes of strong artifacts that can be observed in the results of many prior methods. To address this we propose a new strategy for sampling image patches during training. We also introduce multiple architectural improvements in the deep network modules used for photorealism enhancement. We confirm the benefits of our contributions in controlled experiments and report substantial gains in stability and realism in comparison to recent image-to-image translation methods and a variety of other baselines.