pwc-archive/papers-with-abstracts huggingface dataset:
A Computational Efficient Maximum Likelihood Direct Position Determination Approach for Multiple Emitters Using Angle and Doppler Measurements
Authors: Ziqiang Wang, Yimao Sun, Qun Wan, Lei Xie, Ning Liu
Date: 2021-12-04 00:00:00
Tasks: Position

Emitter localization is widely applied in the military and civilian _elds. In this paper, we tackle the problem of position estimation for multiple stationary emitters using Doppler frequency shifts and angles by moving receivers. The computational load for the exhaustive maximum likelihood (ML) direct position determination (DPD) search is insu_erable. Based on the Pincus' theorem and importance sampling (IS) concept, we propose a novel non-iterative ML DPD method. The proposed method transforms the original multidimensional grid search into random variables generation with multiple low-dimensional pseudo-probability density functions (PDF), and the circular mean is used for superior position estimation performance. The computational complexity of the proposed method is modest, and the o_-grid problem that most existing DPD techniques face is signi_cantly alleviated. Moreover, it can be implemented in parallel separately. Simulation results demonstrate that the proposed ML DPD estimator can achieve better estimation accuracy than state-of-the-art DPD techniques. With a reasonable parameter choice, the estimation performance of the proposed technique is very close to the Cram_er-Rao lower bound (CRLB), even in the adverse conditions of low signal-to-noise ratios (SNR) levels.
Wireless Power Transmission on Martian Surface for Zero-Energy Devices
Authors: Kürşat Tekbıyık, Dogay Altinel, Mustafa Cansiz, Güneş Karabulut Kurt
Date: 2021-12-03 00:00:00

Exploration of the Red Planet is essential on the way through both human colonization and establishing a habitat on the planet. Due to the high costs of space missions, the use of distributed sensor networks has been investigated to make in situ explorations affordable. Along with this, the devices with ultra-low-power receivers, which are called zero-energy (ZE) devices, can pave the way to further discoveries about the environment of Mars. This study focuses on wireless power transmission to provide the power required by ZE devices on the Martian surface. The main motivation of this study is to investigate whether conventional harvesters and communication units can supply the required power for a long distance. The numerical results show that it is possible to deliver power to ZE devices without utilizing any sophisticated hardware. In addition, the effects of pointing error and dust storms on harvesting performance are investigated. Comprehensive simulation results reveal that harvester selection and design should be done by considering propagation channel and transmitter characteristics.
Closed-Loop Nash Competition for Liquidity
Authors: Alessandro Micheli, Johannes Muhle-Karbe, Eyal Neuman
Date: 2021-12-06 00:00:00

We study a multi-player stochastic differential game, where agents interact through their joint price impact on an asset that they trade to exploit a common trading signal. In this context, we prove that a closed-loop Nash equilibrium exists if the price impact parameter is small enough. Compared to the corresponding open-loop Nash equilibrium, both the agents' optimal trading rates and their performance move towards the central-planner solution, in that excessive trading due to lack of coordination is reduced. However, the size of this effect is modest for plausible parameter values.
Deep brain stimulation with a computational model for the cortex-thalamus-basal-ganglia system and network dynamics of neurological disorders
Authors: Hina Shaheen, Roderick Melnik
Date: 2021-12-06 00:00:00

Deep brain stimulation (DBS) can alleviate the movement disorders like Parkinson's disease (PD). Indeed, it is known that aberrant beta (13-30Hz)oscillations and the loss of dopaminergic neurons in the basal ganglia-thalamus (BGTH) and cortex characterize the akinesia symptoms of PD. However, the relevant biophysical mechanism behind this process still remains unclear. Based on the prior striatal inhibitory model, we propose an extended BGTH model incorporating medium spine neurons (MSNs) and fast-spiking interneurons (FSIs) along with the effect of DBS. We are focusing in this paper on an open-loop DBS mode, where the stimulation parameters stay constant independent of variations in the disease state, and modifications of parameters rely mainly on trial and error of medical experts. Additionally, we propose a novel combined model of the cerebellar-basal-ganglia thalamocortical network, MSNs, and FSIs, and show new results that indicate that Parkinsonian oscillations in the beta-band frequency range emerge from the dynamics of such a network. Our model predicts that DBS can be used to suppress beta oscillations in globus pallidus pars interna (GPi) neurons. This research will help our better understanding of the changes in brain activity caused by DBS, providing new insight for studying PD in the future.
A Neuronal Noise Critique of Integrated Information Theory
Authors: Refath Bari
Date: 2021-12-06 00:00:00

Integrated Information Theory (IIT) is an audacious attempt to pin down the abstract, phenomenological experiences of consciousness into a rigorous, mathematical framework. We show that IIT's stance in regards to neuronal noise is inconsistent with experimental data demonstrating that neuronal noise in the brain plays a critical role in learning, visual recognition, and even categorical representation. IIT predicts that entropy due to noise will reduce the information integration of a physical system, which is inconsistent with experimental data demonstrating that decision-related noise is a necessary condition for learning and visual recognition tasks. IIT must therefore be reformulated to accommodate experimental evidence showing both the successes and failures of noise.
Combining cortical voltage imaging and hippocampal electrophysiology for investigating global, multi-time scale activity interactions in the brain
Authors: Rafael Pedrosa, Francesco Battaglia
Date: 2021-12-06 00:00:00

A new generation of molecular tools for analyzing neural activity has been contributing to elucidate classical open questions in neuroscience. With enhanced GEVIs and advanced optical imaging techniques, voltage-imaging technologies have been increasingly used to observe the dynamics of large circuits. In this chapter, we describe how to combine cortical wide-field voltage imaging with hippocampal electrophysiology in awake behaving mice. Furthermore, we highlight how this method can be useful for different possible investigations.
Connecting the dots in ethology: applying network theory to understand neural and animal collectives
Authors: Adam Gosztolai, Pavan Ramdya
Date: 2021-12-04 00:00:00

A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach thanks to techniques providing access to the connectivity and activity of neuronal ensembles as well as to behaviors among animal collectives. The next challenge using these datasets is to unravel network mechanisms generating population behaviors. This is aided by network theory, a field that studies structure-function relationships in interconnected systems. Here we review studies that have taken a network view on modern datasets to provide unique insights into individual and collective animal behaviors. Specifically, we focus on how analyzing signal propagation, controllability, symmetry, and geometry of networks can tame the complexity of collective system dynamics. These studies illustrate the potential of network theory to accelerate our understanding of behavior across ethological scales.
A New Rapid Test to End COVID
Authors: Mark Ralph Baker, Fleur Conway, Filippo Dal Ben, Elizabeth Lucinda Hawthorne, Licia Iacoviello, A Agodi, Saquib Mukhtar, Hai Phan, Yemurai Rabvukwa, Jessica R Rogge
Date: 2021-12-05 00:00:00

Despite 93.1% to 95.8% of the UK adult population having been vaccinated and currently 83.5% to 89.8% of adults having received at least two doses (1), and despite many households testing twice a week with lateral flow tests (2), R at the time of writing is 0.9 to 1.1, with a growth rate range for England of between -1% and +1% (3). Furthermore, up to 30% of infected individuals are going on to experience Long Covid (4). The crisis is far from over and as new variants of concern like Omicron spread, the situation is not under control, even in the highly vaccinated and tested UK and far less so in many countries. The problem is likely to be replicated in other countries with currently low infection levels as isolation is eased in future, even if these countries reach a high level of vaccination. Additionally, concerns have been raised about fall in immunity by 6 months after receiving the Pfizer and AstraZeneca vaccines (5), and the ability of Omicron to re-infect and cause illness in vaccinated people, with urgent booster jabs now being given to attempt to mitigate this. A solution to stop the spread of all variants of COVID-19 is needed now, and we present it here: CLDC, a rapid test that is 98%+ sensitive, low cost and scalable.
Minimal informational requirements for fitness
Authors: Alexander S. Moffett, Andrew W. Eckford
Date: 2021-12-03 00:00:00

The existing concept of the "fitness value of information" provides a theoretical upper bound on the fitness advantage of using information concerning a fluctuating environment. Using concepts from rate-distortion theory, we develop a theoretical framework to answer a different pair of questions: What is the minimal amount of information needed for a population to achieve a certain growth rate? What is the minimal amount of information gain needed for one sub-population to achieve a certain average selection coefficient over another? We introduce a correspondence between fitness and distortion and solve for the rate-distortion functions of several systems using analytical and numerical methods. Because accurate information processing is energetically costly, our approach provides a theoretical basis for understanding evolutionary "design principles" underlying information-cost trade-offs.
A revised comparison between FF five-factor model and three-factor model,based on China's A-share market
Authors: Zhijing Zhang, Yue Yu, Qinghua Ma, Haixiang Yao
Date: 2021-10-16 00:00:00
Tasks: model, regression

In allusion to some contradicting results in existing research, this paper selects China's latest stock data from 2005 to 2020 for empirical analysis. By choosing this periods' data, we avoid the periods of China's significant stock market reforms to reduce the impact of the government's policy on the factor effect. In this paper, the redundant factors (HML, CMA) are orthogonalized, and the regression analysis of 5*5 portfolio of Size-B/M and Size-Inv is carried out with these two orthogonalized factors. It found that the HML and the CMA are still significant in many portfolios, indicating that they have a strong explanatory ability, which is also consistent with the results of GRS test. All these show that the five-factor model has a better ability to explain the excess return rate. In the concrete analysis, this paper uses the methods of the five-factor 25-group portfolio returns calculation, the five-factor regression analysis, the orthogonal treatment, the five-factor 25-group regression and the GRS test to more comprehensively explain the excellent explanatory ability of the five-factor model to the excess return. Then, we analyze the possible reasons for the strong explanatory ability of the HML, CMA and RMW from the aspects of price to book ratio, turnover rate and correlation coefficient. We also give a detailed explanation of the results, and analyze the changes of China's stock market policy and investors' investment style recent years. Finally, this paper attempts to put forward some useful suggestions on the development of asset pricing model and China's stock market.
The Cost-Benefit Fallacy: Why Cost-Benefit Analysis Is Broken and How to Fix It
Authors: Bent Flyvbjerg, Dirk W. Bester
Date: 2021-10-12 00:00:00

Most cost-benefit analyses assume that the estimates of costs and benefits are more or less accurate and unbiased. But what if, in reality, estimates are highly inaccurate and biased? Then the assumption that cost-benefit analysis is a rational way to improve resource allocation would be a fallacy. Based on the largest dataset of its kind, we test the assumption that cost and benefit estimates of public investments are accurate and unbiased. We find this is not the case with overwhelming statistical significance. We document the extent of cost overruns, benefit shortfalls, and forecasting bias in public investments. We further assess whether such inaccuracies seriously distort effective resource allocation, which is found to be the case. We explain our findings in behavioral terms and explore their policy implications. Finally, we conclude that cost-benefit analysis of public investments stands in need of reform and we outline four steps to such reform.
Market Microstructure of Non Fungible Tokens
Authors: Mayukh Mukhopadhyay, Kaushik Ghosh
Date: 2021-10-08 00:00:00

Non Fungible Token (NFT) Industry has been witnessing multi-million dollar trade in recent times. With rapid innovation of the NFT market environment by technology, innovation, and decentralization, it is becoming hard to distinguish between genuine NFT from fads and scams. This article discuss the NFT market microstructure, with a focus on price formation, market structure, transparency, and applications to other financial areas. Market manipulation in NFT market with the context of wash-sale patterns has also been surveyed. The article concludes by providing pointers on due-diligence activity that can be adopted by investors to mitigate NFT trading risk.
A stochastic control approach to bid-ask price modelling
Authors: Engel John C. Dela Vega, Robert J. Elliott
Date: 2021-12-04 00:00:00

This paper develops a model for the bid and ask prices of a European type asset by formulating a stochastic control problem. The state process is governed by a modified geometric Brownian motion whose drift and diffusion coefficients depend on a Markov chain. A Girsanov theorem for Markov chains is implemented for the change of coefficients, including the diffusion coefficient which cannot be changed by the usual Girsanov theorem for Brownian motion. The price of a European type asset is then determined using an Esscher transform and a system of partial differential equations. A dynamic programming principle and a maximum/minimum principle associated with the stochastic control problem are then derived to model bid and ask prices. These prices are not quotes of traders or market makers but represent estimates in our model on which reasonable quantities could be traded.
Optimal Investment with Risk Controlled by Weighted Entropic Risk Measures
Authors: Jianming Xia
Date: 2021-12-04 00:00:00

A risk measure that is consistent with the second-order stochastic dominance and additive for sums of independent random variables can be represented as a weighted entropic risk measure (WERM). The expected utility maximization problem with risk controlled by WERM and a related risk minimization problem are investigated in this paper. The latter is same to a problem of maximizing a weighted average of constant-absolute-risk-aversion (CARA) certainty equivalents. The solutions of all the optimization problems are explicitly characterized and an iterative method of the solutions is provided.
Optimal order execution under price impact: A hybrid model
Authors: Marina Di Giacinto, Claudio Tebaldi, Tai-Ho Wang
Date: 2021-12-04 00:00:00

In this paper we explore optimal liquidation in a market populated by a number of heterogeneous market makers that have limited inventory-carrying and risk-bearing capacity. We derive a reduced form model for the dynamic of their aggregated inventory considering a proper scaling limit. The resulting price impact profile is shown to depend on the characteristics and relative importance of their inventories. The model is flexible enough to reproduce the empirically documented power law behavior of the price impact function. For any choice of the market makers characteristics, optimal execution within this modeling approach can be recast as a linear-quadratic stochastic control problem in which the value function and the associated optimal trading rate can be obtained semi-explicitly subject to solving a differential matrix Riccati equation. Numerical simulations are conducted to illustrate the performance of the resulting optimal liquidation strategy in relation to standard benchmarks. Remarkably, they show that the increase in performance is determined by a substantial reduction of higher order moment risk.
Deep differentiable reinforcement learning and optimal trading
Authors: Thibault Jaisson
Date: 2021-12-06 00:00:00
Tasks: Portfolio Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In many reinforcement learning applications, the underlying environment reward and transition functions are explicitly known differentiable functions. This enables us to use recent research which applies machine learning tools to stochastic control to find optimal action functions. In this paper, we define differentiable reinforcement learning as a particular case of this research. We find that incorporating deep learning in this framework leads to more accurate and stable solutions than those obtained from more generic actor critic algorithms. We apply this deep differentiable reinforcement learning (DDRL) algorithm to the problem of one asset optimal trading strategies in various environments where the market dynamics are known. Thanks to the stability of this method, we are able to efficiently find optimal strategies for complex multi-scale market models. We also extend these methods to simultaneously find optimal action functions for a wide range of environment parameters. This makes it applicable to real life financial signals and portfolio optimization where the expected return has multiple time scales. In the case of a slow and a fast alpha signal, we find that the optimal trading strategy consists in using the fast signal to time the trades associated to the slow signal.
CBI-time-changed Lévy processes for multi-currency modeling
Authors: Claudio Fontana, Alessandro Gnoatto, Guillaume Szulda
Date: 2021-12-04 00:00:00

We develop a stochastic volatility framework for modeling multiple currencies based on CBI-time-changed L\'evy processes. The proposed framework captures the typical risk characteristics of FX markets and is coherent with the symmetries of FX rates. Moreover, due to the self-exciting behavior of CBI processes, the volatilities of FX rates exhibit self-exciting dynamics. By relying on the theory of affine processes, we show that our approach is analytically tractable and that the model structure is invariant under a suitable class of risk-neutral measures. A semi-closed pricing formula for currency options is obtained by Fourier methods. We propose two calibration methods, also by relying on deep-learning techniques, and show that a simple specification of the model can achieve a good fit to market data on a currency triangle.
Market making by an FX dealer: tiers, pricing ladders and hedging rates for optimal risk control
Authors: Alexander Barzykin, Philippe Bergault, Olivier Guéant
Date: 2021-12-04 00:00:00
Tasks: Position

Dealers make money by providing liquidity to clients but face flow uncertainty and thus price risk. They can efficiently skew their prices and wait for clients to mitigate risk (internalization), or trade with other dealers in the open market to hedge their position and reduce their inventory (externalization). Of course, the better control associated with externalization comes with transaction costs and market impact. The internalization vs. externalization dilemma has been a topic of recent active discussion within the foreign exchange (FX) community. This paper offers an optimal control framework for market making tackling both pricing and hedging, thus answering a question well known to dealers: `to hedge, or not to hedge?'
Optimal Income Crossover for Two-Class Model Using Particle Swarm Optimization
Authors: Paulo H. dos Santos, Igor D. S. Siciliani, M. H. R. Tragtenberg
Date: 2021-12-04 00:00:00
Tasks: Vocal Bursts Valence Prediction

Personal income distribution may exhibit a two-class structure, such that the lower income class of the population (85-98%) is described by exponential Boltzmann-Gibbs distribution, whereas the upper income class (15-2%) has a Pareto power-law distribution. We propose a method, based on a theoretical and numerical optimization scheme, which allows us to determine the crossover income between the distributions, the temperature of the Boltzmann-Gibbs distribution and the Pareto index. Using this method, the Brazilian income distribution data provided by the National Household Sample Survey was studied. The data was stratified into two dichotomies (sex/gender and color/race), so the model was tested using different subsets along with accessing the economic differences between these groups. Lastly, we analyse the temporal evolution of the parameters of our model and the Gini coefficient discussing the implication on the Brazilian income inequality. To our knowledge, for the first time an optimization method is proposed in order to find a continuous two-class income distribution, which is able to delimit the boundaries of the two distributions. It also gives a measure of inequality which is a function that depends only on the Pareto index and the percentage of people in the high income region. It was found a temporal dynamics relation, that may be general, between the Pareto and the percentage of people described by the Pareto tail.
TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion
Authors: Yiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu
Date: 2021-12-04 00:00:00
Tasks: Transfer Learning

The prosperity of mobile and financial technologies has bred and expanded various kinds of financial products to a broader scope of people, which contributes to advocating financial inclusion. It has non-trivial social benefits of diminishing financial inequality. However, the technical challenges in individual financial risk evaluation caused by the distinct characteristic distribution and limited credit history of new users, as well as the inexperience of newly-entered companies in handling complex data and obtaining accurate labels, impede further promoting financial inclusion. To tackle these challenges, this paper develops a novel transfer learning algorithm (i.e., TransBoost) that combines the merits of tree-based models and kernel methods. The TransBoost is designed with a parallel tree structure and efficient weights updating mechanism with theoretical guarantee, which enables it to excel in tackling real-world data with high dimensional features and sparsity in $O(n)$ time complexity. We conduct extensive experiments on two public datasets and a unique large-scale dataset from Tencent Mobile Payment. The results show that the TransBoost outperforms other state-of-the-art benchmark transfer learning algorithms in terms of prediction accuracy with superior efficiency, shows stronger robustness to data sparsity, and provides meaningful model interpretation. Besides, given a financial risk level, the TransBoost enables financial service providers to serve the largest number of users including those who would otherwise be excluded by other algorithms. That is, the TransBoost improves financial inclusion.