pwc-archive/papers-with-abstracts huggingface dataset:
The Large Core of College Admission Markets: Theory and Evidence
Authors: Péter Biró, Avinatan Hassidim, Assaf Romm, Ran I. Shorrer, Sándor Sóvágó
Date: 2020-10-16 00:00:00

We study stable allocations in college admissions markets where students can attend the same college under different financial terms. The deferred acceptance algorithm identifies a stable allocation where funding is allocated based on merit. While merit-based stable allocations assign the same students to college, non-merit-based stable allocations may differ in the number of students assigned to college. In large markets, this possibility requires heterogeneity in applicants' sensitivity to financial terms. In Hungary, where such heterogeneity is present, a non-merit-based stable allocation would increase the number of assigned applicants by 1.9%, and affect 8.3% of the applicants relative to any merit-based stable allocation. These findings contrast sharply with findings from the matching (without contracts) literature.
Exploring the Design Space of Lunar GNSS in Frozen Orbit Conditions
Authors: Filipe Pereira, Daniel Selva
Date: 2020-10-17 00:00:00

The past decade has witnessed a growing interest in lunar exploration missions. The autonomy of lunar surface and in-orbit missions is, however, dependent on accurate and instantaneous navigation services. These services can not be provided by current Global Navigation Satellite Systems (GNSS) whose signals suffer from poor geometry and coverage in the vicinity of the Moon. Preliminary results of a systems architecture study on a new satellite navigation system orbiting the moon are presented. Lunar frozen orbit conditions under J2, C22 and third-body perturbations are assumed. The formulation includes the following design decisions: (1) Orbit semi-major axis, (2) Number of satellites, (3) Number of orbital planes, (4) Satellite phasing in adjacent planes, (5) Orbit eccentricity and (6) Argument of periapsis.The Borg Multi-Objective Evolutionary Algorithm (MOEA) framework is used to optimize the satellite constellation design problem, with a fitness function that takes into account performance, cost, availability and station-keeping deltaV. The performance metric is based on the Geometric Dilution of Precision (GDOP), which is computed over a grid of 500 equidistant points on the lunar surface. Additionally, the input satellite orbits used in the GDOP computation are obtained from high-fidelity orbit propagation using NASA General Mission Analysis Toolbox. Finally, satellite costs are based on satellite dry mass estimates derived from the power budget analysis assuming a satellite lifetime of 10 years. Results show that Lunar GNSS constellation with 20 satellites at frozen orbits can achieve satisfactory performance at mid-latitudes but not at the poles.
A Model of Choice with Minimal Compromise
Authors: Mario Vazquez Corte
Date: 2020-10-17 00:00:00
Tasks: model

I formulate and characterize the following two-stage choice behavior. The decision maker is endowed with two preferences. She shortlists all maximal alternatives according to the first preference. If the first preference is decisive, in the sense that it shortlists a unique alternative, then that alternative is the choice. If multiple alternatives are shortlisted, then, in a second stage, the second preference vetoes its minimal alternative in the shortlist, and the remaining members of the shortlist form the choice set. Only the final choice set is observable. I assume that the first preference is a weak order and the second is a linear order. Hence the shortlist is fully rationalizable but one of its members can drop out in the second stage, leading to bounded rational behavior. Given the asymmetric roles played by the underlying binary relations, the consequent behavior exhibits a minimal compromise between two preferences. To our knowledge it is the first Choice function that satisfies Sen's $\beta$ axiom of choice,but not $\alpha$.
About Robustness of Control Systems Embedding an Internal Model
Authors: Michelangelo Bin, Daniele Astolfi, Lorenzo Marconi
Date: 2020-10-17 00:00:00

Robustness is a basic property of any control system. In the context of linear output regulation, it was proved that embedding an internal model of the exogenous signals is necessary and sufficient to achieve tracking of the desired reference signals in spite of external disturbances and parametric uncertainties. This result is commonly known as "internal model principle". A complete extension of such linear result to general nonlinear systems is still an open problem, which is exacerbated by the large number of alternative definitions of uncertainty and desired control goals that are possible in a nonlinear setting. In this paper, we develop a general framework in which all these different notions can be formally characterized in a unifying way. Classical results are reinterpreted in the proposed setting, and new results and insights are presented with a focus on robust rejection/tracking of arbitrary harmonic content. Moreover, we show by counter-example that, in the relevant case of continuous unstructured uncertainties, there are problems for which no smooth finite-dimensional robust regulator exists ensuring exact regulation.
Synchronization analysis between exchange rates on the basis of purchasing power parity using the Hilbert transform
Authors: Makoto Muto, Yoshitaka Saiki
Date: 2020-10-17 00:00:00

Synchronization is a phenomenon in which a pair of fluctuations adjust their rhythms when interacting with each other. We measure the degree of synchronization between the U.S. dollar (USD) and euro exchange rates and between the USD and Japanese yen exchange rates on the basis of purchasing power parity (PPP) over time. We employ a method of synchronization analysis using the Hilbert transform, which is common in the field of nonlinear science. We find that the degree of synchronization is high most of the time, suggesting the establishment of PPP. The degree of synchronization does not remain high across periods with economic events with asymmetric effects, such as the U.S. real estate bubble.
PLS for V2I Communications Using Friendly Jammer and Double kappa-mu Shadowed Fading
Authors: Neji Mensi, Danda B. Rawat, Elyes Balti
Date: 2020-10-17 00:00:00
Tasks: Diversity

The concept of intelligent transportation systems (ITS) is considered to be a highly promising area of research due to its diversity of unique features. It is based mainly on the wireless vehicular network (WVN), where vehicles can perform sophisticated services such as sharing real-time safety information. To ensure high-quality service, WVN needs to solve the security challenges like eavesdropping, where malicious entities try to intercept the confidential transmitted signal. In this paper, we are going to provide a security scheme under the Double kappa-mu Shadowed fading. Our solution is based on the use of a friendly jammer that will transmit an artificial noise (AN) to jam the attacker's link and decrease its eavesdropping performances. To evaluate the efficiency of our solution, we investigated the outage probability for two special cases: Nakagami-m and Rician shadowed while taking into consideration the density of the blockage and the shadowing effects. We also studied the average secrecy capacity via deriving closed-form expressions of the ergodic capacity at the legitimate receiver and the attacker for the special case: Nakagami-m fading distribution.
Regional Synchronization during Economic Contraction: The Case of the U.S. and Japan
Authors: Makoto Muto, Tamotsu Onozaki, Yoshitaka Saiki
Date: 2020-10-17 00:00:00

Two decades of studies have found significant regional differences in the timing of transitions in national business cycles and their durations. Earlier studies partly detect regional synchronization during business cycle expansions and contractions in Europe, the United States, and Japan. We examine this possibility applying a sophisticated method for identifying the time-varying degree of synchronization to regional business cycle data in the U.S. and Japan. The method is prominent in nonlinear sciences but has been infrequently applied in business cycle studies.We find that synchronization in regional business cycles increased during contractions and decreased during expansions throughout the period under study.Such asymmetry between the contraction and expansion phases of a business cycle will contribute our better understanding of the phenomenon of business cycles.
Empirical likelihood and uniform convergence rates for dyadic kernel density estimation
Authors: Harold D. Chiang, Bing Yang Tan
Date: 2020-10-17 00:00:00
Tasks: Clustering, Density Estimation

This paper studies the asymptotic properties of and alternative inference methods for kernel density estimation (KDE) for dyadic data. We first establish uniform convergence rates for dyadic KDE. Secondly, we propose a modified jackknife empirical likelihood procedure for inference. The proposed test statistic is asymptotically pivotal regardless of presence of dyadic clustering. The results are further extended to cover the practically relevant case of incomplete dyadic data. Simulations show that this modified jackknife empirical likelihood-based inference procedure delivers precise coverage probabilities even with modest sample sizes and with incomplete dyadic data. Finally, we illustrate the method by studying airport congestion in the United States.
A Decomposition Approach to Counterfactual Analysis in Game-Theoretic Models
Authors: Nathan Canen, Kyungchul Song
Date: 2020-10-17 00:00:00
Tasks: counterfactual

Decomposition methods are often used for producing counterfactual predictions in non-strategic settings. When the outcome of interest arises from a game-theoretic setting where agents are better off by deviating from their strategies after a new policy, such predictions, despite their practical simplicity, are hard to justify. We present conditions in Bayesian games under which the decomposition-based predictions coincide with the equilibrium-based ones. In many games, such coincidence follows from an invariance condition for equilibrium selection rules. To illustrate our message, we revisit an empirical analysis in Ciliberto and Tamer (2009) on firms' entry decisions in the airline industry.
Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk
Authors: Tetsuo Kurosaki, Young Shin Kim
Date: 2020-10-18 00:00:00
Tasks: Portfolio Optimization, Time Series, Time Series Analysis

We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) model with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on the time series model, we optimize the portfolio in terms of Foster-Hart risk. Those sophisticated techniques are not yet documented in the context of cryptocurrency. Statistical tests suggest that the MNTS distributed GARCH model fits better with cryptocurrency returns than the competing GARCH-type models. We find that Foster-Hart optimization yields a more profitable portfolio with better risk-return balance than the prevailing approach.
Progress towards 3D-printing diamond for medical implants: A review
Authors: Aaqil Rifai, Shadi Houshyar, Kate Fox
Date: 2020-10-18 00:00:00

Additive manufacturing or 3D-printing is used to create bespoke items in many fields, such as defence, aerospace and medicine. Despite the progress made in 3D-printed orthopaedic implants, significant challenges remain in terms of creating a material capable of osseointegration while inhibiting bacterial colonisation of the implant. Diamond is rapidly emerging as a material with an extensive range of biomedical applications, especially due to its excellent biocompatibility. However, diamond is a difficult material to fabricate, owing to its extreme level of hardness and its brittleness. New methods of fabrication including additive manufacturing, have overcome some of these challenges and given rise to an increase in the use of diamond-based implants in both soft and hard tissue applications. Therefore, due to the unique properties of diamond, it is being considered as a facilitator of bone growth and subsequent tissue integration. This review outlines the recent progress in fabricating diamond for orthopaedic application, specifically focusing on the different fabrication approaches and their applicability in vitro and in vivo. The prospects and challenges for using diamond in medical implant technologies are also discussed.
Comparing antiviral strategies against COVID-19 via multiscale within-host modelling
Authors: Farzad Fatehi, Richard J Bingham, Eric C Dykeman, Peter G Stockley, Reidun Twarock
Date: 2020-10-18 00:00:00

Within-host models of COVID-19 infection dynamics enable the merits of different forms of antiviral therapy to be assessed in individual patients. A stochastic agent-based model of COVID-19 intracellular dynamics is introduced here, that incorporates essential steps of the viral life cycle targeted by treatment options. Integration of model predictions with an intercellular ODE model of within-host infection dynamics, fitted to patient data, generates a generic profile of disease progression in patients that have recovered in the absence of treatment. This is contrasted with the profiles obtained after variation of model parameters pertinent to the immune response, such as effector cell and antibody proliferation rates, mimicking disease progression in immunocompromised patients. These profiles are then compared with disease progression in the presence of antiviral and convalescent plasma therapy against COVID-19 infections. The model reveals that using both therapies in combination can be very effective in reducing the length of infection, but these synergistic effects decline with a delayed treatment start. Conversely, early treatment with either therapy alone can actually increase the duration of infection, with infectious virions still present after the decline of other markers of infection. This suggests that usage of these treatments should remain carefully controlled in a clinical environment.
Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction
Authors: Rintaro Ikeshita, Tomohiro Nakatani, Shoko Araki
Date: 2020-10-18 00:00:00

In this paper, we address the problem of extracting all super-Gaussian source signals from a linear mixture in which (i) the number of super-Gaussian sources $K$ is less than that of sensors $M$, and (ii) there are up to $M - K$ stationary Gaussian noises that do not need to be extracted. To solve this problem, independent vector extraction (IVE) using a majorization minimization and block coordinate descent (BCD) algorithms has been developed, attaining robust source extraction and low computational cost. We here improve the conventional BCDs for IVE by carefully exploiting the stationarity of the Gaussian noise components. We also newly develop a BCD for a semiblind IVE in which the transfer functions for several super-Gaussian sources are given a priori. Both algorithms consist of a closed-form formula and a generalized eigenvalue decomposition. In a numerical experiment of extracting speech signals from noisy mixtures, we show that when $K = 1$ in a blind case or at least $K - 1$ transfer functions are given in a semiblind case, the convergence of our proposed BCDs is significantly faster than those of the conventional ones.
Evolutionary dynamics in financial markets with heterogeneities in strategies and risk tolerance
Authors: Wen-Juan Xu, Chen-Yang Zhong, Fei Ren, Tian Qiu, Rong-Da Chen, Yun-Xin He, Li-Xin Zhong
Date: 2020-10-18 00:00:00

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.
Sparse Symmetric Linear Arrays with Low Redundancy and a Contiguous Sum Co-Array
Authors: Robin Rajamäki, Visa Koivunen
Date: 2020-10-18 00:00:00

Sparse arrays can resolve significantly more scatterers or sources than sensor by utilizing the co-array - a virtual array structure consisting of pairwise differences or sums of sensor positions. Although several sparse array configurations have been developed for passive sensing applications, far fewer active array designs exist. In active sensing, the sum co-array is typically more relevant than the difference co-array, especially when the scatterers are fully coherent. This paper proposes a general symmetric array configuration suitable for both active and passive sensing. We first derive necessary and sufficient conditions for the sum and difference co-array of this array to be contiguous. We then study two specific instances based on the Nested array and the Kl{\o}ve-Mossige basis, respectively. In particular, we establish the relationship between the minimum-redundancy solutions of the two resulting symmetric array configurations, and the previously proposed Concatenated Nested Array (CNA) and Kl{\o}ve Array (KA). Both the CNA and KA have closed-form expressions for the sensor positions, which means that they can be easily generated for any desired array size. The two array structures also achieve low redundancy, and a contiguous sum and difference co-array, which allows resolving vastly more scatterers or sources than sensors
Information Design in Optimal Auctions
Authors: Yi-Chun Chen, Xiangqian Yang
Date: 2020-10-18 00:00:00

We study the information design problem in a single-unit auction setting. The information designer controls independent private signals according to which the buyers infer their binary private values. Assuming that the seller adopts the optimal auction due to Myerson (1981) in response, we characterize both the buyer-optimal information structure, which maximizes the buyers' surplus, and the sellerworst information structure, which minimizes the seller's revenue. We translate both information design problems into finite-dimensional, constrained optimization problems in which one can explicitly solve for the optimal information structures. In contrast to the case with one buyer (Roesler and Szentes, 2017), we show that with two or more buyers, the symmetric buyer-optimal information structure is different from the symmetric seller-worst information structure. The good is always sold under the seller-worst information structure but not under the buyer-optimal information structure. Nevertheless, as the number of buyers goes to infinity, both symmetric information structures converge to no disclosure. We also show that in an ex ante symmetric setting, an asymmetric information structure is never seller-worst but can generate a strictly higher surplus for the buyers than the symmetric buyer-optimal information structure.
Analysis of the impact of maker-taker fees on the stock market using agent-based simulation
Authors: Isao Yagi, Mahiro Hoshino, Takanobu Mizuta
Date: 2020-10-18 00:00:00

Recently, most stock exchanges in the U.S. employ maker-taker fees, in which an exchange pays rebates to traders placing orders in the order book and charges fees to traders taking orders from the order book. Maker-taker fees encourage traders to place many orders that provide market liquidity to the exchange. However, it is not clear how maker-taker fees affect the total cost of a taking order, including all the charged fees and the market impact. In this study, we investigated the effect of maker-taker fees on the total cost of a taking order with our artificial market model, which is an agent-based model for financial markets. We found that maker-taker fees encourage market efficiency but increase the total costs of taking orders.
The Duopoly Analysis of Graphics Card Market
Authors: Nan Miles Xi
Date: 2020-10-18 00:00:00

By analyzing the duopoly market of computer graphics cards, we categorized the effects of enterprise's technological progress into two types, namely, cost reduction and product diversification. Our model proved that technological progress is the most effective means for enterprises in this industry to increase profits. Due to the technology-intensive nature of this industry, monopolistic enterprises face more intense competition compared with traditional manufacturing. Therefore, they have more motivation for technological innovation. Enterprises aiming at maximizing profits have incentives to reduce costs and achieve a higher degree of product differentiation through technological innovation.
Modified EP MIMO Detection Algorithm with Deep Learning Parameters Selection
Authors: Hang Chen, Guoqiang Yao, Jianhao Hu
Date: 2020-10-19 00:00:00
Tasks: Deep Learning

Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO) detector is regarded as a state-of-the-art MIMO detector because of its exceptional performance. However, we find that the EP MIMO detector cannot guarantee to achieve the optimal performance due to the empirical parameter selection, including initial variance and damping factors. According to the influence of the moment matching and parameter selection for the performance of the EP MIMO detector, we propose a modified EP MIMO detector (MEPD). In order to obtain the optimal initial variance and damping factors, we adopt a deep learning scheme, in which we unfold the iterative processing of MEPD to establish MEPNet for parameters training. The simulation results show that MEPD with off-line trained parameters outperforms the original one in various MIMO scenarios. Besides, the proposed MEPD with deep learning parameters selection is more robust than EPD in practical scenarios.
Strong Convergence to the Mean-Field Limit of A Finite Agent Equilibrium
Authors: Masaaki Fujii, Akihiko Takahashi
Date: 2020-10-19 00:00:00

We study an equilibrium-based continuous asset pricing problem for the securities market. In the previous work [16], we have shown that a certain price process, which is given by the solution to a forward backward stochastic differential equation of conditional McKean-Vlasov type, asymptotically clears the market in the large population limit. In the current work, under suitable conditions, we show the existence of a finite agent equilibrium and its strong convergence to the corresponding mean-field limit given in [16]. As an important byproduct, we get the direct estimate on the difference of the equilibrium price between the two markets; one consisting of heterogeneous agents of finite population size and the other of homogeneous agents of infinite population size.