pwc-archive/papers-with-abstracts huggingface dataset:
Data-Based Models for Hurricane Evolution Prediction: A Deep Learning Approach
Authors: Rikhi Bose, Adam Pintar, Emil Simiu
Date: 2021-10-30 00:00:00
Tasks: Prediction, Trajectory Prediction

Fast and accurate prediction of hurricane evolution from genesis onwards is needed to reduce loss of life and enhance community resilience. In this work, a novel model development methodology for predicting storm trajectory is proposed based on two classes of Recurrent Neural Networks (RNNs). The RNN models are trained on input features available in or derived from the HURDAT2 North Atlantic hurricane database maintained by the National Hurricane Center (NHC). The models use probabilities of storms passing through any location, computed from historical data. A detailed analysis of model forecasting error shows that Many-To-One prediction models are less accurate than Many-To-Many models owing to compounded error accumulation, with the exception of $6-hr$ predictions, for which the two types of model perform comparably. Application to 75 or more test storms in the North Atlantic basin showed that, for short-term forecasting up to 12 hours, the Many-to-Many RNN storm trajectory prediction models presented herein are significantly faster than ensemble models used by the NHC, while leading to errors of comparable magnitude.
Maximum Likelihood Estimation of Differentiated Products Demand Systems
Authors: Greg Lewis, Bora Ozaltun, Georgios Zervas
Date: 2021-11-24 00:00:00

We discuss estimation of the differentiated products demand system of Berry et al (1995) (BLP) by maximum likelihood estimation (MLE). We derive the maximum likelihood estimator in the case where prices are endogenously generated by firms that set prices in Bertrand-Nash equilibrium. In Monte Carlo simulations the MLE estimator outperforms the best-practice GMM estimator on both bias and mean squared error when the model is correctly specified. This remains true under some forms of misspecification. In our simulations, the coverage of the ML estimator is close to its nominal level, whereas the GMM estimator tends to under-cover. We conclude the paper by estimating BLP on the car data used in the original Berry et al (1995) paper, obtaining similar estimates with considerably tighter standard errors.
On Recoding Ordered Treatments as Binary Indicators
Authors: Evan K. Rose, Yotam Shem-Tov
Date: 2021-11-24 00:00:00

Researchers using instrumental variables to investigate ordered treatments often recode treatment into an indicator for any exposure. We investigate this estimand under the assumption that the instruments shift compliers from no treatment to some but not from some treatment to more. We show that when there are extensive margin compliers only (EMCO) this estimand captures a weighted average of treatment effects that can be partially unbundled into each complier group's potential outcome means. We also establish an equivalence between EMCO and a two-factor selection model and apply our results to study treatment heterogeneity in the Oregon Health Insurance Experiment.
The Performance of Recent Methods for Estimating Skill Prices in Panel Data
Authors: Michael J. Böhm, Hans-Martin von Gaudecker
Date: 2021-11-24 00:00:00
Tasks: Sensitivity

This paper explores different methods to estimate prices paid per efficiency unit of labor in panel data. We study the sensitivity of skill price estimates to different assumptions regarding workers' choice problem, identification strategies, the number of occupations considered, skill accumulation processes, and estimation strategies. In order to do so, we conduct careful Monte Carlo experiments designed to generate similar features as in German panel data. We find that once skill accumulation is appropriately modelled, skill price estimates are generally robust to modelling choices when the number of occupations is small, i.e., switches between occupations are rare. When switching is important, subtle issues emerge and the performance of different methods varies more strongly.
A Game Theoretic Analysis of Liquidity Events in Convertible Instruments
Authors: Ron van der Meyden
Date: 2021-11-24 00:00:00

Convertible instruments are contracts, used in venture financing, which give investors the right to receive shares in the venture in certain circumstances. In liquidity events, investors may have the option to either receive back their principal investment, or to receive a proportional payment after conversion of the contract to a shareholding. In each case, the value of the payment may depend on the choices made by other investors who hold such convertible contracts. A liquidity event therefore sets up a game theoretic optimization problem. The paper defines a general model for such games, which is shown to cover all instances of the Y Combinator Simple Agreement for Future Equity (SAFE) contracts, a type of convertible instrument that is commonly used to finance startup ventures. The paper shows that, in general, pure strategy Nash equilibria do not necessarily exist in this model, and there may not exist an optimum pure strategy Nash equilibrium in cases where pure strategy Nash equilibria do exist. However, it is shown when all contracts are uniformly one of the SAFE contract types, an optimum pure strategy Nash equilibrium exists. Polynomial time algorithms for computing (optimum) pure strategy Nash equilibria in these cases are developed.
Energy Efficient Video Decoding for VVC Using a Greedy Strategy Based Design Space Exploration
Authors: Matthias Kränzler, Christian Herglotz, André Kaup
Date: 2021-11-23 00:00:00
Tasks: 4k, Decoder

IP traffic has increased significantly in recent years, and it is expected that this progress will continue. Recent studies report that the viewing of online video content accounts for a share of 1% of the global greenhouse gas emissions. To reduce the data traffic of video streaming, the new standard Versatile Video Coding (VVC) has been finalized in 2020. In this paper, the energy efficiency of two different VVC decoders is analyzed in detail. Furthermore, we propose a design space exploration that uses an algorithm based on a greedy strategy to derive coding tool profiles that optimize the energy demand of the decoder. We show that the algorithm derives optimal coding tool profiles for a subset of coding tools. Additionally, we propose profiles that reduce the energy demand of VVC decoders and provide energy savings of more than 50% for sequences with 4K resolution. Thereby, we will also show that the proposed profiles can have a lower decoding energy demand than comparable HEVC-encoded bit streams while also having a significantly lower bit rate.
Dual Domain Waveform Design for Joint Communication and Sensing Systems
Authors: Luca Rinaldi, Dario Tagliaferri, Francesco Linsalata, Marouan Mizmizi, Maurizio Magarini, Umberto Spagnolini
Date: 2021-11-24 00:00:00
Tasks: Management

The evolution of wireless communication systems towards millimeter-wave ($30-100$ GHz) and sub-THz ($>100$ GHz) frequency bands highlighted the need for accurate and fast beam management and a proactive link-blockage prediction in high-mobility scenarios. Joint Communication and Sensing (JC\&S) systems aim at equipping communication terminals with sensing capabilities using the same time/frequency/space communication resources to solve, or alleviate, the aforementioned issues. For an efficient implementation, a suitable waveform design that combines communication and sensing capabilities is of utmost importance. This paper proposes a novel dual-domain waveform design approach that superimposes onto the Frequency-Time (FT) domain both the legacy orthogonal frequency division multiplexing modulation scheme and a sensing signal, purposely designed in the Delay-Doppler (DD) domain. The power of the two signals is properly allocated in FT and DD domains, respectively, to reduce their mutual interference and optimize both communication and sensing tasks. Numerical results show the effectiveness of the proposed JC\&S waveform design approach, yielding target communication and sensing performance with a full time-frequency resource sharing.
Federated Dynamic Neural Network for Deep MIMO Detection
Authors: Yuwen Yang, Feifei Gao, Jiang Xue, Ting Zhou, Zongben Xu
Date: 2021-11-24 00:00:00

In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) detector and the OAMPNet detector as two independent network branches, the DDNet detector performs sample-wise dynamic routing to adaptively select a better one between the IDetNet and the OAMPNet detectors for every samples under different system conditions. To avoid the prohibitive transmission overhead of dataset collection in centralized learning (CL), we propose the federated averaging (FedAve)-DDNet detector, where all raw data are kept at local clients and only locally trained model parameters are transmitted to the central server for aggregation. To further reduce the transmission overhead, we develop the federated gradient sparsification (FedGS)-DDNet detector by randomly sampling gradients with elaborately calculated probability when uploading gradients to the central server. Based on simulation results, the proposed DDNet detector consistently outperforms other detectors under all system conditions thanks to the sample-wise dynamic routing. Moreover, the federated DDNet detectors, especially the FedGS-DDNet detector, can reduce the transmission overhead by at least 25.7\% while maintaining satisfactory detection accuracy.
Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling
Authors: Chen Huang, Ruisi He, Bo Ai, Andreas F. Molisch, Buon Kiong Lau, Katsuyuki Haneda, Bo Liu, Cheng-Xiang Wang, Mi Yang, Claude Oestges, Zhangdui Zhong
Date: 2021-11-24 00:00:00

This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well.
Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization
Authors: Chen Huang, Ruisi He, Bo Ai, Andreas F. Molisch, Buon Kiong Lau, Katsuyuki Haneda, Bo Liu, Cheng-Xiang Wang, Mi Yang, Claude Oestges, Zhangdui Zhong
Date: 2021-11-24 00:00:00

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper.
Multifrequency 3D Elasticity Reconstruction withStructured Sparsity and ADMM
Authors: Shahed Mohammed, Mohammad Honarvar, Qi Zeng, Hoda Hashemi, Robert Rohling, Piotr Kozlowski, Septimiu Salcudean
Date: 2021-11-23 00:00:00

We introduce a model-based iterative method to obtain shear modulus images of tissue using magnetic resonance elastography. The method jointly finds the displacement field that best fits multifrequency tissue displacement data and the corresponding shear modulus. The displacement satisfies a viscoelastic wave equation constraint, discretized using the finite element method. Sparsifying regularization terms in both shear modulus and the displacement are used in the cost function minimized for the best fit. The formulated problem is bi-convex. Its solution can be obtained iteratively by using the alternating direction method of multipliers. Sparsifying regularizations and the wave equation constraint filter out sensor noise and compressional waves. Our method does not require bandpass filtering as a preprocessing step and converges fast irrespective of the initialization. We evaluate our new method in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal to noise ratios. Results from an in vivo liver imaging study show elastograms with mean elasticity comparable to other values reported in the literature.
Non-asymptotic estimation of risk measures using stochastic gradient Langevin dynamics
Authors: Jiarui Chu, Ludovic Tangpi
Date: 2021-11-24 00:00:00

In this paper we will study the approximation of arbitrary law invariant risk measures. As a starting point, we approximate the average value at risk using stochastic gradient Langevin dynamics, which can be seen as a variant of the stochastic gradient descent algorithm. Further, the Kusuoka's spectral representation allows us to bootstrap the estimation of the average value at risk to extend the algorithm to general law invariant risk measures. We will present both theoretical, non-asymptotic convergence rates of the approximation algorithm and numerical simulations.
Completing correlation matrices
Authors: Olaf Dreyer, Horst Köhler, Thomas Streuer
Date: 2021-11-24 00:00:00

We describe a way to complete a correlation matrix that is not fully specified. Such matrices often arise in financial applications when the number of stochastic variables becomes large or when several smaller models are combined in a larger model. We argue that the proper completion to consider is the matrix that maximizes the entropy of the distribution described by the matrix. We then give a way to construct this matrix starting from the graph associated with the incomplete matrix. If this graph is chordal our construction will result in a proper correlation matrix. We give a detailed description of the construction for a cross-currency model with six stochastic variables and describe extensions to larger models involving more currencies.
Nonlinear Multi-Objective Flux Balance Analysis of the Warburg Effect
Authors: Yi Zhang, Daniel Boley
Date: 2021-11-23 00:00:00

Due to its implication in cancer treatment, the Warburg Effect has received extensive in silico investigation. Flux Balance Analysis (FBA), based on constrained optimization, was successfully applied in the Warburg Effect modelling. Yet, the assumption that cell types have one invariant cellular objective severely limits the applicability of the previous FBA models. Meanwhile, we note that cell types with different objectives show different extents of the Warburg Effect. To extend the applicability of the previous model and model the disparate cellular pathway preferences in different cell types, we built a Nonlinear Multi-Objective FBA (NLMOFBA) model by including three key objective terms (ATP production rate, lactate generation rate and ATP yield) into one objective function through linear scalarization. By constructing a cellular objective map and iteratively varying the objective weights, we showed disparate cellular pathway preferences manifested by different cell types driven by their unique cellular objectives, and we gained insights about the causal relationship between cellular objectives and the Warburg Effect. In addition, we obtained other biologically consistent results by using our NLMOFBA model. For example, augmented with the constraint associated with inefficient mitochondria function or limited substrate, NLMOFBA predicts cellular pathways supported by the biology literature. Collectively, NLMOFBA can help build a complete understanding towards the Warburg Effect in different cell types.
Portfolio optimisation with options
Authors: Jonathan Raimana Chan, Thomas Huckle, Antoine Jacquier, Aitor Muguruza
Date: 2021-11-24 00:00:00

We develop a new analysis for portfolio optimisation with options, tackling the three fundamental issues with this problem: asymmetric options' distributions, high dimensionality and dependence structure. To do so, we propose a new dependency matrix, built upon conditional probabilities between options' payoffs, and show how it can be computed in closed form given a copula structure of the underlying asset prices. The empirical evidence we provide highlights that this approach is efficient, fast and easily scalable to large portfolios of (mixed) options.
Conditional Estimates of Diffusion Processes for Evaluating the Positive Feedback Trading
Authors: Aihua Li
Date: 2021-11-17 00:00:00

Positive feedback trading, which buys when prices rise and sells when prices fall, has long been criticized for being destabilizing as it moves prices away from the fundamentals. Motivated by the relationship between positive feedback trading and investors cognitive bias, this paper provides a quantitative measurement of the bias based on the conditional estimates of diffusion processes. We prove the asymptotic properties of the estimates, which helps to interpret the investment behaviors that if a feedback trader finds a security perform better than his expectation, he will expect the future return to be higher, while in the long term, this bias will converge to zero. Furthermore, the observed deviations between the return forecast and its realized value lead to adaptive expectations in reality, for which we raise an exponential smoothing model as an adjustment method. In the empirical study on the stock market in China, we show the effectiveness of the ES method in bringing the biased expectation closer to the fundamental level, and suggest that the feedback traders, who are often over-optimistic about the return, are likely to suffer from downside risk and aggravate the speculative bubbles in the market.
Is the empirical out-of-sample variance an informative risk measure for the high-dimensional portfolios?
Authors: Taras Bodnar, Nestor Parolya, Erik Thorsén
Date: 2021-11-24 00:00:00

The main contribution of this paper is the derivation of the asymptotic behaviour of the out-of-sample variance, the out-of-sample relative loss, and of their empirical counterparts in the high-dimensional setting, i.e., when both ratios $p/n$ and $p/m$ tend to some positive constants as $m\to\infty$ and $n\to\infty$, where $p$ is the portfolio dimension, while $n$ and $m$ are the sample sizes from the in-sample and out-of-sample periods, respectively. The results are obtained for the traditional estimator of the global minimum variance (GMV) portfolio, for the two shrinkage estimators introduced by \cite{frahm2010} and \cite{bodnar2018estimation}, and for the equally-weighted portfolio, which is used as a target portfolio in the specification of the two considered shrinkage estimators. We show that the behaviour of the empirical out-of-sample variance may be misleading is many practical situations. On the other hand, this will never happen with the empirical out-of-sample relative loss, which seems to provide a natural normalization of the out-of-sample variance in the high-dimensional setup. As a result, an important question arises if this risk measure can safely be used in practice for portfolios constructed from a large asset universe.
Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks
Authors: Tomer Boyarski, Wenbo Wang, Amir Leshem
Date: 2021-10-25 00:00:00
Tasks: Scheduling

In recent years there is a growing effort to provide learning algorithms for spectrum collaboration. In this paper we present a medium access control protocol which allows spectrum collaboration with minimal regret and high spectral efficiency in highly loaded networks. We present a fully-distributed algorithm for spectrum collaboration in congested ad-hoc networks. The algorithm jointly solves both the channel allocation and access scheduling problems. We prove that the algorithm has an optimal logarithmic regret. Based on the algorithm we provide a medium access control protocol which allows distributed implementation of the algorithm in ad-hoc networks. The protocol utilizes single-channel opportunistic carrier sensing to carry out a low-complexity distributed auction in time and frequency. We also discuss practical implementation issues such as bounded frame size and speed of convergence. Computer simulations comparing the algorithm to state-of-the-art distributed medium access control protocols show the significant advantage of the proposed scheme.
Topological and Algebraic Structures of Atanassov's Intuitionistic Fuzzy-Values Space
Authors: Xinxing Wu, Tao Wang, Qian Liu, Peide Liu, Guanrong Chen, Xu Zhang
Date: 2021-11-17 00:00:00
Tasks: Negation

We prove that the space of intuitionistic fuzzy values (IFVs) with a linear order based on a score function and an accuracy function has the same algebraic structure as the one induced by a linear order based on a similarity function and an accuracy function. By introducing a new operator for IFVs via the linear order based on a score function and an accuracy function, we show that such an operator is a strong negation on IFVs. Moreover, we observe that the space of IFVs is a complete lattice and a Kleene algebra with the new operator. We also demonstrate that the topological space of IFVs with the order topology induced by the above two linear orders is not separable and metrizable but compact and connected. From some new perspectives,our results partially answer three open problems posed by Atanassov [Intuitionistic Fuzzy Sets: Theory and Applications, Springer, 1999] and [On Intuitionistic Fuzzy Sets Theory, Springer, 2012]. Furthermore, we construct an isomorphism between the spaces of IFVs and q-rung orthopedic fuzzy values (q-ROFVs) under the corresponding linear orders. To this end, we introduce the concept of admissible similarity measures with particular orders for IFSs, extending the existing definition of the similarity measure for IFSs, and construct an admissible similarity measure with a linear order based on a score function and an accuracy function, which is effectively applied to a pattern recognition problem about the classification of building materials.
Parametric study of basin saline water in single slope solar still using CFD modeling
Authors: R. Dhivagar1, a), S. Balaji2, A. Prakash3, and A. Sivaramakrishnan4
Date: 2021-08-28 00:00:00
Proceeding: AIP Conference Proceedings 2396, 020009 2021 8

In this simulation, the parametric performance of basin saline water in single slope solar still has been analyzed using computational fluid dynamics (CFD). The two-phase and three dimensional solar still model has been created using ANSYS FLUENT v19.2 software to analyze the water volume, water density, water temperature, vapor velocity, vapor density and vapor volume. The entire solar still model was considered under the quasi-static conditions and the results observed in mid, right and left planes. The prediction of heat, mass and momentum transfers in saline water and vapor were differed in each planes. The maximum hourly productivity of about 680 ml was observed during the simulation. The predicted results confirmed that, the CFD is a much effective tool for design and modeling the thermal equipment.