pwc-archive/papers-with-abstracts huggingface dataset:
Pricing the Information Quantity in Artworks
Authors: Lan Ju, Zhiyong Tu, Changyong Xue
Date: 2020-11-18 00:00:00

In the traditional art pricing models, the variables that capture the painting's content are often missing. Recent research starts to apply the computer graphic techniques to extract the information from the painting content. Most of the research concentrates on the reading of the color information from the painting images and analyzes how different color compositions can affect the sales prices of paintings. This paper takes a different approach, and tries to abstract away from the interpretation of the content information, while only focus on measuring the quantity of information contained. We extend the concept of Shannon entropy in information theory to the painting's scenario, and suggest using the variances of a painting's composing elements, i.e., line, color, value, shape/form and space, to measure the amount of information in the painting. These measures are calculated at the pixel level based on a picture's digital image. We include them into the traditional hedonic regression model to test their significance based on the auction samples from two famous artists (Picasso and Renoir). We find that all the variance measurements can significantly explain the sales price either at 1% or 5% level. The adjusted R square is also increased by more than ten percent. Our method greatly improves the traditional pricing models, and may also find applications in other areas such as art valuation and authentication.
WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation
Date: 2020-11-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Separation

This paper aims at eliminating the interfering speakers' speech, additive
noise, and reverberation from the noisy multi-talker speech mixture that
benefits automatic speech recognition (ASR) backend. While the recently
proposed Weighted Power minimization Distortionless response (WPD) beamformer
can perform separation and dereverberation simultaneously, the noise
cancellation component still has the potential to progress. We propose an
improved neural WPD beamformer called "WPD++" by an enhanced beamforming module
in the conventional WPD and a multi-objective loss function for the joint
training. The beamforming module is improved by utilizing the spatio-temporal
correlation. A multi-objective loss, including the complex spectra domain
scale-invariant signal-to-noise ratio (C-Si-SNR) and the magnitude domain mean
square error (Mag-MSE), is properly designed to make multiple constraints on
the enhanced speech and the desired power of the dry clean signal. Joint
training is conducted to optimize the complex-valued mask estimator and the
WPD++ beamformer in an end-to-end way. The results show that the proposed WPD++
outperforms several state-of-the-art beamformers on the enhanced speech quality
and word error rate (WER) of ASR.
Small-Gain Theorem for Safety Verification of Interconnected Systems
Date: 2020-11-18 00:00:00

A small-gain theorem in the formulation of barrier function is developed in
this work for safety verification of interconnected systems. This result is
helpful to verify input-to-state safety (ISSf) of the overall system from the
safety information encoded in the subsystem's ISSf-barrier function. Also, it
can be used to obtain a safety set in a higher dimensional space from the
safety sets in two lower dimensional spaces.
Sparsity-aware Robust Community Detection(SPARCODE)
Authors: Aylin Tastan, Michael Muma, Abdelhak M. Zoubir
Date: 2020-11-18 00:00:00
Tasks: Community Detection

Community detection refers to finding densely connected groups of nodes in graphs. In important applications, such as cluster analysis and network modelling, the graph is sparse but outliers and heavy-tailed noise may obscure its structure. We propose a new method for Sparsity-aware Robust Community Detection (SPARCODE). Starting from a densely connected and outlier-corrupted graph, we first extract a preliminary sparsity improved graph model where we optimize the level of sparsity by mapping the coordinates from different clusters such that the distance of their embedding is maximal. Then, undesired edges are removed and the graph is constructed robustly by detecting the outliers using the connectivity of nodes in the improved graph model. Finally, fast spectral partitioning is performed on the resulting robust sparse graph model. The number of communities is estimated using modularity optimization on the partitioning results. We compare the performance to popular graph and cluster-based community detection approaches on a variety of benchmark network and cluster analysis data sets. Comprehensive experiments demonstrate that our method consistently finds the correct number of communities and outperforms existing methods in terms of detection performance, robustness and modularity score while requiring a reasonable computation time.
Distributional uncertainty of the financial time series measured by G-expectation
Authors: Shige Peng, Shuzhen Yang
Date: 2020-11-18 00:00:00
Tasks: Time Series, Time Series Analysis

Based on law of large numbers and central limit theorem under nonlinear expectation, we introduce a new method of using G-normal distribution to measure financial risks. Applying max-mean estimators and small windows method, we establish autoregressive models to determine the parameters of G-normal distribution, i.e., the return, maximal and minimal volatilities of the time series. Utilizing the value at risk (VaR) predictor model under G-normal distribution, we show that the G-VaR model gives an excellent performance in predicting the VaR for a benchmark dataset comparing to many well-known VaR predictors.
A Risk Based approach for the Solvency Capital requirement for Health Plans
Authors: Fabio Baione, Davide Biancalana, Paolo De Angelis
Date: 2020-11-18 00:00:00
Tasks: regression

The study deals with the assessment of risk measures for Health Plans in order to assess the Solvency Capital Requirement. For the estimation of the individual health care expenditure for several episode types, we suggest an original approach based on a three-part regression model. We propose three Generalized Linear Models (GLM) to assess claim counts, the allocation of each claim to a specific episode and the severity average expenditures respectively. One of the main practical advantages of our proposal is the reduction of the regression models compared to a traditional approach, where several two-part models for each episode types are requested. As most health plans require co-payments or co-insurance, considering at this stage the non-linearity condition of the reimbursement function, we adopt a Montecarlo simulation to assess the health plan costs. The simulation approach provides the probability distribution of the Net Asset Value of the Health Plan and the estimate of several risk measures.
Allocating marketing resources over social networks: A long-term analysis
Authors: Vineeth S. Varma, Samson Lasaulce, Julien Mounthanyvong, Irinel-Constantin Morarescu
Date: 2020-11-17 00:00:00
Tasks: Marketing

In this paper, we consider a network of consumers who are under the combined influence of their neighbors and external influencing entities (the marketers). The consumers' opinion follows a hybrid dynamics whose opinion jumps are due to the marketing campaigns. By using the relevant static game model proposed recently in [1], we prove that although the marketers are in competition and therefore create tension in the network, the network reaches a consensus. Exploiting this key result, we propose a coopetition marketing strategy which combines the one-shot Nash equilibrium actions and a policy of no advertising. Under reasonable sufficient conditions, it is proved that the proposed coopetition strategy profile Pareto-dominates the one-shot Nash equilibrium strategy. This is a very encouraging result to tackle the much more challenging problem of designing Pareto-optimal and equilibrium strategies for the considered dynamical marketing game.
"Discrimination of virgin olive oils with different geographical origin: a rapid untargeted chromatographic approach based on volatile compounds"
Authors: Rosa Palagano, Enrico Valli, Chiara Cevoli, Alessandra Bendini, Tullia Gallina Toschi
Date: 2020-11-18 00:00:00

Many studies have shown that the geographic origin is one of the most influencing factors in the choice of olive oil. To avoid misleading, European regulation has established specific rules to report the geographical origin of extra virgin and virgin olive oils on the product label, but an official analytical procedure to verify this information has not been yet defined. In this work, a flash gas chromatography untargeted approach for determination of volatile compounds, followed by a chemometric data elaboration, is proposed for discrimination of EVOOs and VOOs according to their geographical origin (EU and Extra-EU). A set of 210 samples was analyzed and two different classification techniques were used, one linear and one non-linear. The two models were also validated using an external data set. Satisfactory results were obtained for both chemometric approaches: considering the PLS-DA, 89% and 81% of EU and Extra-EU samples, respectively, were correctly classified; for ANN the percentages were 93% and 89%, respectively. These results confirm the reliability of the method as a rapid approach to discriminate EVOOs and VOOs according to their geographical provenance.
3D imaging from multipath temporal echoes
Authors: Alex Turpin, Valentin Kapitany, Jack Radford, Davide Rovelli, Kevin Mitchell, Ashley Lyons, Ilya Starshynov, Daniele Faccio
Date: 2020-11-17 00:00:00
Tasks: Retrieval

Echo-location is a broad approach to imaging and sensing that includes both man-made RADAR, LIDAR, SONAR and also animal navigation. However, full 3D information based on echo-location requires some form of scanning of the scene in order to provide the spatial location of the echo origin-points. Without this spatial information, imaging objects in 3D is a very challenging task as the inverse retrieval problem is strongly ill-posed. Here, we show that the temporal information encoded in the return echoes that are reflected multiple times within a scene is sufficient to faithfully render an image in 3D. Numerical modelling and an information theoretic perspective prove the concept and provide insight into the role of the multipath information. We experimentally demonstrate the concept by using both radio-frequency and acoustic waves for imaging individuals moving in a closed environment.
Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants
Authors: Sinjini Mitra
Date: 2020-11-17 00:00:00
Tasks: Density Estimation

Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health issues such as cerebral palsy, asthma and sudden infant death syndrome. One of the leading health complications in preterm infants is bradycardia - which is defined as the slower than expected heart rate, generally beating lower than 60 beats per minute. Bradycardia is often accompanied by low oxygen levels and can cause additional long term health problems in the premature infant. The implementation of a non-parametric method to predict the onset of bradycardia is presented. This method assumes no prior knowledge of the data and uses kernel density estimation to predict the future onset of bradycardia events. The data is preprocessed, and then analyzed to detect the peaks in the ECG signals, following which different kernels are implemented to estimate the shared underlying distribution of the data. The performance of the algorithm is evaluated using various metrics and the computational challenges and methods to overcome them are also discussed. It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.
Distributional properties of semantic interference in picture naming: Bayesian meta-analyses
Authors: Pamela Fuhrmeister, Audrey Bueurki
Date: 2020-11-18 00:00:00

Studies of word production often make use of picture naming tasks, including the picture word interference task. In this task, participants name pictures with superimposed distractor words. They typically need more time to name pictures when the distractor word is semantically related to the picture than when it is unrelated (the semantic interference effect). The present study examines the distributional properties of this effect in a series of Bayesian meta-analyses. Meta-analytic estimates of the semantic interference effect first show that the effect is present throughout the reaction time distribution and that it increases throughout the distribution. Second, we find a correlation between a participant's mean semantic interference effect and the change in the effect in the tail of the reaction time distribution, which has been argued to reflect the involvement of selective inhibition in the naming task. Finally, we show with simulated data that this correlation emerges even when no inhibition is used to generate the data, which suggests that inhibition is not needed to explain this relationship.
MetaLocalization: Reconfigurable Intelligent Surface Aided Multi-user Wireless Indoor Localization
Authors: Haobo Zhang, Hongliang Zhang, Boya Di, Kaigui Bian, Zhu Han, Lingyang Song
Date: 2020-11-18 00:00:00
Tasks: Indoor Localization

The received signal strength (RSS) based technique is extensively utilized for localization in the indoor environments. Since the RSS values of neighboring locations may be similar, the localization accuracy of the RSS based technique is limited. To tackle this problem, in this paper, we propose to utilize reconfigurable intelligent surface (RIS) for the RSS based multi-user localization. As the RIS is able to customize the radio channels by adjusting the phase shifts of the signals reflected at the surface, the localization accuracy in the RIS aided scheme can be improved by choosing the proper phase shifts with significant differences of RSS values among adjacent locations. However, it is challenging to select the optimal phase shifts because the decision function for location estimation and the phase shifts are coupled. To tackle this challenge, we formulate the optimization problem for the RIS-aided localization, derive the optimal decision function, and design the phase shift optimization (PSO) algorithm to solve the formulated problem efficiently. Analysis of the proposed RIS aided technique is provided, and the effectiveness is validated through simulation.
A Likelihood Ratio-Based Detector for QTMS Radar and Noise Radar
Authors: David Luong, Bhashyam Balaji, Sreeraman Rajan
Date: 2020-11-18 00:00:00
Tasks: LEMMA

We derive a detector function for quantum two-mode squeezing (QTMS) radars and noise radars that is based on the use of a likelihood ratio (LR) test for distinguishing between the presence and absence of a target. In addition to an explicit expression for the LR detector, we derive a detector function which approximates the LR detector in the limit where the target is small, far away, or otherwise difficult to detect. When the number of integrated samples is large, we derive a theoretical expression for the receiver operating characteristic (ROC) curve of the radar when the LR detector is used. When the number of samples is small, we use simulations to understand the ROC curve behavior of the detector. One interesting finding is there exists a parameter regime in which a previously-studied detector outperforms the LR detector, contrary to the intuition that LR tests are optimal. This is because neither the Neyman-Pearson lemma, nor the Karlin-Rubin theorem which generalizes the lemma, hold in this particular problem. However, the LR detector remains a good choice for target detection.
Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
Authors: Rodrigo Cofré, Cesar Maldonado, Bruno Cessac
Date: 2020-11-18 00:00:00

The Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle corresponding, in its simplest form, to the Maximum Entropy principle. It is used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science. In particular, it has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.
Maximum Power Reference Tracking Algorithm for Power Curtailment of Photovoltaic Systems
Authors: Victor Paduani, Lidong Song, Bei Xu, Dr. Ning Lu
Date: 2020-11-18 00:00:00

This paper presents an algorithm for power curtail-ment of photovoltaic (PV) systems under fast solar irradiance intermittency. Based on the Perturb and Observe (P&O) technique, the method contains an adaptive gain that is compensated in real-time to account for moments of lower power availability. In addition, an accumulator is added to the calculation of the step size to reduce the overshoot caused by large irradiance swings. A testbed of a three-phase single-stage, 500 kVA PV system is developed on the OPAL-RT eMEGAsim real-time simulator. Field irradiance data and a regulation signal from PJM (RTO) are used to compare the performance of the proposed method with other techniques found in the literature. Results indicate an operation with smaller overshoot, less dc-link voltage oscillations, and improved power reference tracking capability.
Line Outage Identification Based on AC Power Flow and Synchronized Measurements
Authors: Zhen Dai, Joseph Euzebe Tate
Date: 2020-11-18 00:00:00

This paper proposes a method of identifying single line outages in power systems based on phasor measurement unit (PMU) measurements and ac power flow models. In addition to the main identification algorithm, a rejection filter is introduced so that the preliminary identified results can be further processed and categorized into three types: correctly identified, misidentified and inconclusive (including correct-filtered and misidentified-filtered). The methods are systematically tested using test systems of various sizes for various PMU placements, and the results show that the proposed identification algorithm has a high identification accuracy and the proposed rejection filter is able to reduce the misidentified rate without significantly increasing the number of inconclusive cases.
Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks
Authors: Gojko Cutura, Boning Li, Ananthram Swami, Santiago Segarra
Date: 2020-11-18 00:00:00

We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evolution of the spread leveraging the topology of the network but being agnostic to the precise epidemic model. We overcome this lack of model awareness through a data-driven solution to the inverse problem at hand. In particular, we propose DDmix, a graph conditional variational autoencoder that can be trained from past epidemic spreads and whose latent space seeks to capture key aspects of the underlying (unknown) spreading dynamics. We illustrate the accuracy and generalizability of DDmix and compare it with non-graph-aware learning algorithms through numerical experiments on epidemic spreads simulated on synthetic and real-world networks.
Belief Error and Non-Bayesian Social Learning: Experimental Evidence
Authors: Boğaçhan Çelen, Sen Geng, Huihui Li
Date: 2020-11-19 00:00:00

This paper experimentally studies whether individuals hold a first-order belief that others apply Bayes' rule to incorporate private information into their beliefs, which is a fundamental assumption in many Bayesian and non-Bayesian social learning models. We design a novel experimental setting in which the first-order belief assumption implies that social information is equivalent to private information. Our main finding is that participants' reported reservation prices of social information are significantly lower than those of private information, which provides evidence that casts doubt on the first-order belief assumption. We also build a novel belief error model in which participants form a random posterior belief with a Bayesian posterior belief kernel to explain the experimental findings. A structural estimation of the model suggests that participants' sophisticated consideration of others' belief error and their exaggeration of the error both contribute to the difference in reservation prices.
State-of-charge Estimation of a Li-ion Battery using Deep Learning and Stochastic Optimization
Authors: Alexandre Barbosa de Lima, Maurício B. C. Salles, José Roberto Cardoso
Date: 2020-11-18 00:00:00
Tasks: Stochastic Optimization

This article presents a novel empirical study for the estimation of the State of Charge (SOC) of a lithium-ion (Li-ion) battery which uses a deep learning model with three hidden layers. We model a series of ten vehicle drive cycles that were applied to a Panasonic 18650PF Li-ion cell. Our results show that the choice of the optimization algorithm affects the model performance. The proposed model was able to achieve an error smaller than 1.0% in all drive cycles.
Regioselective chemo-enzymatic syntheses of ferulate conjugates as chromogenic substrates for feruloyl esterases
Authors: Olga Gherbovet, Fernando Ferreira, Apolline Clément, Mélanie Ragon, Julien Durand, Sophie Bozonnet, Michael O'Donohue, Régis Fauré
Date: 2020-11-19 00:00:00

Generally, carbohydrate-active enzymes are studied using chromogenic substrates that provide quick and easy color-based detection of enzyme-mediated hydrolysis. In the case of feruloyl esterases, commercially available chromogenic ferulate derivatives are both costly and limited in terms of their experimental application. In this study, we describe solutions for these two issues, using a chemoenzymatic approach to synthesize different ferulate compounds. The overall synthetic routes towards commercially available 5-bromo-4-chloro-3-indolyl and 4-nitrophenyl O-5-feruloyl-$\alpha$-l-arabinofuranosides 1a and 1b were significantly shortened (7-8 steps reduced to 4-6) and transesterification yields enhanced (from 46 to 73% for 1a and 47 to 86 % for 1b). This was achieved using enzymatic (immobilized Lipolase 100T from Thermomyces lanuginosus) transesterification of unprotected vinyl ferulate to the primary hydroxyl group of $\alpha$-l-arabinofuranosides. Moreover, a novel feruloylated-butanetriol 4-nitrocatechol-1-yl analog 12, containing a cleavable hydroxylated linker was also synthesized in 29% overall yield in 3 steps (convergent synthesis). The latter route combined regioselective functionalization of 4-nitrocatechol and enzymatic transferuloylation. The use of 12 as a substrate to characterize type A feruloyl esterase from Aspergillus niger reveals the advantages of this substrate for the characterizations of feruloyl esterases.