pwc-archive/papers-with-abstracts huggingface dataset:
Necessary and sufficient conditions for the identifiability of isolated loops
Authors: Eduardo Mapurunga, Michel Gevers, Alexandre Sanfelice Bazanella
Date: 2021-11-02 00:00:00

This Letter provides necessary and sufficient conditions on the excitation and measurement pattern (EMP) that guarantee identifiability of a dynamical network that has the structure of a loop. The conditions are extremely simple in their formulation, and they can be checked by visual inspection. They allow one to easily characterize all EMPs that make the loop network identifiable.
Autoregressive conditional duration modelling of high frequency data
Authors: Xiufeng Yan
Date: 2021-11-03 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper explores the duration dynamics modelling under the Autoregressive Conditional Durations (ACD) framework (Engle and Russell 1998). I test different distributions assumptions for the durations. The empirical results suggest unconditional durations approach the Gamma distributions. Moreover, compared with exponential distributions and Weibull distributions, the ACD model with Gamma distributed innovations provide the best fit of SPY durations.
Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice
Authors: Chaohua Dong, Jiti Gao, Bin Peng, Yundong Tu
Date: 2021-11-03 00:00:00
Tasks: Time Series, Time Series Analysis

This paper proposes a class of parametric multiple-index time series models that involve linear combinations of time trends, stationary variables and unit root processes as regressors. The inclusion of the three different types of time series, along with the use of a multiple-index structure for these variables to circumvent the curse of dimensionality, is due to both theoretical and practical considerations. The M-type estimators (including OLS, LAD, Huber's estimator, quantile and expectile estimators, etc.) for the index vectors are proposed, and their asymptotic properties are established, with the aid of the generalized function approach to accommodate a wide class of loss functions that may not be necessarily differentiable at every point. The proposed multiple-index model is then applied to study the stock return predictability, which reveals strong nonlinear predictability under various loss measures. Monte Carlo simulations are also included to evaluate the finite-sample performance of the proposed estimators.
Testing the monocentric standard urban model in a global sample of cities
Authors: Charlotte Liotta, Vincent Viguié, Quentin Lepetit
Date: 2021-11-03 00:00:00

Using a unique dataset containing gridded data on population densities, rents, housing sizes, and transportation in 192 cities worldwide, we investigate the empirical relevance of the monocentric standard urban model (SUM). Overall, the SUM seems surprisingly capable of capturing the inner structure of cities, both in developed and developing countries. As expected, cities spread out when they are richer, more populated, and when transportation or farmland is cheaper. Respectively 100% and 87% of the cities exhibit the expected negative density and rent gradients: on average, a 1% decrease in income net of transportation costs leads to a 21% decrease in densities and a 3% decrease in rents per m2. We also investigate the heterogeneity between cities of different characteristics in terms of monocentricity, informality, and amenities.
TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging
Authors: Alper Güngör, Baris Askin, Damla Alptekin Soydan, Emine Ulku Saritas, Can Barış Top, Tolga Çukur
Date: 2021-11-03 00:00:00
Tasks: Image Reconstruction, Super-Resolution

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging.
Interpolation Estimator for Infinite Sets of Random Vectors
Authors: Anatoli Torokhti
Date: 2021-11-03 00:00:00

We propose an approach to the estimation of infinite sets of random vectors. The problem addressed is as follows. Given two infinite sets of random vectors, find a single estimator that estimates vectors from with a controlled associated error. A new theory for the existence and implementation of such an estimator is studied. In particular, we show that the proposed estimator is asymptotically optimal. Moreover, the estimator is determined in terms of pseudo-inverse matrices and, therefore, it always exists.
End-to-End Learning for Integrated Sensing and Communication
Authors: José Miguel Mateos-Ramos, Jinxiang Song, Yibo Wu, Christian Häger, Musa Furkan Keskin, Vijaya Yajnanarayana, Henk Wymeersch
Date: 2021-11-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.
Blind inverse problems with isolated spikes
Authors: Valentin Debarnot, Pierre Weiss
Date: 2021-11-03 00:00:00
Tasks: Blind Super-Resolution, Super-Resolution

Assume that an unknown integral operator living in some known subspace is observed indirectly, by evaluating its action on a few Dirac masses at unknown locations. Is this information enough to recover the operator and the impulse responses locations stably? We study this question and answer positively under realistic technical assumptions. We illustrate the well-foundedness of this theory on two challenging optical imaging problems: blind super-resolution and deconvolution. This provides a simple, practical and theoretically grounded approach to solve these long resisting problems.
Antenna De-Embedding in FDTD Using Spherical Wave Functions by Exploiting Orthogonality
Authors: Leonardo Mörlein, Lukas Berkelmann, Dirk Manteuffel
Date: 2021-11-03 00:00:00

De-embedding antennas from the channel using Spherical Wave Functions (SWF) is a useful method to reduce the numerical effort in the simulation of wearable antennas. In this paper an analytical solution to the De-embedding problem is presented in form of surface integrals. This new integral solution is helpful on a theoretical level to derive insights and is also well suited for implementation in Finite Difference Time Domain (FDTD) numerical software. The spherical wave function coefficients are calculated directly from near-field values. Furthermore, the presence of a near-field scatterer in the de-embedding problem is discussed on a theoretical level based on the Huygens Equivalence Theorem. This makes it possible to exploit the degrees of freedom in such a way that it is sufficient to only use out-going spherical wave functions and still obtain correct results.
First experimental evaluation of ambient backscatter communications with massive MIMO reader
Authors: Romain Fara, Nada Bel-Haj-Maati, Dinh-Thuy Phan-Huy, Nadine Malhouroux, Marco Di Renzo
Date: 2021-11-02 00:00:00
Tasks: Diversity, TAG

Ambient backscatter communications have been introduced as low-power communications for green networking. This technology is very promising as it recycles ambient radio frequency waves, however such systems have limitations and suffer from poor performance due to their low-power. In this paper, we present and evaluate the performance of an ambient backscatter system equipped with a massive multiple input multiple output (MIMO) antenna at the reader side. A mobile device transmits a signal that is backscattered by a tag and received by the reader. Thanks to the spatial diversity of the massive MIMO antenna, the reader is able to identify, for each received signal, the state of the tag (backscattering or transparent) that corresponds to a bit of the tag message. First, we experimentally determine the channel between the device and the reader for two states of the tag. Then using a minimum mean square error algorithm we evaluate the performance of the ambient backscatter communication. We demonstrate that the performance of the ambient backscatter system is significantly improved by the massive MIMO reader.
Robust Ambient Backscatter Communications with Polarization Reconfigurable Tags
Authors: Romain Fara, Dinh-Thuy Phan-Huy, Abdelwaheb Ourir, Marco Di Renzo, Julien de Rosny
Date: 2021-11-02 00:00:00
Tasks: TAG

Ambient backscatter communication system is an emerging and promising low-energy technology for Internet of Things. In such system, a device named tag, sends a binary message to a reader by backscattering a radio frequency signal generated by an ambient source. Such tag can operate without battery and without generating additional wave. However, the tag-to-reader link suffers from the source-to-reader direct interference. In this paper, for the first time, we propose to exploit a "polarization reconfigurable" antenna to improve robustness of the tag-to-reader link against the source-to-reader direct interference. Our proposed new tag sends its message by backscattering as an usual tag. However, it repeats its message several times, with a different radiation pattern and polarization, each time. We expect one polarization pattern to be better detected by the reader. We show by simulations and experiments, in line-of-sight and in richly scattering environment, that a polarization reconfigurable tag limited to 4 polarization directions outperforms a nonreconfigurable tag and nearly equals an ideally reconfigurable tag in performance.
Gabor frames from contact geometry in models of the primary visual cortex
Authors: Vasiliki Liontou, Matilde Marcolli
Date: 2021-11-03 00:00:00

We analyze the interplay between contact geometry and Gabor filters signal analysis in geometric models of the primary visual cortex. We show in particular that a specific framed lattice and an associated Gabor system is determined by the Legendrian circle bundle structure of the $3$-manifold of contact elements on a surface (which models the V1-cortex), together with the presence of an almost-complex structure on the tangent bundle of the surface (which models the retinal surface). We identify a scaling of the lattice, also dictated by the manifold geometry, that ensures the frame condition is satisfied. We then consider a $5$-dimensional model where receptor profiles also involve a dependence on frequency and scale variables, in addition to the dependence on position and orientation. In this case we show that a proposed profile window function does not give rise to frames (even in a distributional sense), while a natural modification of the same generates Gabor frames with respect to the appropriate lattice determined by the contact geometry.
Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study
Authors: Rahul Biswas, Eli Shlizerman
Date: 2021-11-03 00:00:00
Tasks: Functional Connectivity

Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property -- an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.
An age-of-infection model with both symptomatic and asymptomatic infections
Authors: Fan Bai
Date: 2021-11-03 00:00:00
Tasks: Relation

We formulate a general age-of-infection epidemic model with two pathways: the symptomatic infections and the asymptomatic infections. We then calculate the basic reproduction number $\mathcal{R}_0$ and establish the final size relation. It is shown that the ratio of accumulated counts of symptomatic patients and asymptomatic patients is determined by the symptomatic ratio $f$ which is defined as the probability of eventually becoming symptomatic after being infected. We also formulate and study a general age-of-infection model with disease deaths and with two infection pathways. The final size relation is investigated, and the upper and lower bounds for final epidemic size are given. Several numerical simulations are performed to verify the analytical results.
Testing macroecological theories in cryptocurrency market: neutral models can not describe diversity patterns and their variation
Authors: Edgardo Brigatti, Estevan Augusto Amazonas Mendes
Date: 2021-11-03 00:00:00
Tasks: Diversity

We develop an analysis of the cryptocurrency market borrowing methods and concepts from ecology. This approach makes it possible to identify specific diversity patterns and their variation, in close analogy with ecological systems, and to characterize the cryptocurrency market in an effective way. At the same time, it shows how non-biological systems can have an important role in contrasting different ecological theories and in testing the use of neutral models. The study of the cryptocurrencies abundance distribution and the evolution of the community structure strongly indicates that these statistical patterns are not consistent with neutrality. In particular, the necessity to increase the temporal change in community composition when the number of cryptocurrencies grows, suggests that their interactions are not necessarily weak. The analysis of the intraspecific and interspecific interdependency supports this fact and demonstrates the presence of a market sector influenced by mutualistic relations. These latest findings challenge the hypothesis of weakly interacting symmetric species, the postulate at the heart of neutral models.
An Open-Source Web App for Creating and Scoring Qualtrics-based Implicit Association Test
Authors: Yong Cui, Jason D. Robinson, Seokhun Kim, George Kypriotakis, Charles E. Green, Sanjay S. Shete, Paul M. Cinciripini
Date: 2021-11-03 00:00:00
Tasks: Survey

The Implicit Association Test (IAT) is a common behavioral paradigm to assess implicit attitudes in various research contexts. In recent years, researchers have sought to collect IAT data remotely using online applications. Compared to laboratory-based assessments, online IAT experiments have several advantages, including widespread administration outside of artificial (i.e., laboratory) environments. Use of survey-software platforms (e.g., Qualtrics) represents an innovative and cost-effective approach that allows researchers to prepare online IAT experiments without any programming expertise. However, there are some drawbacks with the existing survey-software as well as other online IAT preparation tools, such as limited mobile device compatibility and lack of helper functionalities for easy adaptation. To address these issues, we developed an open-source web app (GitHub page: https://github.com/ycui1-mda/qualtrics_iat) for creating mobile-compatible Qualtrics-based IAT experiments and scoring the collected responses. The present study demonstrates the key functionalities of this web app and describes feasibility data that were collected and scored using the app to show the tool's validity. We show that the web app provides a complete and easy-to-adapt toolset for researchers to construct Qualtrics-based IAT experiments and process the derived IAT data.
A group theoretic approach to model comparison with simplicial representations
Authors: Sean T. Vittadello, Michael P. H. Stumpf
Date: 2021-11-03 00:00:00

The complexity of biological systems, and the increasingly large amount of associated experimental data, necessitates that we develop mathematical models to further our understanding of these systems. As biological systems are generally not well understood, most mathematical models of these systems are based on experimental data, resulting in a seemingly heterogeneous collection of models that ostensibly represent the same system. To understand the system we therefore need to know how the different models are related, with a view to obtaining a unified mathematical description. This goal is complicated by the fact that distinct mathematical formalisms may be used to represent the same system, making direct comparison of the models very difficult. In previous work we developed an appropriate framework for model comparison where we represent models as labelled simplicial complexes and compare them with two general methodologies: comparison by distance or equivalence. In this article we continue the development of our model comparison methodology in two directions. First, we present a rigorous and automatable methodology for the core process of comparison by equivalence, namely determining the vertices in a simplicial representation, corresponding to model components, that are conceptually related and the identification of these vertices via simplicial operations. Our methodology is based on considerations of vertex symmetry in the simplicial representation, for which we develop the required mathematical theory of group actions on simplicial complexes. This methodology greatly simplifies and expedites the process of determining model equivalence. Second, we provide an alternative mathematical framework for our model-comparison methodology by representing models as groups, which allows for the direct application of group-theoretic techniques within our model-comparison methodology.
Deep Learning Algorithms for Hedging with Frictions
Authors: Xiaofei Shi, Daran Xu, Zhanhao Zhang
Date: 2021-11-02 00:00:00
Tasks: Deep Learning, Time Series Analysis, Transfer Learning

This work studies the deep learning-based numerical algorithms for optimal hedging problems in markets with general convex transaction costs on the trading rates, focusing on their scalability of trading time horizon. Based on the comparison results of the FBSDE solver by Han, Jentzen, and E (2018) and the Deep Hedging algorithm by Buehler, Gonon, Teichmann, and Wood (2019), we propose a Stable Transfer Hedging (ST-Hedging) algorithm, to aggregate the convenience of the leading-order approximation formulas and the accuracy of the deep learning-based algorithms. Our ST-Hedging algorithm achieves the same state-of-the-art performance in short and moderately long time horizon as FBSDE solver and Deep Hedging, and generalize well to long time horizon when previous algorithms become suboptimal. With the transfer learning technique, ST-Hedging drastically reduce the training time, and shows great scalability to high-dimensional settings. This opens up new possibilities in model-based deep learning algorithms in economics, finance, and operational research, which takes advantages of the domain expert knowledge and the accuracy of the learning-based methods.
Predicting Cancer Using Supervised Machine Learning: Mesothelioma
Authors: Avishek Choudhury
Date: 2021-10-31 00:00:00
Tasks: BIG-bench Machine Learning, Prognosis

Background: Pleural Mesothelioma (PM) is an unusual, belligerent tumor that rapidly develops into cancer in the pleura of the lungs. Pleural Mesothelioma is a common type of Mesothelioma that accounts for about 75% of all Mesothelioma diagnosed yearly in the U.S. Diagnosis of Mesothelioma takes several months and is expensive. Given the risk and constraints associated with PM diagnosis, early identification of this ailment is essential for patient health. Objective: In this study, we use artificial intelligence algorithms recommending the best fit model for early diagnosis and prognosis of MPM. Methods: We retrospectively retrieved patients clinical data collected by Dicle University, Turkey, and applied multilayered perceptron (MLP), voted perceptron (VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic gradient decent SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and primal estimated sub-gradient solver for support vector machine (s-Pegasos). We evaluated the models, compared and tested using paired T-test (corrected) at 0.05 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers characteristic curve (ROC), and precision-recall curve (PRC). Results: In phase-1, SGD, AdaBoost. M1, KLR, MLP, VFDT generate optimal results with the highest possible performance measures. In phase 2, AdaBoost, with a classification accuracy of 71.29%, outperformed all other algorithms. C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate Mesothelioma. Conclusion: This study confirms that data obtained from Biopsy and imagining tests are strong predictors of Mesothelioma but are associated with a high cost; however, they can identify Mesothelioma with optimal accuracy.