pwc-archive/papers-with-abstracts huggingface dataset:
Fuzzy Arrovian Theorems when preferences are complete
Authors: Armajac Raventós-Pujol
Date: 2021-11-04 00:00:00

In this paper we study the aggregation of fuzzy preferences on non-necessarily finite societies. We characterize in terms of possibility and impossibility a family of models of complete preferences in which the transitivity is defined for any t-norm. For that purpose, we have described each model by means of some crisp binary relations and we have applied the results obtained by Kirman and Sondermann.
Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT)
Authors: Guanhua Wang, Jeffrey A. Fessler
Date: 2021-11-04 00:00:00
Tasks: compressed sensing, Image Reconstruction, Stochastic Optimization

There is growing interest in learning Fourier domain sampling strategies (particularly for magnetic resonance imaging, MRI) using optimization approaches. For non-Cartesian sampling patterns, the system models typically involve non-uniform FFT (NUFFT) operations. Commonly used NUFFT algorithms contain frequency domain interpolation, which is not differentiable with respect to the sampling pattern, complicating the use of gradient methods. This paper describes an efficient and accurate approach for computing approximate gradients involving NUFFTs. Multiple numerical experiments validated the improved accuracy and efficiency of the proposed approximation. As an application to computational imaging, the NUFFT Jacobians were used to optimize non-Cartesian MRI sampling trajectories via data-driven stochastic optimization. Specifically, the sampling patterns were learned with respect to various model-based reconstruction algorithms, including quadratic regularized reconstruction and compressed sensing-based reconstruction. The proposed approach enables sampling optimization for image sizes that are infeasible with standard auto-differentiation methods due to memory limits. The synergistic acquisition and reconstruction lead to remarkably improved image quality. In fact, we show that model-based image reconstruction (MBIR) methods with suitably optimized imaging parameters can perform nearly as well as CNN-based methods.
Access Delay Constrained Activity Detection in Massive Random Access
Authors: Jyotish Robin, Elza Erkip
Date: 2021-11-04 00:00:00
Tasks: Action Detection, Activity Detection

In 5G and future generation wireless systems, massive IoT networks with bursty traffic are expected to co-exist with cellular systems to serve several latency-critical applications. Thus, it is important for the access points to identify the active devices promptly with minimal resource consumption to enable massive machine-type communication without disrupting the conventional traffic. In this paper, a frequency-multiplexed strategy based on group testing is proposed for activity detection which can take into account the constraints on network latency while minimizing the overall resource utilization. The core idea is that during each time-slot of active device discovery, multiple subcarriers in frequency domain can be used to launch group tests in parallel to reduce delay. Our proposed scheme is functional in the asymptotic and non-asymptotic regime of the total number of devices $(n)$ and the number of concurrently active devices $(k)$. We prove that, asymptotically, when the number of available time-slots scale as $\Omega\big(\log (\frac{n}{k})\big)$, the frequency-multiplexed group testing strategy requires $O\big(k\log (\frac{n}{k})\big)$ time-frequency resources which is order-optimal and results in an $O(k)$ reduction in the number of time-slots with respect to the optimal strategy of fully-adaptive generalized binary splitting. Furthermore, we establish that the frequency-multiplexed GT strategy shows significant tolerance to estimation errors in $k$. Comparison with 3GPP standardized random access protocol for NB-IoT indicates the superiority of our proposed strategy in terms of access delay and overall resource utilization.
A Fiber Measurement System with Approximate Deconvolution Based on the Analysis of Fault Clusters in Linearized Bregman Iterations
Authors: Yuneisy Garcia Guzman, Felipe Calliari, Gustavo C. Amaral, Michael Lunglmayr
Date: 2021-11-04 00:00:00
Tasks: Event Detection, Management

Automatic detection of faults in optical fibers is an active area of research that plays a significant role in the design of reliable and stable optical networks. A fiber measurement system that combines automated data acquisition and processing represents a disruptive impact in the management of optical fiber networks with fast and reliable event detection. It has been shown in the literature that the linearized Bregman iterations (LBI) algorithm and variations can be successfully used for processing and accurately identifying faults in a fiber profile. One of the factors that impact the performance of these algorithms is the degradation of spatial resolution, which is mainly caused by the appearance of fault clusters due to a reduced number of iterations. In this paper, a method is proposed based on an approximate deconvolution approach for increasing the spatial resolution, possible after a thorough analysis of fault clusters that appear in the algorithm's output. The effect of such approximate deconvolution is shown to extend beyond the improvement of spatial resolution, allowing for better performances to be reached at shorter processing times. An efficient hardware architecture that implements the approximate deconvolution, compatible with the hardware structure recently presented for the LBI algorithm, is also proposed and discussed.
Radio Sensing with Large Intelligent Surface for 6G
Authors: Cristian J. Vaca-Rubio, Pablo Ramirez-Espinosa, Kimmo Kansanen, Zheng-Hua Tan, Elisabeth de Carvalho
Date: 2021-11-04 00:00:00
Tasks: Template Matching

This paper leverages the potential of Large Intelligent Surface (LIS) for radio sensing in 6G wireless networks. Major research has been undergone about its communication capabilities but it can be exploited as a formidable tool for radio sensing. By taking advantage of arbitrary communication signals occurring in the scenario, we apply direct processing to the output signal from the LIS to obtain a radio map that describes the physical presence of passive devices (scatterers, humans) which act as virtual sources due to the communication signal reflections. We then assess the usage of machine learning (k-means clustering), image processing and computer vision (template matching and component labeling) to extract meaningful information from these radio maps. As an exemplary use case, we evaluate this method for both active and passive user detection in an indoor setting. The results show that the presented method has high application potential as we are able to detect around 98% of humans passively and 100% active users by just using communication signals of commodity devices even in quite unfavorable Signal-to-Noise Ratio (SNR) conditions.
Physics Assisted Deep Learning for Indoor Imaging using Phaseless Wi-Fi Measurements
Authors: Samruddhi Deshmukh, Amartansh Dubey, Dingfei Ma, Qifeng Chen, Ross Murch
Date: 2021-11-04 00:00:00
Tasks: Deep Learning

A physics assisted deep learning framework to perform accurate indoor imaging using phaseless Wi-Fi measurements is proposed. It is able to image objects that are large (compared to wavelength) and have high permittivity values, that existing radio frequency (RF) inverse scattering techniques find very challenging, making it suitable for indoor RF imaging. The technique utilizes a Rytov based inverse scattering model with a deep learning framework. The inverse scattering model is based on an extended Rytov approximation (xRA) that pre-reconstructs the RF measurements. Under strong scattering conditions, this pre-reconstruction is related to the actual permittivity profile by a non-linear function, which is learned by a modified U-Net model to obtain the permittivity profile of the object. Thus, our proposed approach not only reconstructs the shape of objects, but also estimates their permittivity values accurately. We demonstrate its imaging performance using simulations as well as experimental results in an actual indoor environment using 2.4 GHz Wi-Fi phaseless measurements. For incident wavelength $\lambda_0$, the proposed framework can reconstruct objects with relative permittivity as high as 77 and electrical size as large as $40 \lambda$, where $\lambda =\lambda_0/\sqrt{77}$. This is in contrast to existing phaseless imaging techniques which cannot reconstruct permittivity values beyond 3 or 4. Thus, our proposed method is the first inverse scattering-based deep learning framework which can image large scatterers with high permittivity and achieve accurate indoor RF imaging using phaseless Wi-Fi measurements.
Meta-learning for RIS-assisted NOMA Networks
Authors: Yixuan Zou, Yuanwei Liu, Kaifeng Han, Xiao Liu, Kok Keong Chai
Date: 2021-11-04 00:00:00
Tasks: Clustering, Meta-Learning

A novel reconfigurable intelligent surfaces (RISs)-based transmission framework is proposed for downlink non-orthogonal multiple access (NOMA) networks. We propose a quality-of-service (QoS)-based clustering scheme to improve the resource efficiency and formulate a sum rate maximization problem by jointly optimizing the phase shift of the RIS and the power allocation at the base station (BS). A model-agnostic meta-learning (MAML)-based learning algorithm is proposed to solve the joint optimization problem with a fast convergence rate and low model complexity. Extensive simulation results demonstrate that the proposed QoS-based NOMA network achieves significantly higher transmission throughput compared to the conventional orthogonal multiple access (OMA) network. It can also be observed that substantial throughput gain can be achieved by integrating RISs in NOMA and OMA networks. Moreover, simulation results of the proposed QoS-based clustering method demonstrate observable throughput gain against the conventional channel condition-based schemes.
MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor
Authors: Win-Ken Beh, Yi-Hsuan Wu, An-Yeu, Wu
Date: 2021-11-03 00:00:00
Tasks: Electrocardiography (ECG), Sleep Quality

This paper describes an open-access database focusing on the study of mental workload (MW) assessment system for wearable devices. A wristband photoplethysmogram (PPG) was provided as a representative of wearable devices. In addition, a clinical device that can record Electrocardiography (ECG) , galvanic skin response (GSR) and, fingertip PPG was included in the database as a reference. The MW was induced by performing the N-back task with 22 subjects. The participants were asked to answer the Pittsburgh Sleep Quality Index (PSQI) questionnaire at the beginning of the experiment and the NASA Task Load Index (NASA-TLX) questionnaire after each N-back task. The result of data analysis show the potential uses of the recorded modalities and the feasibility of the MW elicitation protocol. Finally the MAUS dataset is now available for academic use (The MAUS dataset is available at IEEE Dataport: https://ieee-dataport.org/open-access/maus-dataset-mental-workload-assessment-n-back-task-using-wearable-sensor). Besides, we also presents a reproducible baseline system as a preliminary benchmark (The code of the baseline system on MAUS dataset is available on Github: https://github.com/rickwu11/MAUS\_dataset\_baseline\_system), which testing accuracy are 71.6 %, 66.7 %, and 59.9 % in ECG, fingertip PPG, wristband PPG, respectively.
Channel modeling for in-body optical wireless communications
Authors: Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos, Nestor D. Chatzidiamantis, George K. Karagiannidis
Date: 2021-11-03 00:00:00

Next generation in-to-out-of body biomedical applications have adopted optical wireless communications (OWCs). However, by delving into the published literature, a gap is recognised in modeling the in-to-out-of channel, since most published contributions neglect the particularities of different type of tissues. Towards this direction, in this paper we present a novel pathloss and scattering models for in-to-out-of OWC links. Specifically, we derive extract analytical expressions that accurately describe the absorption of the five main tissues' constituents, namely fat, water, melanin, oxygenated and de-oxygenated blood. Moreover, we formulate a model for the calculation of the absorption coefficient of any generic biological tissue. Next, by incorporating the impact of scattering in the aforementioned model we formulate the complete pathloss model. The developed theoretical framework is verified by means of comparisons between the estimated pathloss and experimental measurements from independent research works. Finally, we illustrate the accuracy of the theoretical framework in estimating the optical properties of any generic tissue based on its constitution. The extracted channel model is capable of boosting the design of optimized communication protocols for a plethora of biomedical applications.
Polarization-Based Reconfigurable Tags for Robust Ambient Backscatter Communications
Authors: Romain Fara, Dinh-Thuy Phan-Huy, Abdelwaheb Ourir, Yvan Kokar, Jean-Christophe Prévotet, Maryline Hélard, Marco Di Renzo, Julien de Rosny
Date: 2021-11-02 00:00:00
Tasks: TAG

Ambient backscatter communication is an emerging and promising low-energy technology for the Internet of Things. In such a system, a tag sends a binary message to a reader by backscattering a radio frequency signal generated by an ambient source. The tag can operate without battery and without generating additional radio waves. However, the tag-to-reader link suffers from the source-to-reader interference. In this paper, we propose a polarization-based reconfigurable antenna in order to improve the robustness of the tag-to-reader link against the source-to-reader direct interference. More precisely, we compare different types of tags' antennas, different tags' encoding schemes, and different detectors at the reader. By using analysis, numerical simulations, and experiments, we show that a polarization-based reconfigurable tag with four polarization directions significantly outperforms a non-reconfigurable tag, and provides almost the same performance as an ideal reconfigurable tag with a large number of reconfigurable polarization patterns.
Callable convertible bonds under liquidity constraints and hybrid priorities
Authors: David Hobson, Gechun Liang, Edward Wang
Date: 2021-11-03 00:00:00

This paper investigates the callable convertible bond problem in the presence of a liquidity constraint modelled by Poisson signals. We assume that neither the bondholder nor the firm has absolute priority when they stop the game simultaneously, but instead, a proportion $m\in[0,1]$ of the bond is converted to the firm's stock and the rest is called by the firm. The paper thus generalizes the special case studied in [Liang and Sun, Dynkin games with Poisson random intervention times, SIAM Journal on Control and Optimization, 57 (2019), 2962-2991] where the bondholder has priority ($m=1$), and presents a complete solution to the callable convertible bond problem with liquidity constraint. The callable convertible bond is an example of a Dynkin game, but falls outside the standard paradigm since the payoffs do not depend in an ordered way upon which agent stops the game. We show how to deal with this non-ordered situation by introducing a new technique which may be of interest in its own right, and then apply it to the bond problem.
Decoupled coordinates for machine learning-based molecular fragment linking
Authors: Markus Fleck, Noah Weber, Christopher Trummer
Date: 2021-11-01 00:00:00
Tasks: BIG-bench Machine Learning

Recent developments in machine-learning based molecular fragment linking have demonstrated the importance of informing the generation process with structural information specifying the relative orientation of the fragments to be linked. However, such structural information has not yet been provided in the form of a complete relative coordinate system. Mathematical details for a decoupled set of bond lengths, bond angles and torsion angles are elaborated and the coordinate system is demonstrated to be complete. Significant impact on the quality of the generated linkers is demonstrated numerically. The amount of reliable information within the different types of degrees of freedom is investigated. Ablation studies and an information-theoretical analysis are performed. The presented benefits suggest the application of a complete and decoupled relative coordinate system as a standard good practice in linker design.
Did the Black Death reach the Kingdom of Poland in the middle of the 14th century?
Authors: Piotr Guzowski
Date: 2021-11-04 00:00:00

The Black Death is regarded as a turning point in late medieval European history. Recent studies have shown that even regions that have so far been perceived in the literature as not or only marginally affected by the epidemic, suffered from its profound demographic and economic consequences. The scale and geographical range of the plague in Central Europe, the Kingdom of Poland included remains, however, a matter of dispute and from the beginning scholar's views on this matter have been divided. What is particularly important, the outbreak of the plague in Western Europe coincided with the reign of Casimir of the Piast dynasty, the only ruler of Poland to receive the nickname the Great, who is associated with the modernization and extraordinary development of his kingdom which was entering the golden age of its history.
Compositional modelling of immune response and virus transmission dynamics
Authors: William Waites, Matteo Cavaliere, Vincent Danos, Ruchira Datta, Rosalind M. Eggo, Timothy B. Hallett, David Manheim, Jasmina Panovska-Griffiths, Timothy W. Russell, Veronika I. Zarnitsyna
Date: 2021-11-03 00:00:00

Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they don't allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory.
Mobility-Dependent and Mobility-Compensated Effective Reproduction Number of COVID-19 Viral Variants: New Metric for Infectivity Evaluation
Authors: Sachiko Kodera, Essam A. Rashed, Akimasa Hirata
Date: 2021-11-01 00:00:00
Tasks: Management, Open-Ended Question Answering

During epidemics, estimation of the effective reproduction number (ERN) associated with infectious disease is a challenging topic for policy development and medical resource management. There is still an open question about the dominant factors to characterize in corona virus disease 2019 (COVID-19), although recent studies based on nonlinear regression with machine learning suggested mobility. The emergence of new viral variants is common in widespread pandemics. However, understanding the potential ERN of new variants is required for policy revision, including lockdown constraints. In this study, we proposed time-averaged mobility at transit stations as a surrogate to correlate with ERN using data from three prefectures in Japan. The latency and duration to average over the mobility were 6-8 days and 6-7, respectively (R2 was 0.109-0.512 in Tokyo, 0.365-0.607 in Osaka, and 0.317-0.631 in Aichi). The same linear correlation was confirmed in Singapore and London. The mobility-adjusted ERN of the alpha variant was 15%-30%, and was 20%-40% higher than the standard type in Osaka, Aichi, and London. Similarly, the ERN of the delta variant was 20%-40% higher than that of the standard type in Osaka and Aichi. The proposed metric would be useful for proper evaluation of the infectivity of different variants in terms of ERN.
On Similarity
Authors: Luciano da F. Costa
Date: 2021-11-02 00:00:00

The objective quantification of similarity between two mathematical structures constitutes a recurrent issue in science and technology. In the present work, we developed a principled approach that took the Kronecker's delta function of two scalar values as the prototypical reference for similarity quantification and then derived for more yielding indices, three of which bound between 0 and 1. Generalizations of these indices to take into account the sign of the scalar values were then presented and developed to multisets, vectors, and functions in real spaces. Several important results have been obtained, including the interpretation of the Jaccard index as a yielding implementation of the Kronecker's delta function. When generalized to real functions, the four described similarity indices become respective functionals, which can then be employed to obtain associated operations of convolution and correlation.
Numerical Approximation in CFD Problems Using Physics Informed Machine Learning
Authors: Siddharth Rout, Vikas Dwivedi, Balaji Srinivasan
Date: 2021-11-01 00:00:00
Tasks: BIG-bench Machine Learning, Physics-informed machine learning

The thesis focuses on various techniques to find an alternate approximation method that could be universally used for a wide range of CFD problems but with low computational cost and low runtime. Various techniques have been explored within the field of machine learning to gauge the utility in fulfilling the core ambition. Steady advection diffusion problem has been used as the test case to understand the level of complexity up to which a method can provide solution. Ultimately, the focus stays over physics informed machine learning techniques where solving differential equations is possible without any training with computed data. The prevalent methods by I.E. Lagaris et.al. and M. Raissi et.al are explored thoroughly. The prevalent methods cannot solve advection dominant problems. A physics informed method, called as Distributed Physics Informed Neural Network (DPINN), is proposed to solve advection dominant problems. It increases the lexibility and capability of older methods by splitting the domain and introducing other physics-based constraints as mean squared loss terms. Various experiments are done to explore the end to end possibilities with the method. Parametric study is also done to understand the behavior of the method to different tunable parameters. The method is tested over steady advection-diffusion problems and unsteady square pulse problems. Very accurate results are recorded. Extreme learning machine (ELM) is a very fast neural network algorithm at the cost of tunable parameters. The ELM based variant of the proposed model is tested over the advection-diffusion problem. ELM makes the complex optimization simpler and Since the method is non-iterative, the solution is recorded in a single shot. The ELM based variant seems to work better than the simple DPINN method. Simultaneously scope for various development in future are hinted throughout the thesis.