pwc-archive/papers-with-abstracts huggingface dataset:
Volumetric Image Projection Super-Resolution Ultrasound (VIP-SR) with a 1D Unfocused Linear Array
Authors: B. Wang, K. Riemer, M. Toulemonde, J. Yan, X. Zhou, M. Tang
Date: 2022-06-08 00:00:00
Tasks: Super-Resolution

Super-Resolution Ultrasound (SRUS) through localizing spatially isolated microbubbles has been demonstrated to overcome the wave diffraction limit and reveal the microvascular structure and flow information at the microscopic scale. However, 3D SRUS imaging remains a challenge due to the fabrication and computational complexity of 2D matrix array probes and connections. Inspired by X-ray radiography which can present volumetric information in a single projection image with much simpler hardware than X-ray CT, this study investigates the feasibility of volumetric image projection super-resolution (VIP-SR) ultrasound using a 1D unfocused linear array. Both simulation and experiments were conducted on 3D microvessel phantoms using a 1D linear array with or without an elevational focus, and a 2D matrix array as the reference. Results show that, VIP-SR, using an unfocused 1D array probe can capture significantly more volumetric information than the conventional 1D elevational focused probe. Compared with the 2D projection image of the full 3D SRUS results using the 2D array probe with the same aperture size, VIP-SR has similar volumetric coverage using 32 folds less independent elements. The impact of bubble concentration and vascular density on the VIP-SR US was also investigated. This study demonstrates the ability of high-resolution volumetric imaging of microvascular structures at significantly reduced costs with VIP-SR.
Integrated Sensing and Communication with Delay Alignment Modulation
Authors: Zhiqiang Xiao, Yong Zeng
Date: 2022-06-08 00:00:00
Tasks: Integrated sensing and communication, ISAC

Delay alignment modulation (DAM) has been recently proposed to enable inter-symbol interference (ISI)-free single-carrier (SC) communication without relying on sophisticated channel equalization. The key idea of DAM is to pre-introduce deliberate symbol delays at the transmitter side, so that all multi-path signal components may arrive at the receiver simultaneously and be superimposed constructively, rather than causing the detrimental ISI. Compared to the classic orthogonal frequency division multiplexing (OFDM) transmission, DAM has several appealing advantages, including low peak-to-average-power ratio (PAPR) and high tolerance for Doppler frequency shift, which makes DAM also appealing for radar sensing. Therefore, in this paper, DAM is investigated for the emerging integrated sensing and communication (ISAC) setup. We first derive the output signal-to-noise ratios (SNRs) for ISI-free communication and radar sensing, respectively, and then propose an efficient beamforming design for DAM-ISAC to maximize the communication SNR while guaranteeing the sensing performance. The comparison analysis of DAM versus OFDM for ISAC is developed, and it is revealed that DAM enables higher sensing SNR and larger Doppler frequency estimation. Simulation results are provided to show the great potential of DAM for ISAC
Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network
Authors: Hongzhi Zhu, Yongliang Guo, Wei Xu, Xiaohu You
Date: 2022-06-08 00:00:00
Tasks: Generative Adversarial Network

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter.
Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI
Authors: Mihir Joshi, Aaron Pruitt, Chong Chen, Yingmin Liu, Rizwan Ahmad
Date: 2022-06-08 00:00:00
Tasks: compressed sensing

For an effective application of compressed sensing (CS), which exploits the underlying compressibility of an image, one of the requirements is that the undersampling artifact be incoherent (noise-like) in the sparsifying transform domain. For cardiovascular MRI (CMR), several pseudo-random sampling methods have been proposed that yield a high level of incoherence. In this technical report, we present a collection of five pseudo-random Cartesian sampling methods that can be applied to 2D cine and flow, 3D volumetric cine, and 4D flow imaging. Four out of the five presented methods yield fast computation for on-the-fly generation of the sampling mask, without the need to create and store pre-computed look-up tables. In addition, the sampling distribution is parameterized, providing control over the sampling density. For each sampling method in the report, (i) we briefly describe the methodology, (ii) list default values of the pertinent parameters, and (iii) provide a publicly available MATLAB implementation.
Tensor-Based Channel Estimation for RIS-Assisted Networks Operating Under Imperfections
Authors: Paulo R. B. Gomes, Gilderlan T. de Araújo, Bruno Sokal, André L. F. de Almeida, Behrooz Makki, Gábor Fodor
Date: 2022-06-07 00:00:00

Reconfigurable intelligent surface (RIS) is a candidate technology for future wireless networks. It enables to shape the wireless environment to reach massive connectivity and enhanced data rate. The promising gains of RIS-assisted networks are, however, strongly depends on the accuracy of the channel state information. Due to the passive nature of the RIS elements, channel estimation may become challenging. This becomes most evident when physical imperfections or electronic impairments affect the RIS due to its exposition to different environmental effects or caused by hardware limitations from the circuitry. In this paper, we propose an efficient and low-complexity tensor-based channel estimation approach in RIS-assisted networks taking different imperfections into account. By assuming a short-term model in which the RIS imperfections behavior, modeled as unknown amplitude and phase shifts deviations, is non-static with respect to the channel coherence time, we formulate a closed-form higher order singular value decomposition based algorithm for the joint estimation of the involved channels and the unknown impairments. Furthermore, the identifiability and computational complexity of the proposed algorithm are analyzed, and we study the effect of different imperfections on the channel estimation quality. Simulation results demonstrate the effectiveness of our proposed tensor-based algorithm in terms of the estimation accuracy and computational complexity compared to competing tensor-based iterative alternating solutions.
Model for prognostic of symptomatic, asymptomatic and hospitalized COVID-19 cases with correct demography evolution
Authors: Antonio Rafael Selva Castañeda, Erick Eduardo Ramirez-Torres, Luis Eugenio Valdés-García, Hilda María Morandeira-Padrón, Diana Sedal Yanez, Juan I. Montijano, Luis Enrique Bergues Cabrales
Date: 2022-06-08 00:00:00

The aim of this study is to propose a modified Susceptible-Exposed-Infectious-Removed (SEIR) model that describes the behaviour of symptomatic, asymptomatic and hospitalized patients of COVID-19 epidemic, including the effect of demographic variation of population. It is shown that considering a population growth proportional to the total population leads to solutions with a qualitative behaviour different from the behaviour obtained in many studies, where constant growth ratio is assumed. An exhaustive theoretical study is carried out and the basic reproduction number $R_0$ is computed from the model equations. It is proved that if $R_0<1$ then the disease-free manifold is globally asymptotically stable, that is, the epidemics remits. Global and local stability of the equilibrium points is also studied. Numerical simulations are used to show the agreement between numerical results and theoretical properties. The model is fitted to experimental data corresponding to the pandemic evolution in the Rep\'ublica de Cuba, showing a proper behaviour of infected cases which let us think that can provide a correct estimation of asymptomatic cases. In conclusion, the model seems to be an adequate tool for the study and control of infectious diseases in particular the COVID-19 disease transmission.
Interpretability of artificial neural network models in artificial Intelligence vs. neuroscience
Authors: Kohitij Kar, Simon Kornblith, Evelina Fedorenko
Date: 2022-06-07 00:00:00
Tasks: Decision Making

Computationally explicit hypotheses of brain function derived from machine learning (ML)-based models have recently revolutionized neuroscience. Despite the unprecedented ability of these artificial neural networks (ANNs) to capture responses in biological neural networks (brains), and our full access to all internal model components (unlike the brain), ANNs are often referred to as black-boxes with limited interpretability. Interpretability, however, is a multi-faceted construct that is used differently across fields. In particular, interpretability, or explainability, efforts in Artificial Intelligence (AI) focus on understanding how different model components contribute to its output (i.e., decision making). In contrast, the neuroscientific interpretability of ANNs requires explicit alignment between model components and neuroscientific constructs (e.g., different brain areas or phenomena, like recurrence or top-down feedback). Given the widespread calls to improve the interpretability of AI systems, we here highlight these different notions of interpretability and argue that the neuroscientific interpretability of ANNs can be pursued in parallel with, but independently from, the ongoing efforts in AI. Certain ML techniques (e.g., deep dream) can be leveraged in both fields, to ask what stimulus optimally activates the specific model features (feature visualization by optimization), or how different features contribute to the model's output (feature attribution). However, without appropriate brain alignment, certain features will remain uninterpretable to neuroscientists.
Structural identifiability of compartmental models for infectious disease transmission is influenced by data type
Authors: Emmanuelle A. Dankwa, Andrew F. Brouwer, Christl A. Donnelly
Date: 2022-06-08 00:00:00
Tasks: Vocal Bursts Type Prediction

If model identifiability is not confirmed, inferences from infectious disease transmission models may not be reliable, so they might lead to misleading recommendations. Structural identifiability analysis characterizes whether it is possible to obtain unique solutions for all unknown model parameters, given the model structure. In this work, we studied the structural identifiability of some typical deterministic compartmental models for infectious disease transmission, focusing on the influence of the data type considered as model output on the identifiability of unknown model parameters, including initial conditions. We defined 26 model versions, each having a unique combination of underlying compartmental structure and data type(s) considered as model output(s). Four compartmental model structures and three common data types in disease surveillance (incidence, prevalence and detected vector counts) were studied. The structural identifiability of some parameters varied depending on the type of model output. In general, models with multiple data types as outputs had more structurally identifiable parameters, than did models with a single data type as output. This study highlights the importance of a careful consideration of data types as an integral part of the inference process with compartmental infectious disease transmission models.
Topological Data Analysis Ball Mapper for Finance
Authors: Pawel Dlotko, Wanling Qiu, Simon Rudkin
Date: 2022-06-08 00:00:00
Tasks: Decision Making, Topological Data Analysis

Finance is heavily influenced by data-driven decision-making. Meanwhile, our ability to comprehend the full informational content of data sets remains impeded by the tools we apply in analysis, especially where the data is high-dimensional. Presenting the Topological Data Analysis Ball Mapper algorithm this paper illuminates a new means of seeing the detail in data from data shape. With comparisons to existing approaches and illustrative examples, the value of the new tool is shown. Directions for employing Ball Mapper in practice are given and the benefits are reviewed.
Market-to-book Ratio in Stochastic Portfolio Theory
Authors: Donghan Kim
Date: 2022-06-08 00:00:00

We study market-to-book ratios of stocks in the context of Stochastic Portfolio Theory. Functionally generated portfolios that depend on auxiliary economic variables other than relative capitalizations ("sizes") are developed in two ways, together with their relative returns with respect to the market. This enables us to identify the value factor (i.e., market-to-book ratio) in returns of such generated portfolios when the auxiliary variables are stocks' book values. Examples of portfolios, as well as their empirical results, are given, with the evidence that, in addition to size, the value factor does affect the performance of the portfolio.
Predictable Forward Performance Processes in Complete Markets
Authors: Bahman Angoshtari
Date: 2022-06-07 00:00:00

We establish existence of Predictable Forward Performance Processes (PFPPs) in complete markets, which has been previously shown only in the binomial setting. Our market model can be a discrete-time or a continuous-time model, and the investment horizon can be finite or infinite. We show that the main step in construction of PFPPs is solving a one-period problem involving an integral equation, which is the counterpart of the functional equation found in the binomial case. Although this integral equation has been partially studied in the existing literature, we provide a new solution method using the Fourier transform for tempered distributions. We also provide closed-form solutions for PFPPs with inverse marginal functions that are completely monotonic and establish uniqueness of PFPPs within this class. We apply our results to two special cases. The first one is the binomial market and is included to relate our work to the existing literature. The second example considers a generalized Black-Scholes model which, to the best of our knowledge, is a new result.
Multivariate backtests and copulas for risk evaluation
Authors: Boris David, Gilles Zumbach
Date: 2022-06-08 00:00:00
Tasks: Time Series, Time Series Analysis

Risk evaluation is a forecast, and its validity must be backtested. Probability distribution forecasts are used in this work and allow for more powerful validations compared to point forecasts. Our aim is to use bivariate copulas in order to characterize the in-sample copulas and to validate out-of-sample a bivariate forecast. For both set-ups, probability integral transforms (PIT) and Rosenblatt transforms are used to map the problem into an independent copula. For this simple copula, statistical tests can be applied to validate the choice of the in-sample copula or the validity of the bivariate forecast. The salient results are that a Student copula describes well the dependencies between financial time series (regardless of the correlation), and that the bivariate forecasts provided by a risk methodology based on historical innovations performs correctly out-of-sample. A prerequisite is to remove the heteroskedasticity in order to have stationary time series, in this work a long-memory ARCH volatility model is used.
Formalization of the principles of brain Programming (Brain Principles Programming)
Authors: E. E. Vityaev, A. G. Kolonin, A. V. Kurpatov A. A. Molchanov
Date: 2022-05-13 00:00:00

In the monograph "Strong artificial intelligence. On the Approaches to Superintelligence" contains an overview of general artificial intelligence (AGI). As an anthropomorphic research area, it includes Brain Principles Programming (BPP) -- the formalization of universal mechanisms (principles) of the brain work with information, which are implemented at all levels of the organization of nervous tissue. This monograph contains a formalization of these principles in terms of category theory. However, this formalization is not enough to develop algorithms for working with information. In this paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed earlier, which modeling cognitive functions and base on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin Theory of Functional Brain Systems, Eleanor Rosch prototypical categorization theory, Bob Rehder theory of causal models and "natural" classification. As a result, a formalization of BPP is obtained and computer experiments demonstrating the operation of algorithms are presented.
TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation
Authors: El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed
Date: 2022-05-27 00:00:00
Proceeding: OSACT (LREC) 2022 6
Tasks: Machine Translation, Semantic Similarity, Semantic Textual Similarity, Translation

We present TURJUMAN, a neural toolkit for translating from 20 languages into Modern Standard Arabic (MSA). TURJUMAN exploits the recently-introduced text-to-text Transformer AraT5 model, endowing it with a powerful ability to decode into Arabic. The toolkit offers the possibility of employing a number of diverse decoding methods, making it suited for acquiring paraphrases for the MSA translations as an added value. To train TURJUMAN, we sample from publicly available parallel data employing a simple semantic similarity method to ensure data quality. This allows us to prepare and release AraOPUS-20, a new machine translation benchmark. We publicly release our translation toolkit (TURJUMAN) as well as our benchmark dataset (AraOPUS-20).
Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation
Authors: Yongjia Xu, Xinzheng Lu, Yifan Fei, Yuli Huang
Date: 2022-04-29 00:00:00

An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection.
Solving the apparent diversity-accuracy dilemma of recommender systems
Authors: Tao Zhoua, Zoltán Kuscsika, Jian-Guo Liua, Matúš Medoa, Joseph Rushton Wakelinga, Yi-Cheng Zhanga
Date: 2010-03-09 00:00:00
Proceeding: PNAS ∣ March 9 ∣ vol. 107 ∣ no. 10 ∣ 4511–4515 2010 3
Tasks: Diversity, Recommendation Systems

Recommender systems use data on past user preferences to predict
possible future likes and interests. A key challenge is that while the
most useful individual recommendations are to be found among
diverse niche objects, the most reliably accurate results are ob-
tained by methods that recommend objects based on user or object
similarity. In this paper we introduce a new algorithm specifically to
address the challenge of diversity and show how it can be used to
resolve this apparent dilemma when combined in an elegant
hybrid with an accuracy-focused algorithm. By tuning the hybrid
appropriately we are able to obtain, without relying on any seman-
tic or context-specific information, simultaneous gains in both
accuracy and diversity of recommendations.
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms
Authors: Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang
Date: 2022-06-09 00:00:00
Tasks: Classification, Multi-class Classification

The theoretical analysis of multi-class classification has proved that the existing multi-class classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multi-class classification has not been solved: how to improve the classification accuracy of multi-class classification problems when only imprecise observations are available. Hence, in this paper, we propose a novel framework to address a new realistic problem called multi-class classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations. Firstly, we give the theoretical analysis of the MCIMO problem based on fuzzy Rademacher complexity. Then, two practical algorithms based on support vector machine and neural networks are constructed to solve the proposed new problem. Experiments on both synthetic and real-world datasets verify the rationality of our theoretical analysis and the efficacy of the proposed algorithms.