pwc-archive/papers-with-abstracts huggingface dataset:
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution
Authors: Si-Cun Chen, Yi-Hsin Chen, Yen-Yu Lin, Wen-Hsiao Peng
Date: 2023-07-16 00:00:00
Proceeding: ICCV 2023 1
Tasks: Motion Interpolation, Space-time Video Super-resolution, Super-Resolution, Video Super-Resolution

This work addresses continuous space-time video super-resolution (C-STVSR) that aims to up-scale an input video both spatially and temporally by any scaling factors. One key challenge of C-STVSR is to propagate information temporally among the input video frames. To this end, we introduce a space-time local implicit neural function. It has the striking feature of learning forward motion for a continuum of pixels. We motivate the use of forward motion from the perspective of learning individual motion trajectories, as opposed to learning a mixture of motion trajectories with backward motion. To ease motion interpolation, we encode sparsely sampled forward motion extracted from the input video as the contextual input. Along with a reliability-aware splatting and decoding scheme, our framework, termed MoTIF, achieves the state-of-the-art performance on C-STVSR. The source code of MoTIF is available at https://github.com/sichun233746/MoTIF.
Distributed Radar Imaging Based on Accelerated ADMM
Authors: Ahmed Murtada, Bhavani Shankar Mysore Rama Rao, Udo Schroeder
Date: 2023-07-17 00:00:00

The ability of widely distributed radar systems to capture diverse spatial scattering properties substantially improves radar imaging performance. Traditional imaging methods leverage regularized optimization techniques to reconstruct sparse images from local sensors and later combine them to create a global image. Alternatively, we proposed in an earlier work a joint reconstruction technique based on two problem formulations according to the optimization framework of the Alternating Direction Method of Multipliers (ADMM). The joint reconstruction of the global image offers faster convergence, flexible implementation, and a general distributed reconstruction framework. However, despite its benefits, ADMM framework still exhibits a slow convergence rate, making its employment in some contexts impractical. In this paper, we introduce a heuristic method to accelerate the convergence of the previously proposed ADMM formulations based on the gradual elimination of the already converged pixels in accordance with a predetermined criterion. In addition to reducing running time, the accelerated implementation offers reduced computational complexity and lower communication cost between the sensors during iterative updates.
Interferometric single-pixel imaging with a multicore fiber
Authors: Olivier Leblanc, Matthias Hofer, Siddharth Sivankutty, Hervé Rigneault, Laurent Jacques
Date: 2023-07-17 00:00:00

Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of a Hermitian \emph{interferometric} matrix -- a matrix encoding the spectral content of the sample image. In this model, each SROP is induced by the complex \emph{sketching} vector shaping the incident light wavefront with a spatial light modulator (SLM), while the projected interferometric matrix collects up to $O(Q^2)$ image frequencies for a $Q$-core MCF. While this scheme subsumes previous sensing modalities, such as raster scanning (RS) imaging with beamformed illumination, we demonstrate that collecting the measurements of $M$ random SLM configurations -- and thus acquiring $M$ SROPs -- allows us to estimate an image of interest if $M$ and $Q$ scale linearly (up to log factors) with the image sparsity level, hence requiring much fewer observations than RS imaging or a complete Nyquist sampling of the $Q \times Q$ interferometric matrix. This demonstration is achieved both theoretically, with a specific restricted isometry analysis of the sensing scheme, and with extensive Monte Carlo experiments. Experimental results made on an actual MCF system finally demonstrate the effectiveness of this imaging procedure on a benchmark image.
Subsampling Methods for Fast Electron Backscattered Diffraction Analysis
Authors: Zoë Broad, Daniel Nicholls, Jack Wells, Alex W. Robinson, Amirafshar Moshtaghpour, Robert Masters, Louise Hughes, Nigel D. Browning
Date: 2023-07-17 00:00:00
Tasks: Dictionary Learning, Position

Despite advancements in electron backscatter diffraction (EBSD) detector speeds, the acquisition rates of 4-Dimensional (4D) EBSD data, i.e., a collection of 2-dimensional (2D) diffraction maps for every position of a convergent electron probe on the sample, is limited by the capacity of the detector. Such 4D data enables computation of, e.g., band contrast and Inverse Pole Figure (IPF) maps, used for material characterisation. In this work we propose a fast acquisition method of EBSD data through subsampling 2-D probe positions and inpainting. We investigate reconstruction of both band contrast and IPF maps using an unsupervised Bayesian dictionary learning approach, i.e., Beta process factor analysis. Numerical simulations achieve high quality reconstructed images from 10% subsampled data.
Towards Zero-power 3D Imaging: VLC-assisted Passive ToF Sensing
Authors: Faisal Ahmed, Miguel Heredia Conde, Paula López Martínez
Date: 2023-07-17 00:00:00

Passive Time-of-Flight (ToF) imaging can be enabled by optical wireless communication (OWC). The lighting infrastructure is the backbone of emerging light-based wireless communication. To this end, communication sources are used as opportunity illuminators to probe the scene, and an array of time-resolved pixels are exploited to demodulate the return, provided that the ToF camera can be externally synchronized. Our work employs a direct line-of-sight path to synchronize the camera externally. Together with the indirect path given by the reflections from the scene, this yields a bistatic configuration. Each Time-of-Flight (ToF) measurement induced a solution space of ellipsoidal shape and redefined the image formation model based on the bistatic configuration. In this demo, we showcase a passive ToF camera capable of delivering intensity and depth information in practice without emitting photons from the ToF camera. Our passive modality can eliminate built-in illumination sources, thus coping with optical power constraints, as is desired in future ToF cameras.
Deep Learning-based Power Allocation in Rate Splitting Optical Wireless Networks
Authors: Khulood D. Alazwary, Ahmad Adnan Qidan, T. E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2023-07-17 00:00:00

Optical wireless communication (OWC) provides high aggregate data rates in the range of Terabits per second (Tb/s). Specifically, OWC using infrared lasers as transmitters has been considered as a strong candidate in the next generation of wireless communication. Rate splitting (RS) is a transmission scheme derived to improve spectral efficiency in dense wireless networks. In RS, the transmitted power is allocated to different messages, common and private messages, serving multiple users simultaneously, where each user can decode the desired message following a certain procedure. Moreover, two-tier precoding RS scheme has been proposed to overcome the limitations of traditional RS in multi-group scenarios. In this context, power allocation (PA) is a crucial issue, which can affect the performance of RS. Therefore, we formulate a PA optimization problem to enhance the data rates of RS-based OWC networks. However, such optimization problems are complex due to the use of different messages intended to the users. In this paper, we design and train a deep neural network (DNN) model to determine the power allocated to the messages of RS, while fulfilling the demands of users. The results show the accuracy of our trained DNN model when used in an online phase.
A Hybrid NOMA-OMA Scheme for Inter-plane Intersatellite Communications in Massive LEO Constellations
Authors: Donatella Darsena, Giacinto Gelli, Ivan Iudice, Francesco Verde
Date: 2023-07-17 00:00:00
Tasks: Fairness

Communication between satellites in low-Earth orbit (LEO) constellations takes place through inter-satellite links (ISLs). Unlike intra-plane ISLs, which interconnect satellites belonging to the same orbital plane with fixed relative distance, inter-plane ISLs experience significant Doppler frequency shifts, since satellites belonging to different orbital planes exhibit timevarying relative distance (required, e.g., to minimize the risk of physical collisions between satellites). In this paper, we consider the problem of connecting multiple satellites, belonging to a massive LEO constellation, to a receiving satellite, referred to as the sink. Specifically, we consider a hybrid multiple access scheme, which employs a combination of non-orthogonal multiple access (NOMA), where radio-frequency ISLs share the same time-frequency resource blocks, and orthogonal multiple access (OMA), where ISLs employ orthogonal resource blocks. The set of satellites transmitting towards the sink is divided into groups, where NOMA is employed within each group, whereas OMA is used to separate different groups. Such a scheme subsumes as special cases both pure-OMA and pure-NOMA. Our study highlights that similar Doppler frequency shifts have a significant impact on the individual rates of the satellites in a pure-NOMA scheme, thus reducing the network fairness of this technique. Motivated by such a fact, we develop design strategies of the proposed hybrid NOMA-OMA scheme, which exploit inter-plane Doppler frequency diversity to enhance fairness among the satellites, while ensuring a significantly higher sumrate capacity compared to the pure-OMA technique. Numerical results corroborate our theoretical analysis, by demonstrating both the fairness enhancement of the proposed techniques over the pure-NOMA scheme, as well as their capacity improvement over the pure-OMA one.
Machine Learning for Ranking f-wave Extraction Methods in Single-Lead ECGs
Authors: Noam Ben-Moshe, Shany Biton, Kenta Tsutsui, Mahmoud Suleiman, Leif Sörnmo, Joachim A. Behar
Date: 2023-07-17 00:00:00
Tasks: Benchmarking

Introduction: The presence of fibrillatory waves (f-waves) is important in the diagnosis of atrial fibrillation (AF), which has motivated the development of methods for f-wave extraction. We propose a novel approach to benchmarking methods designed for single-lead ECG analysis, building on the hypothesis that better-performing AF classification using features computed from the extracted f-waves implies better-performing extraction. The approach is well-suited for processing large Holter data sets annotated with respect to the presence of AF. Methods: Three data sets with a total of 300 two- or three-lead Holter recordings, performed in the USA, Israel and Japan, were used as well as a simulated single-lead data set. Four existing extraction methods based on either average beat subtraction or principal component analysis (PCA) were evaluated. A random forest classifier was used for window-based AF classification. Performance was measured by the area under the receiver operating characteristic (AUROC). Results: The best performance was found for PCA-based extraction, resulting in AUROCs in the ranges 0.77--0.83, 0.62--0.78, and 0.87--0.89 for the data sets from USA, Israel, and Japan, respectively, when analyzed across leads; the AUROC of the simulated single-lead, noisy data set was 0.98. Conclusions: This study provides a novel approach to evaluating the performance of f-wave extraction methods, offering the advantage of not using ground truth f-waves for evaluation, thus being able to leverage real data sets for evaluation. The code is open source (following publication).
How time window influences biometrics performance: an EEG-based fingerprints connectivity study
Authors: Luca Didaci, Sara Maria Pani, Claudio Frongia, Matteo Fraschini
Date: 2023-07-17 00:00:00
Tasks: EEG

EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how the performance of an EEG biometric system varies with respect to different time windows to understand if it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. Overall, the results have shown a pronounced effect of the time window on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase of the biometric performance with an increase of the time window. In conclusion, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as EEG fingerprint and that, in this context, it is very important to define a sufficient time window able to collect the subject specific features. Moreover, our preliminary results show that extending the window size beyond a certain maximum does not improve biometric systems' performance.
Delay Alignment Modulation with Hybrid beamforming for Spatially Sparse Communications
Authors: Jieni Zhang, Yong Zeng
Date: 2023-07-17 00:00:00

For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed. The key ideas of DAM are delay pre-compensation and path-based beamforming. However, existing research on DAM is based on fully digital beamforming, where the number of radio frequency (RF) chains required is equal to the number of antennas. This leads to high hardware cost and power consumption for mmWave/THz massive multiple-input multiple-output (MIMO) communications. Thus, this paper proposes the hybrid analog/digital beamforming based DAM. The analog and digital beamforming matrices are designed to achieve performance close to DAM based on fully digital beamforming. The effectiveness of the proposed technique is verified by simulation results.
Channel Modeling for Heterogeneous Vehicular ISAC System with Shared Clusters
Authors: Baiping Xiong, Zaichen Zhang, Yingmeng Ge, Haibo Wang, Hao Jiang, Liang Wu, Ziyang Zhang
Date: 2023-07-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we consider the channel modeling of a heterogeneous vehicular integrated sensing and communication (ISAC) system, where a dual-functional multi-antenna base station (BS) intends to communicate with a multi-antenna vehicular receiver (MR) and sense the surrounding environments simultaneously. The time-varying complex channel impulse responses (CIRs) of the sensing and communication channels are derived, respectively, in which the sensing and communication channels are correlated with shared clusters. The proposed models show great generality for the capability in covering both monostatic and bistatic sensing scenarios, and as well for considering both static clusters/targets and mobile clusters/targets. Important channel statistical characteristics, including time-varying spatial cross-correlation function (CCF) and temporal auto-correlation function (ACF), are derived and analyzed. Numerically results are provided to show the propagation characteristics of the proposed ISAC channel model. Finally, the proposed model is validated via the agreement between theoretical and simulated as well as measurement results.
The faces of Convolution: from the Fourier theory to algebraic signal processing
Authors: Feng Ji, Wee Peng Tay, Antonio Ortega
Date: 2023-07-16 00:00:00

In this expository article, we provide a self-contained overview of the notion of convolution embedded in different theories: from the classical Fourier theory to the theory of algebraic signal processing. We discuss their relations and differences. Toward the end, we provide an opinion on whether there is a consistent approach to convolution that unifies seemingly different approaches by different theories.
Direction-of-Arrival Estimation for Constant Modulus Signals Using a Structured Matrix Recovery Technique
Authors: Xunmeng Wu, Zai Yang, Zhiqiang Wei, Zongben Xu
Date: 2023-07-15 00:00:00
Tasks: Direction of Arrival Estimation

This paper addresses the problem of direction-of-arrival (DOA) estimation for constant modulus (CM) source signals using a uniform or sparse linear array. Existing methods typically exploit either the Vandermonde structure of the steering matrix or the CM structure of source signals only. In this paper, we propose a structured matrix recovery technique (SMART) for CM DOA estimation via fully exploiting the two structures. In particular, we reformulate the highly nonconvex CM DOA estimation problems in the noiseless and noisy cases as equivalent rank-constrained Hankel-Toeplitz matrix recovery problems, in which the Vandermonde structure is captured by a series of Hankel-Toeplitz block matrices, of which the number equals the number of snapshots, and the CM structure is guaranteed by letting the block matrices share a same Toeplitz submatrix. The alternating direction method of multipliers (ADMM) is applied to solve the resulting rank-constrained problems and the DOAs are uniquely retrieved from the numerical solution. Extensive simulations are carried out to corroborate our analysis and confirm that the proposed SMART outperforms state-of-the-art algorithms in terms of the maximum number of locatable sources and statistical efficiency.
Multichannel Frequency Estimation in Challenging Scenarios via Structured Matrix Embedding and Recovery (StruMER)
Authors: Xunmeng Wu, Zai Yang, Zongben Xu
Date: 2023-07-15 00:00:00
Tasks: Miscellaneous

Multichannel frequency estimation with incomplete data and miscellaneous noises arises in array signal processing, modal analysis, wireless communications, and so on. In this paper, we consider maximum-likelihood(-like) optimization methods for frequency estimation in which proper objective functions are adopted subject to observed data patterns and noise types. We propose a universal signal-domain approach to solve the optimization problems by embedding the noiseless multichannel signal of interest into a series of low-rank positive-semidefinite block matrices of Hankel and Toeplitz submatrices and formulating the original parameter-domain optimization problems as equivalent structured matrix recovery problems. The alternating direction method of multipliers (ADMM) is applied to solve the resulting matrix recovery problems in which both subproblems of ADMM are solved in (nearly) closed form. The proposed approach is termed as structured matrix embedding and recovery (StruMER). Extensive numerical simulations are provided to demonstrate that StruMER has improved threshold performances in various challenging scenarios, e.g., limited data, low signal-to-noise ratio, impulsive noise, and closely spaced frequencies, as compared with state-of-the-art methods.
The weighted total cophenetic index: A novel balance index for phylogenetic networks
Authors: Linda Knüver, Mareike Fischer, Marc Hellmuth, Kristina Wicke
Date: 2023-07-17 00:00:00

Phylogenetic networks play an important role in evolutionary biology as, other than phylogenetic trees, they can be used to accommodate reticulate evolutionary events such as horizontal gene transfer and hybridization. Recent research has provided a lot of progress concerning the reconstruction of such networks from data as well as insight into their graph theoretical properties. However, methods and tools to quantify structural properties of networks or differences between them are still very limited. For example, for phylogenetic trees, it is common to use balance indices to draw conclusions concerning the underlying evolutionary model, and more than twenty such indices have been proposed and are used for different purposes. One of the most frequently used balance index for trees is the so-called total cophenetic index, which has several mathematically and biologically desirable properties. For networks, on the other hand, balance indices are to-date still scarce. In this contribution, we introduce the \textit{weighted} total cophenetic index as a generalization of the total cophenetic index for trees to make it applicable to general phylogenetic networks. As we shall see, this index can be determined efficiently and behaves in a mathematical sound way, i.e., it satisfies so-called locality and recursiveness conditions. In addition, we analyze its extremal properties and, in particular, we investigate its maxima and minima as well as the structure of networks that achieve these values within the space of so-called level-$1$ networks. We finally briefly compare this novel index to the two other network balance indices available so-far.
Trajectory inference for a branching SDE model of cell differentiation
Authors: Elias Ventre, Aden Forrow, Nitya Gadhiwala, Parijat Chakraborty, Omer Angel, Geoffrey Schiebinger
Date: 2023-07-15 00:00:00

A core challenge for modern biology is how to infer the trajectories of individual cells from population-level time courses of high-dimensional gene expression data. Birth and death of cells present a particular difficulty: existing trajectory inference methods cannot distinguish variability in net proliferation from cell differentiation dynamics, and hence require accurate prior knowledge of the proliferation rate. Building on Global Waddington-OT (gWOT), which performs trajectory inference with rigorous theoretical guarantees when birth and death can be neglected, we show how to use lineage trees available with recently developed CRISPR-based measurement technologies to disentangle proliferation and differentiation. In particular, when there is neither death nor subsampling of cells, we show that we extend gWOT to the case with proliferation with similar theoretical guarantees and computational cost, without requiring any prior information. In the case of death and/or subsampling, our method introduces a bias, that we describe explicitly and argue to be inherent to these lineage tracing data. We demonstrate in both cases the ability of this method to reliably reconstruct the landscape of a branching SDE from time-courses of simulated datasets with lineage tracing, outperforming even a benchmark using the experimentally unavailable true branching rates.
Is (independent) subordination relevant in option pricing?
Authors: Michele Azzone, Roberto Baviera
Date: 2023-07-14 00:00:00

Monroe (1978) demonstrates that any local semimartingale can be represented as a time-changed Brownian Motion (BM). A natural question arises: does this representation theorem hold when the BM and the time-change are independent? We prove that a local semimartingale is not equivalent to a BM with a time-change that is independent from the BM. Our result is obtained utilizing a class of additive processes: the additive normal tempered stable (ATS). This class of processes exhibits an exceptional ability to accurately calibrate the equity volatility surface. We notice that the sub-class of additive processes that can be obtained with an independent additive subordination is incompatible with market data and shows significantly worse calibration performances than the ATS, especially on short time maturities. These results have been observed every business day in a semester on a dataset of S&P 500 and EURO STOXX 50 options.