pwc-archive/papers-with-abstracts huggingface dataset:
Perceptual Quality Assessment of HEVC and VVC Standards for 8K Video
Authors: Charles Bonnineau, Wassim Hamidouche, Jerome Fournier, Naty Sidaty, Jean-Francois Travers, Olivier Deforges
Date: 2021-09-14 00:00:00
Tasks: 4k, 8k, MS-SSIM, SSIM, Video Compression

With the growing data consumption of emerging video applications and users requirement for higher resolutions, up to 8K, a huge effort has been made in video compression technologies. Recently, versatile video coding (VVC) has been standardized by the moving picture expert group (MPEG), providing a significant improvement in compression performance over its predecessor high efficiency video coding (HEVC). In this paper, we provide a comparative subjective quality evaluation between VVC and HEVC standards for 8K resolution videos. In addition, we evaluate the perceived quality improvement offered by 8K over UHD 4K resolution. The compression performance of both VVC and HEVC standards has been conducted in random access (RA) coding configuration, using their respective reference software, VVC test model (VTM-11) and HEVC test model (HM-16.20). Objective measurements, using PSNR, MS-SSIM and VMAF metrics have shown that the bitrate gains offered by VVC over HEVC for 8K video content are around 31%, 26% and 35%, respectively. Subjectively, VVC offers an average of 40% of bitrate reduction over HEVC for the same visual quality. A compression gain of 50% has been reached for some tested video sequences regarding a Student t-test analysis. In addition, for most tested scenes, a significant visual difference between uncompressed 4K and 8K has been noticed.
WINNet: Wavelet-inspired Invertible Network for Image Denoising
Authors: Jun-Jie Huang, Pier Luigi Dragotti
Date: 2021-09-14 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring, Image Denoising, Noise Estimation

Image denoising aims to restore a clean image from an observed noisy image. The model-based image denoising approaches can achieve good generalization ability over different noise levels and are with high interpretability. Learning-based approaches are able to achieve better results, but usually with weaker generalization ability and interpretability. In this paper, we propose a wavelet-inspired invertible network (WINNet) to combine the merits of the wavelet-based approaches and learningbased approaches. The proposed WINNet consists of K-scale of lifting inspired invertible neural networks (LINNs) and sparsity-driven denoising networks together with a noise estimation network. The network architecture of LINNs is inspired by the lifting scheme in wavelets. LINNs are used to learn a non-linear redundant transform with perfect reconstruction property to facilitate noise removal. The denoising network implements a sparse coding process for denoising. The noise estimation network estimates the noise level from the input image which will be used to adaptively adjust the soft-thresholds in LINNs. The forward transform of LINNs produce a redundant multi-scale representation for denoising. The denoised image is reconstructed using the inverse transform of LINNs with the denoised detail channels and the original coarse channel. The simulation results show that the proposed WINNet method is highly interpretable and has strong generalization ability to unseen noise levels. It also achieves competitive results in the non-blind/blind image denoising and in image deblurring.
Transoceanic Phase and Polarization Fiber Sensing using Real-Time Coherent Transceiver
Authors: Mikael Mazur, Jorge C. Castellanos, Roland Ryf, Erik Borjeson, Tracy Chodkiewicz, Valey Kamalov, Shuang Yin, Nicolas K. Fontaine, Haoshuo Chen, Lauren Dallachiesa, Steve Corteselli, Philip Copping, Jurgen Gripp, Aurelien Mortelette, Benoit Kowalski, Rodney Dellinger, David T. Neilson, Per Larsson-Edefors
Date: 2021-09-14 00:00:00

We implement a real-time coherent transceiver with fast streaming outputs for environmental sensing. Continuous sensing using phase and equalizer outputs over 12800km of a submarine cable is demonstrated to enable time resolved spectroscopy in broad spectral range of 10mHz - 1kHz.
Acoustic Source Localization in Shallow Water: A Probabilistic Focalization Approach
Authors: Florian Meyer, Kay L. Gemba
Date: 2021-09-14 00:00:00

This paper presents a Bayesian estimation method for the passive localization of an acoustic source in shallow water. Our probabilistic focalization approach estimates the time-varying source location by associating direction of arrival (DOA) observations to DOAs predicted based on a statistical model. Embedded ray tracing makes it possible to incorporate environmental parameters and characterize the nonlinear acoustic waveguide. We demonstrate performance advantages of our approach compared to matched field processing using data collected during the SWellEx-96 experiment.
Transient Performance Analysis of the $\ell_1$-RLS
Authors: Wei Gao, Jie Chen, Cédric Richard, Wentao Shi, Qunfei Zhang
Date: 2021-09-14 00:00:00

The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems. Nevertheless few works have studied its stochastic behavior, in particular its transient performance. In this letter, we derive analytical models of the transient behavior of the $\ell_1$-RLS in the mean and mean-square sense. Simulation results illustrate the accuracy of these models.
Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization
Authors: S. Shi, Y. Cai, Q. Hu, B. Champagne, L. Hanzo
Date: 2021-09-14 00:00:00

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital (AD) beamforming can be used to attain a high directional gain without requiring a dedicated radio frequency (RF) chain for each antenna element, which substantially reduces both the hardware costs and power consumption. While massive MIMO transceiver design typically relies on the conventional mean-square error (MSE) criterion, directly minimizing the symbol error rate (SER) can lead to a superior performance. In this paper, we first mathematically formulate the problem of hybrid transceiver design under the minimum SER (MSER) optimization criterion and then develop a MSER-based gradient descent (GD) iterative algorithm to find the related stationary points. We then propose a deep-unfolding neural network (NN), in which the iterative GD algorithm is unfolded into a multi-layer structure wherein a set of trainable parameters are introduced for accelerating the convergence and enhancing the overall system performance. To implement the training stage, the relationship between the gradients of adjacent layers is derived based on the generalized chain rule (GCR). The deep-unfolding NN is developed for both quadrature phase shift keying (QPSK) and for $M$-ary quadrature amplitude modulated (QAM) signals and its convergence is investigated theoretically. Furthermore, we analyze the transfer capability, computational complexity, and generalization capability of the proposed deep-unfolding NN. Our simulation results show that the latter significantly outperforms its conventional counterpart at a reduced complexity.
Potential of the Sterile Insect Technique for Control of Deer Ticks, $\textit{Ixodes scapularis}$
Authors: Thomas G. Kirby, Julie C. Blackwood
Date: 2021-09-13 00:00:00

The deer tick, $\textit{Ixodes scapularis}$, is a vector for numerous human diseases, including Lyme disease, anaplasmosis, and babesiosis. Concern is rising in the US and abroad as the population and range of this species grow and new diseases emerge. Herein I consider the potential for control of $\textit{I. scapularis}$ using the Sterile Insect Technique (SIT), which acts by reducing net fertility through release of sterile males. I construct a population model with density-dependent and -independent growth, migration, and an Allee effect (decline of the population when it is small), and use this model to simulate sterile tick release in both single- and multi-patch frameworks. I test two key concerns with implementing $\textit{I. scapularis}$ SIT: that the ticks' lengthy life course could make control take too long and that low migration might mean sterile males need thorough manual dispersal to all parts of the control area. Results suggest that typical $\textit{I. scapularis}$ SIT programs will take about eight years, a prediction near the normal range for the technique, but that thorough distribution of sterile ticks over the control area is indeed critical, increasing expense substantially by necessitating aerial release. With particularly high rearing costs also expected for $\textit{I. scapularis}$, the latter finding suggests that cost-effectiveness improvements to aerial release may be a prerequisite to $\textit{I. scapularis}$ SIT.
Techniques of Model Reductions in Biochemical Cell Signaling Pathways
Authors: Hemn Mohammed Rasool, Sarbaz H. A. Khoshnaw
Date: 2021-09-14 00:00:00
Tasks: Translation

There are many mathematical models of biochemical cell signaling pathways that contain a large number of elements (species and reactions). This is sometimes a big issue for identifying critical model elements and describing the model dynamics. Thus, techniques of model reduction can be used as a mathematical tool in order to minimize the number of variables and parameters. In this thesis, we review some well-known methods of model reduction for cell signaling pathways. We have also developed some approaches that provide us a great step forward in model reduction. The techniques are quasi steady state approximation (QSSA), quasi equilibrium approximation (QEA), lumping of species and entropy production analysis. They are applied on protein translation pathways with microRNA mechanisms, chemical reaction networks, extracellular signal regulated kinase (ERK) pathways, NFkB signal transduction pathways, elongation factors EFTu and EFTs signaling pathways and Dihydrofolate reductase (DHFR) pathways. The main aim of this thesis is to reduce the complex cell signaling pathway models. This provides one a better understanding of the dynamics of such models and gives an accurate approximate solution. Results show that there is a good agreement between the original models and the simplified models.
Common Complexes of Decompositions and Complex Balanced Equilibria of Chemical Reaction Networks
Authors: Lauro L. Fontanil, Eduardo R. Mendoza
Date: 2021-09-13 00:00:00

A decomposition of a chemical reaction network (CRN) is produced by partitioning its set of reactions. The partition induces networks, called subnetworks, that are "smaller" than the given CRN which, at this point, can be called parent network. A complex is called a common complex if it occurs in at least two subnetworks in a decomposition. A decomposition is said to be incidence independent if the image of the incidence map of the parent network is the direct sum of the images of the subnetworks' incidence maps. It has been recently discovered that the complex balanced equilibria of the parent network and its subnetworks are fundamentally connected in an incidence independent decomposition. In this paper, we utilized the set of common complexes and a developed criterion to investigate decomposition's incidence independence properties. A framework was also developed to analyze decomposition classes with similar structure and incidence independence properties. We identified decomposition classes that can be characterized by their sets of common complexes and studied their incidence independence. Some of these decomposition classes occur in some biological and chemical models. Finally, a sufficient condition was obtained for the complex balancing of some power law kinetic (PLK) systems with incidence independent and complex balanced decompositions. This condition led to a generalization of the Defficiency Zero Theorem for some PLK systems.
Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy
Authors: Gabrielle M. Schroeder, Fahmida A. Chowdhury, Mark J. Cook, Beate Diehl, John S. Duncan, Philippa J. Karoly, Peter N. Taylor, Yujiang Wang
Date: 2021-09-14 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

A seizure's electrographic dynamics are characterised by its spatiotemporal evolution, also termed dynamical "pathway" and the time it takes to complete that pathway, which results in the seizure's duration. Both seizure pathways and durations can vary within the same patient, producing seizures with different dynamics, severity, and clinical implications. However, it is unclear whether seizures following the same pathway will have the same duration or if these features can vary independently. We compared within-subject variability in these seizure features using 1) epilepsy monitoring unit intracranial EEG (iEEG) recordings of 31 patients (mean 6.7 days, 16.5 seizures/subject), 2) NeuroVista chronic iEEG recordings of 10 patients (mean 521.2 days, 252.6 seizures/subject), and 3) chronic iEEG recordings of 3 dogs with focal-onset seizures (mean 324.4 days, 62.3 seizures/subject). While the strength of the relationship between seizure pathways and durations was highly subject-specific, in most subjects, changes in seizure pathways were only weakly to moderately associated with differences in seizure durations. The relationship between seizure pathways and durations was weakened by seizures that 1) had a common pathway, but different durations ("elastic pathways"), or 2) had similar durations, but followed different pathways ("duplicate durations"). Even in subjects with distinct populations of short and long seizures, seizure durations were not a reliable indicator of different seizure pathways. These findings suggest that seizure pathways and durations are modulated by different processes. Uncovering such modulators may reveal novel therapeutic targets for reducing seizure duration and severity.
I wanna draw like you: Inter-and intra-individual differences in orang-utan drawings
Authors: Marie Pelé, Gwendoline Thomas, Alaïs Liénard, Nagi Eguchi, Masaki Shimada, Cédric Sueur
Date: 2021-09-14 00:00:00

Recently discovered, the oldest human abstract drawing is around 73,000 years. Although the origins of drawing behaviour have remained an enigma to this day, light may be shone on the subject through its study among our closest neighbours, the great apes. This study analyses 749 drawings of five female Bornean orang-utans (Pongo pygmaeus) at Tama Zoological Park in Japan. We searched for differences between individuals but also tried to identify possible temporal changes among the drawings of one individual, Molly, who drew almost 1,300 drawings from 2006 to 2016. A classical analysis of the drawings was carried out after collecting quantitative and qualitative variables. Our findings reveal evidence of differences in the drawing style of the five individuals as well as creative changes in Molly's drawing style throughout her lifetime. Individuals differed in terms of the colours used, the space they filled but also the shapes (fan patterns, circles or loops) they drew. Molly drew less and less as she grew older, and we found a significant difference between drawings produced in winter, when orang-utans were kept inside and had less activity, and those produced during other seasons. Our results suggest that the drawing behaviour of these five orangutans is not random and that differences among individuals might reflect differences of styles, states of mind but also motivation to draw. These novel results are a significant contribution to our understanding of how drawing behaviour evolved in hominids.
Evolutionary dynamics of zero-determinant strategies in repeated multiplayer games
Authors: Fang Chen, Te Wu, Long Wang
Date: 2021-09-14 00:00:00

Since Press and Dyson's ingenious discovery of ZD (zero-determinant) strategy in the repeated Prisoner's Dilemma game, several studies have confirmed the existence of ZD strategy in repeated multiplayer social dilemmas. However, few researches study the evolutionary performance of multiplayer ZD strategies, especially from a theoretical perspective. Here, we use a newly proposed state-clustering method to theoretically analyze the evolutionary dynamics of two representative ZD strategies: generous ZD strategies and extortionate ZD strategies. Apart from the competitions between the two strategies and some classical strategies, we consider two new settings for multiplayer ZD strategies: competitions in the whole ZD strategy space and competitions in the space of all memory-1 strategies. Besides, we investigate the influence of level of generosity and extortion on the evolutionary dynamics of generous and extortionate ZD, which was commonly ignored in previous studies. Theoretical results show players with limited generosity are at an advantageous place and extortioners extorting more severely hold their ground more readily. Our results may provide new insights into better understanding the evolutionary dynamics of ZD strategies in repeated multiplayer games.
Trade-offs between chemotaxis and proliferation shape the phenotypic structuring of invading waves
Authors: Tommaso Lorenzi, Kevin J Painter
Date: 2021-09-08 00:00:00

Chemotaxis-driven invasions have been proposed across a broad spectrum of biological processes, from cancer to ecology. The influential system of equations introduced by Keller and Segel has proven a popular choice in the modelling of such phenomena, but in its original form restricts to a homogeneous population. To account for the possibility of phenotypic heterogeneity, we extend to the case of a population continuously structured across space, time and phenotype, where the latter determines variation in chemotactic responsiveness, proliferation rate, and the level of chemical environment modulation. The extended model considered here comprises a non-local partial differential equation for the local phenotype distribution of cells which is coupled, through an integral term, with a differential equation for the concentration of an attractant, which is sensed and degraded by the cells. In the framework of this model, we concentrate on a chemotaxis/proliferation trade-off scenario, where the cell phenotypes span a spectrum of states from highly-chemotactic but minimally-proliferative to minimally-chemotactic but highly-proliferative. Using a combination of numerical simulation and formal asymptotic analysis, we explore the properties of travelling-wave solutions. The results of our study demonstrate how incorporating phenotypic heterogeneity may lead to a highly-structured wave profile, where cells in different phenotypic states dominate different spatial positions across the invading wave, and clarify how the phenotypic structuring of the wave can be shaped by trade-offs between chemotaxis and proliferation.
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs
Authors: Bing He, Yao Xiao, Haodong Liang, Qianhui Huang, Yuheng Du, Yijun Li, David Garmire, Duxin Sun, Lana X. Garmire
Date: 2021-09-14 00:00:00
Tasks: Drug Response Prediction

Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose a new drug recommendation system called: A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD defines a novel drug score predicting drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. We tested ASGARD on multiple diseases, including breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC of 0.92) compared to two other bulk-cell-based drug repurposing methods (AUC of 0.80 and 0.76). It is also considerably better (AUC of 0.82) than other cell cluster level predicting methods (AUC of 0.67 and 0.55). In addition, ASGARD is also validated by the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. Many top-ranked drugs are either approved by FDA or in clinical trials treating corresponding diseases. In silico cell-type specific drop-out experiments using triple-negative breast cancers show the importance of T cells in the tumor microenvironment in affecting drug predictions. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD.
How to address cellular heterogeneity by distribution biology
Authors: Niko Komin, Alexander Skupin
Date: 2021-09-13 00:00:00
Tasks: Diversity

Cellular heterogeneity is an immanent property of biological systems that covers very different aspects of life ranging from genetic diversity to cell-to-cell variability driven by stochastic molecular interactions, and noise induced cell differentiation. Here, we review recent developments in characterizing cellular heterogeneity by distributions and argue that understanding multicellular life requires the analysis of heterogeneity dynamics at single cell resolution by integrative approaches that combine methods from non-equilibrium statistical physics, information theory and omics biology.
A consumption-investment model with state-dependent lower bound constraint on consumption
Authors: Chonghu Guan, Zuo Quan Xu, Fahuai Yi
Date: 2021-09-14 00:00:00

This paper studies a life-time consumption-investment problem under the Black-Scholes framework, where the consumption rate is subject to a lower bound constraint that linearly depends on her wealth. It is a stochastic control problem with state-dependent control constraint to which the standard stochastic control theory cannot be directly applied. We overcome this by transforming it into an equivalent stochastic control problem in which the control constraint is state-independent so that the standard theory can be applied. We give an explicit optimal consumption-investment strategy when the constraint is homogeneous. When the constraint is non-homogeneous, it is shown that the value function is third-order continuously differentiable by differential equation approach, and a feedback form optimal consumption-investment strategy is provided. According to our findings, if one is concerned with long-term more than short-term consumption, then she should always consume as few as possible; otherwise, she should consume optimally when her wealth is above a threshold, and consume as few as possible when her wealth is below the threshold.