pwc-archive/papers-with-abstracts huggingface dataset:
Using Decoupled Features for Photo-realistic Style Transfer
Authors: Trevor D. Canham, Adrián Martín, Marcelo Bertalmío, Javier Portilla
Date: 2022-12-05 00:00:00
Tasks: Style Transfer

In this work we propose a photorealistic style transfer method for image and video that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of sample statistics. The novel aspects of our approach include matching decoupled moments of higher order than in common style transfer approaches, and matching a descriptor of the power spectrum so as to characterize and transfer diffusion effects between source and target, which is something that has not been considered before in the literature. The results are of high visual quality, without spatio-temporal artifacts, and validation tests in the form of observer preference experiments show that our method compares very well with the state-of-the-art. The computational complexity of the algorithm is low, and we propose a numerical implementation that is amenable for real-time video application. Finally, another contribution of our work is to point out that current deep learning approaches for photorealistic style transfer don't really achieve photorealistic quality outside of limited examples, because the results too often show unacceptable visual artifacts.
Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound
Authors: Kyurae Kim, Simon Maskell, Jason F. Ralph
Date: 2022-12-07 00:00:00
Tasks: Direction of Arrival Estimation

Imaging methods based on array signal processing often require a fixed propagation speed of the medium, or speed of sound (SoS) for methods based on acoustic signals. The resolution of the images formed using these methods is strongly affected by the assumed SoS, which, due to multipath, nonlinear propagation, and non-uniform mediums, is challenging at best to select. In this letter, we propose a Bayesian approach to marginalize the influence of the SoS on beamformers for imaging. We adapt Bayesian direction-of-arrival estimation to an imaging setting and integrate a popular minimum variance beamformer over the posterior of the SoS. To solve the Bayesian integral efficiently, we use numerical Gauss quadrature. We apply our beamforming approach to shallow water sonar imaging where multipath and nonlinear propagation is abundant. We compare against the minimum variance distortionless response (MVDR) beamformer and demonstrate that its Bayesian counterpart achieves improved range and azimuthal resolution while effectively suppressing multipath artifacts.
Superimposed Pilot-based Channel Estimation for RIS-Assisted IoT Systems Using Lightweight Networks
Authors: Chaojin Qing, Li Wang, Lei Dong, Guowei Ling, Jiafan Wang
Date: 2022-12-07 00:00:00

Conventional channel estimation (CE) for Internet of Things (IoT) systems encounters challenges such as low spectral efficiency, high energy consumption, and blocked propagation paths. Although superimposed pilot-based CE schemes and the reconfigurable intelligent surface (RIS) could partially tackle these challenges, limited researches have been done for a systematic solution. In this paper, a superimposed pilot-based CE with the reconfigurable intelligent surface (RIS)-assisted mode is proposed and further enhanced the performance by networks. Specifically, at the user equipment (UE), the pilot for CE is superimposed on the uplink user data to improve the spectral efficiency and energy consumption for IoT systems, and two lightweight networks at the base station (BS) alleviate the computational complexity and processing delay for the CE and symbol detection (SD). These dedicated networks are developed in a cooperation manner. That is, the conventional methods are employed to perform initial feature extraction, and the developed neural networks (NNs) are oriented to learn along with the extracted features. With the assistance of the extracted initial feature, the number of training data for network training is reduced. Simulation results show that, the computational complexity and processing delay are decreased without sacrificing the accuracy of CE and SD, and the normalized mean square error (NMSE) and bit error rate (BER) performance at the BS are improved against the parameter variance.
Semantic Communication for Internet of Vehicles: A Multi-User Cooperative Approach
Authors: Wenjun Xu, Yimeng Zhang, Fengyu Wang, Zhijin Qin, Chenyao Liu, Ping Zhang
Date: 2022-12-06 00:00:00
Tasks: Image Retrieval, Retrieval, Semantic Communication

Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security. A variety of emerging applications/services bring explosively growing demands for mobile data traffic between connected vehicles and roadside units (RSU), imposing the significant challenge of spectrum scarcity to IoV. In this paper, we propose a cooperative semantic-aware architecture to convey essential semantics from collaborated users to servers for lowering the data traffic. In contrast to current solutions that are mainly based on piling up highly complex signal processing techniques and multiple access capabilities in terms of syntactic communications, this paper puts forth the idea of semantic-aware content delivery in IoV. Specifically, the successful transmission of essential semantics of the source data is pursued, rather than the accurate reception of symbols regardless of its meaning as in conventional syntactic communications. To assess the benefits of the proposed architecture, we provide a case study of the image retrieval task for vehicles in intelligent transportation systems. Simulation results demonstrate that the proposed architecture outperforms the existing solutions with fewer radio resources, especially in a low signal-to-noise-ratio (SNR) regime, which can shed light on the potential of the proposed architecture in extending the applications in extreme environments.
RISnet: a Dedicated Scalable Neural Network Architecture for Optimization of Reconfigurable Intelligent Surfaces
Authors: Bile Peng, Finn Siegismund-Poschmann, Eduard A. Jorswieck
Date: 2022-12-06 00:00:00

The reconfigurable intelligent surface (RIS) is a promising technology for next-generation wireless communication. It comprises many passive antennas, which reflect signals from the transmitter to the receiver with adjusted phases without changing the amplitude. The large number of the antennas enables a huge potential of signal processing despite the simple functionality of a single antenna. However, it also makes the RIS configuration a high dimensional problem, which might not have a closed-form solution and has a high complexity and, as a result, severe difficulty in online real-time application if we apply iterative numerical solutions. In this paper, we introduce a machine learning approach to maximize the weighted sum-rate (WSR). We propose a dedicated neural network architecture called RISNet. The RIS optimization is designed according to the RIS property of product and direct channel and homogeneous RIS antennas. The architecture is scalable due to the fact that the number of trainable parameters is independent from the number of RIS antennas (because all antennas share the same parameters). The weighted minimum mean squared error (WMMSE) precoding is applied and an alternating optimization (AO) training procedure is designed. Testing results show that the proposed approach outperforms the state-of-the-art block coordinate descent (BCD) algorithm. Moreover, although the training takes several hours, online testing with trained model (application) is almost instant, which makes it feasible for real-time application. Compared to it, the BCD algorithm requires much more convergence time. Therefore, the proposed method outperforms the state-of-the-art algorithm in both performance and complexity.
CNN-based Timing Synchronization for OFDM Systems Assisted by Initial Path Acquisition in Frequency Selective Fading Channel
Authors: Chaojin Qing, Na Yang, Shuhai Tang, Chuangui Rao, Jiafan Wang, Jinliang Chen
Date: 2022-12-06 00:00:00
Tasks: compressed sensing

Multi-path fading seriously affects the accuracy of timing synchronization (TS) in orthogonal frequency division multiplexing (OFDM) systems. To tackle this issue, we propose a convolutional neural network (CNN)-based TS scheme assisted by initial path acquisition in this paper. Specifically, the classic cross-correlation method is first employed to estimate a coarse timing offset and capture an initial path, which shrinks the TS search region. Then, a one-dimensional (1-D) CNN is developed to optimize the TS of OFDM systems. Due to the narrowed search region of TS, the CNN-based TS effectively locates the accurate TS point and inspires us to construct a lightweight network in terms of computational complexity and online running time. Compared with the compressed sensing-based TS method and extreme learning machine-based TS method, simulation results show that the proposed method can effectively improve the TS performance with the reduced computational complexity and online running time. Besides, the proposed TS method presents robustness against the variant parameters of multi-path fading channels.
Homeopathic priors?
Authors: James Theiler
Date: 2022-12-06 00:00:00

The problem of composite hypothesis testing is considered in the context of Bayesian detection of weak target signals in cluttered backgrounds. (A specific example is the detection of sub-pixel targets in multispectral imagery.) In this model, the target strength (call it $a$) is an unknown parameter, and that lack of knowledge can be addressed by incorporating a prior over possible parameter values. The performance of the detector depends on the choice of prior, and -- with the motivation of enabling better performance at low target abundances -- a family of priors are investigated in which singular weight is associated with the $a\to 0$ limit. Careful treatment of this limiting process leads to a situation in which components of the prior with infinitesimal weight have nontrivial effects. Similar claims have been made for homeopathic medicines.
Algebraic network reconstruction of discrete dynamical systems
Authors: Heather A. Harrington, Mike Stillman, Alan Veliz-Cuba
Date: 2022-12-05 00:00:00

We present a computational algebra solution to reverse engineering the network structure of discrete dynamical systems from data. We use monomial ideals to determine dependencies between variables that encode constraints on the possible wiring diagrams underlying the process generating the discrete-time, continuous-space data. Our work assumes that each variable is either monotone increasing or decreasing. We prove that with enough data, even in the presence of small noise, our method can reconstruct the correct unique wiring diagram.
A new lipid-structured model to investigate the opposing effects of LDL and HDL on atherosclerotic plaque macrophages
Authors: Keith L Chambers, Mary R Myerscough, Helen M Byrne
Date: 2022-12-07 00:00:00

Atherosclerotic plaques form in artery walls due to a chronic inflammatory response driven by lipid accumulation. A key component of the inflammatory response is the interaction between monocyte-derived macrophages and extracellular lipid. Although concentrations of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) particles in the blood are known to affect plaque progression, their impact on the lipid load of plaque macrophages remains unexplored. In this paper, we develop a lipid-structured mathematical model to investigate the impact of blood LDL/HDL levels on plaque composition, and lipid distribution in plaque macrophages. A reduced subsystem, derived by summing the equations of the full model, describes the dynamics of biophysical quantities relating to plaque composition (e.g. total number of macrophages, total amount of intracellular lipid). We also derive a continuum approximation of the model to facilitate analysis of the macrophage lipid distribution. The results, which include time-dependent numerical solutions and asymptotic analysis of the unique steady state solution, indicate that plaque lipid content is sensitive to the influx of LDL relative to HDL capacity. The macrophage lipid distribution evolves in a wave-like manner towards an equilibrium profile which may be monotone decreasing, quasi-uniform or unimodal, attaining its maximum value at a non-zero lipid level. Our model also reveals that macrophage uptake may be severely impaired by lipid accumulation. We conclude that lipid accumulation in plaque macrophages may serve as a partial explanation for the defective uptake of apoptotic cells (efferocytosis) often reported in atherosclerotic plaques.
Ancestry-specific analyses of genome-wide data confirm the settlement sequence of Polynesia
Authors: Alexander G. Ioannidis, Javier Blanco-Portillo, Erika Hagelberg, Juan Esteban Rodríguez-Rodríguez, Keolu Fox, Adrian V. S. Hill, Carlos D. Bustamante, Marcus W. Feldman, Alexander J. Mentzer, Andrés Moreno-Estrada
Date: 2022-12-06 00:00:00

By demonstrating the role that historical population replacements and waves of admixture have played around the world, the genetics work of Reich and colleagues has provided a paradigm for understanding human history [Reich et al. 2009; Reich et al. 2012; Patterson et al. 2012]. Although we show in Ioannidis et al. [2021] that the peopling of Polynesia was a range expansion, and not, as suggested by Huang et al. [2022], yet another example of waves of admixture and large-scale gene flow between populations, we believe that our result in this recently settled oceanic expanse is the exception that proves the rule.
Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process
Authors: Jinlu Liu, Sara Wade, Natalia Bochkina
Date: 2022-12-05 00:00:00
Tasks: Clustering, Imputation

Single-cell RNA sequencing (scRNA-seq) is powerful technology that allows researchers to understand gene expression patterns at the single-cell level. However, analysing scRNA-seq data is challenging due to issues and biases in data collection. In this work, we construct an integrated Bayesian model that simultaneously addresses normalization, imputation and batch effects and also nonparametrically clusters cells into groups across multiple datasets. A Gibbs sampler based on a finite-dimensional approximation of the HDP is developed for posterior inference.
Complete cavity map of the C. elegans connectome
Authors: Bo Liu, Rongmei Yang, Hao Wang, Linyuan Lü
Date: 2022-12-07 00:00:00

Network structure or topology is the basis for understanding complex systems. Recently, higher-order structures have been considered as a new research direction that can provide new perspectives and phenomena. However, most existing studies have focused on simple 1-cycles and triangles, and few have explored the higher-order and non-trivial cycles. The current study focused on the cavity, which is a non-trivial cycle. We proposed a method to compute cavities with different orders based on pruning in the boundary matrix and calculated all cavities of the neural network of Caenorhabditis elegans (\emph{C. elegans}) neural network. This study reports for the first time a complete cavity map of C. elegans neural network, developing a new method for mining higher-order structures that can be applied by researchers in neuroscience, network science and other interdisciplinary fields to explore higher-order structural markers of complex systems.
On the emergence of agency
Authors: Aliza T. Sloan, J. A. Scott Kelso
Date: 2022-12-06 00:00:00

How do human beings make sense of their relation to the world and realize their ability to effect change? Applying modern concepts and methods of coordination dynamics we demonstrate that patterns of movement and coordination in 3-4 month-olds may be used to identify states and behavioral phenotypes of emergent agency. By means of a complete coordinative analysis of baby and mobile motion and their interaction, we show that the emergence of agency takes the form of a punctuated self-organizing process, with meaning found both in movement and stillness.
Effects of Racial Segregation on Economic Productivity in U.S. Cities
Authors: Andrew J. Stier, Sina Sajjadi, Luıs M. A. Bettencourt, Fariba Karimi, Marc G. Berman
Date: 2022-12-06 00:00:00

Homophily and heterophobia, the tendency for people with similar characteristics to preferentially interact with (or avoid) each other are pervasive in human social networks. Here, we develop an extension of the mathematical theory of urban scaling which describes the effects of homophily and heterophobia on social interactions and resulting economic outputs of cities. Empirical tests of our model show that increased residential racial heterophobia and segregation in U.S. cities are associated with reduced economic outputs and that the strength of this relationship increased throughout the 2010s. Our findings provide the means for the formal incorporation of general homophilic and heterophobic effects into theories of modern urban science and suggest that racial segregation is increasingly and adversely impacting the economic performance and connectivity of urban societies in the U.S.
Genetic Sequence compression using Machine Learning and Arithmetic Encoding Decoding Techniques
Authors: Mehedi Hasan Sarkar, Adnan Ferdous Ashrafi
Date: 2022-12-06 00:00:00

We live in a period where bio-informatics is rapidly expanding, a significant quantity of genomic data has been produced as a result of the advancement of high-throughput genome sequencing technology, raising concerns about the costs associated with data storage and transmission. The question of how to properly compress data from genomic sequences is still open. Previously many researcher proposed many compression method on this topic DNA Compression without machine learning and with machine learning approach. Extending a previous research, we propose a new architecture like modified DeepDNA and we have propose a new methodology be deploying a double base-ed strategy for compression of DNA sequences. And validated the results by experimenting on three sizes of datasets are 100, 243, 356. The experimental outcomes highlight our improved approach's superiority over existing approaches for analyzing the human mitochondrial genome data, such as DeepDNA.
The human antigen R as an actionable super-hub within the network of cancer cell persistency and plasticity
Authors: Rastine Merat
Date: 2022-12-06 00:00:00

In this perspective article, a clinically inspired phenotype-driven experimental approach is put forward to address the challenge of the adaptive response of solid cancers to small-molecule targeted therapies. A list of conditions is derived, including an experimental quantitative assessment of cell plasticity and an information theory-based detection of in vivo dependencies, for the discovery of post-transcriptional druggable mechanisms capable of preventing at multiple levels the emergence of plastic dedifferentiated slow-proliferating cells. The approach is illustrated by the author's own work in the example case of the adaptive response of BRAFV600-melanoma to BRAF inhibition. A bench-to-bedside and back to bench effort leads to a therapeutic strategy in which the inhibition of the baseline activity of the interferon-gamma-activated inhibitor of translation (GAIT) complex, incriminated in the expression insufficiency of the RNA-binding protein HuR in a minority of cells, results in the suppression of the plastic, intermittently slow-proliferating cells involved in the adaptive response. A similar approach is recommended for the validation of other classes of mechanisms that we seek to modulate to overcome this complex challenge of modern cancer therapy.
Asymptotic Analysis for Optimal Dividends in a Dual Risk Model
Authors: Arash Fahim, Lingjiong Zhu
Date: 2016-01-13 00:00:00

The dual risk model is a popular model in finance and insurance, which is often used to model the wealth process of a venture capital or high tech company. Optimal dividends have been extensively studied in the literature for a dual risk model. It is well known that the value function of this optimal control problem does not yield closed-form solutions except in some special cases. In this paper, we study the asymptotics of the optimal dividend problem when the parameters of the model go to either zero or infinity. Our results provide insights to the optimal strategies and the optimal values when the parameters are extreme.
From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems
Authors: Tom Young
Date: 2022-11-14 00:00:00

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.
Synthetic Expertise
Authors: Ron Fulbright, Grover Walters
Date: 2022-11-11 00:00:00

We will soon be surrounded by artificial systems capable of cognitive performance rivaling or exceeding a human expert in specific domains of discourse. However, these cogs need not be capable of full general artificial intelligence nor able to function in a stand-alone manner. Instead, cogs and humans will work together in collaboration each compensating for the weaknesses of the other and together achieve synthetic expertise as an ensemble. This paper reviews the nature of expertise, the Expertise Level to describe the skills required of an expert, and knowledge stores required by an expert. By collaboration, cogs augment human cognitive ability in a human/cog ensemble. This paper introduces six Levels of Cognitive Augmentation to describe the balance of cognitive processing in the human/cog ensemble. Because these cogs will be available to the mass market via common devices and inexpensive applications, they will lead to the Democratization of Expertise and a new cognitive systems era promising to change how we live, work, and play. The future will belong to those best able to communicate, coordinate, and collaborate with cognitive systems.
Caching Contents with Varying Popularity using Restless Bandits
Authors: Pavamana K J, Chandramani Kishore Singh
Date: 2022-10-31 00:00:00

Mobile networks are experiencing prodigious increase in data volume and user density , which exerts a great burden on mobile core networks and backhaul links. An efficient technique to lessen this problem is to use caching i.e. to bring the data closer to the users by making use of the caches of edge network nodes, such as fixed or mobile access points and even user devices. The performance of a caching depends on contents that are cached. In this paper, we examine the problem of content caching at the wireless edge(i.e. base stations) to minimize the discounted cost incurred over infinite horizon. We formulate this problem as a restless bandit problem, which is hard to solve. We begin by showing an optimal policy is of threshold type. Using these structural results, we prove the indexability of the problem, and use Whittle index policy to minimize the discounted cost.