pwc-archive/papers-with-abstracts huggingface dataset:
Transfer Generative Adversarial Networks (T-GAN)-based Terahertz Channel Modeling
Authors: Zhengdong Hu, Yuanbo Li, Chong Han
Date: 2023-01-03 00:00:00
Tasks: Generative Adversarial Network, SSIM, Transfer Learning

Terahertz (THz) communications are envisioned as a promising technology for 6G and beyond wireless systems, providing ultra-broad bandwidth and thus Terabit-per-second (Tbps) data rates. However, as foundation of designing THz communications, channel modeling and characterization are fundamental to scrutinize the potential of the new spectrum. Relied on physical measurements, traditional statistical channel modeling methods suffer from the problem of low accuracy with the assumed certain distributions and empirical parameters. Moreover, it is time-consuming and expensive to acquire extensive channel measurement in the THz band. In this paper, a transfer generative adversarial network (T-GAN) based modeling method is proposed in the THz band, which exploits the advantage of GAN in modeling the complex distribution, and the benefit of transfer learning in transferring the knowledge from a source task to improve generalization about the target task with limited training data. Specifically, to start with, the proposed GAN is pre-trained using the simulated dataset, generated by the standard channel model from 3rd generation partnerships project (3GPP). Furthermore, by transferring the knowledge and fine-tuning the pre-trained GAN, the T-GAN is developed by using the THz measured dataset with a small amount. Experimental results reveal that the distribution of PDPs generated by the proposed T-GAN method shows good agreement with measurement. Moreover, T-GAN achieves good performance in channel modeling, with 9 dB improved root-mean-square error (RMSE) and higher Structure Similarity Index Measure (SSIM), compared with traditional 3GPP method.
Interpretation and Analysis of the Steady-State Neural Response to Complex Sequential Structures: a Methodological Note
Authors: Nai Ding
Date: 2023-01-03 00:00:00

Frequency tagging is a powerful approach to investigate the neural processing of sensory features, and is recently adapted to study the neural correlates of superordinate structures, i.e., chunks, in complex sequences such as speech and music. The nesting of sequence structures, the necessity to control the periodicity in sensory features, and the low-frequency nature of sequence structures pose new challenges for data analysis and interpretation. Here, I discuss how to interpret the frequency of a sequential structure, and factors that need to be considered when analyzing the periodicity in a signal. Finally, a safe procedure is recommended for the analysis of frequency-tagged responses.
Optimal Grid Layouts for Hybrid Offshore Assets in the North Sea under Different Market Designs
Authors: Stephen Hardy, Andreas Themelis, Kaoru Yamamoto, Hakan Ergun, Dirk Van Hertem
Date: 2023-01-03 00:00:00

This work examines the Generation and Transmission Expansion (GATE) planning problem of offshore grids under different market clearing mechanisms: a Home Market Design (HMD), a zonal cleared Offshore Bidding Zone (zOBZ) and a nodal cleared Offshore Bidding Zone (nOBZ). It aims at answering two questions. 1) Is knowing the market structure a priori necessary for effective generation and transmission expansion planning? 2) Which market mechanism results in the highest overall social welfare? To this end a multi-period, stochastic GATE planning formulation is developed for both nodal and zonal market designs. The approach considers the costs and benefits among stake-holders of Hybrid Offshore Assets (HOA) as well as gross consumer surplus (GCS). The methodology is demonstrated on a North Sea test grid based on projects from the European Network of Transmission System Operators' (ENTSO-E) Ten Year Network Development Plan (TYNDP). An upper bound on potential social welfare in zonal market designs is calculated and it is concluded that from a generation and transmission perspective, planning under the assumption of an nOBZ results in the best risk adjusted return.
High Altitude Platform Station (HAPS)-Aided GNSS for Urban Areas
Authors: Hongzhao Zheng, Mohamed Atia, Halim Yanikomeroglu
Date: 2023-01-02 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Today the global averaged civilian positioning accuracy is still at meter level for all existing Global Navigation Satellite Systems (GNSSs), and the civilian positioning performance is even worse in regions such as the Arctic region and the urban areas. In this work, we examine the positioning performance of the High Altitude Platform Station (HAPS)-aided GPS system in an urban area via both simulation and physical experiment. HAPS can support GNSS in many ways, herein we treat the HAPS as an additional ranging source. From both simulation and experiment results, we can observe that HAPS can improve the horizontal dilution of precision (HDOP) and the 3D positioning accuracy. The simulated positioning performance of the HAPS-aided GPS system is subject to the estimation accuracy of the receiver clock offset. This work also presents the future work and challenges in modelling the pseudorange of HAPS.
Analysis of a HAPS-Aided GNSS in Urban Areas using a RAIM Algorithm
Authors: Hongzhao Zheng, Mohamed Atia, Halim Yanikomeroglu
Date: 2023-01-02 00:00:00

The global averaged civilian positioning accuracy is still at meter level for all existing Global Navigation Satellite Systems (GNSSs), and the performance is even worse in urban areas. At lower altitudes than satellites, high altitude platform stations (HAPS) offer several benefits, such as lower latency, less pathloss, and likely smaller overall estimation error for the parameters associated in the pseudorange equation. HAPS can support GNSSs in many ways, and in this paper we treat the HAPS as another type of ranging source. In so doing, we examine the positioning performance of a HAPS-aided GPS system in an urban area using both a simulation and physical experiment. The HAPS measurements are unavailable today; therefore, they are modeled in a rather simple but logical manner in both the simulation and physical experiment. We show that the HAPS can improve the horizontal dilution of precision (HDOP), the vertical dilution of precision (VDOP), and the 3D positioning accuracy of GPS in both suburban and dense urban areas. We also demonstrate the applicability of a RAIM algorithm for the HAPS-aided GPS system, especially in the dense urban area.
A plug-in graph neural network to boost temporal sensitivity in fMRI analysis
Authors: Irmak Sivgin, Hasan A. Bedel, Şaban Öztürk, Tolga Çukur
Date: 2023-01-01 00:00:00
Tasks: Functional Connectivity, Graph Neural Network, Sensitivity, Time Series, Time Series Analysis

Learning-based methods have recently enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) time series. Deep learning models that receive as input functional connectivity (FC) features among brain regions have been commonly adopted in the literature. However, many models focus on temporally static FC features across a scan, reducing sensitivity to dynamic features of brain activity. Here, we describe a plug-in graph neural network that can be flexibly integrated into a main learning-based fMRI model to boost its temporal sensitivity. Receiving brain regions as nodes and blood-oxygen-level-dependent (BOLD) signals as node inputs, the proposed GraphCorr method leverages a node embedder module based on a transformer encoder to capture temporally-windowed latent representations of BOLD signals. GraphCorr also leverages a lag filter module to account for delayed interactions across nodes by computing cross-correlation of windowed BOLD signals across a range of time lags. Information captured by the two modules is fused via a message passing algorithm executed on the graph, and enhanced node features are then computed at the output. These enhanced features are used to drive a subsequent learning-based model to analyze fMRI time series with elevated sensitivity. Comprehensive demonstrations on two public datasets indicate improved classification performance and interpretability for several state-of-the-art graphical and convolutional methods that employ GraphCorr-derived feature representations of fMRI time series as their input.
Learn to Rapidly and Robustly Optimize Hybrid Precoding
Authors: Ortal Lavi, Nir Shlezinger
Date: 2023-01-01 00:00:00

Hybrid precoding plays a key role in realizing massive multiple-input multiple-output (MIMO) transmitters with controllable cost. MIMO precoders are required to frequently adapt based on the variations in the channel conditions. In hybrid MIMO, here precoding is comprised of digital and analog beamforming, such an adaptation involves lengthy optimization and depends on accurate channel state information (CSI). This affects the spectral efficiency when the channel varies rapidly and when operating with noisy CSI. In this work we employ deep learning techniques to learn how to rapidly and robustly optimize hybrid precoders, while being fully interpretable. We leverage data to learn iteration-dependent hyperparameter settings of projected gradient sum-rate optimization with a predefined number of iterations. The algorithm maps channel realizations into hybrid precoding settings while preserving the interpretable flow of the optimizer and improving its convergence speed. To cope with noisy CSI, we learn to optimize the minimal achievable sum-rate among all tolerable errors, proposing a robust hybrid precoding based on the projected conceptual mirror prox minimax optimizer. Numerical results demonstrate that our approach allows using over ten times less iterations compared to that required by conventional optimization with shared hyperparameters, while achieving similar and even improved sum-rate performance.
Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security
Authors: Fuwang Dong, Wei Wang, Xin Li, Fan Liu, Sheng Chen, Lajos Hanzo
Date: 2023-01-01 00:00:00

The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation. In this paper, we propose sophisticated beamforming designs for multi-user DFRC systems by additionally taking the physical layer security (PLS) into account. We show that appropriately designed radar waveforms can also act as the traditional artificial noise conceived for drowning out the eavesdropping channel and for attaining increased design degrees of freedom (DoF). The joint beamforming design is formulated as a non-convex optimization problem for striking a compelling trade-off amongst the conflicting design objectives of radar transmit beampattern, communication quality of service (QoS), and the PLS level. Then, we propose a semidefinite relaxation (SDR)-based algorithm and a reduced-complexity version to tackle the non-convexity, where the globally optimal solutions are found. Moreover, a robust beamforming method is also developed for considering realistic imperfect channel state information (CSI) knowledge. Finally, simulation results are provided for corroborating our theoretical results and show the proposed methods' superiority.
The Functional Wiener Filter
Authors: Benjamin Colburn, Luis G. Sanchez Giraldo, Jose C. Principe
Date: 2022-12-31 00:00:00
Tasks: Time Series, Time Series Analysis

This paper presents a close form solution in Reproducing Kernel Hilbert Space (RKHS) for the famed Wiener filter, which we called the functional Wiener filter(FWF). Instead of using the Wiener-Hopf factorization theory, here we define a new lagged RKHS that embeds signal statistics based on the correntropy function. In essence, we extend Parzen$'$s work on the autocorrelation function RKHS to nonlinear functional spaces. The FWF derivation is also quite different from kernel adaptive filtering (KAF) algorithms, which utilize a search approach. The analytic FWF solution is derived in the Gaussian kernel RKHS with a constant computational complexity similar to the Wiener solution, and never composes nor employs the error as in conventional optimal modeling. Because of the lack of congruence between the Gaussian RKHS and the space of time series, we compare performance of two pre-imaging algorithms: a fixed-point optimization (FWFFP) that finds and approximate solution in the RKHS, and a local model implementation named FWFLM. The experimental results show that the FWF performance is on par with the KAF for time series modeling, and it requires far less computation.
Activity Detection for Grant-Free NOMA in Massive IoT Networks
Authors: Mehrtash Mehrabi, Mostafa Mohammadkarimi, Masoud Ardakani
Date: 2022-12-23 00:00:00
Tasks: Action Detection, Activity Detection

Recently, grant-free transmission paradigm has been introduced for massive Internet of Things (IoT) networks to save both time and bandwidth and transmit the message with low latency. In order to accurately decode the message of each device at the base station (BS), first, the active devices at each transmission frame must be identified. In this work, first we investigate the problem of activity detection as a threshold comparing problem. We show the convexity of the activity detection method through analyzing its probability of error which makes it possible to find the optimal threshold for minimizing the activity detection error. Consequently, to achieve an optimum solution, we propose a deep learning (DL)-based method called convolutional neural network (CNN)-activity detection (AD). In order to make it more practical, we consider unknown and time-varying activity rate for the IoT devices. Our simulations verify that our proposed CNN-AD method can achieve higher performance compared to the existing non-Bayesian greedy-based methods. This is while existing methods need to know the activity rate of IoT devices, while our method works for unknown and even time-varying activity rates
Symmetries and normalization in 3-compartment epidemic models I: The replacement number dynamics
Authors: Florian Nill
Date: 2022-12-31 00:00:00

As shown recently by the author, constant population SI(R)S models map to Hethcote's classic endemic model originally proposed in 1973. This unifies a whole class of models with up to 10 parameters, all being isomorphic to a simple 2-parameter master model for endemic bifurcation. In this work this procedure is extended to a 14-parameter SSISS Model, including social behavior parameters, a (diminished) susceptibility of the R-compartment and unbalanced constant per capita birth and death rates, thus covering many prominent models in the literature. Under mild conditions, in the dynamics for fractional variables in this model all vital parameters become redundant at the cost of possibly negative incidence rates. There is a symmetry group G acting on parameter space A, such that systems with G-equivalent parameters are isomorphic and map to the same normalized system. Using (Xrep,I) as canonical coordinates, Xrep the replacement number, normalization reduces to parameter space A/G with 5 parameters only. This approach reveals unexpected relations between various models in the literature. Part two of this work will analyze equilibria, stability and backward bifurcation and part three will further reduce the number of essential parameters from 5 to 3.
Modelling cortical network dynamics
Authors: Gerald K. Cooray, Richard E. Rosch, Karl J. Friston
Date: 2023-01-03 00:00:00

We consider the theoretical constraints on interactions between coupled cortical columns. Each column comprises a set of neural populations, where each population is modelled as a neural mass. The existence of semi-stable states within a cortical column has been shown to be dependent on the type of interaction between the constituent neuronal subpopulations, i.e., the form of the implicit synaptic convolution kernels. Current-to-current coupling has been shown, in contrast to potential-to-current coupling, to create semi-stable states within a cortical column. In this analytic and numerical study, the interaction between semi-stable states is characterized by equations of motion for ensemble activity. We show that for small excitations, the dynamics follow the Kuramoto model. However, in contrast to previous work, we derive coupled equations between phase and amplitude dynamics. This affords the possibility of defining connectivity as a dynamic variable. The turbulent flow of phase dynamics found in networks of Kuramoto oscillators indicate turbulent changes in dynamic connectivity for coupled cortical columns. Crucially, this is something that has been recorded in epileptic seizures. We used the results we derived to estimate a seizure propagation model, which allowed for relatively straightforward inversions using variational Laplace (a.k.a., Dynamic Causal Modelling). The face validity of the ensuing seizure propagation model was established using simulated data as a prelude to future work; which will investigate dynamic connectivity from empirical data. This model also allows predictions of seizure evolution, induced by virtual lesions to synaptic connectivity: something that could be of clinical use, when applied to epilepsy surgical cases.
Mapping effective connectivity by virtually perturbing a surrogate brain
Authors: Zixiang Luo, Kaining Peng, Zhichao Liang, Shengyuan Cai, Chenyu Xu, Dan Li, Yu Hu, Changsong Zhou, Quanying Liu
Date: 2022-12-31 00:00:00

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in humans due to being invasive and having limited spatial coverage of stimulations. To address this gap, we present Neural Perturbational Inference (NPI), a data-driven framework designed to map EC across the entire brain. NPI employs an artificial neural network trained to learn large-scale neural dynamics as a computational surrogate of the brain. NPI maps EC by perturbing each region of the surrogate brain and observing the resulting responses in all other regions. NPI captures the directionality, strength, and excitatory/inhibitory properties of brain-wide EC. Our validation of NPI, using models having ground-truth EC, shows its superiority over Granger causality and dynamic causal modeling. Applying NPI to resting-state fMRI data from diverse datasets reveals consistent and structurally supported EC. Further validation using a cortico-cortical evoked potentials dataset reveals a significant correlation between NPI-inferred EC and real stimulation propagation pathways. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain's functional architecture and facilitating both neuroscience research and clinical applications.
Experimental Evidence Supporting a New "Osmosis Law & Theory" Derived New Formula that Improves van't Hoff Osmotic Pressure Equation
Authors: Rongqing Xie, Gaochao Lin, Hung-Chung Huang
Date: 2012-01-02 00:00:00
Tasks: Miscellaneous

Experimental data were used to support a new concept of osmotic force and a new osmotic law that can explain the osmotic process without the difficulties encountered with van't Hoff osmotic pressure theory. Derived new osmotic formula with curvilinear equation (via new osmotic law) overcomes the limitations and incompleteness of van't Hoff (linear) osmotic pressure equation, $\pi=(n/v)RT$, (for ideal dilute solution only). The application of this classical theory often resulted in contradiction regardless of miscellaneous explaining efforts. This is due to the lack of a scientific concept like "osmotic force" that we believe can elaborate the osmotic process. Via this new concept, the proposed new osmotic law and derived new osmotic pressure equation will greatly complete and improve the theoretical consistency within the scientific framework of osmosis.
A Fast and Scalable Method for Inferring Phylogenetic Networks from Trees by Aligning Lineage Taxon Strings
Authors: Louxin Zhang, Niloufar Abhari, Caroline Colijn, Yufeng Wu
Date: 2023-01-03 00:00:00

The reconstruction of phylogenetic networks is an important but challenging problem in phylogenetics and genome evolution, as the space of phylogenetic networks is vast and cannot be sampled well. One approach to the problem is to solve the minimum phylogenetic network problem, in which phylogenetic trees are first inferred, then the smallest phylogenetic network that displays all the trees is computed. The approach takes advantage of the fact that the theory of phylogenetic trees is mature and there are excellent tools available for inferring phylogenetic trees from a large number of biomolecular sequences. A tree-child network is a phylogenetic network satisfying the condition that every non-leaf node has at least one child that is of indegree one. Here, we develop a new method that infers the minimum tree-child network by aligning lineage taxon strings in the phylogenetic trees. This algorithmic innovation enables us to get around the limitations of the existing programs for phylogenetic network inference. Our new program, named ALTS, is fast enough to infer a tree-child network with a large number of reticulations for a set of up to 50 phylogenetic trees with 50 taxa that have only trivial common clusters in about a quarter of an hour on average.
Density-dependent and independent mechanisms jointly reduce species performance under nitrogen enrichment
Authors: David Sampson Issaka, Or Gross, Itunuoluwa Ayilara, Talia Schabes, Niv DeMalach
Date: 2023-01-02 00:00:00

Nitrogen (N) deposition is a primary driver of species loss in plant communities globally. However, the mechanisms by which high N availability causes species loss remain unclear. Many hypotheses for species loss with increasing N availability highlight density-dependent mechanisms, i.e., changes in species interactions. However, an alternative set of hypotheses highlights density-independent detrimental effects of nitrogen (e.g., N toxicity). We tested the role of density-dependent and density-independent mechanisms in reducing species performance. For this aim, we used 120 experimental plant communities comprised of annual species growing together in containers under four fertilization treatments: (1) no nutrient addition(, (2) all nutrients except N (P, K, and micronutrients), (3) Low N, and (4) high N. Each fertilization treatment included two sowing densities to differentiate between the effects of competition (N * density interactions) and other detrimental effects of N. We focused on three performance attributes: the probability of reaching the reproduction period, biomass growth, and population growth. We found that individual biomass and population growth rates decreased with increasing sowing density in all nutrient treatments, implying that species interactions were predominantly negative. The common grass had a higher biomass and population growth under N enrichment, regardless of sowing density. In contrast, the legume showed a density-independent reduction in biomass growth with increasing N. Lastly, the small forb showed a density-dependent reduction in population growth, i.e., the decline occurred only under high density. Our results demonstrate that density-dependent and density-independent mechanisms operate simultaneously to reduce species performance under high N availability. Yet, their relative importance varies among species and life stages.