pwc-archive/papers-with-abstracts huggingface dataset:
Understanding the Role and Design Space of Demand Sinks in Low-carbon Power Systems
Authors: Sam van der Jagt, Neha Patankar, Jesse Jenkins
Date: 2023-11-07 00:00:00

As the availability of weather-dependent, zero marginal cost resources such as wind and solar power increases, a variety of flexible electricity loads, or `demand sinks', could be deployed to use intermittently available low-cost electricity to produce valuable outputs. This study provides a general framework to evaluate any potential demand sink technology and understand its viability to be deployed cost-effectively in low-carbon power systems. We use an electricity system optimization model to assess 98 discrete combinations of capital costs and output values that collectively span the range of feasible characteristics of potential demand sink technologies. We find that candidates like hydrogen electrolysis, direct air capture, and flexible electric heating can all achieve significant installed capacity (>10% of system peak load) if lower capital costs are reached in the future. Demand sink technologies significantly increase installed wind and solar capacity while not significantly affecting battery storage, firm generating capacity, or the average cost of electricity.
Structural Balance of Complex Weighted Graphs and Multi-partite Consensus
Authors: Honghui Wu, Ahmet Taha Koru, Guanxuan Wu, Frank L. Lewis, Hai Lin
Date: 2023-11-07 00:00:00

The structural balance of a signed graph is known to be necessary and sufficient to obtain a bipartite consensus among agents with friend-foe relationships. In the real world, relationships are multifarious, and the coexistence of different opinions is ubiquitous. We are therefore motivated to study the multi-partite consensus problem of multi-agent systems, for which we extend the concept of structural balance to graphs with complex edge weights. It is shown that the generalized structural balance property is necessary and sufficient for achieving multi-partite consensus.
Freeness in cognitive science
Authors: Ewa Gudowska-Nowak, Maciej A. Nowak
Date: 2023-11-07 00:00:00

In this mini-review, dedicated to the Jubilee of Professor Tadeusz Marek, we highlight in a popular way the power of so-called free random variables (hereafter FRV) calculus, viewed as a potential probability calculus for the XXI century, in applications to the broad area of cognitive sciences. We provide three examples: (i) inference of noisy signals from multivariate correlation data from the brain; (ii) distinguished role of non-normality in real neuronal models; (iii) applications to the field of deep learning in artificial neural networks.
Inferring stochastic rates from heterogeneous snapshots of particle positions
Authors: Christopher E. Miles, Scott A. McKinley, Fangyuan Ding, Richard B. Lehoucq
Date: 2023-11-08 00:00:00

Many imaging techniques for biological systems -- like fixation of cells coupled with fluorescence microscopy -- provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.
Molecular mechanism of anion permeation through aquaporin 6
Authors: Eiji Yamamoto, Keehyoung Joo, Jooyoung Lee, Mark S. P. Sansom, Masato Yasui
Date: 2023-11-08 00:00:00

Aquaporins (AQPs) are recognized as transmembrane water channels that facilitate selective water permeation through their monomeric pores. Among the AQP family, AQP6 has a unique characteristic as an anion channel, which is allosterically controlled by pH conditions and is eliminated by a single amino acid mutation. However, the molecular mechanism of anion permeation through AQP6 remains unclear. Using molecular dynamics simulations in the presence of a transmembrane voltage utilizing an ion concentration gradient, we show that chloride ions permeate through the pore corresponding to the central axis of the AQP6 homotetramer. Under low pH conditions, a subtle opening of the hydrophobic selective filter (SF), located near the extracellular part of the central pore, becomes wetted and enables anion permeation. Our simulations also indicate that a single mutation (N63G) in human AQP6, located at the central pore, significantly reduces anion conduction, consistent with experimental data. Moreover, we demonstrate the pH-sensing mechanism in which the protonation of H184 and H189 under low pH conditions allosterically triggers the gating of the SF region. These results suggest a unique pH-dependent allosteric anion permeation mechanism in AQP6 and could clarify the role of the central pore in some of the AQP tetramers.
Pattern formation in dense populations studied by inference of nonlinear diffusion-reaction mechanisms
Authors: Siddhartha Srivastava, Krishna Garikipati
Date: 2023-11-04 00:00:00
Tasks: Form

Reaction-diffusion systems have been proposed as a model for pattern formation and morphogenesis. The Fickian diffusion typically employed in these constructions model the Brownian motion of particles. The biological and chemical elements that form the basis of this process, like cells and proteins, occupy finite mass and volume and interact during migration. We propose a Reaction-diffusion system with Maxwell-Stefan formulation to construct the diffusive flux. This formulation relies on inter-species force balance and provides a more realistic model for interacting elements. We also present a variational system inference-based technique to extract these models from spatiotemporal data for these processes. We show that the inferred models can capture the characteristics of local Turing instability that instigates the pattern formation process. Moreover, the equilibrium solutions of the inferred models form similar patterns to the observed data.
Dual band wireless transmission over 75-150GHz millimeter wave carriers using frequency-locked laser pairs
Authors: Zichuan Zhou, Amany Kassem, James Seddon, Eric Sillekens, Izzat Darwazeh, Polina Bayvel, Zhixin Liu
Date: 2023-10-27 00:00:00

We generate and transmit 75-GHz-bandwidth OFDM signals over the air using three mutually frequency-locked lasers, achieving minimal frequency gap between the wireless W and D bands using optical-assisted approaches, resulting in 173.5 Gb/s detected capacity.
Global Vulnerability Assessment of Mobile Telecommunications Infrastructure to Climate Hazards using Crowdsourced Open Data
Authors: Edward J. Oughton, Tom Russell, Jeongjin Oh, Sara Ballan, Jim W. Hall
Date: 2023-11-07 00:00:00

The ongoing change in Earth`s climate is causing an increase in the frequency and severity of climate-related hazards, for example, from coastal flooding, riverine flooding, and tropical cyclones. There is currently an urgent need to quantify the potential impacts of these events on infrastructure and users, especially for hitherto neglected infrastructure sectors, such as telecommunications, particularly given our increasing dependence on digital technologies. In this analysis a global assessment is undertaken, quantifying the number of mobile cells vulnerable to climate hazards using open crowdsourced data equating to 7.6 million 2G, 3G, 4G and 5G assets. For a 0.01% annual probability event under a high emissions scenario (RCP8.5), the number of affected cells is estimated at 2.26 million for tropical cyclones, equating to USD 1.01 billion in direct damage (an increase against the historical baseline of 14% and 44%, respectively). Equally, for coastal flooding the number of potentially affected cells for an event with a 0.01% annual probability under RCP8.5 is 109.9 thousand, equating to direct damage costs of USD 2.69 billion (an increase against the baseline of 70% and 78%, respectively). The findings demonstrate the need for risk analysts to include mobile communications (and telecommunications more broadly) in future critical national infrastructure assessments. Indeed, this paper contributes a proven assessment methodology to the literature for use in future research for assessing this critical infrastructure sector.
1SPU: 1-step Speech Processing Unit
Authors: Karan Singla, Shahab Jalalvand, Yeon-Jun Kim, Antonio Moreno Daniel, Srinivas Bangalore, Andrej Ljolje, Ben Stern
Date: 2023-11-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), named-entity-recognition, Named Entity Recognition, speech-recognition, Speech Recognition

Recent studies have made some progress in refining end-to-end (E2E) speech recognition encoders by applying Connectionist Temporal Classification (CTC) loss to enhance named entity recognition within transcriptions. However, these methods have been constrained by their exclusive use of the ASCII character set, allowing only a limited array of semantic labels. We propose 1SPU, a 1-step Speech Processing Unit which can recognize speech events (e.g: speaker change) or an NL event (Intent, Emotion) while also transcribing vocal content. It extends the E2E automatic speech recognition (ASR) system's vocabulary by adding a set of unused placeholder symbols, conceptually akin to the <pad> tokens used in sequence modeling. These placeholders are then assigned to represent semantic events (in form of tags) and are integrated into the transcription process as distinct tokens. We demonstrate notable improvements on the SLUE benchmark and yields results that are on par with those for the SLURP dataset. Additionally, we provide a visual analysis of the system's proficiency in accurately pinpointing meaningful tokens over time, illustrating the enhancement in transcription quality through the utilization of supplementary semantic tags.
Selective HuBERT: Self-Supervised Pre-Training for Target Speaker in Clean and Mixture Speech
Authors: Jingru Lin, Meng Ge, Wupeng Wang, Haizhou Li, Mengling Feng
Date: 2023-11-08 00:00:00

Self-supervised pre-trained speech models were shown effective for various downstream speech processing tasks. Since they are mainly pre-trained to map input speech to pseudo-labels, the resulting representations are only effective for the type of pre-train data used, either clean or mixture speech. With the idea of selective auditory attention, we propose a novel pre-training solution called Selective-HuBERT, or SHuBERT, which learns the selective extraction of target speech representations from either clean or mixture speech. Specifically, SHuBERT is trained to predict pseudo labels of a target speaker, conditioned on an enrolled speech from the target speaker. By doing so, SHuBERT is expected to selectively attend to the target speaker in a complex acoustic environment, thus benefiting various downstream tasks. We further introduce a dual-path training strategy and use the cross-correlation constraint between the two branches to encourage the model to generate noise-invariant representation. Experiments on SUPERB benchmark and LibriMix dataset demonstrate the universality and noise-robustness of SHuBERT. Furthermore, we find that our high-quality representation can be easily integrated with conventional supervised learning methods to achieve significant performance, even under extremely low-resource labeled data.
An End-Cloud Computing Enabled Surveillance Video Transmission System
Authors: Dingxi Yang, Zhijin Qin, Liting Wang, Xiaoming Tao, Fang Cui, Hengjiang Wang
Date: 2023-11-08 00:00:00
Tasks: Cloud Computing, SSIM, Super-Resolution, Video Super-Resolution

The enormous data volume of video poses a significant burden on the network. Particularly, transferring high-definition surveillance videos to the cloud consumes a significant amount of spectrum resources. To address these issues, we propose a surveillance video transmission system enabled by end-cloud computing. Specifically, the cameras actively down-sample the original video and then a redundant frame elimination module is employed to further reduce the data volume of surveillance videos. Then we develop a key-frame assisted video super-resolution model to reconstruct the high-quality video at the cloud side. Moreover, we propose a strategy of extracting key frames from source videos for better reconstruction performance by utilizing the peak signal-to-noise ratio (PSNR) of adjacent frames to measure the propagation distance of key frame information. Simulation results show that the developed system can effectively reduce the data volume by the end-cloud collaboration and outperforms existing video super-resolution models significantly in terms of PSNR and structural similarity index (SSIM).
A human brain atlas of chi-separation for normative iron and myelin distributions
Authors: Kyeongseon Min, Beomseok Sohn, Woo Jung Kim, Chae Jung Park, Soohwa Song, Dong Hoon Shin, Kyung Won Chang, Na-Young Shin, Minjun Kim, Hyeong-Geol Shin, Phil Hyu Lee, Jongho Lee
Date: 2023-11-08 00:00:00

Iron and myelin are primary susceptibility sources in the human brain. These substances are essential for healthy brain, and their abnormalities are often related to various neurological disorders. Recently, an advanced susceptibility mapping technique, which is referred to as chi-separation, has been proposed, successfully disentangling paramagnetic iron from diamagnetic myelin. This method opened a potential for generating high resolution iron and myelin maps in the brain. Utilizing this technique, this study constructs a normative chi-separation atlas from 106 healthy human brains. The resulting atlas provides detailed anatomical structures associated with the distributions of iron and myelin, clearly delineating subcortical nuclei, thalamic nuclei, and white matter fiber bundles. Additionally, susceptibility values in a number of regions of interest are reported along with age-dependent changes. This atlas may have direct applications such as localization of subcortical structures for deep brain stimulation or high-intensity focused ultrasound and also serve as a valuable resource for future research.
A labeled Clinical-MRI dataset of Nigerian brains
Authors: Eberechi Wogu, Patrick Filima, Bradley Caron, Daniel Levitas, Peer Herholz, Catherine Leal, Mohammed F. Mehboob, Soichi Hayashi, Simisola Akintoye, George Ogoh, Tawe Godwin, Damian Eke, Franco Pestilli
Date: 2023-11-08 00:00:00

We describe a Magnetic Resonance Imaging (MRI) dataset from individuals from the African nation of Nigeria. The dataset contains pseudonymized structural MRI (T1w, T2w, FLAIR) data of clinical quality. The dataset contains data from 36 images from healthy control subjects, 32 images from individuals diagnosed with age-related dementia and 20 from individuals with Parkinson's disease. There is currently a paucity of data from the African continent. Given the potential for Africa to contribute to the global neuroscience community, this first MRI dataset represents both an opportunity and benchmark for future studies to share data from the African continent.
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging
Authors: Huidong Xie, Weijie Gan, Bo Zhou, Xiongchao Chen, Qiong Liu, Xueqi Guo, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Chi Liu
Date: 2023-11-07 00:00:00
Tasks: Denoising, Image Denoising, Medical Image Denoising

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either apply a penalty term along the z-axis to remove inconsistencies or reconstruct the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise levels in PET images are affected by several other factors in clinical settings, e.g. scan time, medical history, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3D low-dose PET imaging (DDPET-3D) to address these challenges. We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models. The code is available at: https://github.com/xxx/xxx.
Electromagnetic manifold characterization of antenna arrays
Authors: Miguel R. Castellanos, Robert W. Heath Jr
Date: 2023-11-08 00:00:00

Antenna behaviors such as mutual coupling, near-field propagation, and polarization cannot be neglected in signal and channel models for wireless communication. We present an electromagnetic-based array manifold that accounts for several complicated behaviors and can model arbitrary antenna configurations. We quantize antennas into a large number of Hertzian dipoles to develop a model for the radiated array field. The resulting abstraction provides a means to predict the electric field for general non-homogeneous array geometries through a linear model that depends on the point source location, the position of each Hertzian dipole, and a set of coefficients obtained from electromagnetic simulation. We then leverage this model to formulate a beamforming gain optimization that can be adapted to account for polarization of the receive field as well as constraints on the radiated power density. Numerical results demonstrate that the proposed method achieves accuracy that is close to that of electromagnetic simulations. By leveraging the developed array manifold for beamforming, systems can achieve higher beamforming gains compared to beamforming with less accurate models.
Integrated Distributed Semantic Communication and Over-the-air Computation for Cooperative Spectrum Sensing
Authors: Peng Yi, Yang Cao, Xin Kang, Ying-Chang Liang
Date: 2023-11-08 00:00:00
Tasks: Semantic Communication

Cooperative spectrum sensing (CSS) is a promising approach to improve the detection of primary users (PUs) using multiple sensors. However, there are several challenges for existing combination methods, i.e., performance degradation and ceiling effect for hard-decision fusion (HDF), as well as significant uploading latency and non-robustness to noise in the reporting channel for soft-data fusion (SDF). To address these issues, an integrated communication and computation (ICC) framework is proposed in this paper. Specifically, distributed semantic communication (DSC) jointly optimizes multiple sensors and the fusion center to minimize the transmitted data without degrading detection performance. Moreover, over-the-air computation (AirComp) is utilized to further reduce spectrum occupation in reporting channel, taking advantage of characteristics of wireless channel to enable data aggregation. Under the ICC framework, a particular system, namely ICC-CSS, is designed and implemented, which is theoretically proved to be equivalent to the optimal estimator-correlator (E-C) detector with equal gain SDF when the PU signal samples are independent and identically distributed. Extensive simulations verify the superiority of ICC-CSS compared with various conventional CSS schemes in terms of detection performance, robustness to SNR variations in both sensing and reporting channels, as well as scalability with respect to the number of samples and sensors.
Superimposed Chirp Waveforms for SWIPT with Diplexer-based Integrated Receivers
Authors: Arijit Roy, Constantinos Psomas, Ioannis Krikidis
Date: 2023-11-08 00:00:00

In this paper, we present the superposition of chirp waveforms for simultaneous wireless information and power transfer (SWIPT) applications. Exploiting the chirp waveform characteristics enables us to superimpose multiple chirps, thereby allowing transmission of the same number of waveforms over less bandwidth. This enables us to perform subband selection when operating over set of orthogonal subbands. Furthermore, we consider a user equipped with a diplexer-based integrated receiver (DIR), which enables to extract radio frequency power and decode information from the same signal without splitting. Thereby, incorporating chirp superposition and subband selection, a transmission scheme is proposed to exploit both the diode's nonlinearity and frequency diversity. We derive novel closed-form analytical expressions of the average harvested energy (HE) via transmission of superimposed chirp over selected subbands based on tools from order statistics. We also analyze the downlink information rate achieved at the user. Through our analytical and numerical results, for the considered system setup, we show that superimposed chirp-based SWIPT provides an improvement of 30$\%$ in average HE performance as compared to multisine waveforms consisting of a set of fixed-frequency cosine signals, improves the minimum level of HE in a multiuser network, and extends the operating range of energy transfer as compared to fixed-frequency waveforms. Furthermore, we illustrate that the inclusion of DIR at the receiver for SWIPT enlarges the energy-information transfer region when compared to the widely considered power splitting receiver.