pwc-archive/papers-with-abstracts huggingface dataset:
Simulation of Muon Tomography Projections to Image the Pyramids of Giza
Authors: Mira Liu
Date: 2024-02-27 00:00:00

Purpose: A geometric simulation of a possible two-plane detector was developed to test the abilities of the detector to generate high-resolution images of the Great Pyramid using muon tomography. Methods and Materials: Trajectory range, angular resolution, and acceptance of the detector were calculated with a simulation. Trajectories and the corresponding sinogram space covered were simulated first with one detector in one location, and then two moving detectors on adjacent sides of the pyramid. The resolution at the center slice of the pyramid was calculated using the angular resolution of the detector. Results: The simulation returned trajectory range encompassing the pyramid and peak angular resolution of .0004sr. Sinogram space covered by one position was inadequate, however two moving detectors on adjacent sides of the pyramid cover a significant portion. Resolution at the center of the pyramid is roughly 3m. Conclusions: The simulation provides a way to calculate the detector positions needed to cover an adequate amount of sinogram space for high-resolution cosmic-ray tomographic reconstruction of the Great Pyramids. Key Words: high-resolution muon tomography, one-sided tomography, sinogram simulation, detector simulation
Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure
Authors: Cheng Bian, Xiaoyu Li, Qi Bi, Guangpu Zhu, Jiegeng Lyu, Weile Zhang, Yelei Li, Zijing Zeng
Date: 2024-02-23 00:00:00
Tasks: Management, Photoplethysmography (PPG)

Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management. Notwithstanding its potential, the invasive nature of ABP measurements confines their utility primarily to clinical environments, limiting their applicability for continuous monitoring beyond medical facilities. The conversion of photoplethysmography (PPG) signals into ABP equivalents has garnered significant attention due to its potential in revolutionizing cardiovascular disease management. Recent strides in PPG-to-ABP prediction encompass the integration of generative and discriminative models. Despite these advances, the efficacy of these models is curtailed by the latent space shift predicament, stemming from alterations in PPG data distribution across disparate hardware and individuals, potentially leading to distorted ABP waveforms. To tackle this problem, we present an innovative solution named the Latent Space Constraint Transformer (LSCT), leveraging a quantized codebook to yield robust latent spaces by employing multiple discretizing bases. To facilitate improved reconstruction, the Correlation-boosted Attention Module (CAM) is introduced to systematically query pertinent bases on a global scale. Furthermore, to enhance expressive capacity, we propose the Multi-Spectrum Enhancement Knowledge (MSEK), which fosters local information flow within the channels of latent code and provides additional embedding for reconstruction. Through comprehensive experimentation on both publicly available datasets and a private downstream task dataset, the proposed approach demonstrates noteworthy performance enhancements compared to existing methods. Extensive ablation studies further substantiate the effectiveness of each introduced module.
Global Estimation of Range Resolved Thermodynamic Profiles from MicroPulse Differential Absorption Lidar
Authors: Matthew Hayman, Robert A. Stillwell, Adam Karboski, Willem J. Marais, Scott M. Spuler
Date: 2024-02-27 00:00:00

We demonstrate thermodynamic profile estimation with data obtained using the MicroPulse DIAL such that the retrieval is entirely self contained. The only external input is surface meteorological variables obtained from a weather station installed on the instrument. The estimator provides products of temperature, absolute humidity and backscatter ratio such that cross dependencies between the lidar data products and raw observations are accounted for and the final products are self consistent. The method described here is applied to a combined oxygen DIAL, potassium HSRL, water vapor DIAL system operating at two pairs of wavelengths (nominally centered at 770 and 828 nm). We perform regularized maximum likelihood estimation through the Poisson Total Variation technique to suppress noise and improve the range of the observations. A comparison to 119 radiosondes indicates that this new processing method produces improved temperature retrievals, reducing total errors to less than 2 K below 3 km altitude and extending the maximum altitude of temperature retrievals to 5 km with less than 3 K error. The results of this work definitively demonstrates the potential for measuring temperature through the oxygen DIAL technique and furthermore that this can be accomplished with low-power semiconductor-based lidar sensors.
Social Learning with Intrinsic Preferences
Authors: Fabian Dvorak, Urs Fischbacher
Date: 2024-02-28 00:00:00

Despite strong evidence for peer effects, little is known about how individuals balance intrinsic preferences and social learning in different choice environments. Using a combination of experiments and discrete choice modeling, we show that intrinsic preferences and social learning jointly influence participants' decisions, but their relative importance varies across choice tasks and environments. Intrinsic preferences guide participants' decisions in a subjective choice task, while social learning determines participants' decisions in a task with an objectively correct solution. A choice environment in which people expect to be rewarded for their choices reinforces the influence of intrinsic preferences, whereas an environment in which people expect to be punished for their choices reinforces conformist social learning. We use simulations to discuss the implications of these findings for the polarization of behavior.
Stochastic User Equilibrium Model with a Bounded Perceived Travel Time
Authors: Songyot Kitthamkesorn, Anthony Chen
Date: 2024-02-28 00:00:00

Stochastic User Equilibrium (SUE) models depict the perception differences in traffic assignment problems. According to the assumption of an unbounded perceived travel time distribution, the conventional SUE problems result in a positive choice probability for all available routes, regardless of their unappealing travel time. This study provides an eUnit-SUE model to relax this assumption. The eUnit model is derived from a bounded probability distribution. This closed-form model aligns with an exponentiated random utility maximization (ERUM) paradigm with the exponentiated uniform distributed random error, where the lower and upper bounds endogeneously determine the route usage. Specifically, a Beckmann-type mathematical programming formulation is presented for the eUnit-SUE problem. The equivalency and uniqueness properties are rigorously proven. Numerical examples reveal that the eUnit bound range between the lower and upper bounds greatly affects the SUE assignment results. A larger bound range increases not only the number of routes in the choice set but also the degree of dispersion in the assignment results due to a larger route-specific perception variance. The misperception is contingent upon the disparity between the shortest and longest travel times and the bounds. As the bound range decreases, the shortest route receives significant flow allocation, and the assignment result approaches the deterministic user equilibrium (DUE) flow pattern.
New characterizations of completely useful topologies in mathematical utility theory
Authors: Gianni Bosi, Roberto Daris, Gabriele Sbaiz
Date: 2024-02-28 00:00:00

Let $X$ be an arbitrary set. Then a topology $t$ on $X$ is said to be completely useful if every upper semicontinuous linear (total) preorder $\precsim$ on $X$ can be represented by an upper semicontinuous real-valued order preserving function. In this paper, appealing, simple and new characterizations of completely useful topologies will be proved, therefore clarifying the structure of such topologies.
Why does music source separation benefit from cacophony?
Authors: Chang-Bin Jeon, Gordon Wichern, François G. Germain, Jonathan Le Roux
Date: 2024-02-28 00:00:00
Tasks: Data Augmentation, Music Source Separation

In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs. These random mixes have mismatched characteristics compared to real music, e.g., the different stems do not have consistent beat or tonality, resulting in a cacophony. In this work, we investigate why random mixing is effective when training a state-of-the-art music source separation model in spite of the apparent distribution shift it creates. Additionally, we examine why performance levels off despite potentially limitless combinations, and examine the sensitivity of music source separation performance to differences in beat and tonality of the instrumental sources in a mixture.
Accelerated Real-time Cine and Flow under In-magnet Staged Exercise
Authors: Preethi Chandrasekaran, Chong Chen, Yingmin Liu, Syed Murtaza Arshad, Christopher Crabtree, Matthew Tong, Yuchi Han, Rizwan Ahmad
Date: 2024-02-27 00:00:00
Tasks: Diagnostic, Image Quality Assessment

Background: Cardiovascular magnetic resonance imaging (CMR) is a well established imaging tool for diagnosing and managing cardiac conditions. The integration of exercise stress with CMR (ExCMR) can enhance its diagnostic capacity. Despite recent advances in CMR technology, quantitative ExCMR during exercise remains technically challenging due to motion artifacts and limited spatial and temporal resolution. Methods: This study investigated the feasibility of biventricular functional and hemodynamic assessment using real-time (RT) ExCMR during a staged exercise protocol in 24 healthy volunteers. We employed high acceleration rates and applied a coil reweighting technique to minimize motion blurring and artifacts. We further applied a beat-selection technique that identified beats from the endexpiratory phase to minimize the impact of respiration-induced through-plane motion on cardiac function quantification. Additionally, results from six patients were presented to demonstrate clinical feasibility. Results: Our findings indicated a consistent decrease in end-systolic volume and stable end-diastolic volume across exercise intensities, leading to increased stroke volume and ejection fraction. The selection of end-expiratory beats modestly enhanced the repeatability of cardiac function parameters, as shown by scan-rescan tests in nine volunteers. High scores from a blinded image quality assessment indicated that coil reweighting effectively minimized motion artifacts. Conclusions: This study demonstrated the feasibility of RT ExCMR with inmagnet exercise in healthy subjects and patients. Our results indicate that high acceleration rates, coil reweighting, and selection of respiratory phase-specific heartbeats enhance image quality and repeatability of quantitative RT ExCMR.
Neural Radiance Fields in Medical Imaging: A Survey
Authors: Xin Wang, Yineng Chen, Shu Hu, Heng Fan, Hongtu Zhu, Xin Li
Date: 2024-02-26 00:00:00
Tasks: NeRF, Survey

Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data. However, they face unique challenges when applied to medical applications. This paper presents a comprehensive examination of applications of NeRFs in medical imaging, highlighting four imminent challenges, including fundamental imaging principles, inner structure requirement, object boundary definition, and color density significance. We discuss current methods on different organs and discuss related limitations. We also review several datasets and evaluation metrics and propose several promising directions for future research.
FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease
Authors: Om Roy, Yashar Moshfeghi, Agustin Ibanez, Francisco Lopera, Mario A Parra, Keith M Smith
Date: 2024-02-28 00:00:00
Tasks: Brain Decoding, EEG, Electroencephalogram (EEG), ERP, Functional Connectivity

Measuring transient functional connectivity is an important challenge in Electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a novel methodology to overcome these problems called Filter Average Short-Term (FAST) functional connectivity. First, long-term, stable, functional connectivity is averaged across an entire study cohort for a given pair of Visual Short Term Memory (VSTM) tasks. The resulting average connectivity matrix, containing information on the strongest general connections for the tasks, is used as a filter to analyse the transient high temporal resolution functional connectivity of individual subjects. In simulations, we show that this method accurately discriminates differences in noisy Event-Related Potentials (ERPs) between two conditions where standard connectivity and other comparable methods fail. We then apply this to analyse activity related to visual short-term memory binding deficits in two cohorts of familial and sporadic Alzheimer's disease. Reproducible significant differences were found in the binding task with no significant difference in the shape task in the P300 ERP range. This allows new sensitive measurements of transient functional connectivity, which can be implemented to obtain results of clinical significance.
RF-Flashlight Testbed for Verification of Real-Time Geofencing of EESS Radiometers and Millimeter-Wave Ground-to-Satellite Propagation Models
Authors: Elliot Eichen, Arvind Aradhya, Ljiljana Simić
Date: 2024-02-28 00:00:00

A simple 'RF-flashlight' (or ground to satellite) interference testbed is proposed to experimentally verify real-time geofencing (RTG) for protecting passive Earth Exploration Satellite Services (EESS) radiometer measurements from 5G or 6G mm-wave transmissions, and ground to satellite propagation models used in the interference modeling of this spectrum coexistence scenario. RTG is a stronger EESS protection mechanism than the current methodology recommended by the ITU based on a worst-case interference threshold while simultaneously enabling dynamic spectrum sharing and coexistence with 5G or 6G wireless networks. Similarly, verifying more sophisticated RF propagation models that include ground topology, buildings, and non-line-of-sight paths will provide better estimates of interference than the current ITU line-of-sight model and, thus, a more reliable basis for establishing a consensus among the spectrum stakeholders.
Impact of new 5G network components on out-of-band emissions at 23.8 GHz
Authors: Elliot Eichen
Date: 2024-02-28 00:00:00

The impact of 5G networks transmitting between 24.25- 27.5 GHz on Earth Exploration Satellite Services (EESS) microwave sounders used to measure atmospheric water vapor and temperature was widely discussed and modeled in preparation for setting emission recommendations by International Telecommunications Union (ITU) at the 2019 World Radio Congress (WRC-19). Since then, two new classes of network devices - 5G repeaters and high transmission power User Equipment (UE) for fixed wireless services - have been introduced and deployed in 28 GHz networks with expectations that they will also be deployed at 24 GHz. This paper discusses the (potentially significant) increase in interference from these new components along with open questions related to their regulatory status. While this paper discusses increases in interference to 23.8 GHz EESS measurements from 5G transmissions in the "24 GHz" band, it is important to recognize that repeaters and high power UEs need to be considered when modeling interference from 5G/6G networks in all bands. This paper also touches on whether the current ITU process and methodology to regulate interference with passive sensors (vendor applied hardware-based filtering based on long-term network forecasts and worst-case Monte Carlo modeling) can keep up with rapidly changing wireless technology and the increased competition for spectrum.
Recursive GNNs for Learning Precoding Policies with Size-Generalizability
Authors: Jia Guo, Chenyang Yang
Date: 2024-02-28 00:00:00
Tasks: Scheduling

Graph neural networks (GNNs) have been shown promising in optimizing power allocation and link scheduling with good size generalizability and low training complexity. These merits are important for learning wireless policies under dynamic environments, which partially come from the matched permutation equivariance (PE) properties of the GNNs to the policies to be learned. Nonetheless, it has been noticed in literature that only satisfying the PE property of a precoding policy in multi-antenna systems cannot ensure a GNN for learning precoding to be generalizable to the unseen number of users. Incorporating models with GNNs helps improve size generalizability, which however is only applicable to specific problems, settings, and algorithms. In this paper, we propose a framework of size generalizable GNNs for learning precoding policies that are purely data-driven and can learn wireless policies including but not limited to baseband and hybrid precoding in multi-user multi-antenna systems. To this end, we first find a special structure of each iteration of two numerical algorithms for optimizing precoding, from which we identify the key characteristics of a GNN that affect its size generalizability. Then, we design size-generalizable GNNs that are with these key characteristics and satisfy the PE properties of precoding policies in a recursive manner. Simulation results show that the proposed GNNs can be well-generalized to the number of users for learning baseband and hybrid precoding policies and require much fewer samples than existing counterparts to achieve the same performance.
Joint Beamforming Design and Stream Allocation for Non-Coherent Joint Transmission in Cell-Free MIMO Networks
Authors: Xi Wang, Xiaotong Zhao, Juncheng Wang, You Li, Qingjiang Shi
Date: 2024-02-28 00:00:00

We consider joint beamforming and stream allocation to maximize the weighted sum rate (WSR) for non-coherent joint transmission (NCJT) in user-centric cell-free MIMO networks, where distributed access points (APs) are organized in clusters to transmit different signals to serve each user equipment (UE). We for the first time consider the common limits of maximum number of receive streams at UEs in practical networks, and formulate a joint beamforming and transmit stream allocation problem for WSR maximization under per-AP transmit power constraints. Since the integer number of transmit streams determines the dimension of the beamformer, the joint optimization problem is mixed-integer and nonconvex with coupled decision variables that is inherently NP-hard. In this paper, we first propose a distributed low-interaction reduced weighted minimum mean square error (RWMMSE) beamforming algorithm for WSR maximization with fixed streams. Our proposed RWMMSE algorithm requires significantly less interaction across the network and has the current lowest computational complexity that scales linearly with the number of transmit antennas, without any compromise on WSR. We draw insights on the joint beamforming and stream allocation problem to decouple the decision variables and relax the mixed-integer constraints. We then propose a joint beamforming and linear stream allocation algorithm, termed as RWMMSE-LSA, which yields closed-form updates with linear stream allocation complexity and is guaranteed to converge to the stationary points of the original joint optimization problem. Simulation results demonstrate substantial performance gain of our proposed algorithms over the current best alternatives in both WSR performance and convergence time.
Computational Offloading in Semantic-Aware Cloud-Edge-End Collaborative Networks
Authors: Zelin Ji, Zhijin Qin
Date: 2024-02-28 00:00:00
Tasks: Cloud Computing, Deep Reinforcement Learning, Distributed Computing, Edge-computing, reinforcement-learning

The trend of massive connectivity pushes forward the explosive growth of end devices. The emergence of various applications has prompted a demand for pervasive connectivity and more efficient computing paradigms. On the other hand, the lack of computational capacity of the end devices restricts the implementation of the intelligent applications, and becomes a bottleneck of the multiple access for supporting massive connectivity. Mobile cloud computing (MCC) and mobile edge computing (MEC) techniques enable end devices to offload local computation-intensive tasks to servers by networks. In this paper, we consider the cloud-edge-end collaborative networks to utilize distributed computing resources. Furthermore, we apply task-oriented semantic communications to tackle the fast-varying channel between the end devices and MEC servers and reduce the communication cost. To minimize long-term energy consumption on constraints queue stability and computational delay, a Lyapunov-guided deep reinforcement learning hybrid (DRLH) framework is proposed to solve the mixed integer non-linear programming (MINLP) problem. The long-term energy consumption minimization problem is transformed into the deterministic problem in each time frame. The DRLH framework integrates a model-free deep reinforcement learning algorithm with a model-based mathematical optimization algorithm to mitigate computational complexity and leverage the scenario information, so that improving the convergence performance. Numerical results demonstrate that the proposed DRLH framework achieves near-optimal performance on energy consumption while stabilizing all queues.
Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models
Authors: Dewei Wang, Bhaskar Mitra, Sameer Nekkalapu, Sohom Datta, Bibi Matthew, Rounak Meyur, Heng Wang, Slaven Kincic
Date: 2024-02-28 00:00:00

As the power system continues to be flooded with intermittent resources, it becomes more important to accurately assess the role of hydro and its impact on the power grid. While hydropower generation has been studied for decades, dependency of power generation on water availability and constraints in hydro operation are not well represented in power system models used in the planning and operation of large-scale interconnection studies. There are still multiple modeling gaps that need to be addressed; if not, they can lead to inaccurate operation and planning reliability studies, and consequently to unintentional load shedding or even blackouts. As a result, it is very important that hydropower is represented correctly in both steady-state and dynamic power system studies. In this paper, we discuss the development and use of the Hydrological Dispatch and Analysis Tool (Hy-DAT) as an interactive graphical user interface, that uses a novel methodology to address the hydropower modeling gaps like water availability and interdependency using a database and algorithms to generate accurate representative models for power system simulation.