pwc-archive/papers-with-abstracts huggingface dataset:
Perceptual evaluation of listener envelopment using spatial granular synthesis
Authors: Stefan Riedel, Matthias Frank, Franz Zotter
Date: 2023-01-24 00:00:00
Tasks: Attribute

Listener envelopment refers to the sensation of being surrounded by sound, either by multiple direct sound events or by a diffuse reverberant sound field. More recently, a specific attribute for the sensation of being covered by sound from elevated directions has been proposed by Sazdov et al. and was termed listener engulfment. This contribution investigates the effect of the temporal and directional density of sound events on listener envelopment and engulfment. A spatial granular synthesis technique is used to precisely control the temporal and directional density of sound events. Experimental results indicate that a directionally uniform distribution of sound events at time intervals $\Delta t < 20$ milliseconds is required to elicit a sensation of diffuse envelopment, whereas longer time intervals lead to localized auditory events. It shows that elevated loudspeaker layers do not increase envelopment, but contribute specifically to listener engulfment. Lowpass-filtered stimuli increase envelopment, but lead to a decreased control over engulfment. The results can be exploited in the technical design and creative application of spatial sound synthesis and reverberation algorithms.
Dirac signal processing of higher-order topological signals
Authors: Lucille Calmon, Michael T. Schaub, Ginestra Bianconi
Date: 2023-01-12 00:00:00

Higher-order networks can sustain topological signals which are variables associated not only to the nodes, but also to the links, to the triangles and in general to the higher dimensional simplices of simplicial complexes. These topological signals can describe a large variety of real systems including currents in the ocean, synaptic currents between neurons and biological transportation networks. In real scenarios topological signal data might be noisy and an important task is to process these signals by improving their signal to noise ratio. So far topological signals are typically processed independently of each other. For instance, node signals are processed independently of link signals, and algorithms that can enforce a consistent processing of topological signals across different dimensions are largely lacking. Here we propose Dirac signal processing, an adaptive, unsupervised signal processing algorithm that learns to jointly filter topological signals supported on nodes, links and triangles of simplicial complexes in a consistent way. The proposed Dirac signal processing algorithm is formulated in terms of the discrete Dirac operator which can be interpreted as "square root" of a higher-order Hodge Laplacian. We discuss in detail the properties of the Dirac operator including its spectrum and the chirality of its eigenvectors and we adopt this operator to formulate Dirac signal processing that can filter noisy signals defined on nodes, links and triangles of simplicial complexes. We test our algorithms on noisy synthetic data and noisy data of drifters in the ocean and find that the algorithm can learn to efficiently reconstruct the true signals outperforming algorithms based exclusively on the Hodge Laplacian.
Revisiting Power Systems Time-domain Simulation Methods and Models
Authors: Jose Daniel Lara, Rodrigo Henriquez-Auba, Deepak Ramasubramanian, Sairaj Dhople, Duncan S. Callaway, Seth Sanders
Date: 2023-01-24 00:00:00

The changing nature of power systems dynamics is challenging present practices related to modeling and study of system-level dynamic behavior. While developing new techniques and models to handle the new modeling requirements, it is also critical to review some of the terminology used to describe existing simulation approaches and the embedded assumptions. This paper provides a first-principles review of the simplifications and transformation commonly used in the formulation of time-domain simulation models. It introduces a taxonomy and classification of time-domain simulation models depending on their frequency bandwidth, network representation, and software availability. Furthermore, it focuses on the fundamental aspects of averaging techniques, and model reduction approaches that result in modeling choices, and discusses the associated challenges and opportunities of applying these methods in systems with large shares of Inverter Based Resources (IBRs). The paper concludes with an illustrative simulation that compares the trajectories of an IBR-dominated system.
Validation and traceability of miniaturized multi-parameter cluster of radiosondes used for atmospheric observations
Authors: Shahbozbek Abdunabiev, Chiara Musacchio, Andrea Merlone, Miryam Paredes, Eros Pasero, Daniela Tordella
Date: 2023-01-24 00:00:00
Tasks: Position

In this work we designed and developed a cluster of light expendable radiosondes that can float passively inside warm clouds to study their micro-physical processes. This involves the tracking of both saturated and unsaturated turbulent air parcels. The aim of this new kind of observation system is to obtain Lagrangian statistics of the intense turbulence inside warm clouds and of the lower intensity turbulence that is typical of the air surrounding such clouds. Each radiosonde in a cluster includes an electronic board, which is mounted onto a small, biodegradable balloon filled with a mixture of helium and air. The cluster is able to float inside clouds for a few hours and to measure air temperature, pressure, humidity and the associated position, velocity, acceleration and magnetic field readings of each radiosonde along their trajectory.
Stability analysis for circulant structured multi-agent molecular communication systems
Authors: Taishi Kotsuka, Yutaka Hori
Date: 2023-01-24 00:00:00

In this paper, we introduce the system theoretic model for the multi-agent MC systems represented by multi-input and multi-output (MIMO) systems using the transfer functions, and then propose a method to analyze the stability for the special case of the circulant structured multi-agent MC systems. The proposed method decomposes the MIMO MC system into multiple single-input and single-output (SISO) systems, which facilitates to analyze of the stability of the large-scale multi-agent MC system. Finally, we demonstrate the proposed method to analyze the stability of a specific MC system.
A fair Peer-to-Peer Electricity Market model for Residential Prosumers
Authors: A. A. Raja, S. Grammatico
Date: 2023-01-23 00:00:00
Tasks: energy trading

In this paper, we propose a bilateral peer-to-peer (P2P) energy trading scheme for residential prosumers with a simplified entry to the market. We formulate the market as an assignment game, a special class of coalitional games. For solving the resulting decision problem, we design a bilateral negotiation mechanism that enables matched buyer-seller pairs to reach a consensus on a set of ``stable" and ``fair" trading contracts. The proposed negotiation process can be executed on possibly time-varying communication networks with virtually minimal information requirements that in turn preserves privacy among prosumers. Numerical simulations illustrate the beneficial features of our P2P market model and negotiation protocol.
Prostate Lesion Estimation using Prostate Masks from Biparametric MRI
Authors: Ahmet Karagoz, Mustafa Ege Seker, Mert Yergin, Tarkan Atak Kan, Mustafa Said Kartal, Ercan Karaarslan, Deniz Alis, Ilkay Oksuz
Date: 2023-01-11 00:00:00

Biparametric MRI has emerged as an alternative to multiparametric prostate MRI, which eliminates the need for the potential harms to the patient due to the contrast medium. One major issue with biparametric MRI is difficulty to detect clinically significant prostate cancer (csPCA). Deep learning algorithms have emerged as an alternative solution to detect csPCA in cohort studies. We present a workflow which predicts csPCA on biparametric prostate MRI PI-CAI 2022 Challenge with over 10,000 carefully-curated prostate MRI exams. We propose to to segment the prostate gland first to the central gland (transition + central zone) and the peripheral gland. Then we utilize these predcitions in combination with T2, ADC and DWI images to train an ensemble nnU-Net model. Finally, we utilize clinical indices PSA and ADC intensity distributions of lesion regions to reduce the false positives. Our method achieves top results on open-validation stage with a AUROC of 0.888 and AP of 0.732.
Continuous tuning & thermally induced frequency drift stabilisation of time delay oscillators such as the optoelectronic oscillator
Authors: Mehedi Hasan, Charles Nicholls, Keegan Pitre, Trevor Hall
Date: 2023-01-12 00:00:00

Delay line oscillators based on photonic components, such as the optoelectronic oscillator (OEO), offer the potential for realization of phase noise levels orders of magnitude lower than achievable by conventional microwave sources. Fibre optic-based delay lines can realize the large delay required for low phase noise systems whilst simultaneously achieving insertion loss levels that can be compensated by available microwave and photonic amplification technologies. However, the long fibre is vulnerable to environmental perturbations such as mechanical vibrations and variations in ambient temperature, which result in short term fluctuations and thermally induced drift of the oscillation frequency. The phase shifter used conventionally to adjust the frequency of an OEO to enable phase lock has a finite range that is insufficient to compensate the delay change resulting from operational temperature ranges. A solution to continuous tuning without mode-hopping and to compensation of thermally induced frequency drift without loss of lock of a time delay oscillator is proposed. The basic concept is to introduce a tuning mechanism that works with Cartesian co-ordinates on the complex plane and to avoid explicit use of polar co-ordinates. Consequently, the transmission of the tuning component may traverse the unit circle in either direction multiple times without range limitation to the phase. Thereby tuning by mode-hopping is avoided and expedients to stabilisation, such as the use of tunable lasers within a control loop or precision temperature stabilisation measures are not required. The concept is verified by Simulink simulations. The method has been experimentally tested successfully using a prototype OEO phase locked to a system reference. Solid lock was maintained even when the OEO was placed in an oven and cycled over a temperature range from ambient to 80 {\deg}C.
Prenatal Sugar Consumption and Late-Life Human Capital and Health: Analyses Based on Postwar Rationing and Polygenic Scores
Authors: Gerard J. van den Berg, Stephanie von Hinke, R. Adele H. Wang
Date: 2023-01-24 00:00:00

Maternal sugar consumption in utero may have a variety of effects on offspring. We exploit the abolishment of the rationing of sweet confectionery in the UK on April 24, 1949, and its subsequent reintroduction some months later, in an era of otherwise uninterrupted rationing of confectionery (1942-1953), sugar (1940-1953) and many other foods, and we consider effects on late-life cardiovascular disease, BMI, height, type-2 diabetes and the intake of sugar, fat and carbohydrates, as well as cognitive outcomes and birth weight. We use individual-level data from the UK Biobank for cohorts born between April 1947-May 1952. We also explore whether one's genetic "predisposition" to the outcome can moderate the effects of prenatal sugar exposure. We find that prenatal exposure to derationing increases education and reduces BMI and sugar consumption at higher ages, in line with the "developmental origins" explanatory framework, and that the sugar effects are stronger for those who are genetically "predisposed" to sugar consumption.
Research on the Impact of Innovative City and Smart City Construction on Digital Economy: Evidence from China
Authors: Zhanpeng Huang
Date: 2023-01-22 00:00:00

Does the national innovation city and smart city pilot policy, as an important institutional design to promote the transformation of old and new dynamics, have an important impact on the digital economy? What are the intrinsic mechanisms? Based on the theoretical analysis of whether smart city and national innovation city policies promote urban digital economy, this paper constructs a multi-temporal double difference model based on a quasi-natural experiment with urban dual pilot policies and systematically investigates the impact of dual pilot policies on the development of digital economy. It is found that both smart cities and national innovation cities can promote the development of digital economy, while there is a synergistic effect between the policies. The mechanism test shows that the smart city construction and national innovation city construction mainly affect the digital economy through talent agglomeration effect, technology agglomeration effect and financial agglomeration effect.
A convolutional neural network of low complexity for tumor anomaly detection
Authors: Vasileios E. Papageorgiou, Pantelis Dogoulis, Dimitrios-Panagiotis Papageorgiou
Date: 2023-01-24 00:00:00
Tasks: Anomaly Detection, Classification, Diagnostic, Image Augmentation

The automated detection of cancerous tumors has attracted interest mainly during the last decade, due to the necessity of early and efficient diagnosis that will lead to the most effective possible treatment of the impending risk. Several machine learning and artificial intelligence methodologies has been employed aiming to provide trustworthy helping tools that will contribute efficiently to this attempt. In this article, we present a low-complexity convolutional neural network architecture for tumor classification enhanced by a robust image augmentation methodology. The effectiveness of the presented deep learning model has been investigated based on 3 datasets containing brain, kidney and lung images, showing remarkable diagnostic efficiency with classification accuracies of 99.33%, 100% and 99.7% for the 3 datasets respectively. The impact of the augmentation preprocessing step has also been extensively examined using 4 evaluation measures. The proposed low-complexity scheme, in contrast to other models in the literature, renders our model quite robust to cases of overfitting that typically accompany small datasets frequently encountered in medical classification challenges. Finally, the model can be easily re-trained in case additional volume images are included, as its simplistic architecture does not impose a significant computational burden.
Practical Synchronization for OTFS
Authors: Mohsen Bayat, Sanoopkumar P. S., Arman Farhang
Date: 2023-01-24 00:00:00

In the existing literature on joint timing and frequency synchronization of orthogonal time frequency space modulation (OTFS), practically infeasible impulse pilot with large peak-to-average power ratio (PAPR) is deployed. Hence, in this paper, we propose a timing offset (TO) and carrier frequency offset (CFO) estimation for OTFS over a linear time-varying (LTV) channel, using a low PAPR pilot structure. The proposed technique utilizes the recently proposed practically feasible pilot structure with a cyclic prefix (PCP). We exploit the periodic properties of PCP in both delay and time domains to find the starting point of each OTFS block. Furthermore, we propose a two-stage CFO estimation technique with over an order of magnitude higher estimation accuracy than the existing estimator using the impulse pilot. In the first stage, a coarse CFO estimate is obtained which is refined in the second stage, through our proposed maximum likelihood (ML) based approach. The proposed ML-based approach deploys the generalized complex exponential basis expansion model (GCE-BEM) to capture the time variations of the channel, absorb them into the pilot and provide an accurate CFO estimate. Since our proposed synchronization technique utilizes the same pilot deployed for channel estimation, it does not require any additional overhead. Finally, we evaluate the performance of our proposed synchronization technique through simulations. We also compare and show the superior performance of our proposed technique to the only other existing joint TO and CFO estimation method in OTFS literature.
Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars
Authors: Ignacio Roldan, Francesco Fioranelli, Alexander Yarovoy
Date: 2023-01-24 00:00:00
Tasks: Decision Making, Self-Supervised Learning

A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network to extrapolate the antenna element's response. Afterward, the trained network is used to enhance the angular resolution of compact, low-cost radars. One million scenarios are simulated in a Monte-Carlo fashion, varying the number of targets, their Radar Cross Section (RCS), and location to evaluate the method's performance. Finally, the method is tested in real automotive data collected outdoors with a commercial radar system. A significant increase in the ability to resolve targets is demonstrated, which can translate to more accurate and faster responses from the planning and decision making system of the vehicle.
Radar Sensing via OTFS Signaling: A Delay Doppler Signal Processing Perspective
Authors: Kecheng Zhang, Weijie Yuan, Shuangyang Li, Fan Liu, Feifei Gao, Pingzhi Fan, Yunlong Cai
Date: 2023-01-24 00:00:00

The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain. Since the range and velocity, which can be derived from the delay and Doppler shifts, are the parameters of interest for radar sensing, it is natural to consider implementing DD signal processing for radar sensing. In this paper, we investigate the potential connections between the OTFS and DD domain radar signal processing. Our analysis shows that the range-Doppler matrix computing process in radar sensing is exactly the demodulation of OTFS with a rectangular pulse shaping filter. Furthermore, we propose a two-dimensional (2D) correlation-based algorithm to estimate the fractional delay and Doppler parameters for radar sensing. Simulation results show that the proposed algorithm can efficiently obtain the delay and Doppler shifts associated with multiple targets.
Training End-to-End Unrolled Iterative Neural Networks for SPECT Image Reconstruction
Authors: Zongyu Li, Yuni Dewaraja, Jeff Fessler
Date: 2023-01-23 00:00:00
Tasks: Image Reconstruction, Rolling Shutter Correction

Training end-to-end unrolled iterative neural networks for SPECT image reconstruction requires a memory-efficient forward-backward projector for efficient backpropagation. This paper describes an open-source, high performance Julia implementation of a SPECT forward-backward projector that supports memory-efficient backpropagation with an exact adjoint. Our Julia projector uses only ~5% of the memory of an existing Matlab-based projector. We compare unrolling a CNN-regularized expectation-maximization (EM) algorithm with end-to-end training using our Julia projector with other training methods such as gradient truncation (ignoring gradients involving the projector) and sequential training, using XCAT phantoms and virtual patient (VP) phantoms generated from SIMIND Monte Carlo (MC) simulations. Simulation results with two different radionuclides (90Y and 177Lu) show that: 1) For 177Lu XCAT phantoms and 90Y VP phantoms, training unrolled EM algorithm in end-to-end fashion with our Julia projector yields the best reconstruction quality compared to other training methods and OSEM, both qualitatively and quantitatively. For VP phantoms with 177Lu radionuclide, the reconstructed images using end-to-end training are in higher quality than using sequential training and OSEM, but are comparable with using gradient truncation. We also find there exists a trade-off between computational cost and reconstruction accuracy for different training methods. End-to-end training has the highest accuracy because the correct gradient is used in backpropagation; sequential training yields worse reconstruction accuracy, but is significantly faster and uses much less memory.
Introduction to Non-Invasive Current Estimation (NICE)
Authors: Jonathan L. Fasig, Christopher K. White, Barry K. Gilbert, Clifton R. Haider
Date: 2023-01-17 00:00:00
Tasks: CPU

It is notoriously difficult to measure instantaneous supply current to a device such as an ASIC, FPGA, or CPU without also affecting the instantaneous supply voltage and compromising the operation of the device [21]. For decades designers have relied on rough estimates of dynamic load currents that stimulate a designed Power Delivery Network (PDN). The consequences of inaccurate load-current characterization can range from excessive PDN cost and lengthened development schedules to poor performance or functional failure. This paper will introduce and describe a method to precisely determine timedomain current waveforms from a pair of measured timedomain voltage waveforms. This NonInvasive Current Estimation (NICE) method is based on established twoport network theory along with component and board modeling techniques that have been validated through measurements on demonstrative circuits. This paper will show that the NICE method works for any transient event that can be captured on a digital oscilloscope. Limitations of the method and underlying measurements are noted where appropriate. The method is applied to a simple PDN with an arbitrary load, and the NICE-derived current waveform is verified against an independent measurement by sense resistor. With careful component and board modeling, it is possible to calculate current waveforms with a root mean square error of less than five percent compared to the reference measurement. Current transients that were previously difficult or impossible to characterize by any means can now be calculated and displayed within seconds of an oscilloscope-trigger event by using NICE. ASIC and FPGA manufacturers can now compute the startup current for their device and publish the actual waveform, or provide a piecewiselinear SPICE model (PWL source) to facilitate design and testing of the regulator and PDN required to support their device.
Comparison of Optical and Electrical Links for Highly-Interconnected Systems
Authors: James Kruchowski, Vladimir Sokolov, Nathan E. Harff, Mark A. Nelson, KY Liou, Graham Cameron, Barry K. Gilbert, Erik S. Daniel
Date: 2023-01-17 00:00:00

As data rates for multi-gigabit serial interfaces within multi-node compute systems approach and exceed 10 Gigabits per second (Gbps), board-to-board and chip-to-chip optical signaling solutions become more attractive, particularly for longer (e.g. 50-100 cm) links. The transition to optical signaling will potentially allow new high performance compute (HPC) system architectures that benefit from characteristics unique to optical links. To examine these characteristics, we built and tested several optical demonstration vehicles; one based on dense wavelength division multiplexing (DWDM), and others based on multiple point-to-point links carried across multimode fibers. All test vehicles were constructed to evaluate applicability to a multi-node compute system. Test results, combined with data from recent research efforts are summarized and compared to equivalent electrical links and the advantages and design characteristics unique to optical signaling are identified.