pwc-archive/papers-with-abstracts huggingface dataset:
Analog Isolated Multilevel Quantizer for Voltage Sensing while Maintaining Galvanic Isolation
Authors: Peter Weber, Antonia Papandreou-Suppappola
Date: 2024-02-28 00:00:00

A low-power, compact device for performing measurements in electrical systems with isolated voltage domains is proposed. Isolated measurements are required in numerous applications. For instance, a measurement of the bus voltage for a system with a high supply voltage and lower isolated local voltage level may be needed for system health monitoring and control. Such a requirement may necessitate the use of isolation amplifiers to provide voltage telemetry for the local system. Isolation amplifiers require dual galvanically isolated supplies and use magnetic, capacitive, or optical barriers between primary and secondary sides. Producing this supplemental voltage requires an extra voltage converter, which consumes power and generates electromagnetic interference which must, in turn, be filtered. Complex designs incorporating feedback are needed to achieve linear response. The proposed Analog Isolated Multilevel Quantizer (AIMQ) addresses these issues by monitoring the primary-side signal and communicating the results to the secondary side using a novel scheme involving Zener diodes, optocouplers, transistors, one-hot coding, and discrete outputs. The result is a low power isolated transducer that can in principle be extended to an arbitrary bit depth.
Timer-Based Coverage Control for Mobile Sensors
Authors: Federico M. Zegers, Sean Phillips, Gregory P. Hicks
Date: 2024-02-28 00:00:00

This work investigates the coverage control problem over a static, compact, and convex workspace and develops a hybrid extension of the continuous-time Lloyd algorithm. Each agent in a multi-agent system (MAS) is equipped with a timer mechanism that generates intermittent measurement and control update events, which may occur asynchronously between agents. Between consecutive event times, as determined by the corresponding timer mechanism, the controller of each agent is held constant. These controllers are shown to drive the configuration of the MAS into a neighborhood of the set of centroidal Voronoi configurations, i.e., the minimizers of the standard locational cost. The combination of continuous-time dynamics with intermittently updated control inputs is modeled as a hybrid system. The coverage objective is posed as a set attractivity problem for hybrid systems, where an invariance-based convergence analysis yields sufficient conditions that ensure maximal solutions of the hybrid system asymptotically converge to a desired set. A brief simulation example is included to showcase the result.
MaxCUCL: Max-Consensus with Deterministic Convergence in Networks with Unreliable Communication
Authors: Apostolos I. Rikos, Themistoklis Charalambous, Karl H. Johansson
Date: 2024-02-28 00:00:00

In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max-consensus is reached in networks characterized by unreliable communication links (i.e., links suffering from packet drops). Our proposed algorithm is the first algorithm that achieves max-consensus in a deterministic manner (i.e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops). Furthermore, it allows nodes to determine whether convergence has been achieved (enabling them to transition to subsequent tasks). The operation of MaxCUCL relies on the deployment of narrowband error-free feedback channels used for acknowledging whether a packet transmission between nodes was successful. We analyze the operation of our algorithm and show that it converges after a finite number of time steps. Finally, we demonstrate our algorithm's effectiveness and practical applicability by applying it to a sensor network deployed for environmental monitoring.
Nonlinear identification algorithm for online and offline study of pulmonary mechanical ventilation
Authors: Diego A. Riva, Carolina A. Evangelista, Paul F. Puleston, Luis Corsiglia, Nahuel Dargains
Date: 2024-02-28 00:00:00
Tasks: parameter estimation

This work presents an algorithm for determining the parameters of a nonlinear dynamic model of the respiratory system in patients undergoing assisted ventilation. Using the pressure and flow signals measured at the mouth, the model's quadratic pressure-volume characteristic is fit to this data in each respiratory cycle by appropriate estimates of the model parameters. Parameter changes during ventilation can thus also be detected. The algorithm is first refined and assessed using data derived from simulated patients represented through a sigmoidal pressure-volume characteristic with hysteresis. As satisfactory results are achieved with the simulated data, the algorithm is evaluated with real data obtained from actual patients undergoing assisted ventilation. The proposed nonlinear dynamic model and associated parameter estimation algorithm yield closer fits than the static linear models computed by respiratory machines, with only a minor increase in computation. They also provide more information to the physician, such as the pressure-volume (P-V) curvature and the condition of the lung (whether normal, under-inflated, or over-inflated). This information can be used to provide safer ventilation for patients, for instance by ventilating them in the linear region of the respiratory system.
Properties of Hagen-Poiseuille flow in channel networks
Authors: A. F. Valente, R. Almeida, R. Dilão
Date: 2024-02-29 00:00:00

We derive the main properties of adaptive Hagen-Poiseuille flows in elastic microchannel networks similar to biological veins found in organisms. We demonstrate that adaptive Hagen-Poiseuille flows effectively simulate key features of \textit{Physarum polycephalum} networks, replicating physiological out-of-equilibrium phenomena such as peristalsis and shuttle streaming, which are associated with the mechanism of nutrient transport in \textit{Physarum}. A new topological steady state has been identified for asynchronous adaptation, supporting out-of-equilibrium laminar fluxes. Adaptive Hagen-Poiseuille flows exhibit saturation effects on the fluxes in contractile veins, as observed in both animal and artificial contractile veins. These results suggest that the non-equilibrium effects observed in \textit{Physarum} have a hydrodynamic origin
Adjusting Dynamics of Hopfield Neural Network via Time-variant Stimulus
Authors: Xuenan Peng, Chengqing Li, Yicheng Zeng, Chun-Lai Li
Date: 2024-01-15 00:00:00

As a paradigmatic model for nonlinear dynamics studies, the Hopfield Neural Network (HNN) demonstrates a high susceptibility to external disturbances owing to its intricate structure. This paper delves into the challenge of modulating HNN dynamics through time-variant stimuli. The effects of adjustments using two distinct types of time-variant stimuli, namely the Weight Matrix Stimulus (WMS) and the State Variable Stimulus (SVS), along with a Constant Stimulus (CS) are reported. The findings reveal that deploying four WMSs enables the HNN to generate either a four-scroll or a coexisting two-scroll attractor. When combined with one SVS, four WMSs can lead to the formation of an eight-scroll or four-scroll attractor, while the integration of four WMSs and multiple SVSs can induce grid-multi-scroll attractors. Moreover, the introduction of a CS and an SVS can significantly disrupt the dynamic behavior of the HNN. Consequently, suitable adjustment methods are crucial for enhancing the network's dynamics, whereas inappropriate applications can lead to the loss of its chaotic characteristics. To empirically validate these enhancement effects, the study employs an FPGA hardware platform. Subsequently, an image encryption scheme is designed to demonstrate the practical application benefits of the dynamically adjusted HNN in secure multimedia communication. This exploration into the dynamic modulation of HNN via time-variant stimuli offers insightful contributions to the advancement of secure communication technologies.
Noise-induced survival resonances during fractional killing of cell populations
Authors: Francesco Puccioni, Johannes Pausch, Paul Piho, Philipp Thomas
Date: 2024-02-29 00:00:00

Fractional killing in response to drugs is a hallmark of non-genetic cellular heterogeneity. Yet how individual lineages evade drug treatment, as observed in bacteria and cancer cells, is not quantitatively understood. We analyse a stochastic population model with age-dependent division and death rates and characterise the emergence of fractional killing as a stochastic phenomenon under constant and periodic drug environments. In constant environments, increasing cell cycle noise induces a phase transition from complete to fractional killing, while increasing death noise can induce the reverse transition. In periodic drug environments, we discover survival resonance phenomena that give rise to peaks in the survival probabilities at division or death times that are multiples of the environment duration not seen in unstructured populations.
Testing Information Ordering for Strategic Agents
Authors: Sukjin Han, Hiroaki Kaido, Lorenzo Magnolfi
Date: 2024-02-29 00:00:00

A key primitive of a strategic environment is the information available to players. Specifying a priori an information structure is often difficult for empirical researchers. We develop a test of information ordering that allows researchers to examine if the true information structure is at least as informative as a proposed baseline. We construct a computationally tractable test statistic by utilizing the notion of Bayes Correlated Equilibrium (BCE) to translate the ordering of information structures into an ordering of functions. We apply our test to examine whether hubs provide informational advantages to certain airlines in addition to market power.
An Empirical Analysis of Scam Tokens on Ethereum Blockchain
Authors: Vahidin Jeleskovic
Date: 2024-02-29 00:00:00

This article presents an empirical investigation into the determinants of total revenue generated by counterfeit tokens on Uniswap. It offers a detailed overview of the counterfeit token fraud process, along with a systematic summary of characteristics associated with such fraudulent activities observed in Uniswap. The study primarily examines the relationship between revenue from counterfeit token scams and their defining characteristics, and analyzes the influence of market economic factors such as return on market capitalization and price return on Ethereum. Key findings include a significant increase in overall transactions of counterfeit tokens on their first day of fraud, and a rise in upfront fraud costs leading to corresponding increases in revenue. Furthermore, a negative correlation is identified between the total revenue of counterfeit tokens and the volatility of Ethereum market capitalization return, while price return volatility on Ethereum is found to have a positive impact on counterfeit token revenue, albeit requiring further investigation for a comprehensive understanding. Additionally, the number of subscribers for the real token correlates positively with the realized volume of scam tokens, indicating that a larger community following the legitimate token may inadvertently contribute to the visibility and success of counterfeit tokens. Conversely, the number of Telegram subscribers exhibits a negative impact on the realized volume of scam tokens, suggesting that a higher level of scrutiny or awareness within Telegram communities may act as a deterrent to fraudulent activities. Finally, the timing of when the scam token is introduced on the Ethereum blockchain may have a negative impact on its success. Notably, the cumulative amount scammed by only 42 counterfeit tokens amounted to almost 11214 Ether.
Impacts of electric carsharing on a power sector with variable renewables
Authors: Adeline Guéret, Wolf-Peter Schill, Carlos Gaete-Morales
Date: 2024-02-29 00:00:00
Tasks: Time Series

Electrifying the car fleet is a major strategy for mitigating emissions in the transport sector. As electrification cannot solve all negative externalities associated with cars, reducing the size of the car fleet would be beneficial. Electric carsharing could allow to reconcile current car usage habits with a smaller fleet, but this may reduce the potential of electric cars to align their grid interactions with variable renewable electricity generation. We investigate how electric carsharing may impact the power sector, combining three methods: sequence clustering of car travel diaries, generation of synthetic electric vehicle time series, and power sector modelling. We show that switching to electric carsharing only moderately increases power sector costs, less than 110 euros per substituted car in our main setting. This effect is largest with bidirectional charging. We conclude that the power sector interactions of shared electric car fleets could still be aligned with variable renewable electricity generation.
Identifying Assumptions and Research Dynamics
Authors: Andrew Ellis, Ran Spiegler
Date: 2024-02-28 00:00:00
Tasks: Causal Inference, valid

A representative researcher has repeated opportunities for empirical research. To process findings, she must impose an "identifying assumption." She conducts research when the assumption is sufficiently plausible (taking into account both current beliefs and the quality of the opportunity), and updates beliefs as if the assumption were perfectly valid. We study the dynamics of this learning process. While the rate of research cannot always increase over time, research slowdown is possible. We characterize environments in which the rate is constant. Long-run beliefs can exhibit history-dependence and "false certitude." We apply the model to stylized examples of empirical methodologies: experiments, various causal-inference techniques, and "calibration."
DeepEMC-T2 Mapping: Deep Learning-Enabled T2 Mapping Based on Echo Modulation Curve Modeling
Authors: Haoyang Pei, Timothy M. Shepherd, Yao Wang, Fang Liu, Daniel K Sodickson, Noam Ben-Eliezer, Li Feng
Date: 2024-02-29 00:00:00
Tasks: parameter estimation

Purpose: Echo modulation curve (EMC) modeling can provide accurate and reproducible quantification of T2 relaxation times. The standard EMC-T2 mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2 mapping, to efficiently estimate accurate T2 maps from fewer echoes without a dictionary. Methods: DeepEMC-T2 mapping was developed using a modified U-Net to estimate both T2 and Proton Density (PD) maps directly from multi-echo spin-echo (MESE) images. The modified U-Net employs several new features to improve the accuracy of T2/PD estimation. MESE datasets from 68 subjects were used for training and evaluation of the DeepEMC-T2 mapping technique. Multiple experiments were conducted to evaluate the impact of the proposed new features on DeepEMC-T2 mapping. Results: DeepEMC-T2 mapping achieved T2 estimation errors ranging from 3%-12% in different T2 ranges and 0.8%-1.7% for PD estimation with 10/7/5/3 echoes, which yielded more accurate parameter estimation than standard EMC-T2 mapping. The new features proposed in DeepEMC-T2 mapping enabled improved parameter estimation. The use of a larger echo spacing with fewer echoes can maintain the accuracy of T2 and PD estimations while reducing the number of 180-degree refocusing pulses. Conclusions: DeepEMC-T2 mapping enables simplified, efficient, and accurate T2 quantification directly from MESE images without a time-consuming dictionary-matching step and requires fewer echoes. This allows for increased volumetric coverage and/or decreased SAR by reducing the number of 180-degree refocusing pulses.
Jointly Learning Selection Matrices For Transmitters, Receivers And Fourier Coefficients In Multichannel Imaging
Authors: Han Wang, Yiming Zhou, Eduardo Perez, Florian Roemer
Date: 2024-02-29 00:00:00

Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This paper introduces the Joint Deep Probabilistic Subsampling (J-DPS) method, which aims to learn optimal selection matrices simultaneously for transmitters, receivers, and Fourier coefficients. This task-based algorithm is realized by introducing a specialized measurement model and integrating a customized Complex Learned FISTA (CL-FISTA) network. We propose a parallel network architecture, partitioned into three segments corresponding to the three matrices, all working toward a shared optimization objective with adjustable loss allocation. A synthetic dataset is designed to reflect practical scenarios, and we provide quantitative comparisons with a traditional CRB-based algorithm, standard DPS, and J-DPS.
Quantification of Tracer Dilution Dynamics: An Exploration into the Mathematical Modeling of Medical Imaging
Authors: Ishmael N. Amartey, Andreas A. Linninger, Thomas Ventimiglia
Date: 2024-02-29 00:00:00

Convolution and deconvolution are essential techniques in various fields, notably in medical imaging, where they play a crucial role in analyzing dynamic processes such as blood flow. This paper explores the convolution and deconvolution of arterial and microvascular signals for determining impulse and residue functions from in vivo or simulated data and the derivation of the relationship between the residue function and perfusion metrics such as the Cerebral Blood Flow (CBF), Mean Transit Time (MTT) and Transit Time to Heterogeneity (TTH). The paper presents the spectral derivatives as a technique for recovering the impulse response function from the residue function, detailing the computational procedures involved and strategies for mitigating noise effects.
Privacy-Preserving Autoencoder for Collaborative Object Detection
Authors: Bardia Azizian, Ivan V. Bajic
Date: 2024-02-29 00:00:00
Tasks: License Plate Recognition, Object, object-detection, Object Detection, Privacy Preserving

Privacy is a crucial concern in collaborative machine vision where a part of a Deep Neural network (DNN) model runs on the edge, and the rest is executed on the cloud. In such applications, the machine vision model does not need the exact visual content to perform its task. Taking advantage of this potential, private information could be removed from the data insofar as it does not significantly impair the accuracy of the machine vision system. In this paper, we present an autoencoder-style network integrated within an object detection pipeline, which generates a latent representation of the input image that preserves task-relevant information while removing private information. Our approach employs an adversarial training strategy that not only removes private information from the bottleneck of the autoencoder but also promotes improved compression efficiency for feature channels coded by conventional codecs like VVC-Intra. We assess the proposed system using a realistic evaluation framework for privacy, directly measuring face and license plate recognition accuracy. Experimental results show that our proposed method is able to reduce the bitrate significantly at the same object detection accuracy compared to coding the input images directly, while keeping the face and license plate recognition accuracy on the images recovered from the bottleneck features low, implying strong privacy protection. Our code is available at https://github.com/bardia-az/ppa-code.
Towards Backward-Compatible Continual Learning of Image Compression
Authors: Zhihao Duan, Ming Lu, Justin Yang, Jiangpeng He, Zhan Ma, Fengqing Zhu
Date: 2024-02-29 00:00:00
Proceeding: CVPR 2024 1
Tasks: Continual Learning, Image Compression, Incremental Learning

This paper explores the possibility of extending the capability of pre-trained neural image compressors (e.g., adapting to new data or target bitrates) without breaking backward compatibility, the ability to decode bitstreams encoded by the original model. We refer to this problem as continual learning of image compression. Our initial findings show that baseline solutions, such as end-to-end fine-tuning, do not preserve the desired backward compatibility. To tackle this, we propose a knowledge replay training strategy that effectively addresses this issue. We also design a new model architecture that enables more effective continual learning than existing baselines. Experiments are conducted for two scenarios: data-incremental learning and rate-incremental learning. The main conclusion of this paper is that neural image compressors can be fine-tuned to achieve better performance (compared to their pre-trained version) on new data and rates without compromising backward compatibility. Our code is available at https://gitlab.com/viper-purdue/continual-compression
Unsupervised Airway Tree Clustering with Deep Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study
Authors: Sneha N. Naik, Elsa D. Angelini, R. Graham Barr, Norrina Allen, Alain Bertoni, Eric A. Hoffman, Ani Manichaikul, Jim Pankow, Wendy Post, Yifei Sun, Karol Watson, Benjamin M. Smith, Andrew F. Laine
Date: 2024-02-28 00:00:00

High-resolution full lung CT scans now enable the detailed segmentation of airway trees up to the 6th branching generation. The airway binary masks display very complex tree structures that may encode biological information relevant to disease risk and yet remain challenging to exploit via traditional methods such as meshing or skeletonization. Recent clinical studies suggest that some variations in shape patterns and caliber of the human airway tree are highly associated with adverse health outcomes, including all-cause mortality and incident COPD. However, quantitative characterization of variations observed on CT segmented airway tree remain incomplete, as does our understanding of the clinical and developmental implications of such. In this work, we present an unsupervised deep-learning pipeline for feature extraction and clustering of human airway trees, learned directly from projections of 3D airway segmentations. We identify four reproducible and clinically distinct airway sub-types in the MESA Lung CT cohort.
KGAMC: A Novel Knowledge Graph Driven Automatic Modulation Classification Scheme
Authors: Yike Li, Lu Yua, Fuhui Zhou, Qihui Wu, Naofal Al-Dhahir, Kai-Kit Wong
Date: 2024-02-29 00:00:00

Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven AMC schemes have achieved high accuracy. However, most of these schemes focus on generating additional prior knowledge or features of blind signals, which consumes longer computation time and ignores the interpretability of the model learning process. To solve these problems, we propose a novel knowledge graph (KG) driven AMC (KGAMC) scheme by training the networks under the guidance of domain knowledge. A modulation knowledge graph (MKG) with the knowledge of modulation technical characteristics and application scenarios is constructed and a relation-graph convolution network (RGCN) is designed to extract knowledge of the MKG. This knowledge is utilized to facilitate the signal features separation of the data-oriented model by implementing a specialized feature aggregation method. Simulation results demonstrate that KGAMC achieves superior classification performance compared to other benchmark schemes, especially in the low signal-to-noise ratio (SNR) range. Furthermore, the signal features of the high-order modulation are more discriminative, thus reducing the confusion between similar signals.