pwc-archive/papers-with-abstracts huggingface dataset:
Omni-directional Pathloss Measurement Based on Virtual Antenna Array with Directional Antennas
Authors: Mengting Li, Fengchun Zhang, Xiang Zhang, Yejian Lyu, Wei Fan
Date: 2022-08-07 00:00:00

Omni-directional pathloss, which refers to the pathloss when omni-directional antennas are used at the link ends, are essential for system design and evaluation. In the millimeter-wave (mm-Wave) and beyond bands, high gain directional antennas are widely used for channel measurements due to the significant signal attenuation. Conventional methods for omni-directional pathloss estimation are based on directional scanning sounding (DSS) system, i.e., a single directional antenna placed at the center of a rotator capturing signals from different rotation angles. The omni-directional pathloss is obtained by either summing up all the powers above the noise level or just summing up the powers of detected propagation paths. However, both methods are problematic with relatively wide main beams and high side-lobes provided by the directional antennas. In this letter, directional antenna based virtual antenna array (VAA) system is implemented for omni-directional pathloss estimation. The VAA scheme uses the same measurement system as the DSS, yet it offers high angular resolution (i.e. narrow main beam) and low side-lobes, which is essential for achieving accurate multipath detection in the power angular delay profiles (PADPs) and thereby obtaining accurate omni-directional pathloss. A measurement campaign was designed and conducted in an indoor corridor at 28-30 GHz to verify the effectiveness of the proposed method.
High-level synthesis design of scalable ultrafast ultrasound beamformer with single FPGA
Authors: Zhengchang Kou, Qi You, Jihun Kim, Zhijie Dong, Matthew R. Lowerison, Nathiya V. Chandra Sekaran, Daniel A. Llano, Pengfei Song, Michael L. Oelze
Date: 2022-08-06 00:00:00
Tasks: High-Level Synthesis

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy (ULM) and functional ultrasound (fUS). Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency (RF) signal, and by the latency of the computationally expensive beamforming process. As such, continuous ultrafast data acquisition and beamforming remain elusive with existing software beamformers based on CPUs or GPUs. To address these challenges, the proposed work introduces a novel method of implementing an ultrafast ultrasound beamformer specifically for ultrafast plane wave imaging (PWI) on a field programmable gate array (FPGA) by using high-level synthesis. A parallelized implementation of the beamformer on a single FPGA was proposed by 1) utilizing a delay compression technique to reduce the delay profile size, which enables both run-time pre-calculated delay profile loading from external memory and delay reuse 2) vectorizing channel data fetching which is enabled by delay reuse, and 3) using fixed summing networks to reduce consumption of logic resources. Our proposed method presents two unique advantages over current FPGA beamformers: 1) high scalability that allows fast adaptation to different FPGA resources and beamforming speed demands by using Xilinx High-Level Synthesis as the development tool, and 2) allow a compact form factor design by using a single FPGA to complete the beamforming instead of multiple FPGAs. With the proposed method, a sustainable average beamforming rate of 4.83 G samples/second in terms of input raw RF sample was achieved. The resulting image quality of the proposed beamformer was compared with the software beamformer on the Verasonics Vantage system for both phantom imaging and in vivo imaging of a mouse brain.
Noise-Type Radars: Probability of Detection vs. Correlation Coefficient and Integration Time
Authors: David Luong, Bhashyam Balaji, Sreeraman Rajan
Date: 2022-08-06 00:00:00
Tasks: Vocal Bursts Type Prediction

Noise radars have the same mathematical description as a type of quantum radar known as quantum two-mode squeezing radar. Although their physical implementations are very different, this mathematical similarity allows us to analyze them collectively. We may consider the two types of radars as forming a single class of radars, called noise-type radars. The target detection performance of noise-type radars depends on two parameters: the number of integrated samples and a correlation coefficient. In this paper, we show that when the number of integrated samples is large and the correlation coefficient is low, the detection performance becomes a function of a single parameter: the number of integrated samples multiplied by the square of the correlation coefficient. We then explore the detection performance of noise-type radars in terms of this emergent parameter; in particular, we determine the probability of detection as a function of this parameter.
A Spatially Separable Attention Mechanism for massive MIMO CSI Feedback
Authors: Sharan Mourya, SaiDhiraj Amuru, Kiran Kumar Kuchi
Date: 2022-08-05 00:00:00
Tasks: Compressive Sensing

Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming. However, for a massive MIMO system, this feedback overhead is huge and grows linearly with the number of antennas. To reduce the feedback overhead several compressive sensing (CS) techniques were implemented in recent years but these techniques are often iterative and are computationally complex to realize in power-constrained user equipment (UE). Hence, a data-based deep learning approach took over in these recent years introducing a variety of neural networks for CSI compression. Specifically, transformer-based networks have been shown to achieve state-of-the-art performance. However, the multi-head attention operation, which is at the core of transformers, is computationally complex making transformers difficult to implement on a UE. In this work, we present a lightweight transformer named STNet which uses a spatially separable attention mechanism that is significantly less complex than the traditional full-attention. Equipped with this, STNet outperformed state-of-the-art models in some scenarios with approximately $1/10^{th}$ of the resources.
Hopf Bifurcations of Reaction Networks with Zero-One Stoichiometric Coefficients
Authors: Xiaoxian Tang, Kaizhang Wang
Date: 2022-08-08 00:00:00

For the reaction networks with zero-one stoichiometric coefficients (or simply zero-one networks), we prove that if a network admits a Hopf bifurcation, then the rank of the stoichiometric matrix is at least four. As a corollary, we show that if a zero-one network admits a Hopf bifurcation, then it contains at least four species and five reactions. As applications, we show that there exist rank-four subnetworks, which have the capacity for Hopf bifurcations/oscillations, in two biologically significant networks: the MAPK cascades and the ERK network. We provide a computational tool for computing all four-species, five-reaction, zero-one networks that have the capacity for Hopf bifurcations.
Dynamic Information Transfer in Stochastic Biochemical Networks
Authors: Anne-Lena Moor, Christoph Zechner
Date: 2022-08-08 00:00:00

We develop numerical and analytical approaches to calculate mutual information between complete paths of two molecular components embedded into a larger reaction network. In particular, we focus on a continuous-time Markov chain formalism, frequently used to describe intracellular processes involving lowly abundant molecular species. Previously, we have shown how the path mutual information can be calculated for such systems when two molecular components interact directly with one another with no intermediate molecular components being present. In this work, we generalize this approach to biochemical networks involving an arbitrary number of molecular components. We present an efficient Monte Carlo method as well as an analytical approximation to calculate the path mutual information and show how it can be decomposed into a pair of transfer entropies that capture the causal flow of information between two network components. We apply our methodology to study information transfer in a simple three-node feedforward network, as well as a more complex positive feedback system that switches stochastically between two metastable modes.
What is the nature of motor adaptation to dynamic perturbations?
Authors: Etienne Moullet, Agnès Roby-Brami, Emmanuel Guigon
Date: 2022-08-07 00:00:00

When human participants repeatedly encounter a velocity-dependent force field that distorts their movement trajectories, they adapt their motor behavior to recover straight trajectories. Computational models suggest that adaptation to a force field occurs at the action selection level through changes in the mapping between goals and actions. The quantitative prediction from these models indicates that early perturbed trajectories before adaptation and late unperturbed trajectories after adaptation should have opposite curvature, i.e. one being a mirror image of the other. We tested these predictions in a human adaptation experiment and we found that the expected mirror organization was either absent or much weaker than predicted by the models. These results are incompatible with adaptation occurring at the action selection level but compatible with adaptation occurring at the goal selection level, as if adaptation corresponds to aiming toward spatially remapped targets.
Complex non-Markovian dynamics and the dual role of astrocytes in Alzheimer's disease development and propagation
Authors: Swadesh Pal, Roderick Melnik
Date: 2022-08-06 00:00:00

Alzheimer's disease (AD) is a common neurodegenerative disorder nowadays. Amyloid-beta (A$\beta$) and tau proteins are among the main contributors to the development or propagation of AD. In AD, A$\beta$ proteins clump together to form plaques and disrupt cell functions. On the other hand, the abnormal chemical change in the brain helps to build sticky tau tangles that block the neuron's transport system. Astrocytes generally maintain a healthy balance in the brain by clearing the A$\beta$ plaques (toxic A$\beta$). However, over-activated astrocytes release chemokines and cytokines in the presence of A$\beta$ and react to pro-inflammatory cytokines, further increasing the production of A$\beta$. In this paper, we construct a mathematical model that can capture astrocytes' dual behaviour. Furthermore, we reveal that the disease propagation depends on the current time instance and the disease's earlier status, called the ``memory effect''. We consider a fractional order network mathematical model to capture the influence of such memory effect on AD propagation. We have integrated brain connectome data into the model and studied the memory effect, the dual role of astrocytes, and the brain's neuronal damage. Based on the pathology, primary, secondary, and mixed tauopathies parameters are considered in the model. Due to the mixed tauopathy, different brain nodes or regions in the brain connectome accumulate different toxic concentrations of A$\beta$ and tau proteins. Finally, we explain how the memory effect can slow down the propagation of such toxic proteins in the brain, decreasing the rate of neuronal damage.
Functional architecture of M1 cells encoding movement direction
Authors: Caterina Mazzetti, Alessandro Sarti, Giovanna Citti
Date: 2022-08-05 00:00:00

In this paper we propose a neurogeometrical model of the behaviour of cells of the arm area of the primary motor cortex (M1). We will mathematically express as a fiber bundle the hypercolumnar organization of this cortical area, first modelled by Georgopoulos in \cite{georgopoulos1982relations, georgopoulos2015columnar}. On this structure, we will consider the selective tuning of M1 neurons of kinematic variables of positions and directions of movement. We will then extend this model to encode the notion of fragments introduced by Hatsopoulos \cite{Encoding} which describes the selectivity of neurons to movement direction varying in time. This leads to consider a higher dimensional geometrical structure where fragments are represented as integral curves. A comparison with the curves obtained through numerical simulations and experimental data will be presented. Moreover, neural activity shows coherent behaviours represented in terms of movement trajectories pointing to a specific pattern of movement decomposition \cite{kadmon2019movement}. Here, we will recover this pattern through a spectral clustering algorithm in the subriemannian structure we introduced, and compare our results with the neurophysiological one of \cite{kadmon2019movement}.
A Comparison Study of the Detection Limit of Omicron SARS-CoV-2 Nucleocapsid by various Rapid Antigen Tests
Authors: Daniela Dobrynin, Iryna Polishchuk, Boaz Pokroy
Date: 2022-07-29 00:00:00

Since the first case of COVID-19 disease in Wuhan in December 2019, there is a worldwide struggle to reduce the transmission of acute respiratory syndrome coronavirus SARS-CoV-2. Many countries worldwide decided to impose local lockdowns in order to reduce person-to-person interactions, masks became obligatory especially in closed spaces, and there was a general requirement for social distance. However, the most efficient method to reduce continuing spreading of infection among the population, and in the meantime maintain a regular daily life, is early detection of infected contagious people. Up to now, the most reliable method for SARS-CoV-2 detection is reverse-transcriptase PCR test (RT-PCR). It is possible to detect the virus even if there is only one RNA strand in the sample, and run hundreds of samples simultaneously. This method has a few disadvantages, such as high cost, is time consuming, the need for medical laboratories and skilled staff to perform the test, and the major flaw: the lack of appropriate number of available tests. The latterly prominent Omicron variant (B.1.1.529) and its derivatives have caused a tremendous increase in the number of infected people due to its enhanced transmissibility. This all emphasizes the high demand for easy-to-use, cheap and available detection tests.
Causality in cardiorespiratory signals in pediatric cardiac patients
Authors: Maciej Rosoł, Jakub S. Gąsior, Iwona Walecka, Bożena Werner, Gerard Cybulski, Marcel Młyńczak
Date: 2022-08-05 00:00:00
Tasks: Causal Inference

Four different Granger causality-based methods - one linear and three nonlinear (Granger Causality, Kernel Granger Causality, large-scale Nonlinear Granger Causality, and Neural Network Granger Causality) were used for assessment and causal-based quantification of the respiratory sinus arrythmia (RSA) in the group of pediatric cardiac patients, based on the single-lead ECG and impedance pneumography signals (the latter as the tidal volume curve equivalent). Each method was able to detect the dependency (in terms of causal inference) between respiratory and cardiac signals. The correlations between quantified RSA and the demographic parameters were also studied, but the results differ for each method.
A mean-variance optimized portfolio constructed for investment in a reference security, for an investor with a preference towards an accepted set of securities
Authors: Sidharth Mallik
Date: 2022-08-08 00:00:00

We consider a reference security, understood to be an attractive investment, with the caveat that an investor is not willing to directly invest in the security, for presence of constraints, either investor specific or pertaining to the security itself. The investor, however, is open to a portfolio constructed with an accepted set of securities, where returns could be considered similar to the reference security. We demonstrate, under a measure of similarity, such a portfolio could be selected with a mean-variance characterization, as defined by Markowitz. Furthermore, we consider the performance relative to the reference security, with the Sharpe Ratio. The objective of the paper is to derive an optimal portfolio to address an investor preference for the accepted set of securities.
Learning Financial Networks with High-frequency Trade Data
Authors: Kara Karpman, Sumanta Basu, David Easley
Date: 2022-08-06 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

Financial networks are typically estimated by applying standard time series analyses to price-based economic variables collected at low-frequency (e.g., daily or monthly stock returns or realized volatility). These networks are used for risk monitoring and for studying information flows in financial markets. High-frequency intraday trade data sets may provide additional insights into network linkages by leveraging high-resolution information. However, such data sets pose significant modeling challenges due to their asynchronous nature, nonlinear dynamics, and nonstationarity. To tackle these challenges, we estimate financial networks using random forests. The edges in our network are determined by using microstructure measures of one firm to forecast the sign of the change in a market measure (either realized volatility or returns kurtosis) of another firm. We first investigate the evolution of network connectivity in the period leading up to the U.S. financial crisis of 2007-09. We find that the networks have the highest density in 2007, with high degree connectivity associated with Lehman Brothers in 2006. A second analysis into the nature of linkages among firms suggests that larger firms tend to offer better predictive power than smaller firms, a finding qualitatively consistent with prior works in the market microstructure literature.
A Historical Interaction between Artificial Intelligence and Philosophy
Authors: Youheng Zhang
Date: 2022-07-23 00:00:00
Tasks: Philosophy

This paper reviews the historical development of AI and representative philosophical thinking from the perspective of the research paradigm. Additionally, it considers the methodology and applications of AI from a philosophical perspective and anticipates its continued advancement. In the history of AI, Symbolism and connectionism are the two main paradigms in AI research. Symbolism holds that the world can be explained by symbols and dealt with through precise, logical processes, but connectionism believes this process should be implemented through artificial neural networks. Regardless of how intelligent machines or programs should achieve their smart goals, the historical development of AI demonstrates the best answer at this time. Still, it is not the final answer of AI research.
Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network
Authors: Xu Liu, Wen Yao, Xiaohu Zheng, Yingchun Xu
Date: 2022-06-15 00:00:00
Tasks: Computational Efficiency

In the complex multi-state system (MSS), reliability analysis is a significant research content, both for equipment design, manufacturing, usage and maintenance. Universal Generating Function (UGF) is an important method in the reliability analysis, which efficiently obtains the system reliability by a fast algebraic procedure. However, when structural relationships between subsystems or components are not clear or without explicit expressions, the UGF method is difficult to use or not applicable at all. Bayesian Network (BN) has a natural advantage in terms of uncertainty inference for the relationship without explicit expressions. For the number of components is extremely large, though, it has the defects of low efficiency. To overcome the respective defects of UGF and BN, a novel reliability analysis method called UGF-BN is proposed for the complex MSS. In the UGF-BN framework, the UGF method is firstly used to analyze the bottom components with a large number. Then probability distributions obtained are taken as the input of BN. Finally, the reliability of the complex MSS is modeled by the BN method. This proposed method improves the computational efficiency, especially for the MSS with the large number of bottom components. Besides, the aircraft reliability-based design optimization based on the UGF-BN method is further studied with budget constraints on mass, power, and cost. Finally, two cases are used to demonstrate and verify the proposed method.
Linking Properties to Microstructure in Liquid Metal Embedded Elastomers via Machine Learning
Authors: Abhijith Thoopul Anantharanga, Mohammad Saber Hashemi, Azadeh Sheidaei
Date: 2022-07-24 00:00:00

Liquid metals (LM) are embedded in an elastomer matrix to obtain soft composites with unique thermal, dielectric, and mechanical properties. They have applications in soft robotics, biomedical engineering, and wearable electronics. By linking the structure to the properties of these materials, it is possible to perform material design rationally. Liquid-metal embedded elastomers (LMEEs) have been designed for targeted electro-thermo-mechanical properties by semi-supervised learning of structure-property (SP) links in a variational autoencoder network (VAE). The design parameters are the microstructural descriptors that are physically meaningful and have affine relationships with the synthetization of the studied particulate composite. The machine learning (ML) model is trained on a generated dataset of microstructural descriptors with their multifunctional property quantities as their labels. Sobol sequence is used for in-silico Design of Experiment (DoE) by sampling the design space to generate a comprehensive dataset of 3D microstructure realizations via a packing algorithm. The mechanical responses of the generated microstructures are simulated using a previously developed Finite Element (FE) model, considering the surface tension induced by LM inclusions, while the linear thermal and dielectric constants are homogenized with the help of our in-house Fast Fourier Transform (FFT) package. Following the training by minimization of an appropriate loss function, the VAE encoder acts as the surrogate of numerical solvers of the multifunctional homogenizations, and its decoder is used for the material design. Our results indicate the satisfactory performance of the surrogate model and the inverse calculator with respect to high-fidelity numerical simulations validated with LMEE experimental results.