pwc-archive/papers-with-abstracts huggingface dataset:
High-resolution chirplet transform: from parameters analysis to parameters combination
Authors: Xiangxiang Zhu, Bei Li, Kunde Yang, Zhuosheng Zhang, Wenting Li
Date: 2021-08-02 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The standard chirplet transform (CT) with a chirp-modulated Gaussian window provides a valuable tool for analyzing linear chirp signals. The parameters present in the window determine the performance of the CT and play a vital role in high-resolution time-frequency (TF) analysis. In this paper, we give the window shape analysis of the CT and compare it with the extension that employs a rotating Gaussian window by the fractional Fourier transform. The given parameters analysis provides theoretical guidance for developing high-resolution CT. We then propose a multi-resolution chirplet transform (MrCT) by combining multiple CTs with different parameter combinations. These are combined geometrically to obtain an improved TF resolution by overcoming the limitations of any single representation of the CT. By deriving a combined instantaneous frequency equation, we further develop a high-concentration TF post-processing approach to improve the readability of the MrCT. Numerical experiments on simulated and real signals verify the effectiveness of the proposed methods.
A Track-Before-Detect Algorithm for UWB Radar Sensor Networks
Authors: B. Yan, A. Giorgetti, E. Paolini
Date: 2021-08-01 00:00:00

Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by multistatic radar network with localization errors of 25.4 cm and 19.7 cm, and detection rate of the two targets is 88.75%, and no false alarm trajectory.
Modeling and Design of IRS-Assisted Multi-Link FSO Systems
Authors: Hedieh Ajam, Marzieh Najafi, Vahid Jamali, Bernhard Schmauss, Robert Schober
Date: 2021-07-31 00:00:00

In this paper, we investigate the modeling and design of intelligent reflecting surface (IRS)-assisted optical communication systems which are deployed to relax the line-of-sight (LOS) requirement in multi-link free space optical (FSO) systems. The FSO laser beams incident on the optical IRSs have a Gaussian power intensity profile and a nonlinear phase profile, whereas the plane waves in radio frequency (RF) systems have a uniform power intensity profile and a linear phase profile. Given these substantial differences, the results available for IRS-assisted RF systems are not applicable to IRS-assisted FSO systems. Therefore, we develop a new analytical channel model for point-to-point IRS-assisted FSO systems based on the Huygens-Fresnel principle. Our analytical model captures the impact of the size, position, and orientation of the IRS as well as its phase shift profile on the end-to-end channel. To allow the sharing of the optical IRS by multiple FSO links, we propose three different protocols, namely the time division (TD), IRS-division (IRSD), and IRS homogenization (IRSH) protocols. The proposed protocols address the specific characteristics of FSO systems including the non-uniformity and possible misalignment of the laser beams. Furthermore, to compare the proposed IRS sharing protocols, we analyze the bit error rate (BER) and the outage probability of IRS-assisted multi-link FSO systems in the presence of inter-link interference. Our simulation results validate the accuracy of the proposed analytical channel model for IRS-assisted FSO systems and confirm that this model is applicable for both large and intermediate IRS-receiver lens distances. Moreover, in the absence of misalignment errors, the IRSD protocol outperforms the other protocols, whereas in the presence of misalignment errors, the IRSH protocol performs significantly better than the IRSD protocol.
Single-Channel EEG Based Arousal Level Estimation Using Multitaper Spectrum Estimation at Low-Power Wearable Devices
Authors: Berken Utku Demirel, Ivan Skelin, Haoxin Zhang, Jack J. Lin, Mohammad Abdullah Al Faruque
Date: 2021-07-31 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This paper proposes a novel lightweight method using the multitaper power spectrum to estimate arousal levels at wearable devices. We show that the spectral slope (1/f) of the electrophysiological power spectrum reflects the scale-free neural activity. To evaluate the proposed feature's performance, we used scalp EEG recorded during anesthesia and sleep with technician-scored Hypnogram annotations. It is shown that the proposed methodology discriminates wakefulness from reduced arousal solely based on the neurophysiological brain state with more than 80% accuracy. Therefore, our findings describe a common electrophysiological marker that tracks reduced arousal states, which can be applied to different applications (e.g., emotion detection, driver drowsiness). Evaluation on hardware shows that the proposed methodology can be implemented for devices with a minimum RAM of 512 KB with 55 mJ average energy consumption.
Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps
Authors: Yue Zhang, Yajie Zou, Lingtao Wuand Wanbing Han
Date: 2021-07-31 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Time Series, Time Series Analysis

Understanding the merging behavior patterns at freeway on-ramps is important for assistanting the decisions of autonomous driving. This study develops a primitive-based framework to identify the driving patterns during merging processes and reveal the evolutionary mechanism at freeway on-ramps in congested traffic flow. The Nonhomogeneous Hidden Markov Model is introduced to decompose the merging processes into primitives containing semantic information. Then, the time-series K-means clustering is utilized to gather these primitives with variable-length time series into interpretable merging behavior patterns. Different from traditional state segmentation methods (e.g. Hidden Markov Model), the model proposed in this study considers the dependence of transition probability on exogenous variables, thereby revealing the influence of covariates on the evolution of driving patterns. This approach is evaluated in the merging area at a freeway on-ramp using the INTERACTION dataset. Results demonstrate that the approach provides an insight about the complicated merging processes. The findings about interpretable merging behavior patterns as well as the evolutionary mechanism can be used to design and improve the merging decision-making for autonomous vehicles.
Mean Field Game of Optimal Relative Investment with Jump Risk
Authors: Lijun Bo, Shihua Wang, Xiang Yu
Date: 2021-08-02 00:00:00

This paper studies the n-player game and the mean field game under the CRRA relative performance on terminal wealth, in which the interaction occurs by peer competition. In the model with n agents, the price dynamics of underlying risky assets depend on a common noise and contagious jump risk modelled by a multi-dimensional nonlinear Hawkes process. With a continuum of agents, we formulate the MFG problem and characterize a deterministic mean field equilibrium in an analytical form under some conditions, allowing us to investigate some impacts of model parameters in the limiting model and discuss some financial implications. Moreover, based on the mean field equilibrium, we construct an approximate Nash equilibrium for the n-player game when n is sufficiently large. The explicit order of the approximation error is also derived.
Spike timing regularity in vestibular afferent neurons: How ionic currents influence sensory encoding mechanisms
Authors: Selina Baeza Loya
Date: 2021-08-02 00:00:00
Tasks: Diversity

Primary vestibular neurons are categorized as either regularly or irregularly firing afferents that use rate and temporal sensory encoding strategies, respectively. While many factors influence firing in these neurons, recent work in mammalian vestibular afferents has demonstrated a rich diversity in ion channels that drive spiking regularity. Here, I review key ionic currents studied in vitro and demonstrate how they may enable sensory encoding strategies demonstrated in vivo.
Discovering sparse control strategies in C. elegans
Authors: Edward D. Lee, Xiaowen Chen, Bryan C. Daniels
Date: 2021-08-02 00:00:00

Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology, and though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity but still characterizes collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations -- ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms -- to neural states and their impact on collective neural behavior. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, "pivotal" neurons that account for most of the system's sensitivity, suggesting a sparse mechanism for control of collective behavior.
What do neuroanatomical networks reveal about the ontology of human cognitive abilities?
Authors: Daniel Kristanto, Xinyang Liu, Werner Sommer, Andrea Hildebrandt, Changsong Zhou
Date: 2021-08-01 00:00:00
Tasks: Open-Ended Question Answering

Over the last decades, cognitive psychology has come to fair consensus about the ontological structure of human intelligence. However, it remains an open question, whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance in 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology is partly consistent with the cognitive performance-derived (psychometric) ontology. However, there are interpretable and complementary differences as well. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and differences between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics and treatment.
Macaque's Cortical Functional Connectivity Dynamics at the Onset of Propofol-Induced Anesthesia
Authors: Eduardo C. Padovani
Date: 2021-08-01 00:00:00
Tasks: Functional Connectivity

Propofol, when administered for general anesthesia, induces oscillatory dynamic brain states that are thought to underlie the drug's pharmacological effects. Despite the elucidation of propofol's mechanisms of action at the molecular level, its effects on neural circuits and overall cortical functioning, which eventually lead to unconsciousness, are still unclear. To identify possible mechanisms, the spatial-temporal patterns of functional connectivity established among specialized cortical areas in anesthetized subjects need to be described. Within this context, the present research involved the analysis of dense sub-dural ECoG electrode array recordings from macaques under propofol anesthetic induction. Granger causality methodology was used to infer functional connectivity interactions in five physiological frequency bands serially over time, every five seconds throughout the experiment. The time-resolved networks obtained permitted us to observe the unfolding of the anesthetic induction and compare the networks obtained under different experimental conditions. About one minute after administering propofol, functional connectivity started to increase for 4-5 minutes, then decreased until the LOC was achieved. A predominant Granger causality flow from the occipital and temporal areas towards the frontal and parietal regions was also evidenced during the transition. During general anesthesia, the local connectivity of the occipital lobe increased, as did the interactions between the occipital and temporal lobes. Conversely, the functional connectivity from the frontal and parietal lobes toward the temporal and occipital regions was mainly impaired. The research is one of the first studies to describe the dynamics of the functional connectivity during the transitional state that precedes the LOC.
Observation of complex functional cortical patterns in brain cognition
Authors: Jasleen Gund, R. K. Brojen Singh
Date: 2021-07-31 00:00:00
Tasks: Functional Connectivity

Synaptic plasticity and neuron cross-talk are some of the important key mechanisms underlying formation of dynamic clusters of active neurons. The essence of this study is to model and decipher the mechanism of emergence of a task-specific functional connectivity among a population of neurons. We have used our proposed neuron activity pattern (NAP) model to define an assorted range of interactions and simulated across a 2D lattice with two-state (firing/non-firing) neurons. We observed the emergence of scale-free, hierarchically organized ordered patterns of active neurons, that we call as functional cortical patterns (FCPs), only at the near-critical phase transition. We have done extensive topological characterization of FCPs and tested its congruency with the functional brain networks obtained from the empirical electrophysiological data of a visual stimulus task. Our results of network theory attributes, fractal analysis and functional cartography have confirmed the transitioning effect while generation of FCPs. This gives us strong implication that the functional neuronal system supports far-from-equilibrium dynamics upon receiving a stimulus. Our investigation for the long, critical and short-ranged interactions has lead to an interpretation that near-critical transition phase is a spectrum of critical temperatures (coupling strengths) being affected by the distance between the neurons. The range of interaction among neurons plays an important role in defining the functional severity of the neuronal circuitry. We propose that interaction range as a function of coupling strength drives connectivity in an augmenting fashion as we observed an increase in coupling strength from short to long-range interactions, among the population of neurons. This gives us insight towards the intensity of cognitive behaviour, which is attained by multiple stimulus attempts.
The evolution of forecasting for decision making in dynamic environments
Authors: Andrew R. Tilman, Vítor V. Vasconcelos, Erol Akçay, Joshua B. Plotkin
Date: 2021-07-30 00:00:00
Tasks: Decision Making, Management

Global change is reshaping ecosystems and societies. Strategic choices that were best yesterday may be sub-optimal tomorrow; and environmental conditions that were once taken for granted may soon cease to exist. In this setting, how people choose behavioral strategies has important consequences for environmental dynamics. Economic and evolutionary theories make similar predictions for strategic behavior in a static environment, even though one approach assumes perfect rationality and the other assumes no cognition whatsoever; but predictions differ in a dynamic environment. Here we explore a middle ground between economic rationality and evolutionary myopia. Starting from a population of myopic agents, we study the emergence of a new type that forms environmental forecasts when making strategic decisions. We show that forecasting types can have an advantage in changing environments, even when the act of forecasting is costly. Forecasting types can invade but not overtake the population, producing a stable coexistence with myopic types. Moreover, forecasters provide a public good by reducing the amplitude of environmental oscillations and increasing mean payoff to forecasting and myopic types alike. We interpret our results for understanding the evolution of different modes of decision-making. And we discuss implications for the management of environmental systems of great societal importance.
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose
Authors: Li Liu, Xianghao Zhan, Xikai Yang, Xiaoqing Guan, Rumeng Wu, Zhan Wang, Zhiyuan Luo, You Wang, Guang Li
Date: 2021-08-02 00:00:00
Tasks: Conformal Prediction, Data Augmentation, Prediction

In machine learning applications, the reliability of predictions is significant for assisted decision and risk control. As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN). However, the conventional CPKNN suffers from high variance and bias and long computational time as the feature dimensionality increases. To address these limitations, a new CP framework-conformal prediction with shrunken centroids (CPSC) is proposed. It regularizes the class centroids to attenuate the irrelevant features and shrink the sample space for predictions and reliability quantification. To compare CPKNN and CPSC, we employed them in the classification of 12 categories of alternative herbal medicine with electronic nose as a case and assessed them in two tasks: 1) offline prediction: the training set was fixed and the accuracy on the testing set was evaluated; 2) online prediction with data augmentation: they filtered unlabeled data to augment the training data based on the prediction reliability and the final accuracy of testing set was compared. The result shows that CPSC significantly outperformed CPKNN in both two tasks: 1) CPSC reached a significantly higher accuracy with lower computation cost, and with the same credibility output, CPSC generally achieves a higher accuracy; 2) the data augmentation process with CPSC robustly manifested a statistically significant improvement in prediction accuracy with different reliability thresholds, and the augmented data were more balanced in classes. This novel CPSC provides higher prediction accuracy and better reliability quantification, which can be a reliable assistance in decision support.
Deep Predictive Learning of Carotid Stenosis Severity
Authors: Yiqun Diao, Oliver Zhao, Priya Kothapalli, Peter Monteleone, Chandrajit Bajaj
Date: 2021-07-31 00:00:00
Tasks: Deep Learning

Carotid artery stenosis is the narrowing of carotid arteries, which supplies blood to the neck and head. In this work, we train a model to predict the severity of the stenosis blockage based on SRUC criteria variables and other patient information. We implement classic machine learning methods, decision trees and random forests, used in a previous experiment. In addition, we improve the accuracy through the use of the state-of-art Augmented Neural ODE deep learning method. Through systematic and theory-rooted analysis, we examine different parameters to achieve an accuracy of about 77%. These results show the strong potential in applying recently developing deep learning methods, while simultaneously suggesting that the current data provided by the SRUC criteria may be insufficient to predict stenosis severity at a high performance level.
Self-organization in embryonic development: myth and reality
Authors: Stuart A. Newman
Date: 2021-08-01 00:00:00

"Self-organization" has become a watchword in developmental biology, characterizing observations in which embryonic or induced stem cells of animals replicate morphological steps and outcomes seen in intact embryos. While the term was introduced in the 18th century by the philosopher Immanuel Kant to describe the goal-directed properties of living systems, it came into modern use for non-living materials in which complex forms and patterns emerge through dynamical, energy-expending physical processes. What is the relationship among these uses of the term? While multicellular forms arose dozens of times from single-celled organisms, only some of these undergo development, and not all developmental processes are self-organizing. The evolution of the animals (metazoans) from unicellular holozoans was accompanied by the addition of novel gene products which mediated the constitution the resulting cell clusters as liquid-, liquid crystal-, and solid-like materials with versatile morphogenetic propensities. Such materials variously exhibited multilayering, lumen formation and elongation, echoing the self-organizing properties of nonliving matter, "generic" based on such parallels, though with biologically based subunit properties and modes of interaction. These effects provided evolutionary templates for embryonic forms and morphological motifs of metazoan lineages. Embryos and organ primordia of present-day animal species continue to generate forms that resemble the outcomes of these physical effects. Their development, however, employs overdetermined, highly evolved mechanisms that are often disconnected from their originating processes. Using the examples of gastrulation, somitogenesis, and limb skeletal development, I provide instances of, and a conceptual framework for understanding, the relationships between transparently physical and evolved types of developmental self-organization.
Endogenous noise trackers in a Radner equilibrium
Authors: Jin Hyuk Choi, Kim Weston
Date: 2021-08-02 00:00:00

We prove the existence of an incomplete Radner equilibrium in a model with exponential investors and an endogenous noise tracker. We analyze a coupled system of ODEs and reduce it to a system of two coupled ODEs in order to establish equilibrium existence. As an application, we study the impact of the endogenous noise tracker on welfare by comparing to a model with an exogenous noise trader. We show that the aggregate welfare in the endogenous noise tracker model is bigger for a sufficiently large stock supply, but the welfare comparison depends in a non-trivial manner on the other model parameters.