pwc-archive/papers-with-abstracts huggingface dataset:
Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets
Authors: Mengjie Shi, Tianrui Zhao, Simeon J. West, Adrien E. Desjardins, Tom Vercauteren, Wenfeng Xia
Date: 2021-11-15 00:00:00

Photoacoustic imaging has shown great potential for guiding minimally invasive procedures by accurate identification of critical tissue targets and invasive medical devices (such as metallic needles). The use of light emitting diodes (LEDs) as the excitation light sources accelerates its clinical translation owing to its high affordability and portability. However, needle visibility in LED-based photoacoustic imaging is compromised primarily due to its low optical fluence. In this work, we propose a deep learning framework based on U-Net to improve the visibility of clinical metallic needles with a LED-based photoacoustic and ultrasound imaging system. To address the complexity of capturing ground truth for real data and the poor realism of purely simulated data, this framework included the generation of semi-synthetic training datasets combining both simulated data to represent features from the needles and in vivo measurements for tissue background. Evaluation of the trained neural network was performed with needle insertions into blood-vessel-mimicking phantoms, pork joint tissue ex vivo and measurements on human volunteers. This deep learning-based framework substantially improved the needle visibility in photoacoustic imaging in vivo compared to conventional reconstruction by suppressing background noise and image artefacts, achieving 5.8 and 4.5 times improvements in signal-to-noise ratio (SNR) and the modified Hausdorff distance (MHD) respectively. Thus, the proposed framework could be helpful for reducing complications during percutaneous needle insertions by accurate identification of clinical needles in photoacoustic imaging.
Experimental Investigation on the Friction-induced Vibration with Periodic Characteristics in a Running-in Process under Lubrication
Authors: Di Sun, Pengfei Xing, Guobin Li, Hongtao Gao, Sifan Yang, Honglin Gao, Hongpeng Zhang
Date: 2021-11-15 00:00:00
Tasks: Friction

This paper investigated the friction-induced vibration (FIV) behavior under the running-in process with oil lubrication. The FIV signal with periodic characteristics under lubrication was identified with the help of the squeal signal induced in an oil-free wear experiment and then extracted by the harmonic wavelet packet transform (HWPT). The variation of the FIV signal from running-in wear stage to steady wear stage was studied by its root mean square (RMS) values. The result indicates that the time-frequency characteristics of the FIV signals evolve with the wear process and can reflect the wear stages of the friction pairs. The RMS evolvement of the FIV signal is in the same trend to the composite surface roughness and demonstrates that the friction pair goes through the running-in wear stage and the steady wear stage. Therefore, the FIV signal with periodic characteristics can describe the evolvement of the running-in process and distinguish the running-in wear stage and the stable wear stage of the friction pair.
Ultra-Low-Power IoT Communications: A novel address decoding approach for wake-up receivers
Authors: Yousef Mafi, Fakhreddin Amirhosseini, Seied Ali Hosseini, Amin Azari, Meysam Masoudi, Mojtaba Vaezi
Date: 2021-11-15 00:00:00
Tasks: Decoder

Providing energy-efficient Internet of Things (IoT) connectivity has attracted significant attention in fifth-generation (5G) wireless networks and beyond. A potential solution for realizing a long-lasting network of IoT devices is to equip each IoT device with a wake-up receiver (WuR) to have always-accessible devices instead of always-on devices. WuRs typically comprise a radio frequency demodulator, sequence decoder, and digital address decoder and are provided with a unique authentication address in the network. Although the literature on efficient demodulators is mature, it lacks research on fast, low-power, and reliable address decoders. As this module continuously monitors the received ambient energy for potential paging of the device, its contribution to WuR's power consumption is crucial. Motivated by this need, a low-power, reliable address decoder is developed in this paper. We further investigate the integration of WuR in low-power uplink/downlink communications and, using system-level energy analysis; we characterize operation regions in which WuR can contribute significantly to energy saving. The device-level energy analysis confirms the superior performance of our decoder. The results show that the proposed decoder significantly outperforms the state-of-the-art with a power consumption of 60 nW, at cost of compromising a negligible increase in decoding delay.
Block-Sparse Recovery Network for Two-Dimensional Harmonic Retrieval
Authors: Rong Fu, Tianyao Huang, Lei Wang, Yimin Liu
Date: 2021-11-15 00:00:00
Tasks: multidimensional harmonic retrieval, Retrieval, Vocal Bursts Valence Prediction

As a typical signal processing problem, multidimensional harmonic retrieval (MHR) has been adapted to a wide range of applications in signal processing. Block-sparse signals, whose nonzero entries appearing in clusters, have received much attention recently. An unfolded network, named Ada-BlockLISTA, was proposed to recover a block-sparse signal at a small computational cost, which learns an individual weight matrix for each block. However, as the number of network parameters is increasingly associated with the number of blocks, the demand for parameter reduction becomes very significant, especially for large-scale MHR. Based on the dictionary characteristics in two-dimensional (2D) harmonic retrieve problems, we introduce a weight coupling structure to shrink Ada-BlockLISTA, which significantly reduces the number of weights without performance degradation. In simulations, our proposed block-sparse reconstruction network, named AdaBLISTA-CP, shows excellent recovery performance and convergence speed in 2D harmonic retrieval problems.
Exploring latent networks in resting-state fMRI using voxel-to-voxel causal modeling feature selection
Authors: Hassan Baker, Austin J. Brockmeier
Date: 2021-11-15 00:00:00
Tasks: feature selection, Network Identification

Functional networks characterize the coordinated neural activity observed by functional neuroimaging. The prevalence of different networks during resting state periods provide useful features for predicting the trajectory of neurodegenerative diseases. Techniques for network estimation rely on statistical correlation or dependence between voxels. Due to the large number of voxels, rather than consider the voxel-to-voxel correlations between all voxels, a small set of seed voxels are chosen. Consequently, the network identification may depend on the selected seeds. As an alternative, we propose to fit first-order linear models with sparse priors on the coefficients to model activity across the entire set of cortical grey matter voxels as a linear combination of a smaller subset of voxels. We propose a two-stage algorithm for voxel subset selection that uses different sparsity-inducing regularization approaches to identify subject-specific causally predictive voxels. To reveal the functional networks among these voxels, we then apply independent component analysis (ICA) to model these voxels' signals as a mixture of latent sources each defining a functional network. Based on the inter-subject similarity of the sources' spatial patterns we identify independent sources that are well-matched across subjects but fail to match the independent sources from a group-based ICA. These are resting state networks, common across subjects that group ICA does not reveal. These complementary networks could help to better identify neurodegeneration, a task left for future work.
Beamspace Multidimensional ESPRIT Approaches for Simultaneous Localization and Communications
Authors: Fan Jiang, Fuxi Wen, Yu Ge, Meifang Zhu, Henk Wymeersch, Fredrik Tufvesson
Date: 2021-11-14 00:00:00

Modern wireless communication systems operating at high carrier frequencies are characterized by a high dimensionality of the underlying parameter space (including channel gains, angles, delays, and possibly Doppler shifts). Estimating these parameters is valuable for communication purposes, but also for localization and sensing, making channel estimation a critical component in any joint communication and localization or sensing application. The high dimensionality make it difficult to use search-based methods such as maximum likelihood. Search-free methods such as ESPRIT provide an attractive alternative, but require a complex decomposition step in both the tensor and matrix version of ESPRIT. To mitigate this, we propose, develop, and analyze a reduced complexity beamspace ESPRIT method. Complexity is reduced both by beampace processing as well as low-complex implementation of the singular value decomposition. A novel perturbation analysis provides important insights for both channel estimation and localization performance. The proposed method is compared to the tensor ESPRIT method, in terms of channel estimation, communication, localization, and sensing performance, further validating the perturbation analysis.
Optimizing the Age of Information in RIS-aided SWIPT Networks
Authors: Wanting Lyu, Yue Xiu, Jun Zhao, Zhongpei Zhang
Date: 2021-11-14 00:00:00
Tasks: Scheduling

In this letter, a reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) network is investigated. To quantify the freshness of the data packets at the information receiver, the age of information (AoI) is considered. To minimize the sum AoI of the information users while ensuring that the power transferred to energy harvesting users is greater than the demanded value, we formulate a scheduling scheme, and a joint transmit beamforming and phase shift optimization at the base station (BS) and RIS, respectively. The alternating optimization (AO) algorithm is proposed to handle the coupling between active beamforming and passive RIS phase shifts, and the successive convex approximation (SCA) algorithm is utilized to tackle the non-convexity of the formulated problems. The improvement in terms of AoI provided by the proposed algorithm and the trade-off between the age of information and energy harvesting is quantified by the numerical simulation results.
Blocking Probability in Obstructed Tunnels with Reconfigurable Intelligent Surface
Authors: Changshan Chen, Cunhua Pan
Date: 2021-11-14 00:00:00
Tasks: Blocking

In the obstructed tunnels, the signal transmission will suffer the risk of ray-path blocking caused by the obstacles owing to the Snell's law. In this letter, the reconfigurable intelligent surface (RIS) that can reflect the electromagnetic waves to any specific directions is introduced to mitigate the signal blocking. The closed-form expressions for blocking probability (BP) for one reflection with single RIS and multiple RISs under various scenarios are derived. Compared with the case without RIS, significant reduction of BP can be found with proper configuration of the RIS. Moreover, the impact of the location of RIS, the height of the transmitter, and the location of the receiver, on the BP is investigated. Finally, the case of multiple obstacles with different distributions is discussed to further verify the effectiveness of RIS on reduction of BP.
Doors in the Sky: Detection, Localization and Classification of Aerial Vehicles using Laser Mesh
Authors: Wahab Khawaja, Ender Ozturk, Ismail Guvenc
Date: 2021-11-14 00:00:00

The stealth technology and unmanned aerial vehicles (UAVs) are expected to dominate current and future aerial warfare. The radar systems at their maximum operating ranges, however, are not always able to detect stealth and small UAVs mainly due to their small radar cross-sections and/or low altitudes. In this paper, a novel technique as an alternative to radar technology is proposed. The proposed approach is based on creating a mesh structure of laser beams initiated from aerial platforms towards the ground. The laser mesh acts as a virtual net in the sky. Any aerial vehicle disrupting the path of the laser beams are detected and subsequently localized and tracked. As an additional feature, steering of the beams can be used for increased coverage and improved localization and classification performance. A database of different types of aerial vehicles is created artificially based on Gaussian distributions. The database is used to develop several machine learning (ML) models using different algorithms to classify a target. Overall, we demonstrated through simulations that our proposed model achieves simultaneous detection, classification, localization, and tracking of a target.
Closed-form Two-way TOA Localization and Synchronization for User Devices with Motion and Clock Drift
Authors: Sihao Zhao, Ningyan Guo, Xiao-Ping Zhang, Xiaowei Cui, Mingquan Lu
Date: 2021-11-13 00:00:00
Tasks: Form

A two-way time-of-arrival (TOA system is composed of anchor nodes (ANs and user devices (UDs . Two-way TOA measurements between AN-UD pairs are obtained via round-trip communications to achieve localization and synchronization (LAS for a UD. Existing LAS method for a moving UD with clock drift adopts an iterative algorithm, which requires accurate initialization and has high computational complexity. In this paper, we propose a new closed-form two-way TOA LAS approach, namely CFTWLAS, which does not require initialization, has low complexity and empirically achieves optimal LAS accuracy. We first linearize the LAS problem by squaring and differencing the two-way TOA equations. We employ two auxiliary variables to simplify the problem to finding the analytical solution of quadratic equations. Due to the measurement noise, we can only obtain a raw LAS estimation from the solution of the auxiliary variables. Then, a weighted least squares step is applied to further refine the raw estimation. We analyze the theoretical error of the new CFTWLAS and show that it empirically reaches the Cramer-Rao lower bound (CRLB with sufficient ANs under the condition with proper geometry and small noise. Numerical results in a 3D scenario verify the theoretical analysis that the estimation accuracy of the new CFTWLAS method reaches CRLB in the presented experiments when the number of the ANs is large, the geometry is appropriate, and the noise is small. Unlike the iterative method whose complexity increases with the iteration count, the new CFTWLAS has constant low complexity.
Delay-Oriented Distributed Scheduling Using Graph Neural Networks
Authors: Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra
Date: 2021-11-13 00:00:00
Tasks: Scheduling

In wireless multi-hop networks, delay is an important metric for many applications. However, the max-weight scheduling algorithms in the literature typically focus on instantaneous optimality, in which the schedule is selected by solving a maximum weighted independent set (MWIS) problem on the interference graph at each time slot. These myopic policies perform poorly in delay-oriented scheduling, in which the dependency between the current backlogs of the network and the schedule of the previous time slot needs to be considered. To address this issue, we propose a delay-oriented distributed scheduler based on graph convolutional networks (GCNs). In a nutshell, a trainable GCN module generates node embeddings that capture the network topology as well as multi-step lookahead backlogs, before calling a distributed greedy MWIS solver. In small- to medium-sized wireless networks with heterogeneous transmit power, where a few central links have many interfering neighbors, our proposed distributed scheduler can outperform the myopic schedulers based on greedy and instantaneously optimal MWIS solvers, with good generalizability across graph models and minimal increase in communication complexity.
Evolutionary paths under catastrophes
Authors: Rinaldo B. Schinazi
Date: 2021-11-15 00:00:00

We introduce a model to study the impact of catastrophes on evolutionary paths. If we do not allow catastrophes the number of changes in the maximum fitness of a population grows logarithmically with respect to time. Allowing catastrophes (no matter how rare) yields a drastically different behavior. When catastrophes are possible the number of changes in the maximum fitness of the population grows linearly with time. Moreover, the evolutionary paths are a lot less predictable when catastrophes are possible. Our results can be seen as supporting the hypothesis that catastrophes speed up evolution by disrupting dominant species and creating space for new species to emerge and evolve.
When can we reconstruct the ancestral state? A unified theory
Authors: Lam Si Tung Ho, Vu Dinh
Date: 2021-11-14 00:00:00
Tasks: valid

Ancestral state reconstruction is one of the most important tasks in evolutionary biology. Conditions under which we can reliably reconstruct the ancestral state have been studied for both discrete and continuous traits. However, the connection between these results is unclear, and it seems that each model needs different conditions. In this work, we provide a unifying theory on the consistency of ancestral state reconstruction for various types of trait evolution models. Notably, we show that for a sequence of nested trees with bounded heights, the necessary and sufficient conditions for the existence of a consistent ancestral state reconstruction method under discrete models, the Brownian motion model, and the threshold model are equivalent. When tree heights are unbounded, we provide a simple counter-example to show that this equivalence is no longer valid.
On the Spontaneous Dynamics of Synaptic Weights in Stochastic Models with Pair-Based STDP
Authors: Philippe Robert, Gaëtan Vignoud
Date: 2021-11-15 00:00:00

We investigate spike-timing dependent plasticity (STPD) in the case of a synapse connecting two neural cells. We develop a theoretical analysis of several STDP rules using Markovian theory. In this context there are two different timescales, fast neural activity and slower synaptic weight updates. Exploiting this timescale separation, we derive the long-time limits of a single synaptic weight subject to STDP. We show that the pairing model of presynaptic and postsynaptic spikes controls the synaptic weight dynamics for small external input, on an excitatory synapse. This result implies in particular that mean-field analysis of plasticity may miss some important properties of STDP. Anti-Hebbian STDP seems to favor the emergence of a stable synaptic weight, but only for high external input. In the case of inhibitory synapse the pairing schemes matter less, and we observe convergence of the synaptic weight to a non-null value only for Hebbian STDP. We extensively study different asymptotic regimes for STDP rules, raising interesting questions for future works on adaptative neural networks and, more generally, on adaptive systems.
Computing with Cognitive States
Authors: Stefan Reimann
Date: 2021-11-03 00:00:00
Tasks: Position, Retrieval

Basic experimental findings about human working memory can be described by an algebra built on high-dimensional binary states, representing information items, and two operations: multiplication for binding and addition for bundling. In contrast to common VSA algebras, bundling is not associative. Consequently bundling a sequence of items preserves their sequential ordering. The cognitive states representing a memorised list exhibit a primacy as well as a recency gradient. The typical concave-up and asymmetrically shaped serial position curve is derived as a linear combination of those gradients. Quantitative implications of the algebra are shown to agree well with empirical data from basic cognitive tasks including storage and retrieval of information in human working memory.
Analysis of chronic diseases progression using stochastic modeling
Authors: Iman Mohammed Attia Ebd-Elkhalik Abo-Elreesh
Date: 2021-11-12 00:00:00

This book handles the fatty liver disease from the bio-statistical point of view . It discusses the disease process in the simple general form of health-disease-death multi-states model . Continuous Time Markov Chains are used to estimate the rate transition matrix utilizing the MLE and Quasi-Newton formula , once obtained , the probability transition matrix can be estimated by exponentiation of the rate matrix . The probability transition matrix can also be obtained by solving the forward Kolmogorov differential equations , which yields more stable solution than exponentiation of rate matrix. The disease process is expanded in 9 states model to explain the transition among the detailed stages of the disease process , in more elaborate form. The probability transition matrix is used to estimate the number of patients in each stage , this matrix along with the rate transition matrix , both are used to estimate life expectancy of patients is each stage. These statistical indices are of great value as they can help the health policy makers and medical insurance managers to allocate the resources for investigating and treating patients in different stages of the disease . This method is of a high potential value to be used in longitudinal studies conducted by the pharmaceutical companies to evaluate the effect of anti-fibrotic drugs used to treat patients within the initial stages of fibrosis . Poisson regression model is also used to relate the high risk covariates such as type 2 diabetes, hypercholesterolemia , obesity and hypertension with the rate of progression and evolution of the stages of the disease over time. The general model , the expanded model and the model with covariates are illustrated by artificial hypothetical examples to demonstrate the mathematical statistical indices .
Selfish optimization and collective learning in populations
Authors: Alex McAvoy, Yoichiro Mori, Joshua B. Plotkin
Date: 2021-11-15 00:00:00

A selfish learner seeks to maximize their own success, disregarding others. When success is measured as payoff in a game played against another learner, mutual selfishness typically fails to produce the optimal outcome for a pair of individuals. However, learners often operate in populations, and each learner may have a limited duration of interaction with any other individual. Here, we compare selfish learning in stable pairs to selfish learning with stochastic encounters in a population. We study gradient-based optimization in repeated games like the prisoner's dilemma, which feature multiple Nash equilibria, many of which are suboptimal. We find that myopic, selfish learning, when distributed in a population via ephemeral encounters, can reverse the dynamics that occur in stable pairs. In particular, when there is flexibility in partner choice, selfish learning in large populations can produce optimal payoffs in repeated social dilemmas. This result holds for the entire population, not just for a small subset of individuals. Furthermore, as the population size grows, the timescale to reach the optimal population payoff remains finite in the number of learning steps per individual. While it is not universally true that interacting with many partners in a population improves outcomes, this form of collective learning achieves optimality for several important classes of social dilemmas. We conclude that na\"{i}ve learning can be surprisingly effective in populations of individuals navigating conflicts of interest.