pwc-archive/papers-with-abstracts huggingface dataset:
Anti-Delay Kalman Filter Fusion Algorithm for Vehicle-borne Sensor Network with Finite-Time Convergence
Authors: Hang Yu, Keren Dai, Haojie Li, Yao Zou, Xiang Ma, Shaojie Ma, He Zhang
Date: 2022-09-20 00:00:00
Tasks: Autonomous Driving

Intelligent vehicles in autonomous driving and obstacle avoidance, the precise relative state of vehicles put forward a higher demand. For a vehicle-borne sensor network with time-varying transmission delays, the problem of coordinate fusion of vehicle state is the focus of this paper. By the ingeniously designed low-complexity integration with a consensus strategy and buffer technology, an anti-delay distributed Kalman filter (DKF) with finite-time convergence is proposed.By introducing the matrix weight to assess local estimates, the optimal fusion state result is available in the sense of linear minimum variance. In addition, to accommodate practical engineering in intelligent vehicles, the communication weight coefficient and directed topology with unidirectional transmission are also considered. From a theoretical perspective, the proof of error covariances upper bounds with different communication topologies with delays are presented. Furthermore, the maximum allowable delays of vehicle-borne sensor network is derived backwards. Simulations verify that while considering various non-ideal factors above, the proposed DFK algorithm produces more accurate and robust fusion estimation state results than existing algorithms, making it more valuable in practical applications. Simultaneously, a mobile car trajectory tracking experiment is carried out, which further verifies the feasibility of the proposed algorithm.
Molecular dynamics simulations with grand-canonical reweighting suggest cooperativity effects in RNA structure probing experiments
Authors: Nicola Calonaci, Mattia Bernetti, Alisha Jones, Michael Sattler, Giovanni Bussi
Date: 2022-09-26 00:00:00

Chemical probing experiments such as SHAPE are routinely used to probe RNA molecules. In this work, we use atomistic molecular dynamics simulations to test the hypothesis that binding of RNA with SHAPE reagents is affected by cooperative effects leading to an observed reactivity that is dependent on the reagent concentration. We develop a general technique that enables the calculation of the affinity for arbitrary molecules as a function of their concentration in the grand-canonical ensemble. Our simulations of an RNA structural motif suggest that, at the concentration typically used in SHAPE experiments, cooperative binding would lead to a measurable concentration-dependent reactivity. We also provide a qualitative validation of this statement by analyzing a new set of experiments collected at different reagent concentrations.
A Reciprocal Heuristic Model for Diffuse Scattering from Walls and Surfaces
Authors: Enrico M. Vitucci, Nicolò Cenni, Franco Fuschini, Vittorio Degli-Esposti
Date: 2022-09-04 00:00:00

Diffuse scattering of electromagnetic waves from natural and artificial surfaces has been extensively studied in various disciplines, including radio wave propagation, and several diffuse scattering models based on different approaches have been proposed over the years, two of the most popular ones being Kirchhoff Theory and the so-called Effective Roughness heuristic model. The latter, although less rigorous than the former, is more flexible and applicable to a wider range of real-world cases, including non-Gaussian surfaces, surfaces with electrically small correlation lengths and scattering from material inhomogeneities that are often present under the surface. Unfortunately, the Effective Roughness model, with the exception of its Lambertian version, does not satisfy reciprocity, which is an important physical-soundness requirement for any propagation model. In the present work, without compromising its effectiveness and its simple and yet sound power-balance approach, we propose a reciprocal version of the Effective Roughness model, which can be easily implemented and replaced to the old version in ray-based propagation models. The new model is analyzed and compared to the old one and to other popular models. Once properly calibrated, it is shown to yield similar - if not better - performance with respect to the old one when checked vs. measurements.
Bayesian Modeling of TVP-VARs Using Regression Trees
Authors: Niko Hauzenberger, Florian Huber, Gary Koop, James Mitchell
Date: 2022-09-24 00:00:00
Tasks: regression

In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART) that models the TVPs as an unknown function of effect modifiers. The novelty of this model arises from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflation measures vary nonlinearly with changes in the effect modifiers.
Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition
Authors: Yuehao Bai, Meng Hsuan Hsieh, Jizhou Liu, Max Tabord-Meehan
Date: 2022-09-23 00:00:00

In this paper we revisit some common recommendations regarding the analysis of matched-pair and stratified experimental designs in the presence of attrition. Our main objective is to clarify a number of well-known claims about the practice of dropping pairs with an attrited unit when analyzing matched-pair designs. Contradictory advice appears in the literature about whether or not dropping pairs is beneficial or harmful, and stratifying into larger groups has been recommended as a resolution to the issue. To address these claims, we derive the estimands obtained from the difference-in-means estimator in a matched-pair design both when the observations from pairs with an attrited unit are retained and when they are dropped. We find limited evidence to support the claims that dropping pairs helps recover the average treatment effect, but we find that it may potentially help in recovering a convex weighted average of conditional average treatment effects. We report similar findings for stratified designs when studying the estimands obtained from a regression of outcomes on treatment with and without strata fixed effects.
Equilibrium pricing of securities in the co-presence of cooperative and non-cooperative populations
Authors: Masaaki Fujii
Date: 2022-09-26 00:00:00

In this work, we develop an equilibrium model for price formation of securities in a market composed of two populations of different types: the first one consists of cooperative agents, while the other one consists of non-cooperative agents. The trading of every cooperative member is assumed to be coordinated by a central planner. In the large population limit, the problem for the central planner is shown to be a conditional extended mean-field control. In addition to the convexity assumptions, if the relative size of the cooperative population is small enough, then we are able to show the existence of a unique equilibrium for both the finite-agent and the mean-field models. The strong convergence to the mean-field model is also proved under the same conditions.
The Signaling Role of Leaders in Global Games
Authors: Panagiotis Kyriazis, Edmund Lou
Date: 2022-09-26 00:00:00
Tasks: valid

How important are leaders' actions in facilitating coordination? In this paper, we investigate their signaling role in a global games framework. A perfectly informed leader and a team of followers face a coordination problem. Despite the endogenous information generated by the leader's action, we provide a necessary and sufficient condition that makes the monotone equilibrium strategy profile uniquely $\Delta$-rationalizable and hence guarantees equilibrium uniqueness. Moreover, the unique equilibrium is fully efficient. This result remains valid when the leader observes a noisy signal about the true state except full efficiency may not be obtained. We discuss the implications of our results for a broad class of phenomena such as adoption of green technology, currency attacks and revolutions.
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models
Authors: Mohammed Rakib, Md. Ismail Hossain, Nabeel Mohammed, Fuad Rahman
Date: 2022-09-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Although over 300M around the world speak Bangla, scant work has been done in improving Bangla voice-to-text transcription due to Bangla being a low-resource language. However, with the introduction of the Bengali Common Voice 9.0 speech dataset, Automatic Speech Recognition (ASR) models can now be significantly improved. With 399hrs of speech recordings, Bengali Common Voice is the largest and most diversified open-source Bengali speech corpus in the world. In this paper, we outperform the SOTA pretrained Bengali ASR models by finetuning a pretrained wav2vec2 model on the common voice dataset. We also demonstrate how to significantly improve the performance of an ASR model by adding an n-gram language model as a post-processor. Finally, we do some experiments and hyperparameter tuning to generate a robust Bangla ASR model that is better than the existing ASR models.
AI-powered Language Assessment Tools for Dementia
Authors: Mahboobeh Parsapoor, Muhammad Raisul Alam, Alex Mihailidis
Date: 2022-09-13 00:00:00
Tasks: Sensitivity, Specificity

The main objective of this paper is to propose an approach for developing an Artificial Intelligence (AI)-powered Language Assessment (LA) tool. Such tools can be used to assess language impairments associated with dementia in older adults. The Machine Learning (ML) classifiers are the main parts of our proposed approach, therefore to develop an accurate tool with high sensitivity and specificity, we consider different binary classifiers and evaluate their performances. We also assess the reliability and validity of our approach by comparing the impact of different types of language tasks, features, and recording media on the performance of ML classifiers.
STEMPO -- dynamic X-ray tomography phantom
Authors: Tommi Heikkilä
Date: 2022-09-26 00:00:00
Tasks: 3D geometry

This is the documentation for Spatio-TEmporal Motor-POwered (STEMPO) phantom for dynamic X-ray tomography. Provided are different measurements designed for testing dynamic tomography reconstruction algorithms. The measured data and additional materials are available at www.fips.fi/dataset.php and doi.org/10.5281/zenodo.7081688 for open use by the scientific community, as long as the data and this documentation are appropriately referenced. The files contain: (1) measurement data (sinograms) from one static scan and two dynamic scans (same movement but different choice of projection directions). All data is provided both for 2D and 3D geometry in varying resolution levels; (2) approximation of the ground truth obtained using the known motion model; and (3) short example codes to showcase how the data could be used to test and validate dynamic tomography algorithms.
Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis
Authors: Jiawen Kang, Hongyang Du, Zonghang Li, Zehui Xiong, Shiyao Ma, Dusit Niyato, Yuan Li
Date: 2022-09-25 00:00:00
Tasks: Image Retrieval, Retrieval, Semantic Communication

Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e.g., smart healthcare. In this paper, we focus on UAV image-sensing-driven task-oriented semantic communications scenarios. The majority of existing work has focused on designing advanced algorithms for high-performance semantic communication. However, the challenges, such as energy-hungry and efficiency-limited image retrieval manner, and semantic encoding without considering user personality, have not been explored yet. These challenges have hindered the widespread adoption of semantic communication. To address the above challenges, at the semantic level, we first design an energy-efficient task-oriented semantic communication framework with a triple-based {\color{black}scene graph} for image information. We then design a new personalized semantic encoder based on user interests to meet the requirements of personalized saliency. Moreover, at the communication level, we study the effects of dynamic wireless fading channels on semantic transmission mathematically and thus design an optimal multi-user resource allocation scheme by using game theory. Numerical results based on real-world datasets clearly indicate that the proposed framework and schemes significantly enhance the personalization and anti-interference performance of semantic communication, and are also efficient to improve the communication quality of semantic communication services.
Analog Image Denoising with an Adaptive Memristive Crossbar Network
Authors: O. Krestinskaya, K. N. Salama, A. P. James
Date: 2022-09-25 00:00:00
Tasks: Denoising, Image Denoising, SSIM

Noise in image sensors led to the development of a whole range of denoising filters. A noisy image can become hard to recognize and often require several types of post-processing compensation circuits. This paper proposes an adaptive denoising system implemented using an analog in-memory neural computing network. The proposed method can learn new noises and can be integrated into or alone with CMOS image sensors. Three denoising network configurations are implemented namely, (1) single layer network, (2) convolution network, and (3) fusion network. The single layer network shows the processing time, energy consumption, and on-chip area of 3.2us, 21nJ per image, and 0.3mm^2 respectively, meanwhile, the convolution denoising network correspondingly shows 72ms, 236uJ, and 0.48mm^2. Among all the implemented networks, it is observed that performance metrics SSIM, MSE, and PSNR show a maximum improvement of 3.61, 21.7, and 7.7 times respectively.
Graph Neural Network and Superpixel Based Brain Tissue Segmentation (Corrected Version)
Authors: Chong Wu, Zhenan Feng, Houwang Zhang, Hong Yan
Date: 2022-09-21 00:00:00
Tasks: Graph Neural Network, Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

Convolutional neural networks (CNNs) are usually used as a backbone to design methods in biomedical image segmentation. However, the limitation of receptive field and large number of parameters limit the performance of these methods. In this paper, we propose a graph neural network (GNN) based method named GNN-SEG for the segmentation of brain tissues. Different to conventional CNN based methods, GNN-SEG takes superpixels as basic processing units and uses GNNs to learn the structure of brain tissues. Besides, inspired by the interaction mechanism in biological vision systems, we propose two kinds of interaction modules for feature enhancement and integration. In the experiments, we compared GNN-SEG with state-of-the-art CNN based methods on four datasets of brain magnetic resonance images. The experimental results show the superiority of GNN-SEG.
A CNN-LSTM-based Fusion Separation Deep Neural Network for 6G Ultra-Massive MIMO Hybrid Beamforming
Authors: Rafid Umayer Murshed, Zulqarnain Bin Ashraf, Abu Horaira Hridhon, Kumudu Munasinghe, Abbas Jamalipour, MD. Farhad Hossain
Date: 2022-09-26 00:00:00

In the sixth-generation (6G) cellular networks, hybrid beamforming would be a real-time optimization problem that is becoming progressively more challenging. Although numerical computation-based iterative methods such as the minimal mean square error (MMSE) and the alternative manifold-optimization (Alt-Min) can already attain near-optimal performance, their computational cost renders them unsuitable for real-time applications. However, recent studies have demonstrated that machine learning techniques like deep neural networks (DNN) can learn the mapping done by those algorithms between channel state information (CSI) and near-optimal resource allocation, and then approximate this mapping in near real-time. In light of this, we investigate various DNN architectures for beamforming challenges in the terahertz (THz) band for ultra-massive multiple-input multiple-output (UM-MIMO) and explore their contextual mathematical modeling. Specifically, we design a sophisticated 1D convolutional neural network and long short-term memory (1D CNN-LSTM) based fusion-separation scheme, which can approach the performance of the Alt-Min algorithm in terms of spectral efficiency (SE) and, at the same time, use significantly less computational effort. Simulation results indicate that the proposed system can attain almost the same level of SE as that of the numerical iterative algorithms, while incurring a substantial reduction in computational cost. Our DNN-based approach also exhibits exceptional adaptability to diverse network setups and high scalability. Although the current model only addresses the fully connected hybrid architecture, our approach can also be expanded to address a variety of other network topologies. INDEX TERMS 6G, CNN, Hybrid Beamforming, LSTM, UM-MIMO
Spatial Signal Design for Positioning via End-to-End Learning
Authors: Steven Rivetti, Josè Miguel Mateos-Ramos, Yibo Wu, Jinxiang Song, Musa Furkan Keskin, Vijaya Yajnanarayana, Christian Häger, Henk Wymeersch
Date: 2022-09-26 00:00:00
Tasks: Position

This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a multiple-input single-output (MISO) scenario, we propose a novel autoencoder (AE) architecture to estimate user-equipment(UE) position with multiple base-stations (BSs) and demonstrate that end-to-end learning can match model-based design, both for angle of departure (AoD) and position estimation, under ideal conditions without model deficits and outperform it in the presence of hardware impairments.
Community Detection in Multi-frequency EEG Networks
Authors: Abdullah Karaaslanli, Meiby Ortiz-Bouza, Tamanna T. K. Munia, Selin Aviyente
Date: 2022-09-26 00:00:00
Tasks: Community Detection, EEG, Electroencephalogram (EEG), Functional Connectivity

Objective: In recent years, the functional connectivity of the human brain has been studied with graph theoretical tools. One such approach is community detection which is fundamental for uncovering the localized networks. Existing methods focus on networks constructed from a single frequency band while ignoring multi-frequency nature of functional connectivity. Therefore, there is a need to study multi-frequency functional connectivity to be able to capture the full view of neuronal connectivity. Methods: In this paper, we use multilayer networks to model multi-frequency functional connectivity. In the proposed model, each layer corresponds to a different frequency band. We then extend the definition of modularity to multilayer networks to develop a new community detection algorithm. Results} The proposed approach is applied to electroencephalogram data collected during a study of error monitoring in the human brain. The differences between the community structures within and across different frequency bands for two response types, i.e. error and correct, are studied. Conclusion: The results indicate that following an error response, the brain organizes itself to form communities across frequencies, in particular between theta and gamma bands while a similar cross-frequency community formation is not observed for the correct response. Moreover, the community structures detected for the error response were more consistent across subjects compared to the community structures for correct response. Significance: The multi-frequency functional connectivity network models combined with multilayer community detection algorithms can reveal changes in cross-frequency functional connectivity network formation across different tasks and response types.
RIS-Aided Physical Layer Security Improvement in Underlay Cognitive Radio Networks
Authors: Majid H. Khoshafa, Telex M. N. Ngatchede, Mohamed H. Ahmed
Date: 2022-09-26 00:00:00

In this paper, a reconfigurable intelligent surface (RIS)-aided underlay cognitive radio network is investigated. An RIS is utilized to improve the secondary network (SN) reliability and robustness while simultaneously increasing the physical layer security of the primary network (PN). Toward this end, closedform expressions for the SN outage probability, PN secrecy outage probability, and PN probability of non-zero secrecy capacity are derived. To increase the eavesdropping signals of the PN, the eavesdropper uses two combining techniques, namely maximal ratio combining and selection combining. Furthermore, the advantages of the proposed system model are verified through numerical and simulation results.
A Compact Online-Learning Spiking Neuromorphic Biosignal Processor
Authors: Chaoming Fang, Ziyang Shen, Fengshi Tian, Jie Yang, Mohamad Sawan
Date: 2022-09-26 00:00:00
Tasks: ECG Classification

Real-time biosignal processing on wearable devices has attracted worldwide attention for its potential in healthcare applications. However, the requirement of low-area, low-power and high adaptability to different patients challenge conventional algorithms and hardware platforms. In this design, a compact online learning neuromorphic hardware architecture with ultra-low power consumption designed explicitly for biosignal processing is proposed. A trace-based Spiking-Timing-Dependent-Plasticity (STDP) lgorithm is applied to realize hardware-friendly online learning of a single-layer excitatory-inhibitory spiking neural network. Several techniques, including event-driven architecture and a fully optimized iterative computation approach, are adopted to minimize the hardware utilization and power consumption for the hardware implementation of online learning. Experiment results show that the proposed design reaches the accuracy of 87.36% and 83% for the Mixed National Institute of Standards and Technology database (MNIST) and ECG classification. The hardware architecture is implemented on a Zynq-7020 FPGA. Implementation results show that the Look-Up Table (LUT) and Flip Flops (FF) utilization reduced by 14.87 and 7.34 times, respectively, and the power consumption reduced by 21.69% compared to state of the art.
Real-Time Power System Event Detection: A Novel Instance Selection Approach
Authors: Gabriel Intriago, Yu Zhang
Date: 2022-09-25 00:00:00
Tasks: Event Detection

Instance selection is a vital technique for energy big data analytics. It is challenging to process a massive amount of streaming data generated at high speed rates by intelligent monitoring devices. Instance selection aims at removing noisy and bad data that can compromise the performance of data-driven learners. In this context, this paper proposes a novel similarity based instance selection (SIS) method for real-time phasor measurement unit data. In addition, we develop a variant of the Hoeffding-Tree learner enhanced with the SIS for classifying disturbances and cyber-attacks. We validate the merits of the proposed learner by exploring its performance under four scenarios that affect either the system physics or the monitoring architecture. Our experiments are simulated by using the datasets of industrial control system cyber-attacks. Finally, we conduct an implementation analysis which shows the deployment feasibility and high-performance potential of the proposed learner, as a part of real-time monitoring applications.