pwc-archive/papers-with-abstracts huggingface dataset:
A Unified Technique for Entropy Enhancement Based Diabetic Retinopathy Detection Using Hybrid Neural Network
Authors: Fatima, M. Imran, A. Ullah, M. Arif, R. Noor
Date: 2020-07-01 00:00:00
Proceeding: journal 2020 7
Tasks: Diabetic Retinopathy Detection, Specificity

In this paper, a unified technique for entropy enhancement-based diabetic retinopathy detection using a hybrid neural network is proposed for diagnosing diabetic retinopathy. Medical images play crucial roles in the diagnosis, but two images representing two different stages of a disease look alike. It, consequently, make the process of diagnosis extraneous and error-prone. Therefore, in this paper, a technique is proposed to address these issues. Firstly, a novel entropy enhancement technique is devised exploiting the discrete wavelet transforms to improve the visibility of the medical images by making the subtle features more prominent. Later, we designed a computationally efficient hybrid neural network that efficiently classifies diabetic retinopathy images. To examine the effectiveness of our technique, we have chosen three datasets: Ultra-Wide Filed (UWF) dataset, Asia Pacific Tele Ophthalmology Society (APTOS) dataset, and MESSIDOR-2 dataset. In the end, we performed extensive experiments to validate the performance of our technique. In addition, the comparison of the proposed scheme – in terms of accuracy, specificity, sensitivity, precision and recall curve, and area under the curve – with some of the best contemporary schemes shows the significant improvement of our techniques in terms of diabetic retinopathy classification.
Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis
Authors: Lukas Folle, Sara Bayat, Arnd Kleyer, Filippo Fagni, Lorenz A Kapsner, Maja Schlereth, Timo Meinderink, Katharina Breininger, Koray Tacilar, Gerhard Krönke, Michael Uder, Michael Sticherling, Sebastian Bickelhaupt, Georg Schett, Andreas Maier, Frank Roemer, David Simon
Date: 2022-03-25 00:00:00
Proceeding: Rheumatology (Oxford) 2022 3

Objectives
To evaluate whether neural networks can distinguish between seropositive rheumatoid arthritis (RA), seronegative RA and psoriatic arthritis (PsA) based on inflammatory patterns from hand MRI and to test how psoriasis patients with subclinical inflammation fit into such patterns.

Methods
ResNet neural networks were utilized to compare (i) seropositive RA vs. PsA, (ii) seronegative RA vs. PsA and (iii) seropositive vs. seronegative RA with respect to hand MRI data. Results from T1 coronal, T2 coronal, T1 coronal and axial fat suppressed contrast-enhanced (CE) and T2 fat suppressed axial sequences were used. The performance of such trained networks was analyzed by the area-under-the-receiver-operating-characteristic curve (AUROC) with and without presentation of demographic and clinical parameters. Additionally, the trained networks were applied to psoriasis patients without clinical arthritis.

Results
MRI scans from 649 patients (135 seronegative RA, 190 seropositive RA, 177 PsA, 147 psoriasis) were fed into ResNet neural networks. AUROC was 75% for seropositive RA vs. PsA, 74% for seronegative RA vs. PsA and 67% for seropositive vs. seronegative RA. All MRI sequences were relevant for classification, however, when deleting contrast agent-based sequences the loss of performance was only marginal. The addition of demographic and clinical data to the networks did not provide significant improvements for classification. Psoriasis patients were mostly assigned to PsA by the neural networks, suggesting that a PsA-like MRI pattern may be present early in the course of psoriatic disease.

Conclusion
Neural networks can be successfully trained to distinguish MRI inflammation related to seropositive RA, seronegative RA, and PsA.
Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns—How Neural Networks Can Tell Us Where to “Deep Dive” Clinically
Authors: Lukas Folle, David Simon, Koray Tascilar, Gerhard Krönke, Anna-Maria Liphardt, Andreas Maier, Georg Schett, Arnd Kleyer
Date: 2022-03-10 00:00:00
Proceeding: Frontiers in Medicine 2022 3

Objective: We investigated whether a neural network based on the shape of joints can differentiate between rheumatoid arthritis (RA), psoriatic arthritis (PsA), and healthy controls (HC), which class patients with undifferentiated arthritis (UA) are assigned to, and whether this neural network is able to identify disease-specific regions in joints.

Methods: We trained a novel neural network on 3D articular bone shapes of hand joints of RA and PsA patients as well as HC. Bone shapes were created from high-resolution peripheral-computed-tomography (HR-pQCT) data of the second metacarpal bone head. Heat maps of critical spots were generated using GradCAM. After training, we fed shape patterns of UA into the neural network to classify them into RA, PsA, or HC.

Results: Hand bone shapes from 932 HR-pQCT scans of 617 patients were available. The network could differentiate the classes with an area-under-receiver-operator-curve of 82% for HC, 75% for RA, and 68% for PsA. Heat maps identified anatomical regions such as bare area or ligament attachments prone to erosions and bony spurs. When feeding UA data into the neural network, 86% were classified as “RA,” 11% as “PsA,” and 3% as “HC” based on the joint shape.

Conclusion: We investigated neural networks to differentiate the shape of joints of RA, PsA, and HC and extracted disease-specific characteristics as heat maps on 3D joint shapes that can be utilized in clinical routine examination using ultrasound. Finally, unspecific diseases such as UA could be grouped using the trained network based on joint shape.
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View Image
Authors: Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, Seong-Whan Lee
Date: 2022-01-05 00:00:00
Proceeding: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022 1
Tasks: Face Generation, Facial Expression Recognition, Facial Expression Recognition (FER), Robust Face Recognition

Although various face-related tasks have significantly advanced in recent years, occlusion and extreme pose still impede the achievement of higher performance. Existing face rotation or de-occlusion methods only have emphasized the aspect of each problem. In addition, the lack of high-quality paired data remains an obstacle for both methods. In this work, we present a self-supervision strategy called Swap-R&R to overcome the lack of ground-truth in a fully unsupervised manner for joint face rotation and de-occlusion. To generate an input pair for self-supervision, we transfer the occlusion from a face in an image to an estimated 3D face and create a damaged face image, as if rotated from a different pose by rotating twice with the roughly de-occluded face. Furthermore, we propose Complete Face Recovery GAN (CFR-GAN) to restore the collapsed textures and disappeared occlusion areas by leveraging the structural and textural differences between two rendered images. Unlike previous works, which have selected occlusion-free images to obtain ground-truths, our approach does not require human intervention and paired data. We show that our proposed method can generate a de-occluded frontal face image from an occluded profile face image. Moreover, extensive experiments demonstrate that our approach can boost the performance of facial recognition and facial expression recognition. The code is publicly available.
Frequency response of diffusion-based molecular communication channels in bounded environment
Authors: Taishi Kotsuka, Yutaka Hori
Date: 2022-03-25 00:00:00

Recently, molecular communication (MC) has been studied as a micro-scale communication between cells or molecular robots. In previous works, the MC channels in unbounded environment was analyzed. However, many of the experimentally implemented MC channels are surrounded by walls, thus the boundary condition should be explicitly considered to analyze the dynamics of MC channels. In this paper, we propose a framework to analyze the frequency response of one-dimensional MC channels based on a diffusion equation with a boundary. In particular, we decompose the MC channel into the diffusion system and the boundary system, and show the relation between the cut-off frequency of the MC channel and the communication distance based on the transfer function. We then analyze the frequency response of a specific MC channel and reveal that the boundary can restrict the communication bandwidth of the MC channel.
Pseudo-Label Transfer from Frame-Level to Note-Level in a Teacher-Student Framework for Singing Transcription from Polyphonic Music
Authors: Sangeun Kum, Jongpil Lee, Keunhyoung Luke Kim, Taehyoung Kim, Juhan Nam
Date: 2022-03-25 00:00:00
Tasks: Pseudo Label, Quantization, Rhythm

Lack of large-scale note-level labeled data is the major obstacle to singing transcription from polyphonic music. We address the issue by using pseudo labels from vocal pitch estimation models given unlabeled data. The proposed method first converts the frame-level pseudo labels to note-level through pitch and rhythm quantization steps. Then, it further improves the label quality through self-training in a teacher-student framework. To validate the method, we conduct various experiment settings by investigating two vocal pitch estimation models as pseudo-label generators, two setups of teacher-student frameworks, and the number of iterations in self-training. The results show that the proposed method can effectively leverage large-scale unlabeled audio data and self-training with the noisy student model helps to improve performance. Finally, we show that the model trained with only unlabeled data has comparable performance to previous works and the model trained with additional labeled data achieves higher accuracy than the model trained with only labeled data.
Making Nonlinear Systems Negative Imaginary via State Feedback
Authors: Kanghong Shi, Ian R. Petersen, Igor G. Vladimirov
Date: 2022-03-25 00:00:00
Tasks: Form

This paper provides a state feedback stabilization approach for nonlinear systems of relative degree less than or equal to two by rendering them nonlinear negative imaginary (NI) systems. Conditions are provided under which a nonlinear system can be made a nonlinear NI system or a nonlinear output strictly negative imaginary (OSNI) system. Roughly speaking, an affine nonlinear system that has a normal form with relative degree less than or equal to two, after possible output transformation, can be rendered nonlinear NI and nonlinear OSNI. In addition, if the internal dynamics of the normal form are input-to-state stable, then there exists a state feedback input that stabilizes the system. This stabilization result is then extended to achieve stability for systems with a nonlinear NI uncertainty.
On the probability of a Condorcet winner among a large number of alternatives
Authors: Lisa Sauermann
Date: 2022-03-25 00:00:00
Tasks: 2k, Cultural Vocal Bursts Intensity Prediction

Consider $2k-1$ voters, each of which has a preference ranking between $n$ given alternatives. An alternative $A$ is called a Condorcet winner, if it wins against every other alternative $B$ in majority voting (meaning that for every other alternative $B$ there are at least $k$ voters who prefer $A$ over $B$). The notion of Condorcet winners has been studied intensively for many decades, yet some basic questions remain open. In this paper, we consider a model where each voter chooses their ranking randomly according to some probability distribution among all rankings. One may then ask about the probability to have a Condorcet winner with these randomly chosen rankings (which, of course, depends on $n$ and $k$, and the underlying probability distribution on the set of rankings). In the case of the uniform probability distribution over all rankings, which has received a lot of attention and is often referred to as the setting of an "impartial culture", we asymptotically determine the probability of having a Condorcet winner for a fixed number $2k-1$ of voters and $n$ alternatives with $n\to \infty$. This question has been open for around fifty years. While some authors suggested that the impartial culture should exhibit the lowest possible probability of having a Condorcet winner, in fact the probability can be much smaller for other distributions. We determine, for all values of $n$ and $k$, the smallest possible probability of having a Condorcet winner (and give an example of a probability distribution over all rankings which achieves this minimum possible probability).
The Companion Model -- a Canonical Model in Graph Signal Processing
Authors: John Shi, Jose M. F. Moura
Date: 2022-03-25 00:00:00
Tasks: model

This paper introduces a $\textit{canonical}$ graph signal model defined by a $\textit{canonical}$ graph and a $\textit{canonical}$ shift, the $\textit{companion}$ graph and the $\textit{companion}$ shift. These are canonical because, under standard conditions, we show that any graph signal processing (GSP) model can be transformed into the canonical model. The transform that obtains this is the graph $z$-transform ($\textrm{G$z$T}$) that we introduce. The GSP canonical model comes closest to the discrete signal processing (DSP) time signal models: the structure of the companion shift decomposes into a line shift and a signal continuation just like the DSP shift and the GSP canonical graph is a directed line graph with a terminal condition reflecting the signal continuation condition. We further show that, surprisingly, in the canonical model, convolution of graph signals is fast convolution by the DSP FFT.
Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks
Authors: Yiliang Liu, Wei Wang, Hsiao-Hwa Chen, Feng Lyu, Liangmin Wang, Weixiao Meng, Xuemin, Shen
Date: 2022-03-25 00:00:00

In this paper, we propose a secure computation offloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security. Existing computation offloading schemes usually neglected time-varying characteristics of channels and their corresponding secrecy rates, resulting in an inappropriate task partition ratio and a large secrecy outage probability. To address these issues, we utilize an ergodic secrecy rate to determine how many tasks are offloaded to the edge, where ergodic secrecy rate represents the average secrecy rate over all realizations in a time-varying wireless channel. Adaptive wiretap code rates are proposed with a secrecy outage constraint to match time-varying wireless channels. In addition, the proposed secure beamforming and artificial noise (AN) schemes can improve the ergodic secrecy rates of uplink and downlink channels even without eavesdropper channel state information (CSI). Numerical results demonstrate that the proposed schemes have a shorter system delay than the strategies neglecting time-varying characteristics.
Energy-Efficient and Physical Layer Secure Computation Offloading in Blockchain-Empowered Internet of Things
Authors: Yiliang Liu, Zhou Su, Yuntao Wang
Date: 2022-03-25 00:00:00
Tasks: Edge-computing

This paper investigates computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical layer security (PLS). After receiving task data, the BS allocates computational resources provided by mobile edge computing (MEC) servers to help sensors perform tasks. Existing blockchain-based computation offloading schemes usually focus on network performance improvements, such as energy consumption minimization or latency minimization, and neglect the Gas fee for computation offloading, resulting in the dissatisfaction of high Gas providers. Also, the secrecy rate during the data uploading process can not be measured by a steady value because of the time-varying characteristics of IRS-based wireless channels, thereby computational resources allocation with a secrecy rate measured before data uploading is inappropriate. In this paper, we design a Gas-oriented computation offloading scheme that guarantees a low degree of dissatisfaction of sensors, while reducing energy consumption. Also, we deduce the ergodic secrecy rate of IRS-assisted PLS transmission that can represent the global secrecy performance to allocate computational resources. The simulations show that the proposed scheme has lower energy consumption compared to existing schemes, and ensures that the node paying higher Gas gets stronger computational resources.
A Robust and Statistically Efficient Maximum-Likelihood Method for DOA Estimation Using Sparse Linear Arrays
Authors: Zai Yang, Xinyao Chen, Xunmeng Wu
Date: 2022-03-25 00:00:00

A recent trend of research on direction-of-arrival (DOA) estimation is to localize more uncorrelated sources than sensors by using a proper sparse linear array (SLA) and the Toeplitz covariance structure, at a cost of robustness to source correlations. In this paper, we make an attempt to achieve the two goals simultaneously by using a single algorithm. In order to statistically efficiently localize a maximal number of uncorrelated sources, we propose an effective algorithm for the stochastic maximum likelihood (SML) method based on elegant problem reformulations and the alternating direction method of multipliers (ADMM). We prove that the SML is robust to source correlations though it is derived under the assumption of uncorrelated sources. The proposed algorithm is usable for arbitrary SLAs (e.g., minimum redundancy arrays, nested arrays and coprime arrays) and is named as {\em m}aximum-likelihood {\em e}stimation via {\em s}equential {\em A}DMM (MESA). Extensive numerical results are provided that collaborate our analysis and demonstrate the statistical efficiency and robustness of MESA among state-of-the-art algorithms.
Beam Training and Alignment for RIS-Assisted Millimeter Wave Systems:State of the Art and Beyond
Authors: Peilan Wang, Jun Fang, Weizheng Zhang, Zhi Chen, Hongbin Li, Wei zhang
Date: 2022-03-25 00:00:00

Reconfigurable intelligent surface (RIS) has recently emerged as a promising paradigm for future cellular networks. Specifically, due to its capability in reshaping the propagation environment, RIS was introduced to address the blockage issue in millimeter Wave (mmWave) or even Terahertz (THz) communications. The deployment of RIS, however, complicates the system architecture and poses a significant challenge for beam training (BT)/ beam alignment (BA), a process that is required to establish a reliable link between the transmitter and the receiver. In this article, we first review several state-of-the-art beam training solutions for RIS-assisted mmWave systems and discuss their respective advantages and limitations. We also present a new multi-directional BT method, which can achieve a decent BA performance with only a small amount of training overhead. Finally, we outline several important open issues in BT for RIS-assisted mmWave systems.
Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave
Authors: Murat Bayraktar, Joan Palacios, Nuria González-Prelcic, Charlie Jianzhong Zhang
Date: 2022-03-24 00:00:00

RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage. In this paper, we study the ability of RIS to also provide enhanced localization capabilities as a by-product of communication. We consider sparse reconstruction algorithms to obtain high resolution channel estimates that are mapped to position information. In RIS-aided mmWave systems, the complexity of sparse recovery becomes a bottleneck, given the large number of elements of the RIS and the large communication arrays. We propose to exploit a multidimensional orthogonal matching pursuit strategy for compressive channel estimation in a RIS-aided millimeter wave system. We show how this algorithm, based on computing the projections on a set of independent dictionaries instead of a single large dictionary, enables high accuracy channel estimation at reduced complexity. We also combine this strategy with a localization approach which does not rely on the absolute time of arrival of the LoS path. Localization results in a realistic 3D indoor scenario show that RIS-aided wireless system can also benefit from a significant improvement in localization accuracy.
Strong Allee effect synaptic plasticity rule in an unsupervised learning environment
Authors: Eddy Kwessi
Date: 2022-03-25 00:00:00

Synaptic plasticity or the ability of a brain to changes one or more of its functions or structures has generated and is sill generating a lot of interest from the scientific community especially neuroscientists. These interests especially went into high gear after empirical evidences were collected that challenged the established paradigm that human brain structures and functions are set from childhood and only modest changes were expected beyond. Early synaptic plasticity rules or laws to that regard include the basic Hebbian rule that proposed a mechanism for strengthening or weakening of synapses (weights) during learning and memory. This rule however did not account from the fact that weights must have bounded growth overtime. Thereafter, many other rules were proposed to complement the basic Hebbian rule and they also possess other desirable properties. In particular, a desirable property in synaptic plasticity rule is that the ambient system must account for inhibition which is often achieved if the rule used allows for a lower bound in synaptic weights. In this paper, we propose a synaptic plasticity rule inspired from the Allee effect, a phenomenon often observed in population dynamics. We show properties such such as synaptic normalization, competition between weights, de-correlation potential, and dynamic stability are satisfied. We show that in fact, an Allee effect in synaptic plasticity can be construed as an absence of plasticity.