pwc-archive/papers-with-abstracts huggingface dataset:
Early diagnosis of carpal tunnel syndrome using electromyogram signals
Authors: R. Astete, S. Chion, A. Figueroa, S. Franco, J. Mena, V. Zavaleta
Date: 2023-07-10 00:00:00
Proceeding: IEEE Latin America Transactions 2023 7

The present investigation proposes to use electromyographic signals for the diagnosis of carpal tunnel with the aim of helping to reduce the number of false positives and negatives that are obtained with the criteria currently used. For this, the EMG signals were analyzed using the Bitalino kit of a patient with carpal tunnel and a control patient. The signals were filtered and analyzed using the Python language. From this it was obtained that the signals presented slight differences in the electrical activity of both patients, a decrease in the interference pattern was observed, which indicates a reduction in the electrical activity recorded in the muscle during contraction and it is concluded that the carpal tunnel patient has a progressive decrease in nerve conduction velocity compared to a control patient.
Addressing detection of left bundle branch block by means of electrocardiogram’s signals using Machine Learning algorithms
Authors: Alessandra Aldave, Jose Huaylinos, Mateo Portal, Rosmy Postigo, Tayel Saavedra, Diego Salvatierra
Date: 2023-10-07 00:00:00
Proceeding: INTERCON 2023 10
Tasks: Prognosis

In this study, we addressed the detection of left bundle branch block (LBBB) through the analysis of electrocardiogram (ECG) signals using Machine Learning algorithms. LBBB is a cardiovascular disease that affects the heart’s electrical conduction system and may indicate the presence of underlying diseases. Early detection of LBBB is crucial for assessing the patient’s prognosis and taking preventive measures. To this end, we acquired datasets of ECGs from patients with LBBB, healthy patients, and patients with atrial fibrillation; we also acquired ECG signals from a volunteer suffering from LBBB through BITalino (r)evolution Plugged kit platform. We performed signal preprocessing, feature extraction, and classification using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Accuracy of classification of 82.54% and 89.14% are obtained for SVM and RF, respectively. F1 score of 97.99% and 97.12%
are obtain for prediction of LBBB with SVM and RF, respectively. Our results demonstrate the effectiveness of these algorithms in
accurately detecting LBBB from ECG signals. This approach could improve the detection and treatment of cardiovascular diseases related to LBBB.
Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis
Authors: Jun-Min Lee, Tae-Bin Ha
Date: 2023-06-19 00:00:00
Tasks: Image Generation, Memorization, Text Generation

Generative Adversarial Networks (GAN) is a model for data synthesis, which creates plausible data through the competition of generator and discriminator. Although GAN application to image synthesis is extensively studied, it has inherent limitations to natural language generation. Because natural language is composed of discrete tokens, a generator has difficulty updating its gradient through backpropagation; therefore, most text-GAN studies generate sentences starting with a random token based on a reward system. Thus, the generators of previous studies are pre-trained in an autoregressive way before adversarial training, causing data memorization that synthesized sentences reproduce the training data. In this paper, we synthesize sentences using a framework similar to the original GAN. More specifically, we propose Text Embedding Space Generative Adversarial Networks (TESGAN) which generate continuous text embedding spaces instead of discrete tokens to solve the gradient backpropagation problem. Furthermore, TESGAN conducts unsupervised learning which does not directly refer to the text of the training data to overcome the data memorization issue. By adopting this novel method, TESGAN can synthesize new sentences, showing the potential of unsupervised learning for text synthesis. We expect to see extended research combining Large Language Models with a new perspective of viewing text as an continuous space.
Low-cost semi-automatic arrhythmia detector using ECG signal
Authors: Dair Alegría, Jorge Medina, Erika Uchuya, Renato Rojas and Fernando Puipulivia
Date: 2023-07-10 00:00:00
Proceeding: IEEE Latin America Transactions 2023 7
Tasks: Rhythm

Cardiac arrhythmias are common heart rhythm
disorders with significant global impact. They are a leading cause
of death worldwide, accounting for 31 percentage of all deaths
according to the World Health Organization. Atrial fibrillation,
ventricular tachycardia, and sinus bradycardia are among the
most frequent types of arrhythmias. Researchers have developed
low-cost devices and machine learning algorithms to detect and
classify arrhythmias using electrocardiogram signals. Improving
access to specialized healthcare, particularly in rural areas, is
crucial in addressing the disparities in diagnosing and managing
cardiac arrhythmias. Early evaluation using cost-effective devices
can be a valuable tool in identifying potential heart rhythm
abnormalities and guiding patients towards appropriate care,
preventing emergencies and long-term complications
PIC-XAI: Post-hoc Image Captioning Explanation using Segmentation
Authors: Modafar Al-Shouha, Gábor Szűcs
Date: 2023-05-23 00:00:00
Proceeding: IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI) 2023 5
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Image Captioning

The rapid advancement in Deep Learning (DL) proposes viable solutions to various real-world problems. However, deploying DL-based models in some applications is hindered by their black-box nature and the inability to explain them. This has pushed Explainable Artificial Intelligence (XAI) research toward DL-based models, aiming to increase the trust by reducing their opacity. Although many XAI algorithms were proposed earlier, they lack the ability to explain certain tasks, i.e. image captioning (IC). This is caused by the IC task nature, e.g. the presence of multiple objects from the same category in the captioned image. In this paper we propose and investigate an XAI approach for this particular task. Additionally, we provide a method to evaluate XAI algorithms performance in the domain.
Encrypted Dynamic Control exploiting Limited Number of Multiplications and a Method using RLWE-based Cryptosystem
Authors: Joowon Lee, Donggil Lee, Junsoo Kim, Hyungbo Shim
Date: 2023-07-07 00:00:00

In this paper, we present a method to encrypt dynamic controllers that can be implemented through most homomorphic encryption schemes, including somewhat, leveled fully, and fully homomorphic encryption. To this end, we represent the output of the given controller as a linear combination of a fixed number of previous inputs and outputs. As a result, the encrypted controller involves only a limited number of homomorphic multiplications on every encrypted data, assuming that the output is re-encrypted and transmitted back from the actuator. A guidance for parameter choice is also provided, ensuring that the encrypted controller achieves predefined performance for an infinite time horizon. Furthermore, we propose a customization of the method for Ring Learning With Errors (RLWE)-based cryptosystems, where a vector of messages can be encrypted into a single ciphertext and operated simultaneously, thus reducing computation and communication loads. Unlike previous results, the proposed customization does not require extra algorithms such as rotation, other than basic addition and multiplication. Simulation results demonstrate the effectiveness of the proposed method.
Dual Inverse Degradation Network for Real-World SDRTV-to-HDRTV Conversion
Authors: Kepeng Xu, Li Xu, Gang He, Xianyun Wu, Zhiqiang Zhang, Wenxin Yu, Yunsong Li
Date: 2023-07-07 00:00:00
Tasks: inverse tone mapping, Inverse-Tone-Mapping, Tone Mapping, Video Restoration

In this study, we address the emerging necessity of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) in light of the limited number of native HDRTV content. A principal technical challenge in this conversion is the exacerbation of coding artifacts inherent in SDRTV, which detrimentally impacts the quality of the resulting HDRTV. To address this issue, our method introduces a novel approach that conceptualizes the SDRTV-to-HDRTV conversion as a composite task involving dual degradation restoration. This encompasses inverse tone mapping in conjunction with video restoration. We propose Dual Inversion Downgraded SDRTV to HDRTV Network (DIDNet), which can accurately perform inverse tone mapping while preventing encoding artifacts from being amplified, thereby significantly improving visual quality. DIDNet integrates an intermediate auxiliary loss function to effectively separate the dual degradation restoration tasks and efficient learning of both artifact reduction and inverse tone mapping during end-to-end training. Additionally, DIDNet introduces a spatio-temporal feature alignment module for video frame fusion, which augments texture quality and reduces artifacts. The architecture further includes a dual-modulation convolution mechanism for optimized inverse tone mapping. Recognizing the richer texture and high-frequency information in HDRTV compared to SDRTV, we further introduce a wavelet attention module to enhance frequency features. Our approach demonstrates marked superiority over existing state-of-the-art techniques in terms of quantitative performance and visual quality.
A review of dynamics design methods for high-speed and high-precision CNC machine tool feed systems
Authors: Xuesong Wang, Dongsheng Zhang, Zheng Zhang
Date: 2023-07-07 00:00:00

With the development of CNC machine tools toward high speed and high precision, the traditional static design methods can hardly meet the demand. Hence, in this paper, the dynamics matching design methods of existing CNC machine tool feed systems were investigated and analyzed. Further, sub-system coupling mechanisms and optimization design studies were carried out for each sub-system. First, the required kinematic indexes must be achieved when designing the feed system dynamics of high-speed, high-precision CNC machine tools. Second, the CNC machine tool feed systems generally have four sub-systems: motion process, control system, motor, and mechanical structure. The coupling effect between the sub-systems should also be considered in the design. Based on the dynamics design, each sub-system should be optimized to maximize the system dynamic performance with minimum resource allocation. Finally, based on the review, future research directions within the field were detected.
A Dynamic Equivalent Method for PMSG Based Wind Farms Under Asymmetrical Faults
Authors: Dongsheng Li, Chen Shen
Date: 2023-07-07 00:00:00

In this paper, a three-machine equivalent method applicable to asymmetrical faults is proposed considering the operating wind speed and fault severity. Firstly, direct-driven permanent magnet synchronous generator wind turbines (PMSGs) are clustered based on their different active power response characteristics considering the wind speed, the fault severity, and the negative sequence control strategy. Further, single-machine equivalent methods are proposed for each cluster of PMSGs. In particular, for the PMSGs with ramp recovery characteristics, a single-machine equivalent model with multi-segmented slope recovery is proposed, which can more accurately reflect the characteristics of the wind farm during the fault recovery. Moreover, an iterative simulation method is proposed to obtain the required clustering indicators before the actual occurrence of faults. Therefore, the proposed equivalent method can be used to analyze any anticipated contingency. Eventually, the effectiveness of the proposed method is verified on a modified IEEE 39-bus system.
On the convexity of static output feedback control synthesis for systems with lossless nonlinearities
Authors: Talha Mushtaq, Peter Seiler, Maziar S. Hemati
Date: 2023-07-06 00:00:00

Computing a stabilizing static output-feedback (SOF) controller is an NP-hard problem, in general. Yet, these controllers have amassed popularity in recent years because of their practical use in feedback control applications, such as fluid flow control and sensor/actuator selection. The inherent difficulty of synthesizing SOF controllers is rooted in solving a series of non-convex problems that make the solution computationally intractable. In this note, we show that SOF synthesis is a convex problem for the specific case of systems with a lossless (i.e., energy-conserving) nonlinearity. Our proposed method ensures asymptotic stability of an SOF controller by enforcing the lossless behavior of the nonlinearity using a quadratic constraint approach. In particular, we formulate a bilinear matrix inequality~(BMI) using the approach, then show that the resulting BMI can be recast as a linear matrix inequality (LMI). The resulting LMI is a convex problem whose feasible solution, if one exists, yields an asymptotically stabilizing SOF controller.
Are there Dragon Kings in the Stock Market?
Authors: Jiong Liu, M. Dashti Moghaddam, R. A. Serota
Date: 2023-07-07 00:00:00

We undertake a systematic study of historic market volatility spanning roughly five preceding decades. We focus specifically on the time series of realized volatility (RV) of the S&P500 index and its distribution function. As expected, the largest values of RV coincide with the largest economic upheavals of the period: Savings and Loan Crisis, Tech Bubble, Financial Crisis and Covid Pandemic. We address the question of whether these values belong to one of the three categories: Black Swans (BS), that is they lie on scale-free, power-law tails of the distribution; Dragon Kings (DK), defined as statistically significant upward deviations from BS; or Negative Dragons Kings (nDK), defined as statistically significant downward deviations from BS. In analyzing the tails of the distribution with RV > 40, we observe the appearance of "potential" DK which eventually terminate in an abrupt plunge to nDK. This phenomenon becomes more pronounced with the increase of the number of days over which the average RV is calculated -- here from daily, n=1, to "monthly," n=21. We fit the entire distribution with a modified Generalized Beta (mGB) distribution function, which terminates at a finite value of the variable but exhibits a long power-law stretch prior to that, as well as Generalized Beta Prime (GB2) distribution function, which has a power-law tail. We also fit the tails directly with a straight line on a log-log scale. In order to ascertain BS, DK or nDK behavior, all fits include their confidence intervals and p-values are evaluated for the data points to check if they can come from the respective distributions.
Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions
Authors: Francis X. Diebold, Glenn D. Rudebusch
Date: 2023-07-07 00:00:00
Tasks: Sensitivity

Arctic sea ice has steadily diminished as atmospheric greenhouse gas concentrations have increased. Using observed data from 1979 to 2019, we estimate a close contemporaneous linear relationship between Arctic sea ice area and cumulative carbon dioxide emissions. For comparison, we provide analogous regression estimates using simulated data from global climate models (drawn from the CMIP5 and CMIP6 model comparison exercises). The carbon sensitivity of Arctic sea ice area is considerably stronger in the observed data than in the climate models. Thus, for a given future emissions path, an ice-free Arctic is likely to occur much earlier than the climate models project. Furthermore, little progress has been made in recent global climate modeling (from CMIP5 to CMIP6) to more accurately match the observed carbon-climate response of Arctic sea ice.
Semantic-Aware Image Compressed Sensing
Authors: BoWen Zhang, Zhijin Qin, Geoffrey Ye Li
Date: 2023-07-06 00:00:00
Tasks: compressed sensing, Decoder, Image Compressed Sensing

Deep learning based image compressed sensing (CS) has achieved great success. However, existing CS systems mainly adopt a fixed measurement matrix to images, ignoring the fact the optimal measurement numbers and bases are different for different images. To further improve the sensing efficiency, we propose a novel semantic-aware image CS system. In our system, the encoder first uses a fixed number of base CS measurements to sense different images. According to the base CS results, the encoder then employs a policy network to analyze the semantic information in images and determines the measurement matrix for different image areas. At the decoder side, a semantic-aware initial reconstruction network is developed to deal with the changes of measurement matrices used at the encoder. A rate-distortion training loss is further introduced to dynamically adjust the average compression ratio for the semantic-aware CS system and the policy network is trained jointly with the encoder and the decoder in an en-to-end manner by using some proxy functions. Numerical results show that the proposed semantic-aware image CS system is superior to the traditional ones with fixed measurement matrices.
Using electrical impedance spectroscopy to identify equivalent circuit models of lubricated contacts with complex geometry: in-situ application to mini traction machine
Authors: Min Yu, Jie Zhang, Arndt Joedicke, Tom Reddyhoff
Date: 2023-07-07 00:00:00

Electrical contact resistance or capacitance as measured between a lubricated contact has been used in tribometers, partially reflecting the lubrication condition. In contrast, the electrical impedance provides rich information of magnitude and phase, which can be interpreted using equivalent circuit models, enabling more comprehensive measurements, including the variation of lubricant film thickness and the asperity (metal to metal) contact area. An accurate circuit model of the lubricated contact is critical as needed for the electrical impedance analysis. However, existing circuit models are hand derived and suited to interfaces with simple geometry, such as parallel plates, concentric and eccentric cylinders. Circuit model identification of lubricated contacts with complex geometry is challenging. This work takes the ball-on-disc lubricated contact in a Mini Traction Machine (MTM) as an example, where screws on the ball, grooves on the disc, and contact close to the disc edge make the overall interface geometry complicated. The electrical impedance spectroscopy (EIS) is used to capture its frequency response, with a group of load, speed, and temperature varied and tested separately. The results enable an identification of equivalent circuit models by fitting parallel resistor-capacitor models, the dependence on the oil film thickness is further calibrated using a high-accuracy optical interferometry, which is operated under the same lubrication condition as in the MTM. Overall, the proposed method is applicable to general lubricated interfaces for the identification of equivalent circuit models, which in turn facilitates in-situ tribo-contacts with electric impedance measurement of oil film thickness. It does not need transparent materials as optical techniques do, or structural modifications for piezoelectric sensor mounting as ultrasound techniques do.
An Anti-Jamming Strategy for Disco Intelligent Reflecting Surfaces Based Fully-Passive Jamming Attacks
Authors: Huan Huang, Hongliang Zhang, Yi Cai, A. Lee Swindlehurst, Zhu Han
Date: 2023-07-07 00:00:00

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, while also pose a huge risk for physical layer security. A disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties, can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS-based fully-passive jammer (FPJ) can be applied to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by the DIRS-based FPJ, an anti-jamming strategy is proposed that requires only the statistical characteristics of DIRS-jammed channels instead of their CSI. Statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is given based on the derived statistical characteristics. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJ.
An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training
Authors: Durgesh Kusuru, Anish C. Turlapaty, Mainak Thakur
Date: 2023-07-07 00:00:00

Recent literature suggests that the surface electromyography (sEMG) signals have non-stationary statistical characteristics specifically due to random nature of the covariance. Thus suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a Compound-Gaussian (CG) model for multivariate sEMG signals in which latent variable of covariance is modeled as a random variable that follows an exponential model. The parameters of the model are estimated using the iterative Expectation Maximization (EM) algorithm. Further, a new dataset, electromyography analysis of human activities database 2 (EMAHA-DB2) is developed. Based on the model fitting analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG model fits more closely to the empirical pdf of sEMG signals than the existing models. The proposed model is validated by visual inspection, further validated by matching central moments and better quantitative metrics in comparison with other models. The proposed compound model provides an improved fit to the statistical behavior of sEMG signals. Further, the estimate of rate parameter of the exponential model shows clear relation to the training weights. Finally, the average signal power estimates of the channels shows distinctive dependency on the training weights, the subject's training experience and the type of activity.
On Unified Adaptive Portfolio Management
Authors: Chi-Lin Li, Chung-Han Hsieh
Date: 2023-07-07 00:00:00
Tasks: Management, Portfolio Optimization, regression

This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible window size adjustment based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive ten-year empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.