pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning for the detection of machining vibration chatter
Authors: Cheick Abdoul Kadir A. Kounta, Lionel Arnaud, Bernard Kamsu-Foguem, Fana Tangara
Date: 2023-03-17 00:00:00
Tasks: Deep Learning

Most of the work on chatter detection is based on laboratory machining tests, thus without the constraints of noise, the variety of situations to be managed in the industry, and the uncertainties on the parameters (sensor position, tool engagement, and sometimes even spindle rotation frequency). This work presents an approach first based on mechanical skills first to identify the optimal signal processing, then based on deep learning to automatically detect the phenomenon of chatter in machining. To do so, we use input images from the Fast Fourier Transform analysis of a vibration signal from the machining of an Alstom industrial railway system. The FFT images are first cropped in height to perform a renormalization of the signal scale. This renormalization step eliminates a bias regularly observed in the literature, which did not eliminate a simple correlation with signal amplitude because chatter is often, but not always, associated with stronger vibrations. Deep learning extracted features from the image data by exploiting pre-trained deep neural networks such as VGG16 and ResNet50. The data is derived from an industrial vibration signal (with noise, a not ideally placed sensors, and uncertainties on process parameters), which was very poorly represented in the literature. It is shown that by depleting the signal (renormalized amplitude and absence of information on the rotation speed), the model obtains a good result (an accuracy of 99% in the training and validation phases), which had never been done in the literature. In addition, since a part of the test dataset is similar to the training dataset, the proposed model obtains an accuracy of 98.82%. The robustness is proved by adding another test dataset containing noisy and more or less ambiguous images to be classified. The model still manages to detect these cases from its training of unambiguous cases, showing that the obtained generalization is efficient with an accuracy of 73.71%.
Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations
Authors: Kexin Zhu, xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao
Date: 2023-03-14 00:00:00
Tasks: EEG, EEG Emotion Recognition, Emotion Recognition, feature selection

Using deep learning methods to classify EEG signals can accurately identify people's emotions. However, existing studies have rarely considered the application of the information in another domain's representations to feature selection in the time-frequency domain. We propose a classification network of EEG signals based on the cross-domain feature fusion method, which makes the network more focused on the features most related to brain activities and thinking changes by using the multi-domain attention mechanism. In addition, we propose a two-step fusion method and apply these methods to the EEG emotion recognition network. Experimental results show that our proposed network, which combines multiple representations in the time-frequency domain and spatial domain, outperforms previous methods on public datasets and achieves state-of-the-art at present.
Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN
Authors: Ahmed Patwa, Muhammad Mahboob Ur Rahman, Tareq Y. Al-Naffouri
Date: 2023-03-12 00:00:00
Tasks: Denoising

Heart murmurs provide valuable information about mechanical activity of the heart, which aids in diagnosis of various heart valve diseases. This work does automatic and accurate heart murmur detection from phonocardiogram (PCG) recordings. Two public PCG datasets (CirCor Digiscope 2022 dataset and PCG 2016 dataset) from Physionet online database are utilized to train and test three custom neural networks (NN): a 1D convolutional neural network (CNN), a long short-term memory (LSTM) recurrent neural network (RNN), and a convolutional RNN (C-RNN). We first do pre-processing which includes the following key steps: denoising, segmentation, re-labeling of noise-only segments, data normalization, and time-frequency analysis of the PCG segments using wavelet scattering transform. We then conduct four experiments, first three (E1-E3) using PCG 2022 dataset, and fourth (E4) using PCG 2016 dataset. It turns out that our custom 1D-CNN outperforms other two NNs (LSTM-RNN and C-RNN). Further, our 1D-CNN model outperforms the related work in terms of accuracy, weighted accuracy, F1-score and AUROC, for experiment E3 (that utilizes the cleaned and re-labeled PCG 2022 dataset). As for experiment E1 (that utilizes the original PCG 2022 dataset), our model performs quite close to the related work in terms of weighted accuracy and F1-score.
Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN
Authors: Youzhi Liang, Wen Liang, Jianguo Jia
Date: 2023-03-11 00:00:00
Tasks: Denoising, Fault Diagnosis, Structural Health Monitoring

Vibration signals have been increasingly utilized in various engineering fields for analysis and monitoring purposes, including structural health monitoring, fault diagnosis and damage detection, where vibration signals can provide valuable information about the condition and integrity of structures. In recent years, there has been a growing trend towards the use of vibration signals in the field of bioengineering. Activity-induced structural vibrations, particularly footstep-induced signals, are useful for analyzing the movement of biological systems such as the human body and animals, providing valuable information regarding an individual's gait, body mass, and posture, making them an attractive tool for health monitoring, security, and human-computer interaction. However, the presence of various types of noise can compromise the accuracy of footstep-induced signal analysis. In this paper, we propose a novel ensemble model that leverages both the ensemble of multiple signals and of recurrent and convolutional neural network predictions. The proposed model consists of three stages: preprocessing, hybrid modeling, and ensemble. In the preprocessing stage, features are extracted using the Fast Fourier Transform and wavelet transform to capture the underlying physics-governed dynamics of the system and extract spatial and temporal features. In the hybrid modeling stage, a bi-directional LSTM is used to denoise the noisy signal concatenated with FFT results, and a CNN is used to obtain a condensed feature representation of the signal. In the ensemble stage, three layers of a fully-connected neural network are used to produce the final denoised signal. The proposed model addresses the challenges associated with structural vibration signals, which outperforms the prevailing algorithms for a wide range of noise levels, evaluated using PSNR, SNR, and WMAPE.
Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity
Authors: Huy Pham, Konstantin Egorov, Alexey Kazakov, Semen Budennyy
Date: 2023-03-10 00:00:00
Tasks: Heart Rate Variability

Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting abnormalities in cardiac activities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel method to detect the disease early could prevent death and complication. The paper presents novel various approaches for classifying cardiac diseases from ECG recordings. The first approach suggests the Poincare representation of ECG signal and deep-learning-based image classifiers (ResNet50 and DenseNet121 were learned over Poincare diagrams), which showed decent performance in predicting AF (atrial fibrillation) but not other types of arrhythmia. XGBoost, a gradient-boosting model, showed an acceptable performance in long-term data but had a long inference time due to highly-consuming calculation within the pre-processing phase. Finally, the 1D convolutional model, specifically the 1D ResNet, showed the best results in both studied CinC 2017 and CinC 2020 datasets, reaching the F1 score of 85% and 71%, respectively, and that was superior to the first-ranking solution of each challenge. The paper also investigated efficiency metrics such as power consumption and equivalent CO2 emissions, with one-dimensional models like 1D CNN and 1D ResNet being the most energy efficient. Model interpretation analysis showed that the DenseNet detected AF using heart rate variability while the 1DResNet assessed AF pattern in raw ECG signals.
Identifiability of the Rooted Tree Parameter under the Cavender-Farris-Neyman Model with a Molecular Clock
Authors: Jane Ivy Coons, Benjamin Hollering
Date: 2023-03-21 00:00:00

Identifiability of the discrete tree parameter is a key property for phylogenetic models since it is necessary for statistically consistent estimation of the tree from sequence data. Algebraic methods have proven to be very effective at showing that tree and network parameters of phylogenetic models are identifiable, especially when the underlying models are group-based. However, since group-based models are time-reversible, only the unrooted tree topology is identifiable and the location of the root is not. In this note we show that the rooted tree parameter of the Cavender-Farris-Neyman Model with a Molecular Clock is generically identifiable by using the invariants of the model which were characterized by Coons and Sullivant.
An agent-based modelling framework to study growth mechanisms in EGFR-L858R mutant alveolar type II cells
Authors: Helena Coggan, Clare E. Weeden, Philip Pearce, Mohit P. Dalwadi, Alastair Magness, Charles Swanton, Karen M. Page
Date: 2023-03-20 00:00:00

Mutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), particularly in never-smoker patients. However, these mutations are not always carcinogenic, and have recently been reported in histologically normal lung tissue from patients with and without lung cancer. To investigate the outcome of EGFR mutation in healthy lung stem cells, we grew murine alveolar type-II organoids monoclonally in a 3D Matrigel. Our experiments showed that the \textit{EGFR-L858R} mutation induced a change in organoid structure: mutated organoids displayed more `budding', in comparison to non-mutant controls, which were nearly spherical. We perform on-lattice computational simulations, which suggest that this can be explained by the concentration of division amongst a small number of cells on the surface of the organoid, which may arise from several possible biological mechanisms. These results suggest that the L858R mutation produces structures which expand quickly from surface protrusions. We are currently unable to distinguish the cell-based mechanisms that lead to this spatial heterogeneity in growth, but suggest a number of future experiments which could be used to do so. We suggest that the likelihood of L858R-fuelled tumorigenesis is affected not just by random fluctuations in cell fitness, but by whether the mutation arises in a spatial environment that allows mutant cells to reproduce without being forced to encounter each other. These data may have implications for cancer prevention strategies and for understanding NSCLC progression.
Representational Tenets for Memory Athletics
Authors: Kevin Schmidt, Othalia Larue, Ray Kulhanek, Dylan Flaute, Razvan Veliche, Christian Manasseh, Nelson Dellis, Scott Clouse, Jared Culbertson, Steve Rogers
Date: 2023-02-22 00:00:00

We describe the current state of world-class memory competitions, including the methods used to prepare for and compete in memory competitions, based on the subjective report of World Memory Championship Grandmaster and co-author Nelson Dellis. We then explore the reported experiences through the lens of the Simulated, Situated, and Structurally coherent Qualia (S3Q) theory of consciousness, in order to propose a set of experiments to help further understand the boundaries of expert memory performance.
Ecology theory disentangles microbial dichotomies
Authors: Luciana L. Couso, Alfonso Soler-Bistue, Ariel A. Aptekmann, Ignacio E. Sanchez
Date: 2023-03-21 00:00:00

Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-specific, sometimes precluding clear-cut results interpretation. We are able to interpret microbial dichotomies as life history strategies by combining ecology theory with Monod curves, a classical laboratory tool of bacterial physiology. Monod curves relate the specific growth rate of a microbe with the concentration of a limiting nutrient, and provide quantities that directly correspond to key ecological parameters in McArthur and Wilsons r/K selection theory, Tilmans resource competition and community structure theory and Grimes triangle of life strategies. The resulting model allows us to reconcile the copiotrophic/oligotrophic and fast/slow-growing dichotomies as different subsamples of a life history strategy triangle that also includes r/K strategists. We analyzed some ecological context by considering the known viable carbon sources for heterotrophic microbes in the framework of community structure theory. This partly explains the microbial diversity observed using metagenomics. In sum, ecology theory in combination with Monod curves can be a unifying quantitative framework for the study of natural microbial communities, calling for the integration of modern laboratory and field experiments.
A mathematical model of Bacteroides thetaiotaomicron, Methanobrevibacter smithii, and Eubacterium rectale interactions in the human gut
Authors: Melissa A. Adrian, Bruce P. Ayati, Ashutosh K. Mangalam
Date: 2023-03-20 00:00:00

The human gut microbiota is a complex ecosystem that affects a range of human physiology. In order to explore the dynamics of the human gut microbiota, we used a system of ordinary differential equations to model mathematically the biomass of three microorganism populations: $\textit{Bacteroides thetaiotaomicron}$, $\textit{Eubacterium rectale}$, and $\textit{Methanobrevibacter smithii}$. Additionally, we modeled the concentrations of relevant nutrients necessary to sustain these populations over time. Our model highlights the interactions and the competition among these three species. These three microorganisms were specifically chosen due to the system's end product, butyrate, which is a short chain fatty acid that aids in developing and maintaining the intestinal barrier in the human gut. The basis of our mathematical model assumes the gut is structured such that bacteria and nutrients exit the gut at a rate proportional to its volume, the rate of volumetric flow, and the biomass or concentration of the particular population or nutrient. We performed global sensitivity analyses using Sobol' sensitivities to estimate the relative importance of model parameters on simulation results.
Cost of Implementation of Basel III reforms in Bangladesh -- A Panel data analysis
Authors: Dipti Rani Hazra, Md. Shah Naoaj, Mohammed Mahinur Alam, Abdul Kader
Date: 2023-03-11 00:00:00

Inspired by the recent debate on the macroeconomic implications of the new bank regulatory standards known as Basel III, we tried to find out in this study that the impact of Basel III liquidity and capital requirements in Bangladesh proposed by Basel Committee on Banking Supervision (BCBS, 2010a). A small set of macro variables, using a sample of 22 private commercial banks operating in Bangladesh for the period of 2010-2014, are used to estimate long-run relationships among the variables. The macroeconomic variables are included The profitability of banks, GDP, banks' lending to private sector, Net Stable Funding Ratio, Tier 1 capital Ratio, Interest rate spread, real interest rate. The cost is quantified using Driscoll and Kraay panel data models with fixed effect. Impact of higher capital and liquidity requirement on Interest rate spread and lending to private sector of banks were considered as the cost to the economy as a whole whereas impact of higher capital and liquidity requirement on profitability of banks(ROE) was considered as the cost of banks. Here it is found that, the interest rate level is positively affected by the tighter liquidity and capital requirements which driven toward lessen of the private sector lending of banks. The return on equity of banks varies negatively with the liquidity and capital. The economic costs are considerably below the estimated positive benefit that the reform should have by reducing the probability of banking crises and the associated banking losses (BCBS, 2010b).
Minimizing Fuzzy Interpretations in Fuzzy Description Logics by Using Crisp Bisimulations
Authors: Linh Anh Nguyen
Date: 2023-03-13 00:00:00
Tasks: Abstract Algebra

The problem of minimizing finite fuzzy interpretations in fuzzy description logics (FDLs) is worth studying. For example, the structure of a fuzzy/weighted social network can be treated as a fuzzy interpretation in FDLs, where actors are individuals and actions are roles. Minimizing the structure of a fuzzy/weighted social network makes it more compact, thus making network analysis tasks more efficient. In this work, we study the problem of minimizing a finite fuzzy interpretation in a FDL by using the largest crisp auto-bisimulation. The considered FDLs use the Baaz projection operator and their semantics is specified using an abstract algebra of fuzzy truth values, which can be any linear and complete residuated lattice. We provide an efficient algorithm with a complexity of $O((m \log{l} + n) \log{n})$ for minimizing a given finite fuzzy interpretation $\mathcal{I}$, where $n$ is the size of the domain of $\mathcal{I}$, $m$ is number of nonzero instances of atomic roles of $\mathcal{I}$ and $l$ is the number of different fuzzy values used for instances of atomic roles of $\mathcal{I}$. We prove that the fuzzy interpretation returned by the algorithm is minimal among the ones that preserve fuzzy TBoxes and ABoxes under certain conditions.
Hierarchical Memory Pool Based Edge Semi-Supervised Continual Learning Method
Authors: Xiangwei Wang, Rui Han, Chi Harold Liu
Date: 2023-01-17 00:00:00
Tasks: Continual Learning

The continuous changes in the world have resulted in the performance regression of neural networks. Therefore, continual learning (CL) area gradually attracts the attention of more researchers. For edge intelligence, the CL model not only needs to overcome catastrophic for-getting, but also needs to face the huge challenge of severely limited resources: the lack of labeled resources and powerful devices. However, the existing classic CL methods usually rely on a large number of labeled samples to maintain the plasticity and stability, and the semi-supervised learning methods often need to pay a large computational and memory overhead for higher accuracy. In response to these prob-lems, a low-cost semi-supervised CL method named Edge Hierarchical Memory Learner (EdgeHML) will be proposed. EdgeHML can effec-tively utilize a large number of unlabeled samples and a small number of labeled samples. It is based on a hierarchical memory pool, lever-age multi-level storage structure to store and replay samples. EdgeHML implements the interaction between different levels through a combination of online and offline strategies. In addition, in order to further reduce the computational overhead for unlabeled samples, EdgeHML leverages a progressive learning method. It reduces the computation cycles of unlabeled samples by controlling the learning process. The experimental results show that on three semi-supervised CL tasks, EdgeHML can improve the model accuracy by up to 16.35% compared with the classic CL method, and the training iterations time can be reduced by more than 50% compared with semi-supervised methods. EdgeHML achieves a semi-supervised CL process with high performance and low overhead for edge intelligence.
Grading Conversational Responses Of Chatbots
Authors: Grant Rosario, David Noever
Date: 2023-02-01 00:00:00
Tasks: Machine Translation, Translation

Chatbots have long been capable of answering basic questions and even responding to obscure prompts, but recently their improvements have been far more significant. Modern chatbots like Open AIs ChatGPT3 not only have the ability to answer basic questions but can write code and movie scripts and imitate well-known people. In this paper, we analyze ChatGPTs' responses to various questions from a dataset of queries from the popular Quora forum. We submitted sixty questions to ChatGPT and scored the answers based on three industry-standard metrics for grading machine translation: BLEU, METEOR, and ROUGE. These metrics allow us to compare the machine responses with the most upvoted human answer to the same question to assess ChatGPT's ability to submit a humanistic reply. The results showed that while the responses and translation abilities of ChatGPT are remarkable, they still fall short of what a typical human reaction would be.
Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models
Authors: Divya Kothandaraman, Tianyi Zhou, Ming Lin, Dinesh Manocha
Date: 2023-03-15 00:00:00

We present a novel method, Aerial Diffusion, for generating aerial views from a single ground-view image using text guidance. Aerial Diffusion leverages a pretrained text-image diffusion model for prior knowledge. We address two main challenges corresponding to domain gap between the ground-view and the aerial view and the two views being far apart in the text-image embedding manifold. Our approach uses a homography inspired by inverse perspective mapping prior to finetuning the pretrained diffusion model. Additionally, using the text corresponding to the ground-view to finetune the model helps us capture the details in the ground-view image at a relatively low bias towards the ground-view image. Aerial Diffusion uses an alternating sampling strategy to compute the optimal solution on complex high-dimensional manifold and generate a high-fidelity (w.r.t. ground view) aerial image. We demonstrate the quality and versatility of Aerial Diffusion on a plethora of images from various domains including nature, human actions, indoor scenes, etc. We qualitatively prove the effectiveness of our method with extensive ablations and comparisons. To the best of our knowledge, Aerial Diffusion is the first approach that performs ground-to-aerial translation in an unsupervised manner.