pwc-archive/papers-with-abstracts huggingface dataset:
High Speed Parallel Signal Crosstalk Cancellation Concept
Authors: Chad M. Smutzer, Michael J. Degerstrom, Barry K. Gilbert, Erik S. Daniel
Date: 2023-01-17 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High performance computing (HPC) systems make extensive use of high speed electrical interconnects, in routing signals among processing elements, or between processing elements and memory. Increasing bandwidth demands result in high density, parallel I/O exposed to crosstalk due to tightly coupled transmission lines. The crosstalk cancellation signaling concept discussed in this paper utilizes the known, predictable theory of coupled transmission lines to cancel crosstalk from neighboring traces with carefully chosen resistive cross-terminations between them. Through simulation and analysis of practical bus architectures, we explore the merits of crosstalk cancellation which could be used in dense interconnect HPC (or other) applications.
PWB Manufacturing Variability Effects on High Speed SerDes Links: Statistical Insights from Thousands of 4-Port SParameter Measurements
Authors: Bart O. McCoy, Robert W. Techentin, Benjamin R. Buhrow, Kevin Buchs, How Lin, Barry K. Gilbert, Erik S. Daniel
Date: 2023-01-17 00:00:00

Variability analysis is important in successfully deploying multi-gigabit backplane printed wiring boards (PWBs) with growing numbers of high-speed SerDes links. We discuss the need for large sample sizes to obtain accurate variability estimates of SI metrics (eye height, phase skew, etc). Using a dataset of 11,961 S-parameters, we demonstrate statistical techniques to extract accurate estimates of PWB SI performance variations. We cite numerical examples illustrating how these variations may contribute to underestimated or overestimated design criteria, causing unnecessary design expense. Tabular summaries of performance variation and key findings of broad interest to the general SI community are highlighted.
Applications of Optimization Routines in Signal Integrity Analysis
Authors: Pat J. Zabinski, Ben R. Buhrow, Barry K. Gilbert, Erik S. Daniel
Date: 2023-01-17 00:00:00

Signal integrity analysis often involves the development of design guidelines through manual manipulation of circuit parameters and judicious interpretation of results. Such an approach can result in significant effort and sub-optimal conclusions. Optimization routines have been well proven to aid analysis across a variety of common tasks. In addition, there are several non-traditional applications where optimization can be useful. This paper begins by describing the basics of optimization followed by two specific case studies where non-traditional optimization provides significant improvements in both analysis efficiency and channel performance.
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data
Authors: María Martínez-García, Fernando Moreno-Pino, Pablo M. Olmos, Antonio Artés-Rodríguez
Date: 2023-01-17 00:00:00
Tasks: Activity Recognition

Sleep constitutes a key indicator of human health, performance, and quality of life. Sleep deprivation has long been related to the onset, development, and worsening of several mental and metabolic disorders, constituting an essential marker for preventing, evaluating, and treating different health conditions. Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. In this work, we propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner, in contrast to traditional sleep assessment methods that usually rely on intrusive and subjective procedures. A Heterogeneous Hidden Markov Model is used to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by tested wearables, proving the effectiveness of the proposed approach and advocating its use to assess sleep without more reliable sources.
Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search
Authors: Mehdi Asadi, Fatemeh Poursalim, Mohammad Loni, Masoud Daneshtalab, Mikael Sjödin, Arash Gharehbaghi
Date: 2023-01-17 00:00:00
Tasks: Generative Adversarial Network, Neural Architecture Search

This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a Generative Adversarial Network (GAN) along with a Neural Architecture Search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certified GAN is statistically validated. Instead of using a general-purpose classifier, the NAS automatically designs a highly accurate convolutional neural network architecture customized for the PxAF classification task. Experimental results show that the accuracy of the proposed framework exhibits a high value of 99% which not only enhances state-of-the-art by up to 5.1%, but also improves the classification performance of the two widely-accepted baseline methods, ResNet-18, and Auto-Sklearn, by 2.2% and 6.1%.
Oscillometric Blood Pressure Measurement Using a Hybrid Deep Morpho-Temporal Representation Learning Framework
Authors: Niloufar Delfan, Mohamad Forouzanfar
Date: 2023-01-12 00:00:00
Tasks: Representation Learning

Oscillometric monitors are the most common automated blood pressure (BP) measurement devices used in non-specialist settings. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. A main limitation of the existing oscillometric monitors is their underlying analysis algorithms that are unable to fully capture the BP information encoded in the pattern of the recorded oscillometric pulses. In this paper, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement
Authors: Che Liu, Sibo Cheng, Weiping Ding, Rossella Arcucci
Date: 2023-01-10 00:00:00
Tasks: Electrocardiography (ECG), Feature Engineering, Time Series Analysis

Electrocardiography (ECG) signals can be considered as multi-variable time-series. The state-of-the-art ECG data classification approaches, based on either feature engineering or deep learning techniques, treat separately spectral and time domains in machine learning systems. No spectral-time domain communication mechanism inside the classifier model can be found in current approaches, leading to difficulties in identifying complex ECG forms. In this paper, we proposed a novel deep learning model named Spectral Cross-domain neural network (SCDNN) with a new block called Soft-adaptive threshold spectral enhancement (SATSE), to simultaneously reveal the key information embedded in spectral and time domains inside the neural network. More precisely, the domain-cross information is captured by a general Convolutional neural network (CNN) backbone, and different information sources are merged by a self-adaptive mechanism to mine the connection between time and spectral domains. In SATSE, the knowledge from time and spectral domains is extracted via the Fast Fourier Transformation (FFT) with soft trainable thresholds in modified Sigmoid functions. The proposed SCDNN is tested with several classification tasks implemented on the public ECG databases \textit{PTB-XL} and \textit{MIT-BIH}. SCDNN outperforms the state-of-the-art approaches with a low computational cost regarding a variety of metrics in all classification tasks on both databases, by finding appropriate domains from the infinite spectral mapping. The convergence of the trainable thresholds in the spectral domain is also numerically investigated in this paper. The robust performance of SCDNN provides a new perspective to exploit knowledge across deep learning models from time and spectral domains. The repository can be found: https://github.com/DL-WG/SCDNN-TS
Analysis of Arrhythmia Classification on ECG Dataset
Authors: Taminul Islam, Arindom Kundu, Tanzim Ahmed, Nazmul Islam Khan
Date: 2023-01-10 00:00:00
Tasks: Arrhythmia Detection, Classification

The heart is one of the most vital organs in the human body. It supplies blood and nutrients in other parts of the body. Therefore, maintaining a healthy heart is essential. As a heart disorder, arrhythmia is a condition in which the heart's pumping mechanism becomes aberrant. The Electrocardiogram is used to analyze the arrhythmia problem from the ECG signals because of its fewer difficulties and cheapness. The heart peaks shown in the ECG graph are used to detect heart diseases, and the R peak is used to analyze arrhythmia disease. Arrhythmia is grouped into two groups - Tachycardia and Bradycardia for detection. In this paper, we discussed many different techniques such as Deep CNNs, LSTM, SVM, NN classifier, Wavelet, TQWT, etc., that have been used for detecting arrhythmia using various datasets throughout the previous decade. This work shows the analysis of some arrhythmia classification on the ECG dataset. Here, Data preprocessing, feature extraction, classification processes were applied on most research work and achieved better performance for classifying ECG signals to detect arrhythmia. Automatic arrhythmia detection can help cardiologists make the right decisions immediately to save human life. In addition, this research presents various previous research limitations with some challenges in detecting arrhythmia that will help in future research.
Co-channel Interference Management for the Next-Generation Heterogeneous Networks using Deep Leaning
Authors: Ishtiaq Ahmad, Aftab Hussain
Date: 2023-01-06 00:00:00
Tasks: Management, Scheduling

The connectivity of public-safety mobile users (MU) in the co-existence of a public-safety network (PSN), unmanned aerial vehicles (UAVs), and LTE-based railway networks (LRN) needs a thorough investigation. UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement for MU. The co-existence of heterogeneous networks gives rise to the issue of co-channel interference due to the utilization of the same frequency band. By considering both sharing and non-sharing of radio access channels (RAC), we analyze co-channel interference in the downlink system of PSN, UAV, and LRN. As the LRN control signal demands high reliability and low latency, we provide higher priority to LRN users when allocating resources from the LRN RAC shared with MUs. Moreover, UAVs are deployed at the cell edge to increase the performance of cell-edge users. Therefore, interference control techniques enable LRN, PSN, and UAVs to cohabit in a scenario of sharing RAC. By offloading more PSN UEs to the LRN or UAVs, the resource utilization of the LRN and UAVs BSs is enhanced. In this paper, we aim to adopt deep learning (DL) based on enhanced inter-cell-interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Among LRN, PSN BS, and UAVs, a DL-based coordinated multipoint (CoMP) link technique is utilized to enhance the performance of PSN MUs. Therefore, if radio access channels are shared, utilization of DL-based FeICIC and CoMP for coordinated scheduling gives the best performance.
Chemical Integration of ODEs using Idealized Abstract Solutions
Authors: Su Hyeong Lee
Date: 2023-01-23 00:00:00
Tasks: Time Series, Time Series Analysis

In this work, we propose a general inversion framework to non-uniquely invert a very large class of ordinary differential equations (ODEs) into chemical reaction networks. A thorough treatment of the relevant chemical reaction network theory from the literature is given. Various simulation results are provided to augment the selection procedure for the inverse framework, where a previously known kineticization strategy is shown to be deterministically excellent but undesirable in chemical simulations. The utility of the framework is verified by simulating reaction network forms of meaningful ODE systems, and their time series are analyzed. In particular, we provide simulations of deterministic chaotic attractors whose newly discovered reaction networks are non-equivalent with any existing chemical interpretations within the literature, as well as presenting exemplary figures which may form a roadmap to the successful biochemical implementation of the integration of ODE systems.
Black-Scholes without stochastics or PDEs
Authors: Richard J. Martin
Date: 2023-01-24 00:00:00

We show how to derive the Black-Scholes model and its generalisation to the `exchange-option' (to exchange one asset for another) via the continuum limit of the Binomial tree. No knowledge of stochastic calculus or partial differential equations is assumed, as we do not use them.
Beyond $\ell_1$ sparse coding in V1
Authors: Ilias Rentzeperis, Luca Calatroni, Laurent Perrinet, Dario Prandi
Date: 2023-01-24 00:00:00

Growing evidence indicates that only a sparse subset from a pool of sensory neurons is active for the encoding of visual stimuli at any instant in time. Traditionally, to replicate such biological sparsity, generative models have been using the $\ell_1$ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. In this work, we use biological vision as a test-bed and show that the soft thresholding operation associated to the use of the $\ell_1$ norm is highly suboptimal compared to other functions suited to approximating $\ell_q$ with $0 \leq q < 1 $ (including recently proposed Continuous Exact relaxations), both in terms of performance and in the production of features that are akin to signatures of the primary visual cortex. We show that $\ell_1$ sparsity produces a denser code or employs a pool with more neurons, i.e. has a higher degree of overcompleteness, in order to maintain the same reconstruction error as the other methods considered. For all the penalty functions tested, a subset of the neurons develop orientation selectivity similarly to V1 neurons. When their code is sparse enough, the methods also develop receptive fields with varying functionalities, another signature of V1. Compared to other methods, soft thresholding achieves this level of sparsity at the expense of much degraded reconstruction performance, that more likely than not is not acceptable in biological vision. Our results indicate that V1 uses a sparsity inducing regularization that is closer to the $\ell_0$ pseudo-norm rather than to the $\ell_1$ norm.
An Optimal Control Strategy for Execution of Large Stock Orders Using LSTMs
Authors: A. Papanicolaou, H. Fu, P. Krishnamurthy, B. Healy, F. Khorrami
Date: 2023-01-23 00:00:00

In this paper, we simulate the execution of a large stock order with real data and general power law in the Almgren and Chriss model. The example that we consider is the liquidation of a large position executed over the course of a single trading day in a limit order book. Transaction costs are incurred because large orders walk the order book, that is, they consume order book liquidity beyond the best bid/ask. We model the order book with a power law that is proportional to trading volume, and thus transaction costs are inversely proportional to a power of trading volume. We obtain a policy approximation by training a long short term memory (LSTM) neural network to minimize transaction costs accumulated when execution is carried out as a sequence of smaller suborders. Using historical S&P100 price and volume data, we evaluate our LSTM strategy relative to strategies based on time-weighted average price (TWAP) and volume-weighted average price (VWAP). For execution of a single stock, the input to the LSTM is the cross section of data on all 100 stocks, including prices, volumes, TWAPs and VWAPs. By using this data cross section, the LSTM should be able to exploit inter-stock co-dependence in volume and price movements, thereby reducing transaction costs for the day. Our tests on S&P100 data demonstrate that in fact this is so, as our LSTM strategy consistently outperforms TWAP and VWAP-based strategies.
Bitcoin Does Not Hedge Inflation
Authors: Mykola Pinchuk
Date: 2023-01-24 00:00:00

This paper examines the response of major cryptocurrencies to macroeconomic news announcements (MNA). While other cryptocurrencies exhibit no reaction to major MNA, Bitcoin responds negatively to inflation surprise. Price of Bitcoin decreases by 24 bps in response to a 1 standard deviation inflationary surprise. This reaction is inconsistent with widely-held beliefs of practitioners that Bitcoin can hedge inflation. I do not find support for the hypothesis that the negative response of Bitcoin to inflation is due to its negative exposure to interest rates. Instead, I find support for the hypothesis that Bitcoin is strongly affected by the shift in consumption-savings decisions, driven by the rise in inflation. Consistent with this view, Bitcoin has negative exposure to a proxy for the consumption-savings ratio.
Methods in Econophysics: Estimating the Probability Density and Volatility
Authors: Moawia Alghalith
Date: 2022-11-03 00:00:00

We discuss and analyze some recent literature that introduced pioneering methods in econophysics. In doing so, we review recent methods of estimating the volatility, volatility of volatility, and probability densities. These methods will have useful applications in econophysics and finance.
Constructing Copulas Using Corrected Hermite Polynomial Expansion for Estimating Cross Foreign Exchange Volatility
Authors: Kenichiro Shiraya, Tomohisa Yamakami
Date: 2023-01-24 00:00:00

Copulas are used to construct joint distributions in many areas. In some problems, it is necessary to deal with correlation structures that are more complicated than the commonly known copulas. A finite order multivariate Hermite polynomial expansion, as an approximation of a joint density function, can handle complex correlation structures. However, it does not construct copulas because the density function can take negative values. In this study, we propose a method to construct a copula based on the finite sum of multivariate Hermite polynomial expansions by applying corrections to the joint density function. Furthermore, we apply this copula to estimate the volatility smile of cross currency pairs in the foreign exchange option market. This method can easily reproduce the volatility smile of cross currency pairs by appropriately adjusting the parameters and following the daily volatility fluctuations even if the higher-order parameters are fixed. In the numerical experiments, we compare the estimation results of the volatility smile of EUR-JPY with those of USD-JPY and EUR-USD for the proposed and other copulas, and show the validity of the proposed copula.
Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)
Authors: Tzu-Ya Lai, Wen Jung Cheng, Jun-En Ding
Date: 2023-01-15 00:00:00
Tasks: Graph Attention

The stock market is characterized by a complex relationship between companies and the market. This study combines a sequential graph structure with attention mechanisms to learn global and local information within temporal time. Specifically, our proposed "GAT-AGNN" module compares model performance across multiple industries as well as within single industries. The results show that the proposed framework outperforms the state-of-the-art methods in predicting stock trends across multiple industries on Taiwan Stock datasets.
Proceedings of the 1st International Workshop on Reading Music Systems
Authors: Jorge Calvo-Zaragoza, Jan Hajič jr., Alexander Pacha
Date: 2022-12-01 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 1st International Workshop on Reading Music Systems, held in Paris on the 20th of September 2018.
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets
Authors: Dennis Eschweiler, Rüveyda Yilmaz, Matisse Baumann, Ina Laube, Rijo Roy, Abin Jose, Daniel Brückner, Johannes Stegmaier
Date: 2023-01-02 00:00:00
Tasks: Denoising, Segmentation

Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method. In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets through an unsupervised and intuitive approach, using rough sketches of desired structures as the starting point. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches and enables the segmentation of diverse datasets without the need for human annotations. This approach holds great promise in streamlining the data generation process and enabling a more efficient and scalable training of segmentation models, as we show in the example of different practical experiments involving various organisms and cell types.