pwc-archive/papers-with-abstracts huggingface dataset:
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems
Authors: Alessandro Gallo, Manh Duong Phung
Date: 2022-05-31 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Motor Imagery

A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data.
Analysis of Augmentations for Contrastive ECG Representation Learning
Authors: Sahar Soltanieh, Ali Etemad, Javad Hashemi
Date: 2022-05-30 00:00:00
Tasks: Arrhythmia Detection, Contrastive Learning, Representation Learning, Self-Supervised Learning

This paper systematically investigates the effectiveness of various augmentations for contrastive self-supervised learning of electrocardiogram (ECG) signals and identifies the best parameters. The baseline of our proposed self-supervised framework consists of two main parts: the contrastive learning and the downstream task. In the first stage, we train an encoder using a number of augmentations to extract generalizable ECG signal representations. We then freeze the encoder and finetune a few linear layers with different amounts of labelled data for downstream arrhythmia detection. We then experiment with various augmentations techniques and explore a range of parameters. Our experiments are done on PTB-XL, a large and publicly available 12-lead ECG dataset. The results show that applying augmentations in a specific range of complexities works better for self-supervised contrastive learning. For instance, when adding Gaussian noise, a sigma in the range of 0.1 to 0.2 achieves better results, while poor training occurs when the added noise is too small or too large (outside of the specified range). A similar trend is observed with other augmentations, demonstrating the importance of selecting the optimum level of difficulty for the added augmentations, as augmentations that are too simple will not result in effective training, while augmentations that are too difficult will also prevent the model from effective learning of generalized representations. Our work can influence future research on self-supervised contrastive learning on bio-signals and aid in selecting optimum parameters for different augmentations.
Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory
Authors: Tilahun M. Getu, Nada T. Golmie, David W. Griffith
Date: 2022-05-30 00:00:00

We provide a new generation solution to the fundamental old problem of a doubly selective fading channel estimation for orthogonal frequency division multiplexing (OFDM) systems. For systems based on OFDM, we propose a deep learning (DL)-based blind doubly selective channel estimator. This estimator does require no pilot symbols, unlike the corresponding state-of-the-art estimators, even during the estimation of a deep fading doubly selective channel. We also provide the first of its kind theory on the testing mean squared error (MSE) performance of our investigated blind OFDM channel estimator based on over-parameterized ReLU FNNs.
Intelligent analysis of EEG signals to assess consumer decisions: A Study on Neuromarketing
Authors: Nikunj Phutela, Abhilash P, Kaushik Sreevathsan, B N Krupa
Date: 2022-05-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Marketing

Neuromarketing is an emerging field that combines neuroscience and marketing to understand the factors that influence consumer decisions better. The study proposes a method to understand consumers' positive and negative reactions to advertisements (ads) and products by analysing electroencephalogram (EEG) signals. These signals are recorded using a low-cost single electrode headset from volunteers belonging to the ages 18-22. A detailed subject dependent (SD) and subject independent (SI) analysis was performed employing machine learning methods like Naive Bayes (NB), Support Vector Machine (SVM), k-nearest neighbour and Decision Tree and the proposed deep learning (DL) model. SVM and NB yielded an accuracy (Acc.) of 0.63 for the SD analysis. In SI analysis, SVM performed better for the advertisement, product and gender-based analysis. Furthermore, the performance of the DL model was on par with that of SVM, especially, in product and ads-based analysis.
Topological Simplification of Signals for Inference and Approximate Reconstruction
Authors: Gary Koplik, Nathan Borggren, Sam Voisin, Gabrielle Angeloro, Jay Hineman, Tessa Johnson, Paul Bendich
Date: 2022-05-25 00:00:00

As Internet of Things (IoT) devices become both cheaper and more powerful, researchers are increasingly finding solutions to their scientific curiosities both financially and computationally feasible. When operating with restricted power or communications budgets, however, devices can only send highly-compressed data. Such circumstances are common for devices placed away from electric grids that can only communicate via satellite, a situation particularly plausible for environmental sensor networks. These restrictions can be further complicated by potential variability in the communications budget, for example a solar-powered device needing to expend less energy when transmitting data on a cloudy day. We propose a novel, topology-based, lossy compression method well-equipped for these restrictive yet variable circumstances. This technique, Topological Signal Compression, allows sending compressed signals that utilize the entirety of a variable communications budget. To demonstrate our algorithm's capabilities, we perform entropy calculations as well as a classification exercise on increasingly topologically simplified signals from the Free-Spoken Digit Dataset and explore the stability of the resulting performance against common baselines.
Bayesian Networks for Brain-Computer Interfaces: A Survey
Authors: Pingsheng Li
Date: 2022-05-24 00:00:00
Tasks: Brain Computer Interface, Survey

Brain-Computer Interface (BCI) is a rapidly developing technology that allows direct communications between the human brain and external devices, such as robotic arms and computers. Bayesian Networks is a powerful tool in machine learning for tackling with problems that requires understanding and modelling the uncertainty and complexity within complex system built by sub-modular components. Therefore, deploying Bayesian Networks in the application of Brain-Computer Interfaces becomes an increasingly popular approach in BCI research. This survey covers related existing works in relatively high-level perspectives, classifies the models and algorithms involved, and also summarizes the application of Bayesian Networks or its variants in the context of Brain-Computer Interfaces.
A Review of Driver Drowsiness Detection Systems: Techniques, Advantages and Limitations
Authors: Ismail Nasri, Mohammed Karrouchi, Kamal Kassmi, Abdelhafid Messaoudi
Date: 2022-05-22 00:00:00

Driver Drowsiness is one of the most factors of road accidents, leading to severe injuries and deaths every year. Drowsiness means difficulty staying awake, which can lead to falling asleep. This paper introduces a literature review of driver drowsiness detection systems based on an analysis of physiological signals, facial features, and driving patterns. The paper also presents and details the recently proposed techniques for each class. We have also provided a comparative study of recently published works regarding the accuracy, reliability, hardware requirement, and intrusiveness. We have summarized and discussed the advantages and limitations of each class. As a result, each class of techniques has advantages and limitations. A hybrid system that combines two and more techniques will be efficient, robust, accurate, and used in real-time to take advantage of each technique.
Demo: low-power communications based on RIS and AI for 6G
Authors: Mingyao Cui, Zidong Wu, Yuhao Chen, Shenheng Xu, Fan Yang, Linglong Dai
Date: 2022-05-21 00:00:00
Tasks: 4k

Ultra-massive multiple-input-multiple-output (UM-MIMO) is promising to meet the high rate requirements for future 6G. However, due to the large number of antennas and high path loss, the hardware power consumption and computing power consumption of UM-MIMO will be unaffordable. To address this problem, we implement a low-power communication system based on reconfigurable intelligent surface (RIS) and artificial intelligence (AI) for 6G. For hardware design, we employ a 256-element RIS at the base station to replace the traditional phased array. Moreover, a 2304-element RIS is developed as a relay to assist communication with much reduced transmit power. For software implementation, we develop an AI-based transmission design to reduce computing power consumption. By jointly designing the hardware and software, this prototype can realize real-time 4K video transmission with much reduced power consumption.
Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease
Authors: Alexandra-Maria Tautan, Elias Casula, Ilaria Borghi, Michele Maiella, Sonia Bonni, Marilena Minei, Martina Assogna, Bogdan Ionescu, Giacomo Koch, Emiliano Santarnecchi
Date: 2022-05-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity

Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain features from individual TMS responses and average them over a low, medium and high density EEG electrode set. Within a leave-one-subject-out validation scenario, the best classification performance for AD vs. HC was obtained using a high-density electrode with a Random Forest classifier. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.2% respectively.
First large-scale genomic prediction in the honey bee
Authors: Richard Bernstein, Manuel Du, Zhipei G. Du, Anja S. Strauss, Andreas Hoppe, Kaspar Bienefeld
Date: 2022-06-15 00:00:00

Genomic selection has increased genetic gain in several livestock species, but due to the complicated genetics and reproduction biology not yet in honey bees. Recently, 2 970 queens were genotyped to gather a reference population. For the application of genomic selection in honey bees, this study analyses the predictive ability and bias of pedigree-based and genomic breeding values for honey yield, three workability traits and two traits for resistance against the parasite Varroa destructor. For breeding value estimation, we use a honey bee-specific model with maternal and direct effects, to account for the contributions of the workers and the queen of a colony to the phenotypes. We conducted a validation for the last generation and a five-fold cross-validation. In the validation for the last generation, the predictive ability of pedigree-based estimated breeding values was 0.06 for honey yield, and ranged from 0.2 to 0.41 for the workability traits. The inclusion of genomic marker data improved these predictive abilities to 0.11 for honey yield, and a range from 0.22 to 0.44 for the workability traits. The inclusion of genomic data did not improve the predictive ability for the disease related traits. Traits with high heritability for maternal effects compared to the heritability for direct effects showed the most promising results. Across all traits, the bias with genomic methods was close to the bias with pedigree-based BLUP. The results show that genomic selection can successfully be applied to honey bees.
Calculating the timing and probability of arrival for sea lice dispersing between salmon farms
Authors: Peter D. Harrington, Danielle L. Cantrell, Michael G. G. Foreman, Ming Guo, Mark A. Lewis
Date: 2022-06-14 00:00:00

Sea lice are a threat to the health of both wild and farmed salmon and an economic burden for salmon farms. With a free living larval stage, sea lice can disperse tens of kilometers in the ocean between salmon farms, leading to connected sea lice populations that are difficult to control in isolation. In this paper we develop a simple analytical model for the dispersal of sea lice between two salmon farms. From the model we calculate the arrival time distribution of sea lice dispersing between farms, as well as the level of cross-infection of sea lice. We also use numerical flows from a hydrodynamic model, coupled with a particle tracking model, to directly calculate the arrival time of sea lice dispersing between two farms in the Broughton Archipelago, BC, in order to fit our analytical model and find realistic parameter estimates. Using the parametrized analytical model we show that there is often an intermediate inter-farm spacing that maximizes the level of cross infection between farms, and that increased temperatures will lead to increased levels of cross infection.
The probability flow in the Stock market and Spontaneous symmetry breaking in Quantum Finance
Authors: Ivan Arraut, Joao Alexandre Lobo Marques, Sergio Gomes
Date: 2022-05-04 00:00:00

The Spontaneous Symmetry breaking in Quantum Finance considers the martingale condition in the stock market as a vacuum state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena ignores completely the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cases. However, when we deal with the Martingale condition, it comes out that the kinetic terms can also behave as potential terms and then reproduce a shift on the effective location of the vacuum (Martingale). In this paper we analyze the effective symmetry breaking patterns and the connected vacuum degeneracy for these special circumstances. Within the same scenario, we analyze the connection between the flow of information and the multiplicity of martingale states, providing in this way powerful tools for analyzing the dynamic of the stock market.
Evaluating and Inducing Personality in Pre-trained Language Models
Date: 2022-05-20 00:00:00
Proceeding: NeurIPS 2023 11

Standardized and quantified evaluation of machine behaviors is a crux of understanding LLMs. In this study, we draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. Originating as a philosophical quest for human behaviors, the study of personality delves into how individuals differ in thinking, feeling, and behaving. Toward building and understanding human-like social machines, we are motivated to ask: Can we assess machine behaviors by leveraging human psychometric tests in a principled and quantitative manner? If so, can we induce a specific personality in LLMs? To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors; MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories. By systematically evaluating LLMs with MPI, we provide the first piece of evidence demonstrating the efficacy of MPI in studying LLMs behaviors. We further devise a Personality Prompting (P^2) method to induce LLMs with specific personalities in a controllable way, capable of producing diverse and verifiable behaviors. We hope this work sheds light on future studies by adopting personality as the essential indicator for various downstream tasks, and could further motivate research into equally intriguing human-like machine behaviors.
Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers
Authors: Mehran Soltani, Francesco Da Ros, Andrea Carena, Darko Zibar
Date: 2022-05-16 00:00:00
Tasks: BIG-bench Machine Learning

We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addresses the amplification in the whole C-band, by optimizing four first-order counter-propagating Raman pumps.
A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus
Authors: Boyang Li, Qing Lu, Weiwen Jiang, Taeho Jung, Yiyu Shi
Date: 2022-05-05 00:00:00
Tasks: Deep Learning, Neural Architecture Search

In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload, thus the computation power of miners will not purely be spent on the hash puzzle. In this way, the hardware and energy will support the blockchain service and deep learning training simultaneously. While the incentive of miners is to earn tokens, individual miners are motivated to join mining pools to become more competitive. In this paper, we are the first to demonstrate a mining pool solution for novel consensuses based on deep learning. The mining pool manager partitions the full searching space into subspaces and all miners are scheduled to collaborate on the Neural Architecture Search (NAS) tasks in the assigned subspace. Experiments demonstrate that the performance of this type of mining pool is more competitive than an individual miner. Due to the uncertainty of miners' behaviors, the mining pool manager checks the standard deviation of the performance of high reward miners and prepares backup miners to ensure the completion of the tasks of high reward miners.
Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach
Authors: Mengwei Yuan, Gang Yang, Shijie Song, Luping Zhou, Robert Minasian, Xiaoke Yi
Date: 2022-05-15 00:00:00

In this paper, we propose a pre-trained-combined neural network (PTCN) as a comprehensive solution to the inverse design of an integrated photonic circuit. By utilizing both the initially pre-trained inverse and forward model with a joint training process, our PTCN model shows remarkable tolerance to the quantity and quality of the training data. As a proof of concept demonstration, the inverse design of a wavelength demultiplexer is used to verify the effectiveness of the PTCN model. The correlation coefficient of the prediction by the presented PTCN model remains greater than 0.974 even when the size of training data is decreased to 17%. The experimental results show a good agreement with predictions, and demonstrate a wavelength demultiplexer with an ultra-compact footprint, a high transmission efficiency with a transmission loss of -2dB, a low reflection of -10dB, and low crosstalk around -7dB simultaneously.
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains
Authors: Julian Gebele, Bonifaz Stuhr, Johann Haselberger
Date: 2022-06-16 00:00:00
Tasks: 2D Semantic Segmentation, Autonomous Driving, Domain Adaptation, Lane Detection, Self-Supervised Learning, Transfer Learning, Unsupervised Domain Adaptation, Unsupervised Pre-training

Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the evaluated domain adaptation methods are high compared to those of fully supervised baselines. This affirms the need for benchmarks such as CARLANE to further strengthen research in Unsupervised Domain Adaptation for lane detection. CARLANE, all evaluated models and the corresponding implementations are publicly available at https://carlanebenchmark.github.io.
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
Authors: Penghao Wu, Xiaosong Jia, Li Chen, Junchi Yan, Hongyang Li, Yu Qiao
Date: 2022-06-16 00:00:00
Tasks: Autonomous Driving, Bench2Drive, CARLA longest6, Trajectory Planning

Current end-to-end autonomous driving methods either run a controller based on a planned trajectory or perform control prediction directly, which have spanned two separately studied lines of research. Seeing their potential mutual benefits to each other, this paper takes the initiative to explore the combination of these two well-developed worlds. Specifically, our integrated approach has two branches for trajectory planning and direct control, respectively. The trajectory branch predicts the future trajectory, while the control branch involves a novel multi-step prediction scheme such that the relationship between current actions and future states can be reasoned. The two branches are connected so that the control branch receives corresponding guidance from the trajectory branch at each time step. The outputs from two branches are then fused to achieve complementary advantages. Our results are evaluated in the closed-loop urban driving setting with challenging scenarios using the CARLA simulator. Even with a monocular camera input, the proposed approach ranks first on the official CARLA Leaderboard, outperforming other complex candidates with multiple sensors or fusion mechanisms by a large margin. The source code is publicly available at https://github.com/OpenPerceptionX/TCP
MAGIC: Microlensing Analysis Guided by Intelligent Computation
Authors: Haimeng Zhao, Wei Zhu
Date: 2022-06-16 00:00:00
Tasks: Time Series, Time Series Analysis

The modeling of binary microlensing light curves via the standard sampling-based method can be challenging, because of the time-consuming light-curve computation and the pathological likelihood landscape in the high-dimensional parameter space. In this work, we present MAGIC, which is a machine-learning framework to efficiently and accurately infer the microlensing parameters of binary events with realistic data quality. In MAGIC, binary microlensing parameters are divided into two groups and inferred separately with different neural networks. The key feature of MAGIC is the introduction of a neural controlled differential equation, which provides the capability to handle light curves with irregular sampling and large data gaps. Based on simulated light curves, we show that MAGIC can achieve fractional uncertainties of a few percent on the binary mass ratio and separation. We also test MAGIC on a real microlensing event. MAGIC is able to locate degenerate solutions even when large data gaps are introduced. As irregular samplings are common in astronomical surveys, our method also has implications for other studies that involve time series.