pwc-archive/papers-with-abstracts huggingface dataset:
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
Authors: Ce Ju, Cuntai Guan
Date: 2022-10-25 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), Motor Imagery, Riemannian optimization

The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG). Over the past few decades, the performance of MI-EEG classifiers has seen gradual enhancement. In this study, we amplify the geometric deep learning-based MI-EEG classifiers from the perspective of time-frequency analysis, introducing a new architecture called Graph-CSPNet. We refer to this category of classifiers as Geometric Classifiers, highlighting their foundation in differential geometry stemming from EEG spatial covariance matrices. Graph-CSPNet utilizes novel manifold-valued graph convolutional techniques to capture the EEG features in the time-frequency domain, offering heightened flexibility in signal segmentation for capturing localized fluctuations. To evaluate the effectiveness of Graph-CSPNet, we employ five commonly-used publicly available MI-EEG datasets, achieving near-optimal classification accuracies in nine out of eleven scenarios. The Python repository can be found at https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet.
Two-Timescale Transmission Design for Wireless Communication Systems Aided by Active RIS
Authors: Zhangjie Peng, Tianshu Li, Cunhua Pan, Xianfu Lei, Shi Jin
Date: 2022-10-25 00:00:00

This paper considers an active reconfigurable intelligent surface (RIS)-aided communication system, where an M-antenna base station (BS) transmits data symbols to a single-antenna user via an N-element active RIS. We use two-timescale channel state information (CSI) in our system, so that the channel estimation overhead and feedback overhead can be decreased dramatically. A closed-form approximate expression of the achievable rate (AR) is derived and the phase shift at the active RIS is optimized. In addition, we compare the performance of the active RIS system with that of the passive RIS system. The conclusion shows that the active RIS system achieves a lager AR than the passive RIS system.
Leveraging Statistical Shape Priors in GAN-based ECG Synthesis
Authors: Nour Neifar, Achraf Ben-Hamadou, Afef Mdhaffar, Mohamed Jmaiel, Bernd Freisleben
Date: 2022-10-22 00:00:00
Tasks: ECG Classification

Electrocardiogram (ECG) data collection during emergency situations is challenging, making ECG data generation an efficient solution for dealing with highly imbalanced ECG training datasets. In this paper, we propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling. Our approach leverages prior knowledge about ECG dynamics to synthesize realistic signals, addressing the complex dynamics of ECG signals. To validate our approach, we conducted experiments using ECG signals from the MIT-BIH arrhythmia database. Our results demonstrate that our approach, which models temporal and amplitude variations of ECG signals as 2-D shapes, generates more realistic signals compared to state-of-the-art GAN based generation baselines. Our proposed approach has significant implications for improving the quality of ECG training datasets, which can ultimately lead to better performance of ECG classification algorithms. This research contributes to the development of more efficient and accurate methods for ECG analysis, which can aid in the diagnosis and treatment of cardiac diseases.
Topological biomarkers for real-time detection of epileptic seizures
Authors: Ximena Fernández, Diego Mateos
Date: 2022-11-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection, Time Series, Time Series Analysis

Real time seizure detection is a fundamental problem in computational neuroscience towards diagnosis and treatment's improvement of epileptic disease. We propose a real-time computational method for tracking and detection of epileptic seizures from raw neurophysiological recordings. Our mechanism is based on the topological analysis of the sliding-window embedding of the time series derived from simultaneously recorded channels. We extract topological biomarkers from the signals via the computation of the persistent homology of time-evolving topological spaces. Remarkably, the proposed biomarkers robustly captures the change in the brain dynamics during the ictal state. We apply our methods in different types of signals including scalp and intracranial electroencephalograms and magnetoencephalograms, in patients during interictal and ictal states, showing high accuracy in a range of clinical situations.
Transcriptome Complexities Across Eukaryotes
Authors: James E. Titus-McQuillan, Adalena V. Nanni, Lauren M. McIntyre, Rebekah L. Rogers
Date: 2022-11-04 00:00:00

Genomic complexity is a growing field of evolution, with case studies for comparative evolutionary analyses in model and emerging non-model systems. Understanding complexity and the functional components of the genome is an untapped wealth of knowledge ripe for exploration. With the "remarkable lack of correspondence" between genome size and complexity, there needs to be a way to quantify complexity across organisms. In this study we use a set of complexity metrics that allow for evaluation of changes in complexity using TranD. We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity is varied. We define three metrics -- TpG, EpT, and EpG in this study to quantify the complexity of the transcriptome that encapsulate the dynamics of alternative splicing. Here we compare complexity metrics across 1) whole genome annotations, 2) a filtered subset of orthologs, and 3) novel genes to elucidate the impacts of ortholog and novel genes in transcriptome analysis. We also derive a metric from Hong et al., 2006, Effective Exon Number (EEN), to compare the distribution of exon sizes within transcripts against random expectations of uniform exon placement. EEN accounts for differences in exon size, which is important because novel genes differences in complexity for orthologs and whole transcriptome analyses are biased towards low complexity genes with few exons and few alternative transcripts. With our metric analyses, we are able to implement changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step forward toward whole transcriptome analysis in the emerging field of non-model evolutionary genomics, with key insights for evolutionary inference of complexity changes on deep timescales across the tree of life. We suggest a means to quantify biases generated in ortholog calling and correct complexity analysis for lineage-specific effects. With these metrics, we directly assay the quantitative properties of newly formed lineage-specific genes as they lower complexity in transcriptomes.
Beyond spiking networks: the computational advantages of dendritic amplification and input segregation
Authors: Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci
Date: 2022-11-04 00:00:00
Tasks: Decision Making, Imitation Learning

The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons, and cannot achieve the state-of-the-art performances in machine learning. Recent works have proposed that segregation of dendritic input (neurons receive sensory information and higher-order feedback in segregated compartments) and generation of high-frequency bursts of spikes would support error backpropagation in biological neurons. However, these approaches require propagating errors with a fine spatio-temporal structure to the neurons, which is unlikely to be feasible in a biological network. To relax this assumption, we suggest that bursts and dendritic input segregation provide a natural support for biologically plausible target-based learning, which does not require error propagation. We propose a pyramidal neuron model composed of three separated compartments. A coincidence mechanism between the basal and the apical compartments allows for generating high-frequency bursts of spikes. This architecture allows for a burst-dependent learning rule, based on the comparison between the target bursting activity triggered by the teaching signal and the one caused by the recurrent connections, providing the support for target-based learning. We show that this framework can be used to efficiently solve spatio-temporal tasks, such as the store and recall of 3D trajectories. Finally, we suggest that this neuronal architecture naturally allows for orchestrating ``hierarchical imitation learning'', enabling the decomposition of challenging long-horizon decision-making tasks into simpler subtasks. This can be implemented in a two-level network, where the high-network acts as a ``manager'' and produces the contextual signal for the low-network, the ``worker''.
Towards Alzheimer's Disease Progression Assessment: A Review of Machine Learning Methods
Authors: Zibin Zhao
Date: 2022-11-01 00:00:00

Alzheimer's Disease (AD), as the most devastating neurodegenerative disease worldwide, has reached nearly 10 million new cases annually. Current technology provides unprecedented opportunities to study the progression and etiology of this disease with the advanced in imaging techniques. With the recent emergence of a society driven by big data and machine learning (ML), researchers have exerted considerable effort to summarize recent advances in ML-based AD diagnosis. Here, we outline some of the most prevalent and recent ML models for assessing the progression of AD and provide insights on the challenges, opportunities, and future directions that could be advantageous to future research in AD using ML.
An approach for benchmarking the numerical solutions of stochastic compartmental models
Authors: Alison C Hale, Christopher P Jewell
Date: 2022-11-04 00:00:00
Tasks: Benchmarking

An approach is introduced for comparing the estimated states of stochastic compartmental models for an epidemic or biological process with analytically obtained solutions from the corresponding system of ordinary differential equations (ODEs). Positive integer valued samples from a stochastic model are generated numerically at discrete time intervals using either the Reed-Frost chain Binomial or Gillespie algorithm. The simulated distribution of realisations is compared with an exact solution obtained analytically from the ODE model. Using this novel methodology this work demonstrates it is feasible to check that the realisations from the stochastic compartmental model adhere to the ODE model they represent. There is no requirement for the model to be in any particular state or limit. These techniques are developed using the stochastic compartmental model for a susceptible-infected-recovered (SIR) epidemic process. The Lotka-Volterra model is then used as an example of the generality of the principles developed here. This approach presents a way of testing/benchmarking the numerical solutions of stochastic compartmental models, e.g. using unit tests, to check that the computer code along with its corresponding algorithm adheres to the underlying ODE model.
Alternative formulations for gilthead seabream diets: towards a more sustainable production
Authors: C. Aragão, M. Cabano, R. Colen, J. Fuentes, J. Dias
Date: 2022-11-03 00:00:00

To support the expected increase in aquaculture production during the next years, a wider range of alternative ingredients to fishmeal is needed, towards contributing to an increase in production sustainability. This study aimed to test diets formulated with non-conventional feed ingredients on gilthead seabream (Sparus aurata) growth performance, feed utilization, apparent digestibility of nutrients and nutrient outputs to the environment. Four isonitrogenous and isoenergetic diets were formulated: a control diet (CTRL) similar to a commercial feed and three experimental diets containing, as main protein sources, plant by-products, glutens and concentrates (PLANT); processed animal proteins (PAP); or micro/macroalgae, insect meals and yeast (EMERG). Diets were tested in triplicate during 80 days. The (EMERG) treatment resulted in lower fish growth performance, higher FCR and lower nutrient and energy retentions than the other treatments. The lowest protein digestibility was found for the EMERG diet, which caused increased nitrogen losses. The PLANT and PAP treatments resulted in better fish growth performance, higher nutrient and energy retentions, and lower FCR than the CTRL treatment. The significant improvement in FCR found for fish fed PLANT and PAP diets and the high protein digestibility of these diets contribute towards minimizing the environmental impacts of seabream production
A Spatially Distributed Model of Brain Metabolism Highlights the Role of Diffusion in Brain Energy Metabolism
Authors: Gideon Idumah, Erkki Somersalo, Daniela Calvetti
Date: 2022-10-24 00:00:00

The different active roles of neurons and astrocytes during neuronal activation are associated with the metabolic processes necessary to supply the energy needed for their respective tasks at rest and during neuronal activation. Metabolism, in turn, relies on the delivery of metabolites and removal of toxic byproducts through diffusion processes and the cerebral blood flow. A comprehensive mathematical model of brain metabolism should account not only for the biochemical processes and the interaction of neurons and astrocytes, but also the diffusion of metabolites. In the present article, we present a computational methodology based on a multidomain model of the brain tissue and a homogenization argument for the diffusion processes. In our spatially distributed compartment model, communication between compartments occur both through local transport fluxes, as is the case within local astrocyte-neuron complexes, and through diffusion of some substances in some of the compartments. The model assumes that diffusion takes place in the extracellular space (ECS) and in the astrocyte compartment. In the astrocyte compartment, the diffusion across the syncytium network is implemented as a function of gap junction strength. The diffusion process is implemented numerically by means of a finite element method (FEM) based spatial discretization, and robust stiff solvers are used to time integrate the resulting large system. Computed experiments show the effects of ECS tortuosity, gap junction strength and spatial anisotropy in the astrocyte network on the brain energy metabolism.
VitalDB
Authors: Hyung-Chul Lee, Yoonsang Park, Soo Bin Yoon, Seong Mi Yang, Dongnyeok Park, Chul-Woo Jung
Date: 2022-06-08 00:00:00
Proceeding: Scientific Data 2022 6
Tasks: Time Series, Time Series Analysis

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series bio-signals and their correlations is a difficult task even for experienced anaesthesiologists. Although recent advanced machine learning technologies have shown promising results in bio-signal analysis, research and development in this area is relatively slow due to the lack of bio-signal datasets for learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset includes high-resolution multi-parameter intraoperative vital signs from 6,388 non-cardiac surgery in the 10 operating rooms at a tertiary academic hospital. A total of 557,622 waveform and numeric data from 197 monitoring parameters were recorded using Vital Recorder and 77 clinical information were extracted from electronic medical records. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable data resource for bio-signal research and development.
Small Language Models for Tabular Data
Authors: Benjamin L. Badger
Date: 2022-11-05 00:00:00
Tasks: Feature Engineering, Representation Learning

Supervised deep learning is most commonly applied to difficult problems defined on large and often extensively curated datasets. Here we demonstrate the ability of deep representation learning to address problems of classification and regression from small and poorly formed tabular datasets by encoding input information as abstracted sequences composed of a fixed number of characters per input field. We find that small models have sufficient capacity for approximation of various functions and achieve record classification benchmark accuracy. Such models are shown to form useful embeddings of various input features in their hidden layers, even if the learned task does not explicitly require knowledge of those features. These models are also amenable to input attribution, allowing for an estimation of the importance of each input element to the model output as well as of which inputs features are effectively embedded in the model. We present a proof-of-concept for the application of small language models to mixed tabular data without explicit feature engineering, cleaning, or preprocessing, relying on the model to perform these tasks as part of the representation learning process.
Multi-blank Transducers for Speech Recognition
Authors: Hainan Xu, Fei Jia, Somshubra Majumdar, Shinji Watanabe, Boris Ginsburg
Date: 2022-11-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper proposes a modification to RNN-Transducer (RNN-T) models for automatic speech recognition (ASR). In standard RNN-T, the emission of a blank symbol consumes exactly one input frame; in our proposed method, we introduce additional blank symbols, which consume two or more input frames when emitted. We refer to the added symbols as big blanks, and the method multi-blank RNN-T. For training multi-blank RNN-Ts, we propose a novel logit under-normalization method in order to prioritize emissions of big blanks. With experiments on multiple languages and datasets, we show that multi-blank RNN-T methods could bring relative speedups of over +90%/+139% to model inference for English Librispeech and German Multilingual Librispeech datasets, respectively. The multi-blank RNN-T method also improves ASR accuracy consistently. We will release our implementation of the method in the NeMo (https://github.com/NVIDIA/NeMo) toolkit.
Intriguing Properties of Compression on Multilingual Models
Authors: Kelechi Ogueji, Orevaoghene Ahia, Gbemileke Onilude, Sebastian Gehrmann, Sara Hooker, Julia Kreutzer
Date: 2022-11-04 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Multilingual models are often particularly dependent on scaling to generalize to a growing number of languages. Compression techniques are widely relied upon to reconcile the growth in model size with real world resource constraints, but compression can have a disparate effect on model performance for low-resource languages. It is thus crucial to understand the trade-offs between scale, multilingualism, and compression. In this work, we propose an experimental framework to characterize the impact of sparsifying multilingual pre-trained language models during fine-tuning. Applying this framework to mBERT named entity recognition models across 40 languages, we find that compression confers several intriguing and previously unknown generalization properties. In contrast to prior findings, we find that compression may improve model robustness over dense models. We additionally observe that under certain sparsification regimes compression may aid, rather than disproportionately impact the performance of low-resource languages.
Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block
Authors: Yunhao Chen, Yunjie Zhu, Zihui Yan, Yifan Huang, Zhen Ren, Jianlu Shen, Lifang Chen
Date: 2022-11-05 00:00:00
Tasks: Audio Classification, Classification, Data Augmentation, Environmental Sound Classification, Genre classification, Music Genre Classification, Sound Classification

Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data augmentation, transfer from large datasets and some other tricks. By utilizing the lightweight nature of audio, we propose an efficient network structure called Paired Inverse Pyramid Structure (PIP) and a network called Paired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\% of Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset and 93.2\% of Music Genre Classification (MGC) on the GTAZN dataset, with only 1 million parameters. Both of the results are achieved without data augmentation or model transfer. Public code is available at: https://github.com/JNAIC/PIPMN
Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach
Authors: Zhen Cheng, Tao Wang, Yong Li, Fenglong Song, Chang Chen, Zhiwei Xiong
Date: 2022-11-06 00:00:00
Tasks: Tone Mapping

Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervised training. In this paper, we argue that, although traditional TMOs exploit efficient dynamic range compression priors, they have several drawbacks on modeling the realistic degradation: information over-preservation, color bias and possible artifacts, making the trained reconstruction networks hard to generalize well to real-world cases. To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions. In specific, we design a conditioned two-stream network with prior tone mapping results as a guidance to synthesize SDRTVs by both global and local transformations. To train the data synthesis network, we form a novel self-supervised content loss to constraint different aspects of the synthesized SDRTVs at regions with different brightness distributions and an adversarial loss to emphasize the details to be more realistic. To validate the effectiveness of our approach, we synthesize SDRTV-HDRTV pairs with our method and use them to train several HDRTV reconstruction networks. Then we collect two inference datasets containing both labeled and unlabeled real-world SDRTVs, respectively. Experimental results demonstrate that, the networks trained with our synthesized data generalize significantly better to these two real-world datasets than existing solutions.