pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
Authors: Xiaozhou Ye, Kouichi Sakurai, Nirmal Nair, Kevin I-Kai Wang
Date: 2024-03-12 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies often assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with less annotated data. This review investigates how machine learning addresses data heterogeneity in HAR, by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.
Agile gesture recognition for low-power applications: customisation for generalisation
Authors: Ying Liu, Liucheng Guo, Valeri A. Makarovc, Alexander Gorbana, Evgeny Mirkesa, Ivan Y. Tyukin
Date: 2024-03-12 00:00:00
Tasks: Few-Shot Learning, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Automated hand gesture recognition has long been a focal point in the AI community. Traditionally, research in this field has predominantly focused on scenarios with access to a continuous flow of hand's images. This focus has been driven by the widespread use of cameras and the abundant availability of image data. However, there is an increasing demand for gesture recognition technologies that operate on low-power sensor devices. This is due to the rising concerns for data leakage and end-user privacy, as well as the limited battery capacity and the computing power in low-cost devices. Moreover, the challenge in data collection for individually designed hardware also hinders the generalisation of a gesture recognition model. In this study, we unveil a novel methodology for pattern recognition systems using adaptive and agile error correction, designed to enhance the performance of legacy gesture recognition models on devices with limited battery capacity and computing power. This system comprises a compact Support Vector Machine as the base model for live gesture recognition. Additionally, it features an adaptive agile error corrector that employs few-shot learning within the feature space induced by high-dimensional kernel mappings. The error corrector can be customised for each user, allowing for dynamic adjustments to the gesture prediction based on their movement patterns while maintaining the agile performance of its base model on a low-cost and low-power micro-controller. This proposed system is distinguished by its compact size, rapid processing speed, and low power consumption, making it ideal for a wide range of embedded systems.
Stochastic Analysis of Touch-Tone Frequency Recognition in Two-Way Radio Systems for Dialed Telephone Number Identification
Authors: Liqiang Yu, Chen Li, Bo Liu, Chang Che
Date: 2024-03-09 00:00:00
Tasks: Decoder

This paper focuses on recognizing dialed numbers in a touch-tone telephone system based on the Dual Tone MultiFrequency (DTMF) signaling technique with analysis of stochastic aspects during the noise and random duration of characters. Each dialed digit's acoustic profile is derived from a composite of two carrier frequencies, distinctly assigned to represent that digit. The identification of each digit is achieved by pinpointing the frequency pair with the highest energy or amplitude in its spectral output, utilizing the Discrete-Time Fourier Transform (DTFT). This analysis includes simulations that illustrate the effects of introducing stochastic variations during the "Mark" and "Space" intervals of the decoding process, offering insights into the technique's efficacy and the impact of random temporal fluctuations. This will reduce the accuracy of decoder decoding and lower the SNR of the system.
Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
Authors: Narges Rashvand, Kenneth Witham, Gabriel Maldonado, Vinit Katariya, Nishanth Marer Prabhu, Gunar Schirner, Hamed Tabkhi
Date: 2024-03-08 00:00:00
Tasks: Automatic Modulation Recognition, Edge-computing

Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing, to address the challenges of efficient AMR. Our transformer network architecture is designed with the mindset of real-time edge computing on IoT devices. Four tokenization techniques are proposed and explored for creating proper embeddings of RF signals, specifically focusing on overcoming the limitations related to the model size often encountered in IoT scenarios. Extensive experiments reveal that our proposed method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. Notably, our model achieves an accuracy of 65.75 on the RML2016 and 65.80 on the CSPB.ML.2018+ dataset.
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Authors: Apolline Mellot, Antoine Collas, Sylvain Chevallier, Denis Engemann, Alexandre Gramfort
Date: 2024-03-07 00:00:00
Tasks: Brain Computer Interface, channel selection, Domain Adaptation, EEG, Electroencephalogram (EEG), Imputation, Source-Free Domain Adaptation, Transfer Learning

Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at train and test time, complicating analysis due to varying sensor numbers and positions across datasets. Simple channel selection discards valuable data, leading to poorer performance, especially with datasets sharing few channels. To address this, we propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field interpolation, facilitating source-free domain adaptation. Leveraging Riemannian geometry classification pipelines and transfer learning steps, our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications. Comparative analysis against a statistical-based approach known as Dimensionality Transcending, a signal-based imputation called ComImp, source-dependent methods, as well as common channel selection and spherical spline interpolation, was conducted with leave-one-dataset-out validation on six public BCI datasets for a right-hand/left-hand classification task. Numerical experiments show that in the presence of few shared channels in train and test, the field interpolation consistently outperforms other methods, demonstrating enhanced classification performance across all datasets. When more channels are shared, field interpolation was found to be competitive with other methods and faster to compute than source-dependent methods.
Phylogenetic diversity indices from an affine and projective viewpoint
Authors: Vincent Moulton, Andreas Spillner, Kristina Wicke
Date: 2024-03-23 00:00:00
Tasks: Diversity

Phylogenetic diversity indices are commonly used to rank the elements in a collection of species or populations for conservation purposes. The derivation of these indices is typically based on some quantitative description of the evolutionary history of the species in question, which is often given in terms of a phylogenetic tree. Both rooted and unrooted phylogenetic trees can be employed, and there are close connections between the indices that are derived in these two different ways. In this paper, we introduce more general phylogenetic diversity indices that can be derived from collections of subsets (clusters) and collections of bipartitions (splits) of the given set of species. Such indices could be useful, for example, in case there is some uncertainty in the topology of the tree being used to derive a phylogenetic diversity index. As well as characterizing some of the indices that we introduce in terms of their special properties, we provide a link between cluster-based and split-based phylogenetic diversity indices that uses a discrete analogue of the classical link between affine and projective geometry. This provides a unified framework for many of the various phylogenetic diversity indices used in the literature based on rooted and unrooted phylogenetic trees, generalizations and new proofs for previous results concerning tree-based indices, and a way to define some new phylogenetic diversity indices that naturally arise as affine or projective variants of each other.
A Markov approach to credit rating migration conditional on economic states
Authors: Michael Kalkbrener, Natalie Packham
Date: 2024-03-21 00:00:00

We develop a model for credit rating migration that accounts for the impact of economic state fluctuations on default probabilities. The joint process for the economic state and the rating is modelled as a time-homogeneous Markov chain. While the rating process itself possesses the Markov property only under restrictive conditions, methods from Markov theory can be used to derive the rating process' asymptotic behaviour. We use the mathematical framework to formalise and analyse different rating philosophies, such as point-in-time (PIT) and through-the-cycle (TTC) ratings. Furthermore, we introduce stochastic orders on the bivariate process' transition matrix to establish a consistent notion of "better" and "worse" ratings. Finally, the construction of PIT and TTC ratings is illustrated on a Merton-type firm-value process.
Competition for binding targets results in paradoxical effects for simultaneous activator and repressor action -- Extended Version
Authors: M. Ali Al-Radhawi, Krishna Manoj, Dhruv D. Jatkar, Alon Duvall, Domitilla Del Vecchio, Eduardo D. Sontag
Date: 2024-03-21 00:00:00

In the context of epigenetic transformations in cancer metastasis, a puzzling effect was recently discovered, in which the elimination (knock-out) of an activating regulatory element leads to increased (rather than decreased) activity of the element being regulated. It has been postulated that this paradoxical behavior can be explained by activating and repressing transcription factors competing for binding to other possible targets. It is very difficult to prove this hypothesis in mammalian cells, due to the large number of potential players and the complexity of endogenous intracellular regulatory networks. Instead, this paper analyzes this issue through an analogous synthetic biology construct which aims to reproduce the paradoxical behavior using standard bacterial gene expression networks. The paper first reviews the motivating cancer biology work, and then describes a proposed synthetic construct. A mathematical model is formulated, and basic properties of uniqueness of steady states and convergence to equilibria are established, as well as an identification of parameter regimes which should lead to observing such paradoxical phenomena (more activator leads to less activity at steady state). A proof is also given to show that this is a steady-state property, and for initial transients the phenomenon will not be observed. This work adds to the general line of work of resource competition in synthetic circuits.
A data-informed mathematical model of microglial cell dynamics during ischemic stroke in the middle cerebral artery
Authors: Sara Amato, Andrea Arnold
Date: 2024-03-22 00:00:00
Tasks: parameter estimation, Time Series Analysis

Neuroinflammation immediately follows the onset of ischemic stroke in the middle cerebral artery. During this process, microglial cells are activated in and recruited to the penumbra. Microglial cells can be activated into two different phenotypes: M1, which can worsen brain injury; or M2, which can aid in long-term recovery. In this study, we contribute a summary of experimental data on microglial cell counts in the penumbra following ischemic stroke induced by middle cerebral artery occlusion (MCAO) in mice and compile available data sets into a single set suitable for time series analysis. Further, we formulate a mathematical model of microglial cells in the penumbra during ischemic stroke due to MCAO. Through use of global sensitivity analysis and Markov Chain Monte Carlo (MCMC)-based parameter estimation, we analyze the effects of the model parameters on the number of M1 and M2 cells in the penumbra and fit identifiable parameters to the compiled experimental data set. We utilize results from MCMC parameter estimation to ascertain uncertainty bounds and forward predictions for the number of M1 and M2 microglial cells over time. Results demonstrate the significance of parameters related to M1 and M2 activation on the number of M1 and M2 microglial cells. Simulations further suggest that potential outliers in the observed data may be omitted and forecast predictions suggest a lingering inflammatory response.
Connectivity of Parameter Regions of Multistationarity for Multisite Phosphorylation Networks
Authors: Nidhi Kaihnsa, Máté L. Telek
Date: 2024-03-25 00:00:00

The parameter region of multistationarity of a reaction network contains all the parameters for which the associated dynamical system exhibits multiple steady states. Describing this region is challenging and remains an active area of research. In this paper, we concentrate on two biologically relevant families of reaction networks that model multisite phosphorylation and dephosphorylation of a substrate at $n$ sites. For small values of $n$, it had previously been shown that the parameter region of multistationarity is connected. Here, we extend these results and provide a proof that applies to all values of $n$. Our techniques are based on the study of the critical polynomial associated with these reaction networks together with polyhedral geometric conditions of the signed support of this polynomial.
An image-computable model of speeded decision-making
Authors: Paul I. Jaffe, Gustavo X. Santiago-Reyes, Robert J. Schafer, Patrick G. Bissett, Russell A. Poldrack
Date: 2024-03-25 00:00:00
Tasks: Decision Making, model

Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.
Altered patterning of neural activity in a tauopathy mouse model
Authors: C. Hoffman, J. Cheng, R. Morales, D. Ji, Y. Dabaghian
Date: 2024-03-23 00:00:00
Tasks: Hippocampus

Alzheimer's disease (AD) is a complex neurodegenerative condition that manifests at multiple levels and involves a spectrum of abnormalities ranging from the cellular to cognitive. Here, we investigate the impact of AD-related tau-pathology on hippocampal circuits in mice engaged in spatial navigation, and study changes of neuronal firing and dynamics of extracellular fields. While most studies are based on analyzing instantaneous or time-averaged characteristics of neuronal activity, we focus on intermediate timescales -- spike trains and waveforms of oscillatory potentials, which we consider as single entities. We find that, in healthy mice, spike arrangements and wave patterns (series of crests or troughs) are coupled to the animal's location, speed, and acceleration. In contrast, in tau-mice, neural activity is structurally disarrayed: brainwave cadence is detached from locomotion, spatial selectivity is lost, the spike flow is scrambled. Importantly, these alterations start early and accumulate with age, which exposes progressive disinvolvement the hippocampus circuit in spatial navigation. These features highlight qualitatively different neurodynamics than the ones provided by conventional analyses, and are more salient, thus revealing a new level of the hippocampal circuit disruptions.
Playing With Neuroscience: Past, Present and Future of Neuroimaging and Games
Authors: Paolo Burelli, Laurits Dixen
Date: 2024-03-06 00:00:00

Videogames have been a catalyst for advances in many research fields, such as artificial intelligence, human-computer interaction or virtual reality. Over the years, research in fields such as artificial intelligence has enabled the design of new types of games, while games have often served as a powerful tool for testing and simulation. Can this also happen with neuroscience? What is the current relationship between neuroscience and games research? what can we expect from the future? In this article, we'll try to answer these questions, analysing the current state-of-the-art at the crossroads between neuroscience and games and envisioning future directions.
Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study
Authors: Elodie Germani, Nikhil Baghwat, Mathieu Dugré, Rémi Gau, Albert Montillo, Kevin Nguyen, Andrzej Sokolowski, Madeleine Sharp, Jean-Baptiste Poline, Tristan Glatard
Date: 2024-02-20 00:00:00

Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to reproduce (re-implementing the experiments with the same data, same method) and replicate (different data and/or method) the models described in [1] to predict individual's PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson's Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in [1] and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction and compare the reproduced results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 \&gt; 0), which is consistent with its findings. Moreover, using derived data provided by the authors of the original study, we were able to make an exact reproduction and managed to obtain results that were close to the original ones. The challenges encountered while reproducing and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.
Semi-Quantitative Analysis and Seroepidemiological Evidence of Past Dengue Virus Infection among HIV-infected patients in Onitsha, Anambra State, Nigeria
Authors: David Nwachukwu, Edith Nnenna Oketah, Chineze Helen Ugwu, Hope Chioma Innocent-Adiele, Chisom Chimbundum Adim, Euslar Nnenna Onu, Ann Onyinyechi Chukwu, Grace Aghaji Nwankwo, Mary Uche Igwe, Phillip O. Okerentugba, Iheanyi Omezuruike Okonko
Date: 2024-03-23 00:00:00

Despite its endemic nature as well as the recent outbreaks, information on the opportunistic DENV in Anambra state has been sparse. This study thus aimed to give seroepidemiological evidence of past dengue virus infection among HIV-infected patients in Onitsha, Anambra State, Nigeria. Plasma from 94 HIV-infected patients who were attending Saint Charles Borromeo Hospital, Onitsha in Anambra State, Nigeria was tested for IgG antibodies specific to the dengue virus by IgG ELISA assay. The prevalence of past dengue virus infection was 61.7% (n = 58/94). This study showed age group 0-15 years (77.30%), female gender (65.1%), married (63.9%) and no formal level (100.0 %) as the highest seropositivity among the study participants. In terms of immunological and virological markers, greater IgG seroprevalence was observed in individuals with a viral load of <40 copies/ml (64.0%) and a CD4 count of >350 cells/ul (63.2%). The high IgG seropositivity of Dengue Virus (DENV) among HIV-infected individuals on Onitsha is cause for concern.
A story of viral co-infection, co-transmission and co-feeding in ticks: how to compute an invasion reproduction number
Authors: Giulia Belluccini, Qianying Lin, Bevelynn Williams, Yijun Lou, Zati Vatansever, Martín López-García, Grant Lythe, Thomas Leitner, Ethan Romero-Severson, Carmen Molina-París
Date: 2024-03-22 00:00:00

With a single circulating vector-borne virus, the basic reproduction number incorporates contributions from tick-to-tick (co-feeding), tick-to-host and host-to-tick transmission routes. With two different circulating vector-borne viral strains, resident and invasive, and under the assumption that co-feeding is the only transmission route in a tick population, the invasion reproduction number depends on whether the model system of ordinary differential equations possesses the property of neutrality. We show that a simple model, with two populations of ticks infected with one strain, resident or invasive, and one population of co-infected ticks, does not have Alizon's neutrality property. We present model alternatives that are capable of representing the invasion potential of a novel strain by including populations of ticks dually infected with the same strain. The invasion reproduction number is analysed with the next-generation method and via numerical simulations.