pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Processing Pipelines for Indoor Human Tracking using FMCW radar
Authors: Dingyang Wang, Francesco Fioranelli, Alexander Yarovoy
Date: 2024-02-29 00:00:00

In this paper, the problem of formulating effective processing pipelines for indoor human tracking is investigated, with the usage of a Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. Specifically, two processing pipelines starting with detections on the Range-Azimuth (RA) maps and the Range-Doppler (RD) maps are formulated and compared, together with subsequent clustering and tracking algorithms and their relevant parameters. Experimental results are presented to validate and assess both pipelines, using a 24 GHz commercial radar platform with 250 MHz bandwidth and 15 virtual channels. Scenarios where 1 and 2 people move in an indoor environment are considered, and the influence of the number of virtual channels and detectors' parameters is discussed. The characteristics and limitations of both pipelines are presented, with the approach based on detections on RA maps showing in general more robust results.
Automatic Radar Signal Detection and FFT Estimation using Deep Learning
Authors: Akila Pemasiri, Zi Huang, Fraser Williams, Ethan Goan, Simon Denman, Terrence Martin, Clinton Fookes
Date: 2024-02-29 00:00:00
Tasks: Binary Classification, Deep Learning, parameter estimation

This paper addresses a critical preliminary step in radar signal processing: detecting the presence of a radar signal and robustly estimating its bandwidth. Existing methods which are largely statistical feature-based approaches face challenges in electronic warfare (EW) settings where prior information about signals is lacking. While alternate deep learning based methods focus on more challenging environments, they primarily formulate this as a binary classification problem. In this research, we propose a novel methodology that not only detects the presence of a signal, but also localises it in the time domain and estimates its operating frequency band at that point in time. To achieve robust estimation, we introduce a compound loss function that leverages complementary information from both time-domain and frequency-domain representations. By integrating these approaches, we aim to improve the efficiency and accuracy of radar signal detection and parameter estimation, reducing both unnecessary resource consumption and human effort in downstream tasks.
Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph
Authors: Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet
Date: 2024-02-28 00:00:00
Tasks: All, Graph Attention, Navigate

In the complex landscape of hematologic samples such as peripheral blood or bone marrow, cell classification, delineating diverse populations into a hierarchical structure, presents profound challenges. This study presents LeukoGraph, a recently developed framework designed explicitly for this purpose employing graph attention networks (GATs) to navigate hierarchical classification (HC) complexities. Notably, LeukoGraph stands as a pioneering effort, marking the application of graph neural networks (GNNs) for hierarchical inference on graphs, accommodating up to one million nodes and millions of edges, all derived from flow cytometry data. LeukoGraph intricately addresses a classification paradigm where for example four different cell populations undergo flat categorization, while a fifth diverges into two distinct child branches, exemplifying the nuanced hierarchical structure inherent in complex datasets. The technique is more general than this example. A hallmark achievement of LeukoGraph is its F-score of 98%, significantly outclassing prevailing state-of-the-art methodologies. Crucially, LeukoGraph's prowess extends beyond theoretical innovation, showcasing remarkable precision in predicting both flat and hierarchical cell types across flow cytometry datasets from 30 distinct patients. This precision is further underscored by LeukoGraph's ability to maintain a correct label ratio, despite the inherent challenges posed by hierarchical classifications.
Difficult control is related to instability in biologically inspired Boolean networks
Authors: Bryan C. Daniels, Enrico Borriello
Date: 2024-02-28 00:00:00

Previous work in Boolean dynamical networks has suggested that the number of components that must be controlled to select an existing attractor is typically set by the number of attractors admitted by the dynamics, with no dependence on the size of the network. Here we study the rare cases of networks that defy this expectation, with attractors that require controlling most nodes. We find empirically that unstable fixed points are the primary recurring characteristic of networks that prove more difficult to control. We describe an efficient way to identify unstable fixed points and show that, in both existing biological models and ensembles of random dynamics, we can better explain the variance of control kernel sizes by incorporating the prevalence of unstable fixed points. In the end, the association of these outliers with dynamics that are unstable to small perturbations reveals them as artifacts of deterministic models, making them less biologically relevant and reinforcing the generality of easy controllability in biological networks.
Stimulation technology for brain and nerves, now and future
Authors: Masaru Kuwabara, Ryota Kanai
Date: 2024-02-29 00:00:00
Tasks: Brain Computer Interface

In individuals afflicted with conditions such as paralysis, the implementation of Brain-Computer-Interface (BCI) has begun to significantly impact their quality of life. Furthermore, even in healthy individuals, the anticipated advantages of brain-to-brain communication and brain-to-computer interaction hold considerable promise for the future. This is attributed to the liberation from bodily constraints and the transcendence of existing limitations inherent in contemporary brain-to-brain communication methods. To actualize a comprehensive BCI, the establishment of bidirectional communication between the brain and the external environment is imperative. While neural input technology spans diverse disciplines and is currently advancing rapidly, a notable absence exists in the form of review papers summarizing the technology from the standpoint of the latest or potential input methods. The challenges encountered encompass the requisite for bidirectional communication to achieve a holistic BCI, as well as obstacles related to information volume, precision, and invasiveness. The review section comprehensively addresses both invasive and non-invasive techniques, incorporating nanotech/micro-device technology and the integration of Artificial Intelligence (AI) in brain stimulation.
A minimal model of pan-immunity maintenance by horizontal gene transfer in the ecological dynamics of bacteria and phages
Authors: Wenping Cui, Jemma M. Fendley, Sriram Srikant, Boris Shraiman
Date: 2024-02-29 00:00:00
Tasks: Diversity

Bacteria and phages have been in an ongoing arms race for billions of years. To resist phages bacteria have evolved numerous defense systems, which nevertheless are still overcome by counter-defense mechanisms of specific phages. These defense/counter-defense systems are a major element of microbial genetic diversity and have been demonstrated to propagate between strains by Horizontal Gene Transfer (HGT). It has been proposed that the totality of defense systems found in microbial communities collectively form a distributed "pan-immune" system with individual elements moving between strains via ubiquitous HGT. Here, we formulate a Lotka-Volterra type model of a bacteria/phage community interacting via a combinatorial variety of defense/counter-defense systems and show that HGT enables stable maintenance of diverse defense/counter-defense genes in the microbial pan-genome even when individual microbial strains inevitably undergo extinction. This stability requires the HGT rate to be sufficiently high to ensure that some descendant of a "dying" strain survives, thanks to the immunity acquired through HGT from the community at large, thus establishing a new strain.This mechanism of persistence for the pan-immune gene pool is fundamentally similar to the "island migration" model of ecological diversity, with genes moving between genomes instead of species migrating between islands.
Prediction of vaccination coverage level in the heterogeneous mixing population
Authors: Fan Bai, Qianyu Chen, Yizhuo Xu
Date: 2024-02-29 00:00:00
Tasks: Decision Making, Management

Heterogeneity of population is a key factor in modeling the transmission of disease among the population and has huge impact on the outcome of the transmission. In order to investigate the decision making process in the heterogeneous mixing population regarding whether to be vaccinated or not, we propose the modeling framework which includes the epidemic models and the game theoretical analysis. We consider two sources of heterogeneity in this paper: the different activity levels and the different relative vaccination costs. It is interesting to observe that, if both sources of heterogeneity are considered, there exist a finite number of Nash equilibria (evolutionary stable strategies (ESS)) of the vaccination game. While if only the difference of activity levels is considered, there are infinitely many Nash equilibira. For the latter case, the initial condition of the decision making process becomes highly sensitive. In the application of public health management, the inclusion of population heterogeneity significantly complicates the prediction of the overall vaccine coverage level.
Lotka-Volterra Model with Mutations and Generative Adversarial Networks
Authors: S. V. Kozyrev
Date: 2024-02-29 00:00:00
Tasks: Generative Adversarial Network, Learning Theory

A model of population genetics of the Lotka-Volterra type with mutations on a statistical manifold is introduced. Mutations in the model are described by diffusion on a statistical manifold with a generator in the form of a Laplace-Beltrami operator with a Fisher-Rao metric, that is, the model combines population genetics and information geometry. This model describes a generalization of the model of machine learning theory, the model of generative adversarial network (GAN), to the case of populations of generative adversarial networks. The introduced model describes the control of overfitting for generative adversarial networks.
Estimation of migrate histories of the Japanese sardine in the Sea of Japan by combining the microscale stable isotope analysis of otoliths and a data assimilation model
Authors: Tomoya Aono, Tatsuya Sakamoto, Toyoho Ishimura, Motomitsu Takahashi, Tohya Yasuda, Satoshi Kitajima, Kozue Nishida, Takayoshi Matsuura, Akito Ikari, Shin-ichi Ito
Date: 2024-02-28 00:00:00

The Japanese sardine (Sardinops melanostictus) is a small pelagic fish found in the Sea of Japan, the marginal sea of the western North Pacific. It is an important species for regional fisheries, but their transportation and migration patterns during early life stages remain unclear. In this study, we analyzed the stable oxygen isotope ratios of otoliths of young-of-the-year (age 0) Japanese sardines collected from the northern offshore and southern coastal areas of the Sea of Japan in 2015 and 2016. The ontogenetic shifts of the geographic distribution were estimated by comparing the profiles of life-long isotope ratios and temporally varying isoscape, which was calculated using the temperature and salinity fields produced by an ocean data assimilation model. Individuals that were collected in the northern and southern areas hatched and stayed in the southern areas (west offshore of Kyushu) until late June, and thereafter, they can be distinguished into two groups: one that migrated northward at shallow layer and one that stayed around the southern area in the deep layer. A comparison of somatic growth trajectories of the two groups, which was reconstructed based on otolith microstructure analysis, suggested that individuals that migrated northward had significantly larger body lengths in late June than those that stayed in the southern area. These results indicate that young-of-the-year Japanese sardines that hatched in the southern area may have been forced to choose one of two strategies to avoid extremely high water temperatures within seasonal and geographical limits. These include migrating northward or moving to deeper layers. Our results indicate that the environmental variabilities in the southern area could critically impact sardine population dynamics in the Sea of Japan.
Cartographie de l'habitat de reproduction du tétras-lyre (Lyrurus tetrix) dans les Alpes françaises
Authors: Alexandre Defossez, Samuel Alleaume, Marc Montadert, Dino Ienco, Sandra Luque
Date: 2024-02-27 00:00:00

The Black Grouse (Lyrurus tetrix) is an emblematic alpine species with high conservation importance. The population size of these mountain bird tends to decline on the reference sites and shows differences according to changes in local landscape characteristics. Habitat changes are at the centre of the identified pressures impacting part or all of its life cycle, according to experts. Hence, an approach to monitor population dynamics, is trough modelling the favourable habitats of Black Grouse breeding (nesting sites). Then, coupling modelling with multi-source remote sensing data (medium and very high spatial resolution), allowed the implementation of a spatial distribution model of the species. Indeed, the extraction of variables from remote sensing helped to describe the area studied at appropriate spatial and temporal scales: horizontal and vertical structure (heterogeneity), functioning (vegetation indices), phenology (seasonal or inter-annual dynamics) and biodiversity. An annual time series of radiometric indices (NDVI, NDWI, BI {\ldots}) from Sentinel-2 has made it possible to generate Dynamic Habitat Indices (DHIs) to derive phenological indications on the nature and dynamics of natural habitats. In addition, very high resolution images (SPOT6) provided access to the fine structure of natural habitats, i.e. the vertical and horizontal organisation by states identified as elementary (mineral, herbaceous, low and high woody). Indeed, one of the essential limiting factors for brood rearing is the presence of a well-developed herbaceous or ericaceous stratum in the northern Alps and larch forests in the southern region. A deep learning model was used to classify elementary strata. Finally, Biomod2 R platform, using an ensemble approach, was applied to model, the favourable habitat of Black Grouse reproduction. Of all the models, Random Forest and Extreme Boosted Gradient are the best performing, with TSS and ROC scores close to 1. For the SDM, we selected only Random Forest models (ensemble modelling) because of their low susceptibility to overfitting and coherent predictions (after comparing model predictions).In this ensemble model, the most important explanatory variables are altitude, the proportion of heathland, and the DHI (NDVI Max and NDWI Max). Results from the habitat model can be used as an operational tool for monitoring forest landscape shifts and changes. In addition, to delimiting potential areas to protect the species habitat, which constitute a valuable decision-making tool for conservation management of mountain open forest.
Deep Mapper: Efficient Visualization of Plausible Conformational Pathways
Authors: Ziyad Oulhaj, Yoshiyuki Ishii, Kento Ohga, Kimihiro Yamazaki, Mutsuyo Wada, Yuhei Umeda, Takashi Kato, Yuichiro Wada, Hiroaki Kurihara
Date: 2024-02-29 00:00:00
Tasks: Drug Discovery, Topological Data Analysis

Acquiring plausible pathways on high-dimensional structural distributions is beneficial in several domains. For example, in the drug discovery field, a protein conformational pathway, i.e. a highly probable sequence of protein structural changes, is useful to analyze interactions between the protein and the ligands, helping to create new drugs. Recently, a state-of-the-art method in drug discovery was presented, which efficiently computes protein pathways using latent variables obtained from an isometric auto-encoding of the space of 3D density maps associated to protein conformations. However, our preliminary experiments show that there is room to significantly reduce the computing time. In this study, we use the Mapper algorithm, which is a Topological Data Analysis method, and present a novel variant to extract plausible conformational pathways from the isometric latent space with comparatively short running time. The extracted pathways are visualized as paths on the resulting Mapper graph. The methodological novelties are described as follows: firstly, the filter function of the Mapper algorithm is optimized so as to extract the pathways via minimization of an energy loss defined on the Mapper graph itself, while filter functions taken in the classical Mapper algorithm are fixed beforehand. The optimization is with respect to parameters of a deep neural network in the filter. Secondly, the clustering method, which defines the vertices and edges of the Mapper graph, of our algorithm, is designed by incorporating domain prior knowledge to assist the extraction. In our numerical experiments, based on an isometric latent space built on the common 50S-ribosomal dataset, the resulting Mapper graph successfully includes all the well-recognized plausible pathways. Moreover, our running time is much shorter than the above state-of-the-art counterpart.
Verif.ai: Towards an Open-Source Scientific Generative Question-Answering System with Referenced and Verifiable Answers
Authors: Miloš Košprdić, Adela Ljajić, Bojana Bašaragin, Darija Medvecki, Nikola Milošević
Date: 2024-02-09 00:00:00
Tasks: Generative Question Answering, Information Retrieval, Misinformation, Question Answering, Retrieval

In this paper, we present the current progress of the project Verif.ai, an open-source scientific generative question-answering system with referenced and verified answers. The components of the system are (1) an information retrieval system combining semantic and lexical search techniques over scientific papers (PubMed), (2) a fine-tuned generative model (Mistral 7B) taking top answers and generating answers with references to the papers from which the claim was derived, and (3) a verification engine that cross-checks the generated claim and the abstract or paper from which the claim was derived, verifying whether there may have been any hallucinations in generating the claim. We are reinforcing the generative model by providing the abstract in context, but in addition, an independent set of methods and models are verifying the answer and checking for hallucinations. Therefore, we believe that by using our method, we can make scientists more productive, while building trust in the use of generative language models in scientific environments, where hallucinations and misinformation cannot be tolerated.
Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network
Authors: Sales Aribe Jr
Date: 2024-01-10 00:00:00
Tasks: Decision Making

Decision making and planning have long relied heavily on AI-driven forecasts. The government and the general public are working to minimize the risks while maximizing benefits in the face of potential future public health uncertainties. This study used an improved method of forecasting utilizing the Random Descending Velocity Inertia Weight (RDV IW) technique to improve the convergence of Particle Swarm Optimization (PSO) and the accuracy of Artificial Neural Network (ANN). The IW technique, inspired by the motions of a golf ball, modified the particles' velocities as they approached the solution point to a parabolically descending structure. Simulation results revealed that the proposed forecasting model with [0.4, 0.9] combination of alpha and alpha_dump exhibits a 6.36% improvement in position error and 11.75% improvement in computational time compared to the old model, thus, improving its convergence. It reached the optimum level at minimal steps with 12.50% improvement as against the old model since it provides better velocity averages when speed stabilization occurs at the 24th iteration. Meanwhile, the computed p-values for NRMSE (0.04889174), MAE (0.02829063), MAPE (0.02226053), WAPE (0.01701545), and R2 (0.00000021) of the proposed algorithm are less than the set 0.05 level of significance, thus the values indicated a significant result in terms of accuracy performance. Applying the modified ANN-PSO using RDV IW technique greatly improved the new HIV/AIDS forecasting model compared with the two models.
Multi-objective Optimal Roadside Units Deployment in Urban Vehicular Networks
Authors: Weian Guo, Zecheng Kang, Dongyang Li, Lun Zhang, Li Li
Date: 2024-01-14 00:00:00

The significance of transportation efficiency, safety, and related services is increasing in urban vehicular networks. Within such networks, roadside units (RSUs) serve as intermediates in facilitating communication. Therefore, the deployment of RSUs is of utmost importance in ensuring the quality of communication services. However, the optimization objectives, such as time delay and deployment cost, are commonly developed from diverse perspectives. As a result, it is possible that conflicts may arise among the objectives. Furthermore, in urban environments, the presence of various obstacles, such as buildings, gardens, lakes, and other infrastructure, poses challenges for the deployment of RSUs. Hence, the deployment encounters significant difficulties due to the existence of multiple objectives, constraints imposed by obstacles, and the necessity to explore a large-scale optimization space. To address this issue, two versions of multi-objective optimization algorithms are proposed in this paper. By utilizing a multi-population strategy and an adaptive exploration technique, the methods efficiently explore a large-scale decision-variable space. In order to mitigate the issue of an overcrowded deployment of RSUs, a calibrating mechanism is adopted to adjust RSU density during the optimization procedures. The proposed methods also take care of data offloading between vehicles and RSUs by setting up an iterative best response sequence game (IBRSG). By comparing the proposed algorithms with several state-of-the-art algorithms, the results demonstrate that our strategies perform better in both high-density and low-density urban scenarios. The results also indicate that the proposed solutions substantially improve the efficiency of vehicular networks.