pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Bayesian inference for community detection and connectivity of functional brain networks
Authors: Lingbin Bian, Nizhuan Wang, Leonardo Novelli, Jonathan Keith, Adeel Razi
Date: 2023-01-18 00:00:00
Tasks: Bayesian Inference, Community Detection

Many functional magnetic resonance imaging (fMRI) studies rely on estimates of hierarchically organised brain networks whose segregation and integration reflect the dynamic transitions of latent cognitive states. However, most existing methods for estimating the community structure of networks from both individual and group-level analysis neglect the variability between subjects and lack validation. In this paper, we develop a new multilayer community detection method based on Bayesian latent block modelling. The method can robustly detect the group-level community structure of weighted functional networks that give rise to hidden brain states with an unknown number of communities and retain the variability of individual networks. For validation, we propose a new community structure-based multivariate Gaussian generative model to simulate synthetic signal. Our result shows that the inferred community memberships using hierarchical Bayesian analysis are consistent with the predefined node labels in the generative model. The method is also tested using real working memory task-fMRI data of 100 unrelated healthy subjects from the Human Connectome Project. The results show distinctive community structure patterns between 2-back, 0-back, and fixation conditions, which may reflect cognitive and behavioural states under working memory task conditions.
fMRI-based Static and Dynamic Functional Connectivity Analysis for Post-stroke Motor Dysfunction Patient: A Review
Authors: Kaichao Wu, Beth Jelfs, Katrina Neville, John Q. Fang
Date: 2022-12-15 00:00:00
Tasks: Denoising, Functional Connectivity

Functional magnetic resonance imaging (fMRI) has been widely utilized to study the motor deficits and rehabilitation following stroke. In particular, functional connectivity(FC) analyses with fMRI at rest can be employed to reveal the neural connectivity rationale behind this post-stroke motor function impairment and recovery. However, the methods and findings have not been summarized in a review focusing on post-stroke functional connectivity analysis. In this context, we broadly review the static functional connectivity network analysis (SFC) and dynamic functional connectivity network analysis (DFC) for post-stroke motor dysfunction patients, aiming to provide method guides and the latest findings regarding post-stroke motor function recovery. Specifically, a brief overview of the SFC and DFC methods for fMRI analysis is provided, along with the preprocessing and denoising procedures that go into these methods. Following that, the current status of research in functional connectivity networks for post-stoke patients under these two views was synthesized individually. Results show that SFC is the most frequent post-stroke functional connectivity analysis method. The SFC findings demonstrate that the stroke lesion reduces FC between motor areas, and the FC increase positively correlates with functional recovery. Meanwhile, the current DFC analysis in post-stroke has just been uncovered as the tip of the iceberg of its prospect, and its exceptionally rapidly progressing development can be expected.
Competition on the edge of an expanding population
Authors: Daniel W. Swartz, Hyunseok Lee, Mehran Kardar, Kirill S. Korolev
Date: 2023-01-18 00:00:00

In growing populations, the fate of mutations depends on their competitive ability against the ancestor and their ability to colonize new territory. Here we present a theory that integrates both aspects of mutant fitness by coupling the classic description of one-dimensional competition (Fisher equation) to the minimal model of front shape (KPZ equation). We solved these equations and found three regimes, which are controlled solely by the expansion rates, solely by the competitive abilities, or by both. Collectively, our results provide a simple framework to study spatial competition.
Danlu Tongdu tablets treat lumbar spinal stenosis through reducing reactive oxygen species and apoptosis by regulating CDK2/CDK4/CDKN1A expression
Authors: Xue Bai, Ayesha T. Tahir, Zhengheng Yu, Wenbo Cheng, Bo Zhang, Jun Kang
Date: 2023-01-18 00:00:00

Lumbar spinal stenosis (LSS) is caused by the compression of the nerve root or cauda equina nerve by stenosis of the lumbar spinal canal or intervertebral foramen, and is manifested as chronic low back and leg pain. Danlu Tongdu (DLTD) tablets can relieve chronic pain caused by LSS, but the molecular mechanism remains largely unknown. In this study, the potential molecular mechanism of DLTD tablets in the treatment of LSS was firstly predicted by network pharmacology method. Results showed that DLTD functions in regulating anti-oxidative, apoptosis, and inflammation signaling pathways. Furthermore, the flow cytometry results showed that DLTD tablets efficiently reduced ROS content and inhibited rat neural stem cell apoptosis induced by hydrogen peroxide. DLTD also inhibited the mitochondrial membrane potential damage induced by hydrogen peroxide. Elisa analysis showed that DLTD induced cell cycle related protein, CDK2 and CDK4 and reduced CDKN1A protein expression level. Taken together, our study provided new insights of DLTD in treating LSS through reducing ROS content, decreasing apoptosis by inhibiting CDKN1A and promoting CDK2 and CDK4 expression levels.
Deep Learning Enables Reduced Gadolinium Dose for Contrast-Enhanced Blood-Brain Barrier Opening
Authors: P. Lee, H. Wei, A. N. Pouliopoulos, B. T. Forsyth, Y. Yang, C. Zhang, A. F. Laine, E. E. Konofagou, C. Wu, J. Guo
Date: 2023-01-18 00:00:00
Tasks: Time Series, Time Series Analysis

Focused ultrasound (FUS) can be used to open the blood-brain barrier (BBB), and MRI with contrast agents can detect that opening. However, repeated use of gadolinium-based contrast agents (GBCAs) presents safety concerns to patients. This study is the first to propose the idea of modeling a volume transfer constant (Ktrans) through deep learning to reduce the dosage of contrast agents. The goal of the study is not only to reconstruct artificial intelligence (AI) derived Ktrans images but to also enhance the intensity with low dosage contrast agent T1 weighted MRI scans. We successfully validated this idea through a previous state-of-the-art temporal network algorithm, which focused on extracting time domain features at the voxel level. Then we used a Spatiotemporal Network (ST-Net), composed of a spatiotemporal convolutional neural network (CNN)-based deep learning architecture with the addition of a three-dimensional CNN encoder, to improve the model performance. We tested the ST-Net model on ten datasets of FUS-induced BBB-openings aquired from different sides of the mouse brain. ST-Net successfully detected and enhanced BBB-opening signals without sacrificing spatial domain information. ST-Net was shown to be a promising method of reducing the need of contrast agents for modeling BBB-opening K-trans maps from time-series Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans.
Distortion in ophthalmic optics: A review of the principal concepts and models
Authors: Jean-Marie Hanssens, Bernard Bourdoncle, Jacques Gresset, Jocelyn Faubert, Pierre Simonet
Date: 2023-01-17 00:00:00

Although all members of the ophthalmic community agree that distortion is an aberration affecting the geometry of an image produced by the periphery of an ophthalmic lens, there are several approaches for analyzing and quantifying this aberration. Various concepts have been introduced: ordinary distortion, stationary distortion and central static distortion are associated with a fixed eye behind the ophthalmic lens, whereas rotatory distortion, peripheral distortion, lateral static distortion, and dynamic distortion require a secondary position of gaze behind the lens. Furthermore, concept definitions vary from one author to another. The goal of this paper is to review the various concepts, analyze their effects on lens design and determine their ability to predict the deformation of an image as perceived by the lens wearer. These entities can be classified within 3 categories: the concepts associated with an ocular rotation, the concepts resulting from an optical approach, and the concepts using a perceptual approach. Among the various concepts reviewed, it appears that the Le Grand-Fry approach for analyzing and displaying distortion is preferable to others and allows modeling of the different possible types of distortions affecting the periphery of an ophthalmic lens.
Modeling Vascular Branching Alterations in Polycystic Kidney Disease
Authors: Timothy L. Kline
Date: 2022-12-20 00:00:00

The analysis of biological networks encompasses a wide variety of fields from genomic research of protein-protein interaction networks, to the physiological study of biologically optimized tree-like vascular networks. It is certain that different biological networks have different optimization criteria and we are interested in those networks optimized for fluid transport within the circulatory system. Many theories currently exist. For instance, distributive vascular geometry data is typically consistent with a theoretical model that requires simultaneous minimization of both the power loss of laminar flow and a cost function proportional to the total volume of material needed to maintain the system (Murray's law). However, how this optimized system breaks down (or is altered) due to disease has yet to be characterized in detail in terms of branching geometry and geometric interrelationships. This is important for understanding how vasculature remodels under changes of functional demands. For instance, in polycystic kidney disease (PKD), drastic cyst development may lead to a significant alteration of the vascular geometry (or vascular changes may be a preceding event). Understanding these changes could lead to a better understanding of early disease as well as development and characterization of treatment interventions. We have developed an optimal transport network model which simulates distributive vascular systems in health as well as disease in order to better understand changes that may occur due to PKD. We found that reduced perfusion territories, dilated distributive vasculature, and vessel rarefaction are all consequences of cyst development derived from this theoretical model and are a direct result of the increased heterogeneity of local renal tissue perfusion demands.
Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning
Authors: Chuting Sun, Qi Wu, Xing Yan
Date: 2023-01-18 00:00:00
Tasks: Portfolio Optimization

The dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. To achieve this, we propose a dynamic generative factor model which uses random variable transformation as an implicit way of distribution modeling and relies on the Attention-GRU network for dynamic learning and forecasting. The proposed model captures the dynamic dependence among multivariate stock returns, especially focusing on the tail-side properties. We also propose a two-step iterative algorithm to train the model and then predict the time-varying model parameters, including the time-invariant tail parameters. At each investment date, we can easily simulate new samples from the learned generative model, and we further perform CVaR portfolio optimization with the simulated samples to form a dynamic portfolio strategy. The numerical experiment on stock data shows that our model leads to wiser investments that promise higher reward-risk ratios and present lower tail risks.
Factors other than climate change are currently more important in predicting how well fruit farms are doing financially
Authors: Fabian Obster, Heidi Bohle, Paul M. Pechan
Date: 2023-01-11 00:00:00

Machine learning and statistical modeling methods were used to analyze the impact of climate change on financial wellbeing of fruit farmers in Tunisia and Chile. The analysis was based on face to face interviews with 801 farmers. Three research questions were investigated. First, whether climate change impacts had an effect on how well the farm was doing financially. Second, if climate change was not influential, what factors were important for predicting financial wellbeing of the farm. And third, ascertain whether observed effects on the financial wellbeing of the farm were a result of interactions between predictor variables. This is the first report directly comparing climate change with other factors potentially impacting financial wellbeing of farms. Certain climate change factors, namely increases in temperature and reductions in precipitation, can regionally impact self-perceived financial wellbeing of fruit farmers. Specifically, increases in temperature and reduction in precipitation can have a measurable negative impact on the financial wellbeing of farms in Chile. This effect is less pronounced in Tunisia. Climate impact differences were observed within Chile but not in Tunisia. However, climate change is only of minor importance for predicting farm financial wellbeing, especially for farms already doing financially well. Factors that are more important, mainly in Tunisia, included trust in information sources and prior farm ownership. Other important factors include farm size, water management systems used and diversity of fruit crops grown. Moreover, some of the important factors identified differed between farms doing and not doing well financially. Interactions between factors may improve or worsen farm financial wellbeing.
Creating awareness about security and safety on highways to mitigate wildlife-vehicle collisions by detecting and recognizing wildlife fences using deep learning and drone technology
Authors: Irene Nandutu, Marcellin Atemkeng, Patrice Okouma, Nokubonga Mgqatsa, Jean Louis Ebongue Kedieng Fendji, Franklin Tchakounte
Date: 2022-12-22 00:00:00

In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences for the classification task. Additionally, we used aerial and still annotated images extracted from the drone and still cameras for the segmentation and detection tasks. The model training results from the drone camera outperformed those from the still camera. Generally, poor model performance is attributed to (1) over-decompression of images and (2) the ability of drone cameras to capture more details on images for the machine learning model to learn as compared to still cameras that capture only the front view of the wildlife fence. We argue that our model can be deployed on client-edge devices to inform people about the presence and significance of wildlife fencing, which minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle collisions.
Image Embedding for Denoising Generative Models
Authors: Andrea Asperti, Davide Evangelista, Samuele Marro, Fabio Merizzi
Date: 2022-12-30 00:00:00
Tasks: Denoising, Image Generation

Denoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent generative quality, and the solid probabilistic foundation. In this article, we address the problem of {\em embedding} an image into the latent space of Denoising Diffusion Models, that is finding a suitable ``noisy'' image whose denoising results in the original image. We particularly focus on Denoising Diffusion Implicit Models due to the deterministic nature of their reverse diffusion process. As a side result of our investigation, we gain a deeper insight into the structure of the latent space of diffusion models, opening interesting perspectives on its exploration, the definition of semantic trajectories, and the manipulation/conditioning of encodings for editing purposes. A particularly interesting property highlighted by our research, which is also characteristic of this class of generative models, is the independence of the latent representation from the networks implementing the reverse diffusion process. In other words, a common seed passed to different networks (each trained on the same dataset), eventually results in identical images.
Site-specific weed management in corn using UAS imagery analysis and computer vision techniques
Authors: Ranjan Sapkota, John Stenger, Michael Ostlie, Paulo Flores
Date: 2022-12-31 00:00:00
Tasks: Management

Currently, weed control in commercial corn production is performed without considering weed distribution information in the field. This kind of weed management practice leads to excessive amounts of chemical herbicides being applied in a given field. The objective of this study was to perform site-specific weed control (SSWC) in a corn field by 1) using an unmanned aerial system (UAS) to map the spatial distribution information of weeds in the field; 2) creating a prescription map based on the weed distribution map, and 3) spraying the field using the prescription map and a commercial size sprayer. In this study, we are proposing a Crop Row Identification (CRI) algorithm, a computer vision algorithm that identifies corn rows on UAS imagery. After being identified, the corn rows were then removed from the imagery and the remaining vegetation fraction was classified as weeds. Based on that information, a grid-based weed prescription map was created and the weed control application was implemented through a commercial-size sprayer. The decision of spraying herbicides on a particular grid was based on the presence of weeds in that grid cell. All the grids that contained at least one weed were sprayed, while the grids free of weeds were not. Using our SSWC approach, we were able to save 26.23\% of the land (1.97 acres) from being sprayed with chemical herbicides compared to the existing method. This study presents a full workflow from UAS image collection to field weed control implementation using a commercial-size sprayer, and it shows that some level of savings can potentially be obtained even in a situation with high weed infestation, which might provide an opportunity to reduce chemical usage in corn production systems.
Model-agnostic machine learning of conservation laws from data
Authors: Shivam Arora, Alex Bihlo, Rüdiger Brecht, Pavel Holba
Date: 2023-01-12 00:00:00

We present a machine learning based method for learning first integrals of systems of ordinary differential equations from given trajectory data. The method is model-agnostic in that it does not require explicit knowledge of the underlying system of differential equations that generated the trajectories. As a by-product, once the first integrals have been learned, also the system of differential equations will be known. We illustrate our method by considering several classical problems from the mathematical sciences.
Deep Multi-Interest Network for Click-through Rate Prediction
Authors: Zhibo Xiao, Luwei Yang, Wen Jiang, Yi Wei, Yi Hu, Hao Wang
Date: 2020-10-01 00:00:00
Proceeding: CIKM 2020 10
Tasks: Click-Through Rate Prediction, Prediction

Click-through rate prediction plays an important role in many fields, such as recommender and advertising systems. It is one of the crucial parts to improve user experience and increase industry revenue. Recently, several deep learning-based models are successfully applied to this area. Some existing studies further model user representation based on user historical behavior sequence, in order to capture dynamic and evolving interests. We observe that users usually have multiple interests at a time and the latent dominant interest is expressed by the behavior. The switch of latent dominant interest results in the behavior changes. Thus, modeling and tracking latent multiple interests would be beneficial. In this paper, we propose a novel method named as Deep Multi-Interest Network (DMIN) which models user's latent multiple interests for click-through rate prediction task. Specifically, we design a Behavior Refiner Layer using multi-head self-attention to capture better user historical item representations. Then the Multi-Interest Extractor Layer is applied to extract multiple user interests. We evaluate our method on three real-world datasets. Experimental results show that the proposed DMIN outperforms various state-of-the-art baselines in terms of click-through rate prediction task.
Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach
Authors: Guru Bhandari, Andreas Lyth, Andrii Shalaginov, Tor-Morten Grønli
Date: 2023-01-06 00:00:00
Proceeding: Electronics 2023 1
Tasks: CPU

Cyberattacks always remain the major threats and challenging issues in the modern digital world. With the increase in the number of internet of things (IoT) devices, security challenges in these devices, such as lack of encryption, malware, ransomware, and IoT botnets, leave the devices vulnerable to attackers that can access and manipulate the important data, threaten the system, and demand ransom. The lessons from the earlier experiences of cyberattacks demand the development of the best-practices benchmark of cybersecurity, especially in modern Smart Environments. In this study, we propose an approach with a framework to discover malware attacks by using artificial intelligence (AI) methods to cover diverse and distributed scenarios. The new method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes Smart Environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on two selected IoT gateways, we observed very promising results, with less than 30 kb/s increase in network bandwidth on average, and just a 2% increase in CPU consumption. Similarly, we noticed minimal physical memory and power consumption, with 0.42 GB and 0.2 GB memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device with the deployed model. The ML models were able to demonstrate nearly 93% of detection accuracy and 92% f1-score on both utilized datasets. The result of the models shows that our framework detects malware and attacks in Smart Environments accurately and efficiently.
C-Norm: a neural approach to few-shot entity normalization
Authors: Arnaud Ferré, Louise Deléger, Robert Bossy, Pierre Zweigenbaum, Claire Nédellec
Date: 2020-12-29 00:00:00
Proceeding: BMC Bioinformatics 2020 12
Tasks: Few-Shot Learning, Medical Concept Normalization

Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics.