pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes
Authors: Qiqi Li, Mike Ludkovski
Date: 2024-09-10 00:00:00
Tasks: Gaussian Processes

We design a Gaussian Process (GP) spatiotemporal model to capture features of day-ahead wind power forecasts. We work with hourly-scale day-ahead forecasts across hundreds of wind farm locations, with the main aim of constructing a fully probabilistic joint model across space and hours of the day. To this end, we design a separable space-time kernel, implementing both temporal and spatial input warping to capture the non-stationarity in the covariance of wind power. We conduct synthetic experiments to validate our choice of the spatial kernel and to demonstrate the effectiveness of warping in addressing nonstationarity. The second half of the paper is devoted to a detailed case study using a realistic, fully calibrated dataset representing wind farms in the ERCOT region of Texas.
Retinal arterial blood flow measured by real-time Doppler holography at 33,000 frames per second
Authors: Yann Fischer, Zacharie Auray, Olivier Martinache, Marius Dubosc, Noé Topéza, Chloé Magnier, Maxime Boy-Arnould, Michael Atlan
Date: 2024-09-24 00:00:00

This study presents a novel quantitative estimation method for total retinal arterial blood flow utilizing real-time Doppler holography at an unprecedented frame rate of 33,000 frames per second. This technique, leveraging high-speed digital holography, enables non-invasive angiographic imaging of the retina, providing detailed blood flow contrasts essential for assessing retinal health. The proposed quantitative analysis method consists of segmenting primary in-plane retinal arteries and calculating local blood velocity using Doppler frequency broadening. The analysis integrates a forward scattering model to achieve blood flow estimation. Our findings highlight the potential of Doppler holography as a powerful tool for diagnosing and monitoring the treatment of retinal vascular conditions, complementary to existing imaging methods.
The exponential distance rule based network model predicts topology and reveals functionally relevant properties of the Drosophila projectome
Authors: Balazs Pentek, Maria Ercsey-Ravasz
Date: 2024-10-02 00:00:00

Studying structural brain networks has witnessed significant advancement in recent decades. Findings have revealed a geometric principle, the exponential distance rule (EDR) showing that the number of neurons decreases exponentially with the length of their axons. An EDR based network model explained various characteristics of inter-areal cortical networks in macaques, mice, and rats. The complete connectome of the Drosophila fruit fly has recently been mapped at the neuronal level. Our study demonstrates that the EDR holds true in Drosophila, and the EDR model effectively accounts for numerous binary and weighted properties of neuropil networks, also called projectome. Our study illustrates that the EDR model is a suitable null model for analyzing networks of brain regions, as it captures geometric and physical constraints in very different species. The importance of the null model lies in its ability to facilitate the identification of functionally significant features that are not caused by inevitable geometric constraints, as we illustrate with the pronounced asymmetry of connection weights important for functional hierarchy.
Robust assessment of asymmetric division in colon cancer cells
Authors: Domenico Caudo, Chiara Giannattasio, Simone Scalise, Valeria de Turris, Fabio Giavazzi, Giancarlo Ruocco, Giorgio Gosti, Giovanna Peruzzi, Mattia Miotto
Date: 2024-09-28 00:00:00

Asymmetric partition of fate determinants during cell division is a hallmark of cell differentiation. Recent works suggested that such a mechanism is hijacked by cancer cells to increase both their phenotypic heterogeneity and plasticity and in turn their fitness. To quantify fluctuations in the partitioning of cellular elements, imaging-based approaches are used, whose accuracy is limited by the difficulty of detecting cell divisions. Our work addresses this gap proposing a general method based on high-throughput flow cytometry measurements coupled with a theoretical frameworks. We applied our method to a panel of both normal and cancerous human colon cells, showing that different kinds of colon adenocarcinoma cells display very distinct extents of fluctuations in their cytoplasm partition, explained by an asymmetric division of their size. To test the accuracy of our population-level protocol, we directly measure the inherited fractions of cellular elements from extensive time-lapses of live-cell laser scanning microscopy, finding excellent agreement across the cell types. Ultimately, our flow cytometry-based method promise to be accurate and easily applicable to a wide range of biological systems where the quantification of partition fluctuations would help accounting for the observed phenotypic heterogeneity and plasticity
The signal synchronization function of myelin
Authors: Zhuonan Yu, Peijun Qin, Ruibing Sun, Sara Khademi, Zhen Xu, Qinchao Sun, Yanlong Tai, Bing Song, Tianruo Guo, Hao Wang
Date: 2024-09-25 00:00:00

The myelinated axons are widely present in both central and peripheral nervous systems. Its unique compact spiraling structure poses significant challenges to understanding its biological functions and developmental mechanisms. Conventionally, myelin is considered as an insulating layer to achieve saltatory conduction for the enhancement of the neural signal speed, which serves as the foundation of neuroscience. However, this insulating hypothesis is inadequate to account for various experimental observations, especially the long unmyelinated tract observed in the cortex. We here show non-random distributions in three ultrastructural features of myelin: the non-random spiraling directions, the localization preferences of myelin outer tongues, and the radial components along boundaries between oppositely spiraled myelin sheaths. These phenomena are predicted by a novel concept of myelin biological function, which we propose as the signal synchronization function. Our findings demonstrate that cytoplasmic channels within myelin may act as coiled inductors, facilitating electromagnetic induction between adjacent myelin sheaths, and thereby promoting signal synchronization between axons. This, in turn, explains the non-random ultrastructural features observed. We believe these insights lay the foundation for a new understanding of myelin inductive function.
bioSBM: a random graph model to integrate epigenomic data in chromatin structure prediction
Authors: Alex Chen Yi Zhang, Angelo Rosa, Guido Sanguinetti
Date: 2024-09-22 00:00:00

The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously, link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing 12 distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line, and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data.
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models
Authors: Alexius Wadell, Anoushka Bhutani, Venkatasubramanian Viswanathan
Date: 2024-09-19 00:00:00
Tasks: Diversity, Molecular Property Prediction, molecular representation, Property Prediction, scientific discovery

Text-based foundation models have become an important part of scientific discovery, with molecular foundation models accelerating advancements in molecular design and materials science. However, existing models are constrained by closed-vocabulary tokenizers which capture only a fraction of molecular space. In this work, we systematically evaluate thirty tokenizers, including 19 chemistry-specific ones, for their coverage of the SMILES molecular representation language, revealing significant gaps. To assess the impact of tokenizer choice, we introduce n-gram language models as a low-cost proxy and validate their effectiveness by training and fine-tuning 18 RoBERTa-style encoders for molecular property prediction. To overcome the limitations of existing tokenizers, we propose two new tokenizers -- Smirk and Smirk-GPE -- with full coverage of the OpenSMILES specification. Our results highlight the need for open-vocabulary modeling and chemically diverse benchmarks in cheminformatics. The proposed tokenizer framework systematically integrates nuclear, electronic, and geometric degrees of freedom; this facilitates applications in pharmacology, agriculture, biology, and energy storage.
AI and Machine Learning Approaches for Predicting Nanoparticles Toxicity The Critical Role of Physiochemical Properties
Authors: Iqra Yousaf
Date: 2024-09-06 00:00:00
Tasks: Computational chemistry

This research investigates the use of artificial intelligence and machine learning techniques to predict the toxicity of nanoparticles, a pressing concern due to their pervasive use in various industries and the inherent challenges in assessing their biological interactions. Employing models such as Decision Trees, Random Forests, and XGBoost, the study focuses on analyzing physicochemical properties like size, shape, surface charge, and chemical composition to determine their influence on toxicity. Our findings highlight the significant role of oxygen atoms, particle size, surface area, dosage, and exposure duration in affecting toxicity levels. The use of machine learning allows for a nuanced understanding of the intricate patterns these properties form in biological contexts, surpassing traditional analysis methods in efficiency and predictive power. These advancements aid in developing safer nanomaterials through computational chemistry, reducing reliance on costly and time-consuming experimental methods. This approach not only enhances our understanding of nanoparticle behavior in biological systems but also streamlines the safety assessment process, marking a significant stride towards integrating computational techniques in nanotoxicology.
ADEPT: A Noninvasive Method for Determining Elastic Parameters of Valve Tissue
Authors: Wensi Wu, Mitchell Daneker, Christian Herz, Hannah Dewey, Jeffrey A. Weiss, Alison M. Pouch, Lu Lu, Matthew A. Jolley
Date: 2024-09-27 00:00:00
Tasks: Image Registration, Image Segmentation, Semantic Segmentation

Computer simulation of "virtual interventions" may inform optimal valve repair for a given patient prior to intervention. However, the paucity of noninvasive methods to determine in vivo mechanical parameters of valves limits the accuracy of computer prediction and their clinical application. To address this, we propose ADEPT: A noninvasive method for Determining Elastic Parameters of valve Tissue. In this work, we demonstrated its application to the tricuspid valve of a child. We first tracked valve displacements from open to closed frames within a 3D echocardiogram time sequence using image registration. Physics-informed neural networks were subsequently applied to estimate the nonlinear mechanical properties from first principles and reference displacements. The simulated model using these patient-specific parameters closely aligned with the reference image segmentation, achieving a mean symmetric distance of less than 1 mm. Our approach doubled the accuracy of the simulated model compared to the generic parameters reported in the literature.
Texture Discrimination via Hilbert Curve Path Based Information Quantifiers
Authors: Aurelio F. Bariviera, Roberta Hansen, Verónica E. Pastor
Date: 2024-09-06 00:00:00
Tasks: Texture Classification

The analysis of the spatial arrangement of colors and roughness/smoothness of figures is relevant due to its wide range of applications. This paper proposes a texture classification method that extracts data from images using the Hilbert curve. Three information theory quantifiers are then computed: permutation entropy, permutation complexity, and Fisher information measure. The proposal exhibits some important properties: (i) it allows to discriminate figures according to varying degrees of correlations (as measured by the Hurst exponent), (ii) it is invariant to rotation and symmetry transformations, (iii) it can be used either in black and white or color images. Validations have been made not only using synthetic images but also using the well-known Brodatz image database.
DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
Authors: Dongyu Luo, Jianyu Wu, Jing Wang, Hairun Xie, Xiangyu Yue, Shixiang Tang
Date: 2024-09-20 00:00:00

We showcase the plain diffusion models with Transformers are effective predictors of fluid dynamics under various working conditions, e.g., Darcy flow and high Reynolds number. Unlike traditional fluid dynamical solvers that depend on complex architectures to extract intricate correlations and learn underlying physical states, our approach formulates the prediction of flow dynamics as the image translation problem and accordingly leverage the plain diffusion model to tackle the problem. This reduction in model design complexity does not compromise its ability to capture complex physical states and geometric features of fluid dynamical equations, leading to high-precision solutions. In preliminary tests on various fluid-related benchmarks, our DiffFluid achieves consistent state-of-the-art performance, particularly in solving the Navier-Stokes equations in fluid dynamics, with a relative precision improvement of +44.8%. In addition, we achieved relative improvements of +14.0% and +11.3% in the Darcy flow equation and the airfoil problem with Euler's equation, respectively. Code will be released at https://github.com/DongyuLUO/DiffFluid upon acceptance.
Comparing DWI image quality of deep-learning-reconstructed EPI with RESOLVE in breast lesions at 3.0T: a pilot study
Authors: Marialena I. Tsarouchi, Antonio Portaluri, Marnix Maas, Ritse M. Mann
Date: 2024-09-15 00:00:00
Tasks: Image Reconstruction

The challenging spatial resolution of DWI could be addressed by deep learning based image reconstruction, by reducing noise without increasing acquisition time. To compare the image quality of the Echo Planar Imaging Deep Learning (EPI DL) DWI sequence with the clinically used simultaneous multi slice (SMS) RESOLVE in breast lesions. EPI DL and RESOLVE breast images from 20 participants were qualitatively evaluated. Quantitative image quality metrics of SNR and CNR on both high b-value (b800) images and ADC maps were calculated. SNR in RESOLVE vs. EP DL differed statistically significantly in manually delineations for b800 (p=0.006), ADC maps (p=0.001), and in ADC circularly delineations (0.001). DWI DL reconstruction may be clinically useful for addressing low-spatial resolution without compromising acquisition time and image quality. Such benefits coupled with the available methods of readout segmentation and SMS acquisitions may further enhance the value of DWI in breast imaging.
CycleBNN: Cyclic Precision Training in Binary Neural Networks
Authors: Federico Fontana, Romeo Lanzino, Anxhelo Diko, Gian Luca Foresti, Luigi Cinque
Date: 2024-09-28 00:00:00
Tasks: Inference Optimization

This paper works on Binary Neural Networks (BNNs), a promising avenue for efficient deep learning, offering significant reductions in computational overhead and memory footprint to full precision networks. However, the challenge of energy-intensive training and the drop in performance have been persistent issues. Tackling the challenge, prior works focus primarily on task-related inference optimization. Unlike prior works, this study offers an innovative methodology integrating BNNs with cyclic precision training, introducing the CycleBNN. This approach is designed to enhance training efficiency while minimizing the loss in performance. By dynamically adjusting precision in cycles, we achieve a convenient trade-off between training efficiency and model performance. This emphasizes the potential of our method in energy-constrained training scenarios, where data is collected onboard and paves the way for sustainable and efficient deep learning architectures. To gather insights on CycleBNN's efficiency, we conduct experiments on ImageNet, CIFAR-10, and PASCAL-VOC, obtaining competitive performances while using 96.09\% less operations during training on ImageNet, 88.88\% on CIFAR-10 and 96.09\% on PASCAL-VOC. Finally, CycleBNN offers a path towards faster, more accessible training of efficient networks, accelerating the development of practical applications. The PyTorch code is available at \url{https://github.com/fedeloper/CycleBNN/}
Public interest in science or bots? Selective amplification of scientific articles on Twitter
Authors: Ashiqur Rahman, Ehsan Mohammadi, Hamed Alhoori
Date: 2024-09-28 00:00:00
Tasks: Articles

With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. Since spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world, this topic warrants critical study and attention. We used the Altmetric dataset in combination with data collected through the Twitter Application Programming Interface (API) and the Botometer API. We combined the data into an extensive dataset with academic articles, several features from the article and a label indicating whether the article had excessive bot activity on Twitter or not. We analyzed the data to see the possibility of bot activity based on different characteristics of the article. We also trained machine-learning models using this dataset to identify possible bot activity in any given article. Our machine-learning models were capable of identifying possible bot activity in any academic article with an accuracy of 0.70. We also found that articles related to "Health and Human Science" are more prone to bot activity compared to other research areas. Without arguing the maliciousness of the bot activity, our work presents a tool to identify the presence of bot activity in the dissemination of an academic article and creates a baseline for future research in this direction.
Hybrid Semantic-Shannon Communications
Authors: Nikos G. Evgenidis, Nikos A. Mitsiou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, Panagiotis Sarigiannidis, Ioannis Krikidis, George K. Karagiannidis
Date: 2024-10-02 00:00:00

Semantic communications are considered a promising beyond-Shannon/bit paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable "similarity" between the transmitted and received signals. Under strict similarity conditions, semantic transmission may not be applicable and bit communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrier Hybrid Semantic-Shannon communication system where, without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-bit transmission selection and power allocation optimization problem is formulated, aiming to minimize two transmission delay metrics widely used in the literature, subject to strict similarity thresholds. Despite their non-convexity, both problems are decomposed into a convex and a mixed linear integer programming problem by using alternating optimization, both of which can be solved optimally. Furthermore, to improve the performance of the proposed hybrid schemes, a novel association of text sentences to subcarriers is proposed based on the data size of the sentences and the channel gains of the subcarriers. We show that the proposed association is optimal in terms of transmission delay. Numerical simulations verify the effectiveness of the proposed hybrid semantic-bit communication scheme and the derived sentence-to-subcarrier association, and provide useful insights into the design parameters of such systems.
FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training
Authors: Raktim Gautam Goswami, Naman Patel, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2024-09-27 00:00:00
Tasks: Metric Learning, Position

Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hinder rapid adaptation to new environments. To address this, we propose FlashMix, which uses a frozen, scene-agnostic backbone to extract local point descriptors, aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude, combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks, examining different regularizations and aggregators, demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://github.com/raktimgg/FlashMix.
Jamming Detection in MIMO-OFDM ISAC Systems Using Variational Autoencoders
Authors: Luca Arcangeloni, Enrico Testi, Andrea Giorgetti
Date: 2024-10-02 00:00:00
Tasks: Integrated sensing and communication, ISAC, Representation Learning

This paper introduces a novel unsupervised jamming detection framework designed specifically for monostatic multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) radar systems. The framework leverages echo signals captured at the base station (BS) and employs the latent data representation learning capability of variational autoencoders (VAEs). The VAE-based detector is trained on echo signals received from a real target in the absence of jamming, enabling it to learn an optimal latent representation of normal network operation. During testing, in the presence of a jammer, the detector identifies anomalous signals by their inability to conform to the learned latent space. We assess the performance of the proposed method in a typical integrated sensing and communication (ISAC)-enabled 5G wireless network, even comparing it with a conventional autoencoder.