pwc-archive/papers-with-abstracts huggingface dataset:
Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation
Authors: Xin Ma, Mingyue Li, Xuguang Liu
Date: 2024-07-10 00:00:00
Tasks: Causal Inference, Collaborative Filtering, Data Poisoning, Deep Reinforcement Learning, Device-Cloud Collaboration, Ethics, Recommendation Systems

Using 286 research papers collected from Web of Science, ScienceDirect, SpringerLink, arXiv, and Google Scholar databases, a systematic review methodology was adopted to review and summarize the current challenges and potential future developments in data, algorithms, and evaluation aspects of RSs. It was found that RSs involve five major research topics, namely algorithmic improvement, domain applications, user behavior & cognition, data processing & modeling, and social impact & ethics. Collaborative filtering and hybrid recommendation techniques are mainstream. The performance of RSs is jointly limited by four types of eight data issues, two types of twelve algorithmic issues, and two evaluation issues. Notably, data-related issues such as cold start, data sparsity, and data poisoning, algorithmic issues like interest drift, device-cloud collaboration, non-causal driven, and multitask conflicts, along with evaluation issues such as offline data leakage and multi-objective balancing, have prominent impacts. Fusing physiological signals for multimodal modeling, defending against data poisoning through user information behavior, evaluating generative recommendations via social experiments, fine-tuning pre-trained large models to schedule device-cloud resource, enhancing causal inference with deep reinforcement learning, training multi-task models based on probability distributions, using cross-temporal dataset partitioning, and evaluating recommendation objectives across the full lifecycle are feasible solutions to address the aforementioned prominent challenges and unlock the power and value of RSs.The collected literature is mainly based on major international databases, and future research will further expand upon it.
Efficient coding with chaotic neural networks: A journey from neuroscience to physics and back
Authors: Jonathan Kadmon
Date: 2024-08-04 00:00:00

This essay, derived from a lecture at "The Physics Modeling of Thought" workshop in Berlin in winter 2023, explores the mutually beneficial relationship between theoretical neuroscience and statistical physics through the lens of efficient coding and computation in cortical circuits. It highlights how the study of neural networks has enhanced our understanding of complex, nonequilibrium, and disordered systems, while also demonstrating how neuroscientific challenges have spurred novel developments in physics. The paper traces the evolution of ideas from seminal work on chaos in random neural networks to recent developments in efficient coding and the partial suppression of chaotic fluctuations. It emphasizes how concepts from statistical physics, such as phase transitions and critical phenomena, have been instrumental in elucidating the computational capabilities of neural networks. By examining the interplay between order and disorder in neural computation, the essay illustrates the deep connection between theoretical neuroscience and the statistical physics of nonequilibrium systems. This synthesis underscores the ongoing importance of interdisciplinary approaches in advancing both fields, offering fresh perspectives on the fundamental principles governing information processing in biological and artificial systems. This multidisciplinary approach not only advances our understanding of neural computation and complex systems but also points toward future challenges at the intersection of neuroscience and physics.
Impact of Network Heterogeneity on Neuronal Synchronization
Authors: J. Used, J. M. Seoane, I. Bashkirtseva, L. Ryashko, M. A. F. Sanjuán
Date: 2024-07-09 00:00:00

Synchronization dynamics is a phenomenon of great interest in many fields of science. One of the most important fields is neuron dynamics, as synchronization in certain regions of the brain is related to some of the most common mental illnesses. To study the impact of the network heterogeneity in the neuronal synchronization, we analyze a small-world network of non-identical Chialvo neurons that are electrically coupled. We introduce a mismatch in one of the model parameters to introduce the heterogeneity of the network. Our study examines the effects of this parameter mismatch, the noise intensity in the stochastic model, and the coupling strength between neurons on synchronization and firing frequency. We have identified critical values of noise intensity, parameter mismatch, and rewiring probability that facilitate effective synchronization within the network. Furthermore, we observe that the balance between excitatory and inhibitory connections plays a crucial role in achieving global synchronization. Our findings offer insights into the mechanisms driving synchronization dynamics in complex neuron networks.
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
Authors: Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis OBrien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
Date: 2024-08-06 00:00:00
Tasks: Weather Forecasting

Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1,000-10,000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part I, we construct an ensemble weather forecasting system based on Spherical Fourier Neural Operators (SFNO), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. Using large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states, and the ML ensemble thus passes a crucial spectral test in the literature. The IFS and ML ensembles have similar Extreme Forecast Indices, and we show that the ML extreme weather forecasts are reliable and discriminating.
Spatial Temporal Approach for High-Resolution Gridded Wind Forecasting across Southwest Western Australia
Authors: Fuling Chen, Kevin Vinsen, Arthur Filoche
Date: 2024-07-26 00:00:00
Tasks: Decision Making

Accurate wind speed and direction forecasting is paramount across many sectors, spanning agriculture, renewable energy generation, and bushfire management. However, conventional forecasting models encounter significant challenges in precisely predicting wind conditions at high spatial resolutions for individual locations or small geographical areas (< 20 km2) and capturing medium to long-range temporal trends and comprehensive spatio-temporal patterns. This study focuses on a spatial temporal approach for high-resolution gridded wind forecasting at the height of 3 and 10 metres across large areas of the Southwest of Western Australia to overcome these challenges. The model utilises the data that covers a broad geographic area and harnesses a diverse array of meteorological factors, including terrain characteristics, air pressure, 10-metre wind forecasts from the European Centre for Medium-Range Weather Forecasts, and limited observation data from sparsely distributed weather stations (such as 3-metre wind profiles, humidity, and temperature), the model demonstrates promising advancements in wind forecasting accuracy and reliability across the entire region of interest. This paper shows the potential of our machine learning model for wind forecasts across various prediction horizons and spatial coverage. It can help facilitate more informed decision-making and enhance resilience across critical sectors.
Shadow Area and Degrees of Freedom for Free-Space Communication
Authors: Mats Gustafsson
Date: 2024-07-30 00:00:00

The number of degrees-of-freedom (NDoF) in a communication system is limited by the number of antenna ports, element shapes, positions, and the propagation environment. As the number of antenna elements increases within a given region, the NDoF eventually saturates due to correlation of the radiated fields. The maximal NDoF can be determined numerically for communication between two regions using singular value decomposition of a channel model representing wave propagation between densely sampled sources at the transmitter and fields at the receiver. This paper provides a straightforward analytical estimate of the NDoF for arbitrarily shaped transmitter and receiver regions. The analysis show that the NDoF for electrically large regions is approximated by the mutual shadow area of the regions, measured in wavelengths. Several setups illustrate the results, which are then compared with numerical evaluations of the singular values of the propagation channel. These new analytical expressions also simplify to previously established results based on Weyl's law and the paraxial approximation.
Compact SPICE model for TeraFET resonant detectors
Authors: Xueqing Liu, Yuhui Zhang, Trond Ytterdal, Michael Shur
Date: 2024-07-27 00:00:00
Tasks: model

This paper presents an improved compact model for TeraFETs employing a nonlinear transmission line approach to describe the non-uniform carrier density oscillations and electron inertia effects in the TeraFET channels. By calculating the equivalent components for each segment of the channel: conductance, capacitance, and inductance, based on the voltages at the segment's nodes, our model accommodates non-uniform variations along the channel. We validate the efficacy of this approach by comparing terahertz (THz) response simulations with experimental data and MOSA1, EKV TeraFET SPICE models, analytical theories, and Multiphysics simulations.
The Role of Biomarkers on Haemodynamics in Atherosclerotic Artery
Authors: Ruchira Ray, Bibaswan Dey
Date: 2024-08-06 00:00:00
Tasks: Specificity

Atherosclerosis, a chronic inflammatory cardiovascular disease, leads to arterial constriction caused by the accumulation of lipids, cholesterol, and various substances within artery walls. Such plaque can rupture, resulting in a blood clot that obstructs major arteries and may initiate myocardial infarction, ischemic stroke, etc. Atherosclerotic plaque formation begins with the accumulation of foam cells and macrophages within the intima layer of the arterial wall. At the latter stage, the smooth muscle cells migrated from deeper artery wall layers, contributing to the fibrous cap formation and plaque stabilizing. A developed plaque gradually enters the lumen and narrows down the lumen to impede blood flow. We introduce a two-phase and macroscopic model to investigate the progression of plaque growth in its advanced stage and analyze the minimum gap (Lumen Clearance) within an atherosclerotic artery so that blood cells can pass through. Cardiac troponin, a high specificity and sensitivity biomarker, facilitates early detection of elevated myocardial infarction, Ischemic stroke, etc. risks. This study aims to establish a relationship between the troponin concentration in atherosclerotic arteries and their internal clearance, which could significantly improve our understanding of disease progression. Our observations show that the plaque undergoes rapid evolution in its initial stages, gradually slowing down over time to reach a steady state. At the same time, the lumen clearance exhibits an opposite behavior, decreasing slowly over time. Our study finds a positive correlation between plaque depth and troponin concentration in the blood and a negative relationship between troponin concentrations and lumen clearance in atherosclerotic arteries.
Population genetics: an introduction for physicists
Authors: Andrea Iglesias-Ramas, Samuele Pio Lipani, Rosalind J. Allen
Date: 2024-08-05 00:00:00

Population genetics lies at the heart of evolutionary theory. This topic forms part of many biological science curricula but is rarely taught to physics students. Since physicists are becoming increasingly interested in biological evolution, we aim to provide a brief introduction to population genetics, written for physicists. We start with two background chapters: chapter 1 provides a brief historical introduction to the topic, while chapter 2 provides some essential biological background. We begin our main content with chapter 3 which discusses the key concepts behind Darwinian natural selection and Mendelian inheritance. Chapter 4 covers the basics of how variation is maintained in populations, while chapter 5 discusses mutation and selection. In chapter 6 we discuss stochastic effects in population genetics using the Wright-Fisher model as our example, and finally we offer concluding thoughts and references to excellent textbooks in chapter 7.
Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling
Authors: Rishikesan Maran, Eli J. Müller, Ben D. Fulcher
Date: 2024-07-29 00:00:00

Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain-stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss: (i) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics; and (ii) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
Authors: Parastoo Semnani, Mihail Bogojeski, Florian Bley, Zizheng Zhang, Qiong Wu, Thomas Kneib, Jan Herrmann, Christoph Weisser, Florina Patcas, Klaus-Robert Müller
Date: 2024-07-10 00:00:00

The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to complex interactions, catalyst design has long relied on trial-and-error, a costly and labor-intensive process leading to scarce data that is heavily biased towards undesired, low-yield catalysts. Despite the rise of ML in this field, most efforts have not focused on dealing with the challenges presented by such experimental data. To address these challenges, we introduce a robust machine learning and explainable AI (XAI) framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components. This framework combines a series of ML practices designed to handle the scarcity and imbalance of catalyst data. We apply the framework to classify the yield of various catalyst compositions in oxidative methane coupling, and use it to evaluate the performance of a range of ML models: tree-based models, logistic regression, support vector machines, and neural networks. These experiments demonstrate that the methods used in our framework lead to a significant improvement in the performance of all but one of the evaluated models. Additionally, the decision-making process of each ML model is analyzed by identifying the most important features for predicting catalyst performance using XAI methods. Our analysis found that XAI methods, providing class-aware explanations, such as Layer-wise Relevance Propagation, identified key components that contribute specifically to high-yield catalysts. These findings align with chemical intuition and existing literature, reinforcing their validity. We believe that such insights can assist chemists in the development and identification of novel catalysts with superior performance.
The Physics of Learning: From Autoencoders to Truly Autonomous Learning Machines
Authors: Alex Ushveridze
Date: 2024-02-12 00:00:00
Tasks: Continual Learning

The fact that accurately predicted information can serve as an energy source paves the way for new approaches to autonomous learning. The energy derived from a sequence of successful predictions can be recycled as an immediate incentive and resource, driving the enhancement of predictive capabilities in AI agents. We propose that, through a series of straightforward meta-architectural adjustments, any unsupervised learning apparatus could achieve complete independence from external energy sources, evolving into a self-sustaining physical system with a strong intrinsic 'drive' for continual learning. This concept, while still purely theoretical, is exemplified through the autoencoder, a quintessential model for unsupervised efficient coding. We use this model to demonstrate how progressive paradigm shifts can profoundly alter our comprehension of learning and intelligence. By reconceptualizing learning as an energy-seeking process, we highlight the potential for achieving true autonomy in learning systems, thereby bridging the gap between algorithmic concepts and physical models of intelligence.
GPU-based data processing for speeding-up correlation plenoptic imaging
Authors: Francesca Santoro, Isabella Petrelli, Gianlorenzo Massaro, George Filios, Francesco V. Pepe, Leonardo Amoruso, Maria Ieronimaki, Samuel Burri, Edoardo Charbon, Paul Mos, Arin Ulku, Michael Wayne, Cristoforo Abbattista, Claudio Bruschini, Milena D'Angelo
Date: 2024-07-30 00:00:00
Tasks: GPU

Correlation Plenoptic Imaging (CPI) is a novel technological imaging modality enabling to overcome drawbacks of standard plenoptic devices, while preserving their advantages. However, a major challenge in view of real-time application of CPI is related with the relevant amount of required frames and the consequent computational-intensive processing algorithm. In this work, we describe the design and implementation of an optimized processing algorithm that is portable to an efficient computational environment and exploits the highly parallel algorithm offered by GPUs. Improvements by a factor ranging from 20x, for correlation measurement, to 500x, for refocusing, are demonstrated. Exploration of the relation between the improvement in performance achieved and actual GPU capabilities, also indicates the feasibility of near-real time processing capability, opening up to the potential use of CPI for practical real-time application.
Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks
Authors: Amir Heydari, Abbas Ahmadi, Tae Hyung Kim, Berkin Bilgic
Date: 2024-08-06 00:00:00
Tasks: Quantitative MRI, Transfer Learning

Quantification of tissue parameters using MRI is emerging as a powerful tool in clinical diagnosis and research studies. The need for multiple long scans with different acquisition parameters prohibits quantitative MRI from reaching widespread adoption in routine clinical and research exams. Accelerated parameter mapping techniques leverage parallel imaging, signal modelling and deep learning to offer more practical quantitative MRI acquisitions. However, the achievable acceleration and the quality of maps are often limited. Joint MAPLE is a recent state-of-the-art multi-parametric and scan-specific parameter mapping technique with promising performance at high acceleration rates. It synergistically combines parallel imaging, model-based and machine learning approaches for joint mapping of T1, T2*, proton density and the field inhomogeneity. However, Joint MAPLE suffers from prohibitively long reconstruction time to estimate the maps from a multi-echo, multi-flip angle (MEMFA) dataset at high resolution in a scan-specific manner. In this work, we propose a faster version of Joint MAPLE which retains the mapping performance of the original version. Coil compression, random slice selection, parameter-specific learning rates and transfer learning are synergistically combined in the proposed framework. It speeds-up the reconstruction time up to 700 times than the original version and processes a whole-brain MEMFA dataset in 21 minutes on average, which originally requires ~260 hours for Joint MAPLE. The mapping performance of the proposed framework is ~2-fold better than the standard and the state-of-the-art evaluated reconstruction techniques on average in terms of the root mean squared error.
X-ray nano-holotomography reconstruction with simultaneous probe retrieval
Authors: Viktor Nikitin, Marcus Carlsson, Doga Gursoy, Rajmund Mokso, Peter Cloetens
Date: 2024-07-29 00:00:00
Tasks: 3D Reconstruction, Retrieval

In conventional tomographic reconstruction, the pre-processing step includes flat-field correction, where each sample projection on the detector is divided by a reference image taken without the sample. When using coherent X-rays as probe, this approach overlooks the phase component of the illumination field (probe), leading to artifacts in phase-retrieved projection images, which are then propagated to the reconstructed 3D sample representation. The problem intensifies in nano-holotomography with focusing optics, that due to various imperfections create high-frequency components in the probe function. Here, we present a new iterative reconstruction scheme for holotomography, simultaneously retrieving the complex-valued probe function. Implemented on GPUs, this algorithm results in 3D reconstruction resolving twice thinner layers in a 3D ALD standard sample measured using nano-holotomography.
Are EU low-carbon structural funds efficient in reducing emissions?
Authors: Marco Dueñas, Antoine Mandel
Date: 2024-08-03 00:00:00
Tasks: Time Series

This paper investigates the effectiveness of the ``low-carbon economy'' expenditures from European Structural and Investment Funds in fostering reductions in greenhouse gas emissions within European regions, focusing on the 2007-2013 and 2014-2020 programme periods. By decomposing emissions time series into trend and cycle components and considering them within a panel data framework, our research highlights that the impacts of low-carbon economy expenditures vary, qualitatively and quantitatively, with the targeted regions' development level. We find significant emissions reductions in developed and transition regions yet less favourable outcomes in less developed areas. Further analysis into specific greenhouse gas emissions types (CO$_2$, CH$_4$, and N$_2$O) reveals inconsistent impacts, underscoring the complexity of achieving emissions reductions. Our findings emphasise the need for tailored environmental strategies that accommodate the economic disparities of regions in the European Union.