Miracle-dz/UniFAR-FTUs huggingface dataset:
"This (Smart) Town Ain't Big Enough": Smart Small Towns and Digital Twins for Sustainable Urban and Regional Development (short paper)

One of the major challenges today lies in the creation of governance concepts for regional development that not only promote growth but, at the same time, ensure promotion of inclusiveness, fairness, and resilience. Digital twins can support policymakers in developing smart, sustainable solutions for cities and regions and, therefore, urban and non-urban environments. The project SCiNDTiLA (Smart Cities aNd Digital Twins in Lower Austria) aims to define the state-of-the-art in the field of smart cities, identify interdependencies, critical components and stakeholders, and provide a roadmap for smart cities with application to both smaller-scale urban and non-urban environments. SCiNDTiLA uses the foundations of complexity theory and computational social science methods to model Austrian towns and regions as smart cities/regions and thus as systems of socio-technical interaction to guide policy decision-making toward sustainable development.
Decoding phase and time-dependent interferograms of high-order harmonics

Interferometric measurements of high-harmonics induced by multiple laser fields in an emerging field of research that promises optimized yield of harmonics, and time and space-resolved nonlinear spectroscopy. Most of the measurements have been done by controlling the time-delay between the pulses. Here, we show that by changing one additional parameter, i.e. the phase-difference between the fields, together with the time-delay, one can, on the one hand, enhance the harmonic yield and, on the other hand, obtain in-depth information about the physical mechanisms that control the electron trajectories contributing to the high-harmonic generation. The two-dimensional interferograms obtained from such investigations can be used to find the values of time-delay and phase between the laser fields that maximize the yield of a particular harmonic. Results show that maximum yields of certain harmonics can be orders of magnitude larger than when using a single field or two fields with zero time-delay and phase difference. Our high-harmonics two-dimensional interferograms-based method paves the way for a simpler analysis of the attosecond electron dynamics in complex molecules and solids.
Analyzing and controlling diversity in quantum-behaved particle swarm optimization

This paper addresses the issues of controlling and analyzing the population diversity in quantum-behaved particle swarm optimization (QPSO), which is an optimization approach motivated by concepts in quantum mechanics and PSO. In order to gain an in-depth understanding of the role the diversity plays in the evolving process, we first define the genotype diversity by the distance to the average point of the particles' positions and the phenotype diversity by the fitness values for the QPSO. Then, the correlations between the two types of diversities and the search performance are tested and analyzed on several benchmark functions, and the distance-to-average-point diversity is showed to have stronger association with the search performance during the evolving processes. Finally, in the light of the performed diversity analyses, two strategies for controlling the distance-to-average-point diversities are proposed for the purpose of improving the search ability of the QPSO algorithm. Empirical studies on the QPSO with the introduced diversity control methods are performed on a set of benchmark functions from the CEC 2005 benchmark suite. The performance of the proposed methods are evaluated and compared with the original QPSO and other PSO variants.
Characterization of Human Balance through a Reinforcement Learning-based Muscle Controller

Balance assessment during physical rehabilitation often relies on rubric-oriented battery tests to score a patient's physical capabilities, leading to subjectivity. While some objective balance assessments exist, they are often limited to tracking the center of pressure (COP), which does not fully capture the whole-body postural stability. This study explores the use of the center of mass (COM) state space and presents a promising avenue for monitoring the balance capabilities in humans. We employ a musculoskeletal model integrated with a balance controller, trained through reinforcement learning (RL), to investigate balancing capabilities. The RL framework consists of two interconnected neural networks governing balance recovery and muscle coordination respectively, trained using Proximal Policy Optimization (PPO) with reference state initialization, early termination, and multiple training strategies. By exploring recovery from random initial COM states (position and velocity) space for a trained controller, we obtain the final BR enclosing successful balance recovery trajectories. Comparing the BRs with analytical postural stability limits from a linear inverted pendulum model, we observe a similar trend in successful COM states but more limited ranges in the recoverable areas. We further investigate the effect of muscle weakness and neural excitation delay on the BRs, revealing reduced balancing capability in different regions. Overall, our approach of learning muscular balance controllers presents a promising new method for establishing balance recovery limits and objectively assessing balance capability in bipedal systems, particularly in humans.
The Effects of Peeling on Finite Element Method -based EEG Source Reconstruction

The problem of reconstructing brain activity from electric potential measurements performed on the surface of a human head is not an easy task: not just because the solution of the related inverse problem is fundamentally ill-posed (not unique), but because the methods utilized in constructing a synthetic forward solution themselves contain many inaccuracies. One of these is the fact that the usual method of modelling primary currents in the human head via dipoles brings about at least 2 modelling errors: one from the singularity introduced by the dipole, and one from placing such dipoles near conductivity discontinuities in the active brain layer boundaries. In this article we observe how the removal of possible source locations from the surfaces of active brain layers affects the localisation accuracy of two inverse methods, sLORETA and Dipole Scan, at different signal-to-noise ratios (SNR), when the H(div) source model is used. We also describe the finite element forward solver used to construct the synthetic EEG data, that was fed to the inverse methods as input, in addition to the meshes that were used as the domains of the forward and inverse solvers. Our results suggest that there is a slight general improvement in the localisation results, especially at lower noise levels. The applied inverse algorithm and brain compartment under observation also affect the accuracy.
Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising

Real-world single image denoising is crucial and practical in computer vision. Bayesian inversions combined with score priors now have proven effective for single image denoising but are limited to white Gaussian noise. Moreover, applying existing score-based methods for real-world denoising requires not only the explicit train of score priors on the target domain but also the careful design of sampling procedures for posterior inference, which is complicated and impractical. To address these limitations, we propose a score priors-guided deep variational inference, namely ScoreDVI, for practical real-world denoising. By considering the deep variational image posterior with a Gaussian form, score priors are extracted based on easily accessible minimum MSE Non-i.i. d Gaussian denoisers and variational samples, which in turn facilitate optimizing the variational image posterior. Such a procedure adaptively applies cheap score priors to denoising. Additionally, we exploit a Non-i.i. d Gaussian mixture model and variational noise posterior to model the real-world noise. This scheme also enables the pixel-wise fusion of multiple image priors and variational image posteriors. Besides, we develop a noise-aware prior assignment strategy that dynamically adjusts the weight of image priors in the optimization. Our method outperforms other single image-based real-world denoising methods and achieves comparable performance to dataset-based unsupervised methods.
Learning multi-domain feature relation for visible and Long-wave Infrared image patch matching

Recently, learning-based algorithms have achieved promising performance on cross-spectral image patch matching, which, however, is still far from satisfactory for practical application. On the one hand, a lack of large-scale dataset with diverse scenes haunts its further improvement for learning-based algorithms, whose performances and generalization rely heavily on the dataset size and diversity. On the other hand, more emphasis has been put on feature relation in the spatial domain whereas the scale dependency between features has often been ignored, leading to performance degeneration especially when encountering significant appearance variations for cross-spectral patches. To address these issues, we publish, to be best of our knowledge, the largest visible and Long-wave Infrared (LWIR) image patch matching dataset, termed VL-CMIM, which contains 1300 pairs of strictly aligned visible and LWIR images and over 2 million patch pairs covering diverse scenes such as asteroid, field, country, build, street and water.In addition, a multi-domain feature relation learning network (MD-FRN) is proposed. Input by the features extracted from a four-branch network, both feature relations in spatial and scale domains are learned via a spatial correlation module (SCM) and multi-scale adaptive aggregation module (MSAG), respectively. To further aggregate the multi-domain relations, a deep domain interactive mechanism (DIM) is applied, where the learnt spatial-relation and scale-relation features are exchanged and further input into MSCRM and SCM. This mechanism allows our model to learn interactive cross-domain feature relations, leading to improved robustness to significant appearance changes due to different modality.
Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey

Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price prediction methods have shown groundbreaking results by utilizing external knowledge to understand the stock market. Despite the importance of these methods, there is a scarcity of scholarly works that systematically synthesize previous studies from the perspective of external knowledge types. Specifically, the external knowledge can be modeled in different data structures, which we group into non-graph-based formats and graph-based formats: 1) non-graph-based knowledge captures contextual information and multimedia descriptions specifically associated with an individual stock; 2) graph-based knowledge captures interconnected and interdependent information in the stock market. This survey paper aims to provide a systematic and comprehensive description of methods for acquiring external knowledge from various unstructured data sources and then incorporating it into stock price prediction models. We also explore fusion methods for combining external knowledge with historical price features. Moreover, this paper includes a compilation of relevant datasets and delves into potential future research directions in this domain.
Opponents and proponents of the war in Ukraine in Russian social media: who are they?

Understanding the personality of Russians who support the war in Ukraine is one of the key steps to understanding how this war became possible. However, during the war, traditional sociological methods are not always applicable. Social media provides an alternative source of what is inside people's heads. In this paper, I compare the political identities, values, and interests of social media users in Russia who hold a strong position for or against the war in Ukraine. I collect data from VK, the most popular Russian social media platform, and analyze self-filled profile information as well as the groups that the users subscribed to. I found that proponents of the war tend to have a weaker political identity (self-identified as"moderate") compared to opponents, who specify it more precisely (often, but not limited to,"liberal"). Additionally, the values of the proponents more frequently align with those promoted by the Russian government, such as orthodoxy and family. Despite these differences, pro-war and anti-war users share many common interests, as evidenced by their subscriptions to the same groups focused on music, history, and sport. When asked to state the most important trait in people (a field users can fill in VK), the most frequent answer for both groups is"kindness and honesty". The analysis results, in addition to contributing to the understanding of public opinion in Russia, can be utilized for predicting one's position on the war based on their social media profile.
A/B Testing: A Systematic Literature Review

In A/B testing two variants of a piece of software are compared in the field from an end user's point of view, enabling data-driven decision making. While widely used in practice, no comprehensive study has been conducted on the state-of-the-art in A/B testing. This paper reports the results of a systematic literature review that analyzed 141 primary studies. The results shows that the main targets of A/B testing are algorithms and visual elements. Single classic A/B tests are the dominating type of tests. Stakeholders have three main roles in the design of A/B tests: concept designer, experiment architect, and setup technician. The primary types of data collected during the execution of A/B tests are product/system data and user-centric data. The dominating use of the test results are feature selection, feature rollout, and continued feature development. Stakeholders have two main roles during A/B test execution: experiment coordinator and experiment assessor. The main reported open problems are enhancement of proposed approaches and their usability. Interesting lines for future research include: strengthen the adoption of statistical methods in A/B testing, improving the process of A/B testing, and enhancing the automation of A/B testing.
Digital Healthcare in the Metaverse: Insights into Privacy and Security

In this article, metaverse healthcare systems are studied from the privacy and security perspectives. We address data communication security for the metaverse, and the privacy and security threats of employing machine learning and artificial intelligence (ML/AI) algorithms for metaverse healthcare. In addition, human-centric data privacy for the social interactions in the metaverse is studied. Our goal is to present new visions and approaches, including physical layer security, semantic metaverse communications, differential privacy, and adversarial machine learning. These approaches have shown promising results in the field of data communications and networking, as well as the computer science domain, showcasing a huge potential to be employed for the metaverse healthcare systems. As a case study, we propose distributed differential privacy for the metaverse healthcare systems, where each virtual clinic perturbs its medical model vector to safeguard privacy against malicious clients and honest-but-curious servers. Through our experiments on the Breast Cancer Wisconsin Dataset, we highlight the privacy-utility tradeoff for different adjustable levels of privacy.
Coupling the Navier–Stokes–Fourier equations with the Johnson–Segalman stress-diffusive viscoelastic model: global-in-time and large-data analysis

We prove that there exists a~large-data and global-in-time weak solution to a~system of partial differential equations describing an unsteady flow of an incompressible heat-conducting rate-type viscoelastic stress-diffusive fluid filling up a~mechanically and thermally isolated container of any dimension. To overcome the~principle difficulties connected with ill-posedness of the~diffusive Oldroyd-B model in three dimensions, we assume that the~fluid admits a~strengthened dissipation mechanism, at least for excessive elastic deformations. All the~relevant material coefficients are allowed to depend continuously on the~temperature, whose evolution is captured by a~thermodynamically consistent equation. In fact, the~studied model is derived from scratch using only the~balance equations for linear momentum and energy, the~formulation of the~second law of thermodynamics and the~constitutive equation for the~internal energy. The~latter is assumed to be a~linear function of temperature, which simplifies the~model. The~concept of our weak solution incorporates both the~temperature and entropy inequalities, and also the~local balance of total energy provided that the~pressure function exists.
Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation

End-to-end weakly supervised semantic segmentation aims at optimizing a segmentation model in a single-stage training process based on only image annotations. Existing methods adopt an online-trained classification branch to provide pseudo annotations for supervising the segmentation branch. However, this strategy makes the classification branch dominate the whole concurrent training process, hindering these two branches from assisting each other. In our work, we treat these two branches equally by viewing them as diverse ways to generate the segmentation map, and add interactions on both their supervision and operation to achieve mutual promotion. For this purpose, a bidirectional supervision mechanism is elaborated to force the consistency between the outputs of these two branches. Thus, the segmentation branch can also give feedback to the classification branch to enhance the quality of localization seeds. Moreover, our method also designs interaction operations between these two branches to exchange their knowledge to assist each other. Experiments indicate our work outperforms existing end-to-end weakly supervised segmentation methods.
A High-efficient Battery Charging System for Electric Vehicle

Nowadays, automobile is facing the trend of electrification. Lithium-ion batteries is widely used as their power supplies. Lithium-ion battery has complex characteristics, as a result, Lithium-ion battery needs optimal charging strategies to make sure it is charged safely and efficiently. This paper focuses on development of a high-efficient charging method for lithium-ion battery. To test different charging strategies, the electric vehicle charging system consisting of a dual active bridge DC-DC converter and a Thevenin battery model is implemented. Multistage constant current charging (MSCC) and multistage constant current reflex charging (MSCC with reflex charging) were proposed. Compared with the traditional constant voltage constant current (CC-CV) charging method, MSCC can reduce 12% of the charging time and 1.1% of the battery loss; MSCC with reflex charging has a 10.45% and a 1.54% reduction of charging time and battery loss separately.
Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI

Generative Artificial Intelligence (AI) has seen mainstream adoption lately, especially in the form of consumer-facing, open-ended, text and image generating models. However, the use of such systems raises significant ethical and safety concerns, including privacy violations, misinformation and intellectual property theft. The potential for generative AI to displace human creativity and livelihoods has also been under intense scrutiny. To mitigate these risks, there is an urgent need of policies and regulations responsible and ethical development in the field of generative AI. Existing and proposed centralized regulations by governments to rein in AI face criticisms such as not having sufficient clarity or uniformity, lack of interoperability across lines of jurisdictions, restricting innovation, and hindering free market competition. Decentralized protections via crowdsourced safety tools and mechanisms are a potential alternative. However, they have clear deficiencies in terms of lack of adequacy of oversight and difficulty of enforcement of ethical and safety standards, and are thus not enough by themselves as a regulation mechanism. We propose a marriage of these two strategies via a framework we call Dual Governance. This framework proposes a cooperative synergy between centralized government regulations in a U.S. specific context and safety mechanisms developed by the community to protect stakeholders from the harms of generative AI. By implementing the Dual Governance framework, we posit that innovation and creativity can be promoted while ensuring safe and ethical deployment of generative AI.
Low-ionization structures in planetary nebulae -- III. The statistical analysis of physico-chemical parameters and excitation mechanisms

Nearly 30 years after the first detailed studies of low-ionization structures (LISs) in planetary nebulae (PNe), we perform a statistical analysis of their physical, chemical and excitation properties, by collecting published data in the literature. The analysis was made through the contrast between LISs and high-ionization structures -- rims or shells -- for a large sample of PNe, in order to highlight significant differences between these structures. Our motivation was to find robust results based on the largest sample of LISs gathered so far. (i) Indeed, LISs have lower electron densities (N$_{e}$[S~II]) than the rims/shells. (ii) The nitrogen electron temperatures (T$_{e}$[N~II]) are similar between the two groups, while a bimodal distribution is observed for the T$_{e}$ based on [O~III] of the rims/shells, although the high- and low-ionization structures have T$_{e}$[O~III] of similar median values. (iii) No significant variations are observed in total abundances of He, N, O, Ne, Ar, Cl and S between the two groups. (iv) Through the analysis of several diagnostic diagrams, LISs are separated from rims/shells in terms of excitation. From two large grids of photoionization and shock models, we show that there is an important overlap between both mechanisms, particularly when low-ionization line-ratios are concerned. We found a good tracer of high-velocity shocks, as well as an indicator of high- and low-velocity shocks that depends on temperature-sensitive line ratios. In conclusion, both excitation mechanisms could be present, however shocks cannot be the main source of excitation for most of the LISs of PNe.
Desiderata for normative models of synaptic plasticity

Normative models of synaptic plasticity use a combination of mathematics and computational simulations to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work on these models, but experimental confirmation is relatively limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata which, when satisfied, are designed to guarantee that a model has a clear link between plasticity and adaptive behavior, consistency with known biological evidence about neural plasticity, and specific testable predictions. We then discuss how new models have begun to improve on these criteria and suggest avenues for further development. As prototypes, we provide detailed analyses of two specific models -- REINFORCE and the Wake-Sleep algorithm. We provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable.