Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel Nonlinear Dynamic System Identification Based on Expectation-Maximization Method

This paper develops a kernel nonlinear system identification approach to dual estimation problems. Given the observation model and the corresponding observation sequence, the unknown state transition function is approximated in the reproducing kernel Hilbert space (RKHS), while the corresponding hidden states are also estimated with the unscented Kalman smoother (UKS). To implement the dual estimation, the optimization solution of the proposed kernel expectation-maximization (EM) cost function is approximated based on the unscented transform (UT). Finally, the kernel nonlinear system identification method is applied to the noisy time-series estimation, where the data are generated by the IKEDA chaotic dynamical system with unknown system parameters. The simulation results show that the noisy data's signal-noise ratio (SNR) is improved significantly and is close to the SNR obtained with the accurate system model. Furthermore, the proposed non-parametric approach is compared with the parametric approach and outperforms obviously the parametric approach, even if the parameterized system model is assumed to be known.
GDLR: A Robust 3D Laser Descriptor for Loop Closure Detection and Registration

In this paper, we present a novel 3D point cloud descriptor GDLR, which aims to realize robust 3D laser loop detection and registration in challenging scenarios with large rotation or inadequate overlapping. We test the proposed method on Kitti datasets and compare our results with the state-of-the-art methods. Experimental results show GDLR performs a better precision than the state-of-the-art methods and achieves far better registration accuracy than other scan registration methods.
Hot or not? Exploring User Perceptions of thermal Human-Robot Interaction*

Haptics is an essential element of interaction between humans and socially assistive robots. However, it is often limited to movements or vibrations and misses key aspects such as temperature. This mixed-methods study explores the potential of enhancing human-robot interaction [HRI] through thermal stimulation to regulate affect during a stress-inducing task. Participants were exposed to thermal stimulation while completing the Mannheim-multicomponent-stress-task (MMST). Findings yielded that human-robot emotional touch may induce comfort and relaxation during the exposure to acute stressors. User affect may be further enhanced through thermal stimulation, which was experienced as comforting, de-stressing, and altered participants’ perception of the robot to be more life-like. Allowing participants to calibrate a temperature they perceived as calming provided novel insights into the temperature ranges suitable for interaction. While neutral temperatures were the most popular amongst participants, findings suggest that cool (4 – 29 ºC), neutral (30 – 32 ºC), and warm (33ºC -36 ºC) temperatures can all induce comforting effects during exposure to stress. The results highlight the potential of thermal HRI in general and, more specifically, the advantages of personalized temperature calibration.
A Real-time Interactive 3D Ultrasound Imaging System

Real-time 3D ultrasound (US) imaging has attracted more attention since it can display spatial 3D images of tissues and organs in real time to provide important information for diagnosis and intervention. In this study, Bezier interpolation and PNN reconstruction algorithms are combined to achieve real-time repeatable 3D reconstruction. A real-time interactive 3D US imaging system with a 2DB-mode probe is developed. The system employs an optical measuring equipment to collect six degrees of freedom (6-DoF) poses of the US probe. 2D US image acquisition using a calibrated probe with the pose information is performed to reconstruct a 3D volume. A multi-thread scheme is proposed to achieve real-time data acquisition, overlapped 3D reconstruction and volume visualization. Interactive functions and image segmentation enable users to freely select regions of interest (ROI) and adjust image color and opacity. The system was evaluated on a 3D printing femur model, a neck phantom and human carotid arteries. The surface point clouds segmented from the reconstructed 3D volumes were rigidly registered with the models whose CT scans were available. The surface matching error was used as the reconstruction error. The 3D volumes were reconstructed with different voxel sizes of (0.5 mm, 1.0 mm, 1.5 mm). The reconstruction errors of the femur and trachea model were (0.85 mm, 0.99 mm, 1.43 mm) and (0.96 mm, 1.17 mm, 1.37 mm), respectively. The 3D reconstruction on real human carotid arteries has confirmed the clinical feasibility.
Feature Pyramid Vision Transformer for MedMNIST Classification Decathlon

MedMNIST is a medical dataset proposed to block the need for medical knowledge, but there is currently no model that can generalize well on all its sub-datasets. Owing to the inadequacy of long-range relation modeling, models based on convolutional neural networks (CNNs) cannot fully learn the information of images. Besides, relying only on high-level features limits the generalization effect as well. All of these remain challenges for MedMNIST Classification Decathlon. In this paper, we proposed Feature Pyramid Vision Transformer (FPViT), a strong alternative for MedMNIST Classification Decathlon. Our FPViT exhibits enhanced feature learning and modeling capabilities, which merits both residual network (ResNet) and Vision Transformer (ViT). Transformers in our model take the features extracted by ResNet as sequences to capture global contexts which compensate for the lack of locality of convolution operations. Moreover, the feature pyramid designed in our model effectively utilizes the multi-scale feature maps from basic layers of ResNet. These multi-scale features from low-level to high level enable our model to have better adaptability. And, the final prediction is based on the multi-scale ViT and the original ResNet heads. Through experiments, our FPViT can achieve superior classification and generalization on MedMNIST than state-of-the-art methods.
A Granular Computing Approach for Multi-Labelled Sequences Classification in IEEE 802.11 Networks

Recent developments in communication networks have made new kind of services possible, which are even more connected due to smart Internet of Things devices and sensor networks. This complex integration of technologies had increased the attack surface on networks that are spread across multiple environments, with different protocols and services, usually based on the wireless medium. Network Intrusion Detection Systems aim to overcome these security issues, analyzing network traffic and applying multiple reconnaissance techniques. For this purpose, state-of-the-art research activities have been focused on Artificial Intelligence and Machine Learning-based approaches in order to scale more efficiently the capabilities of this kind of systems (in terms of both complexity, speed and training data required). Our work further analyzes these security issues, in particular, by introducing a consolidated approach based on the Granular Computing information processing paradigm applied to sequences of WiFi frames. We focused on a subset of attacks collected in the Aegean WiFi Intrusion Detection dataset, that consists in complex multi-step attacks or simpler control frames flooding attacks, not recognizable using a single-frame processing strategy. This kind of structured data heterogeneity implies a non-exclusive labelling strategy, as each frames' sequence could bring information related to different attack classes, making the whole supervised problem more challenging. We show that the proposed solution provides interesting results in terms of classification performances, limited embedding complexity and peculiar white-box trained models.
Vector Symbolic Scene Representation for Semantic Place Recognition

Most state-of-the-art methods do not explicitly use scene semantics for place recognition by the images. We address this problem and propose a new two-stage approach referred to as TSVLoc. It solves the place recognition task as the image retrieval problem and enriches any well-known method. In the first model-agnostic stage, any modern neural network model that does not directly use semantics, e.g., HF-Net, NetVLAD, or Patch-NetVLAD, can be used. In the second stage, we apply the Vector Symbolic Architectures (VSA) framework to construct semantic scene representation. Our method uses semantic segmentation of an image to extract objects and their relations and applies VSA operations to form semantic scene representation. For this, an optional usage of the depth map was considered, which showed promising results. The effectiveness of our approach is demonstrated through extensive experiments on the open large-scale datasets: the indoor HPointLoc dataset built in the Habitat simulation environment and the outdoor Oxford RobotCar dataset. The proposed solution significantly improves the quality of the place recognition.
Nonlinear current-voltage characteristics due to quantum tunneling of phase slips in superconducting Nb nanowire networks

We report on the transport properties of an array of N∼30 interconnected Nb nanowires, grown by sputtering on robust porous Si substrates. The analyzed system exhibits a broad resistive transition in zero magnetic field, H, and highly nonlinear V(I) characteristics as a function of H, which can be both consistently described by quantum tunneling of phase slips.
Two-dimensional spin ice and the sixteen-vertex model

In this thesis we present a thorough study of the static and dynamic properties of the 2D sixteen-vertex model or, in other words, a simplified version of the dipolar spin-ice model. After a general discussion on frustrated magnets, and spin-ice in particular, we motivate the introduction of our model in order to study the collective behaviour of spin-ice. We use a rejection-free continuous-time Monte Carlo algorithm with local spin-flip updates to analyse the equilibrium phases and the critical properties of the 2D model. We compare our results with the integrable cases. We extend the model to be defined on carefully chosen trees and employ a Bethe-Peierls approximation to study its equilibrium properties. The range of validity of the approximation is discussed by comparing the results obtained analytically for the model defined on trees with the exact and numerical results obtained for the 2D model. Motivated by advent of artificial spin-ice realisations, we set the parameters of the model in order to reproduce the experimental situation. We show that the sixteen-vertex model gives an accurate description of the thermodynamics of artificial spin-ice samples. Our theoretical results are in quasi-quantitative agreement with experimental data obtained in as-grown samples away from the expected critical point. The phase diagram of the sixteen-vertex model and the nature of the equilibrium phases is presented in detail. Our model is build as a stochastic extension of the integrable six-vertex model in order to in- clude thermal fluctuations in the form of defects. We study the ordering dynamics of the system following different kind of quenches by means of Monte Carlo simulations. We analysed the evo- lution of the density of defects and we identified the dynamical mechanisms leading the different ordering processes. We showed that the dynamics proceed through coarsening accordingly to the dynamical scaling picture. The interplay between localised and extended topological defects is discussed. We study in detail the existence of a dynamical arrest following a quench as observed in 3D dipolar spin-ice.
Natural Convection Heat Transfer Enhancement using Functionalized Nanodiamonds in Transformer Oil

Efficient transformer cooling is an important factor in increasing the lifetime of transformer and reducing the associated maintenance costs. It has been reported that the lifetime of the transformer is increased by 10% with 1 °C decrease in the core temperature. The transformer oil which is a typically a mineral oil, used commonly in cooling of transformers has an inherent disadvantage of low thermal conductivity. This limits the amount of heat transfer from the core of the transformer to the surroundings. This research discusses the improvement of thermal conductivity of transformer oil by using functionalized nanodiamond (fND) particles in transformer oil. In this study, enhancement in natural convection heat transfer is studied by using two different concentrations (0.2 wt.% and 0.4 wt.%) of fND in transformer oil and compared with the base transformer oil. A tall container insulated from all sides and heated from one side is considered for the experiment's geometry. The natural convection is observed in the container and the experimental results are validated with the theoretical model. The observed enhancement in heat transfer coefficient for 0.2 wt. % sample was 41.7% and for 0.4 wt. % sample was 82.5%.
Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective Optimization

In dimensionality reduction problems, the adopted technique may produce disparities between the representation errors of different groups. For instance, in the projected space, a specific class can be better represented in comparison with another one. In some situations, this unfair result may introduce ethical concerns. Aiming at overcoming this inconvenience, a fairness measure can be considered when performing dimensionality reduction through Principal Component Analysis. However, a solution that increases fairness tends to increase the overall re-construction error. In this context, this paper proposes to address this trade-off by means of a multi-objective-based approach. For this purpose, we adopt a fairness measure associated with the disparity between the representation errors of different groups. Moreover, we investigate if the solution of a classical Principal Component Analysis can be used to find a fair projection. Numerical experiments attest that a fairer result can be achieved with a very small loss in the overall reconstruction error.
Thermal-Aware Thread and Turbo Frequency Throttling Optimization for Parallel Applications

The number of processing cores in multicore pro-cessors has been rising to deal with the levels performance required by modern applications. Concomitantly, the operating temperature of hardware components has become a primary concern due to economic and environmental perspectives. Hence, different software (e.g., thread throttling) and hardware (e.g., dynamic voltage and frequency scaling - DVFS) strategies have also been applied to reduce the processor temperature levels without jeopardizing the application's performance. While thread throttling strategies artificially tune the degree of thread-level parallelism of applications to improve the hardware resources utilization according to their scalability issues, turbo frequencies have been employed to speed up the execution of a given appli-cation by increasing the processor's frequencies above the base. Given that, we propose Urano. It is a thermal-aware strategy that combines thread throttling and turbo mode optimization to diminish the processor operating temperature without penalizing the performance of the application. Through the execution of twelve well-known parallel applications on a modern multicore architecture, we demonstrate that Urano decreases the peak temperature by up to 17% compared to how parallel applications are executed with minimal impact on the performance.
Non-singular and cyclic universe from the modified GUP

In this paper, we investigate the effects of a new version of the generalized uncertainty principle (modified GUP) on the dynamics of the Universe. As the modified GUP will modify the relation between the entropy and area of the apparent horizon, it will also deform the Friedmann equations within Jacobson's approach. We explicitly find these deformed Friedmann equations governing the modified GUP-corrected dynamics of such a Universe. It is shown that the modified GUP-deformed Jacobson's approach implies an upper bound for the density of such a Universe. The Big Bang singularity can therefore also be avoided using the modified GUP-corrections to horizons' thermodynamics. In fact, we are able to analyze the pre Big Bang state of the Universe. Furthermore, the equations imply that the expansion of the Universe will come to a halt and then will immediately be followed by a contracting phase. When the equations are extrapolated beyond the maximum rate of contraction, a cyclic Universe scenario emerges.
E2CM: Early Exit via Class Means for Efficient Supervised and Unsupervised Learning

State-of-the-art neural networks with early exit mechanisms often need considerable amount of training and fine tuning to achieve good performance with low computational cost. We propose a novel early exit technique, Early Exit Class Means (E2CM), based on class means of samples. Unlike most existing schemes, ECM does not require gradient-based training of internal classifiers and it does not modify the base network by any means. This makes it particularly useful for neural network training in low-power devices, as in wireless edge networks. We evaluate the performance and overheads of E2CM over various base neural networks such as MobileNetV3, EfficientNet, ResNet, and datasets such as CIFAR-100, ImageNet, and KMNIST. Our results show that, given a fixed training time budget, E2CM achieves higher accuracy as compared to existing early exit mechanisms. Moreover, if there are no limitations on the training time budget, E2CM can be combined with an existing early exit scheme to boost the latter's performance, achieving a better trade-off between computational cost and network accuracy. We also show that E2CM can be used to decrease the computational cost in unsupervised learning tasks.
When Dynamic Causality Comes to Graph-Temporal Neural Network

Spatial-temporal data forecasting is a core task in many applications, and traffic forecasting is a typical example. Researchers have proposed various methods to explore spatial and temporal characteristics to improve forecasting accuracy, including the recently emerging graph convolution networks. However, most of them only consider the road network's prior knowledge or the graph's static characteristics and thus ignore the dynamic and deeper information hidden in the data. This paper presents a novel module based on dynamic causality analysis and graph convolution to integrate statistical theories and deep learning for better capturing spatial dependencies. Then we apply the module to two specific models. In each model, we introduce the causality adjacency matrix computed by the proposed algorithm into the conventional graph convolution network to reveal the dynamic correlations between nodes in the road network. The temporal neural network is then applied to extract temporal correlations. Extensive experiments demonstrate the superiority of our method, which achieves state-of-the-art prediction accuarcy on two public transportation data sets.
Fractional-Order Learning Systems

From the inaugural steps of McCulloch and Pitts to put forth a composition for an electrical brain, that combined with the conception of an adaptive leaning mechanism by Widrow and Hoff has given rise to the phenomena of intelligent machines, machine learning techniques have gained the status of a miracle solution in a myriad of scientific fields. At the heart of these techniques lies iterative optimisation processes that are derived based on first, and in some cases, second-order derivatives. This manuscript, however, aims to expand the mentioned framework to that of using fractional-order derivatives. The entire format of adaptation is revised form the perspective of fractional-order calculus and the appropriate framework for taking full advantage of the fractional-order calculus in learning and adaptation paradigms is formulated. For rigour, the structure of behavioural analysis and performance prediction of this novel class of learning machines is also forged.
A weak-value model for virtual particles supplying the electric current in graphene: the minimal conductivity and the Schwinger mechanism

We propose a model for the electric current in graphene in which electric carriers are supplied by virtual particles allowed by the uncertainty relations. The process to make a virtual particle real is described by a weak value of a group velocity: the velocity is requisite for the electric field to give the virtual particle the appropriate changes of both energy and momentum. With the weak value, we approximately estimate the electric current, considering the ballistic transport of the electric carriers. The current shows the quasi-Ohmic with the minimal conductivity of the order of e 2 / h ? > per channel. Crossing a certain ballistic time scale, it is brought to obey the Schwinger mechanism.
Machine learning techniques for explaining air pollution prediction

Explaining the results provided by a machine learning model is crucial in the context of its application in prediction problems. Unfortunately, in itself, it is usually a black box whose interpretation is very difficult. The prediction output generated by the ML model is clear, while its significance - not often sufficient. However, such an outcome can be explained by applying the Shapley analysis. In the current study, this analysis, along with the random forest model, will be used to explain the problem of air pollution prediction measured by the particulate matter 10 (PM10) indicator. The input data will be the meteorological information recorded from October 2021 to December 2021 in four European cities: London, Barcelona, Berlin and Krakow. On the basis of the obtained results, it will be shown what factors and how affect the level of PM10. The presented study stresses the importance of air pollution investigation and contributes to the increase of ecological awareness.
A Self Learning System for Emotion Awareness and Adaptation in Humanoid Robots

Humanoid robots provide a unique opportunity for personalized interaction using emotion recognition. However, emotion recognition performed by humanoid robots in complex social interactions is limited in the flexibility of interaction as well as personalization and adaptation in the responses. We designed an adaptive learning system for real-time emotion recognition that elicits its own ground-truth data and updates individualized models to improve performance over time. A Convolutional Neural Network based on off-the-shelf ResNet50 and Inception v3 are assembled to form an ensemble model which is used for real-time emotion recognition through facial expression. Two sets of robot behaviors, general and personalized, are developed to evoke different emotion responses. The personalized behaviors are adapted based on user preferences collected through a pre-test survey. The performance of the proposed system is verified through a 2-stage user study and tested for the accuracy of the self-supervised retraining. We also evaluate the effectiveness of the personalized behavior of the robot in evoking intended emotions between stages using trust, empathy and engagement scales. The participants are divided into two groups based on their familiarity and previous interactions with the robot. The results of emotion recognition indicate a 12% increase in the F1 score for 7 emotions in stage 2 compared to pre-trained model. Higher mean scores for trust, engagement, and empathy are observed in both participant groups. The average similarity score for both stages was 82% and the average success rate of eliciting the intended emotion increased by 8.28% between stages, despite their differences in familiarity thus offering a way to mitigate novelty effect patterns among user interactions.