Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Proximity Estimation for Contact Tracing using a Multi-channel Approach

—Due to the COVID 19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use Bluetooth Low Energy (BLE) signals to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does not always deliver accurate results. In this paper, we present a multi-channel approach to improve proximity estimation, and a novel, publicly available data set that contains matched IEEE 802.11 (2.4 GHz and 5 GHz) and BLE signal strength data, measured in four different environments. We have developed and evaluated a combined classiﬁcation model based on BLE and IEEE 802.11 signals. Our approach signiﬁcantly improves the distance estimation and consequently also the contact tracing accuracy. We are able to achieve good results with our approach in everyday public transport scenarios. However, in our implementation based on IEEE 802.11 probe requests, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.
TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network

The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. However, long-range dependencies are directly neglected in existing CNN fusion approaches, impeding balancing the entire image-level perception for complex scenario fusion. In this paper, therefore, we propose an infrared and visible image fusion algorithm based on the transformer module and adversarial learning. Inspired by the global interaction power, we use the transformer technique to learn the effective global fusion relations. In particular, shallow features extracted by CNN are interacted in the proposed transformer fusion module to refine the fusion relationship within the spatial scope and across channels simultaneously. Besides, adversarial learning is designed in the training process to improve the output discrimination via imposing competitive consistency from the inputs, reflecting the specific characteristics in infrared and visible images. The experimental performance demonstrates the effectiveness of the proposed modules, with superior improvement against the state-of-the-art, generalising a novel paradigm via transformer and adversarial learning in the fusion task.
Effect of two-stage shattered pellet injection on tokamak disruptions

An effective disruption mitigation system in a tokamak reactor should limit the exposure of the wall to localized heat losses and to the impact of high current runaway electron beams, and avoid excessive forces on the structure. We evaluate with respect to these aspects a two-stage deuterium–neon shattered pellet injection in an ITER-like plasma, using simulations with the DREAM framework (Hoppe et al 2021 Comput. Phys. Commun. 268 108098). To minimize the obtained runaway currents an optimal range of injected deuterium quantities is found. This range is sensitive to the opacity of the plasma to Lyman radiation, which affects the ionization degree of deuterium, and thus avalanche runaway generation. The two-stage injection scheme, where dilution cooling is produced by deuterium before a radiative thermal quench caused by neon, reduces both the hot-tail seed and the localized transported heat load on the wall. However, during nuclear operation, additional runaway seed sources from the activated wall and tritium make it difficult to reach tolerably low runaway currents.
Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

Medical entity extraction (EE) is a standard procedure used as a first stage in medical texts processing. Usually Medical EE is a two-step process: named entity recognition (NER) and named entity normalization (NEN). We propose a novel method of doing medical EE from electronic health records (EHR) as a single-step multi-label classification task by fine-tuning a transformer model pretrained on a large EHR dataset. Our model is trained end-to-end in an distantly supervised manner using targets automatically extracted from medical knowledge base. We show that our model learns to generalize for entities that are present frequently enough, achieving human-level classification quality for most frequent entities. Our work demonstrates that medical entity extraction can be done end-to-end without human supervision and with human quality given the availability of a large enough amount of unlabeled EHR and a medical knowledge base.
Reconfiguration of a satellite constellation in circular formation orbit with decentralized model predictive control

Satellite constellation missions, consisting of a large number of spacecraft, are increasingly being launched or planned. Such missions require novel control approaches, in particular for what concerns orbital phasing maneuvers. In this context, we consider the problem of reconfiguration of a satellite constellation in a circular formation. In our scenario, a formation of equally spaced spacecraft need to undergo an autonomous reconfiguration due to the deorbiting of a satellite in the formation. The remaining spacecraft have to reconfigure to form again an equidistant formation. To achieve this goal, we consider two decentralized strategies that rely on different sets of information about the neighboring spacecraft in the formation. In the fully decentralized case, each controller knows only the current states of each spacecraft, i.e. position and velocity, while in the second decentralized strategy with with information sharing, the entire planned nominal trajectory of each spacecraft is available to its neighbors. Our numerical simulation results show that, by increasing the amount of information available to each spacecraft, faster reconfiguration maneuvers with smaller fuel consumption can be achieved.
Study on the Excitation Circuit for Low Perming Error Fluxgate Sensor

An effective method for low perming error of fluxgates is to enlarge the peak value of the excitation current,but large amplitude excitation will increase the power consumption.A narrow pulse excitation current with sufficient peak value can lower perming error with low power consumption but reduce the sensitivity and increase the difficulty of the signal processing.The idealized excitation waveform with high ratio of peak value to RMS value by careful excitation tuning is an efficient solution.This paper presents a circuit that generates an excitation current waveform with high peak value and low RMS value for fluxgates.Different from the tuned excitation circuit,the new circuit is a combination of an integrator and a differentiator with the same squarewave input.The excitation signal is the sum of a triangle wave and a sharp narrow pulse signal.It is easy to adjust the peak value of the narrow pulses for perming error study and no careful tune adjusting is needed.In case of the fixed peak value of the narrow pulses,an improved excitation circuit with only two resistors,two capacitors and one amplifier is also presented in the paper.The experiment results show that 0.5nT perming error can be achieved for 10 mT shock.
Using Deep Reinforcement Learning for Zero Defect Smart Forging

Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces revenue and negatively impacts sustainability and the environment. An essential reason for material waste is a low degree of automation, especially in industries that currently have a low degree of digitalization, such as steel forging. Those industries typically rely on heavy and old machinery such as large induction ovens that are mostly controlled manually or using well-known recipes created by experts. However, standard recipes may fail when unforeseen events happen, such as an unplanned stop in production, which may lead to overheating and thus material degradation during the forging process. In this paper, we develop a digital twin-based optimization strategy for the heating process for a forging line to automate the development of an optimal control policy that adjusts the power for the heating coils in an induction oven based on temperature data observed from pyrometers. We design a digital twin-based deep reinforcement learning (DTRL) framework and train two different deep reinforcement learning (DRL) models for the heating phase using a digital twin of the forging line. The twin is based on a simulator that contains a heating transfer and movement model, which is used as an environment for the DRL training. Our evaluation shows that both models significantly reduce the temperature unevenness and can help to automate the traditional heating process.
A Latent Class Modeling Approach for Generating Synthetic Data and Making Posterior Inferences from Differentially Private Counts

Several algorithms exist for creating differentially private counts from contingency tables, such as two-way or three-way marginal counts. The resulting noisy counts generally do not correspond to a coherent contingency table, so that some post-processing step is needed if one wants the released counts to correspond to a coherent contingency table. We present a latent class modeling approach for post-processing differentially private marginal counts that can be used (i) to create differentially private synthetic data from the set of marginal counts, and (ii) to enable posterior inferences about the confidential counts. We illustrate the approach using a subset of the 2016 American Community Survey Public Use Microdata Sets and the 2004 National Long Term Care Survey.
Quantum vacuum processes in the extremely intense light of relativistic plasma mirror sources

The advent of petawatt-class laser systems allows generating electromagnetic fields of unprecedented strength in a controlled environment, driving increasingly more efforts to probe yet unobserved processes through their interaction with the quantum vacuum. Still, the lowest intensity scale governing these effects lies orders of magnitude beyond foreseen capabilities, so that such endeavor is expected to remain extremely challenging. In recent years, however, plasma mirrors have emerged as a promising bridge across this gap, by enabling the conversion of intense infrared laser pulses into coherently focused Doppler harmonic beams lying in the X-UV range. In this work, we present predictions on the quantum vacuum signatures produced when such beams are focused to intensities between 1024 and 1028 W cm−2, specifically photon–photon scattering and electron–positron pair creation. These signatures are computed via the stimulated vacuum formalism, combined with a model of perfectly focused beam built from PIC-generated harmonics spectra, and implemented on state-of-the-art massively parallel numerical tools. In view of identifying experimentally favorable configurations, we also consider the coupling of the focused harmonic beam with an auxiliary optical beam, and provide comparison with other established schemes. Our results show that a single coherently focused harmonic beam can produce as much scattered photons as two infrared pulses in head-on collision, and confirm that the coupling of the harmonic beam to an auxiliary beam gives rise to significant levels of inelastic scattering, and hence holds the potential to strongly improve the attainable signal to noise ratios in experiments.
Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems

Social choice functions help aggregate individual preferences while differentially private mechanisms provide formal privacy guarantees to release answers of queries operating on sensitive data. However, preserving differential privacy requires introducing noise to the system, and therefore may lead to undesired byproducts. Does an increase in the level of differential privacy for releasing the outputs of social choice functions increase or decrease the level of influence and welfare, and at what rate? In this paper, we mainly address this question in more precise terms in a referendum setting with two candidates when the celebrated randomized response mechanism is used. We show that there is an inversely-proportional relation between welfare and privacy, and also influence and privacy.
A Sybil-Resistant and Decentralized Market Place

Existing centralised market places such as Ebay enable companies to gather large amounts of personal data that can be used to manipulate users. Furthermore, users can frequently perform fraud without severe consequence. Reputation systems only solve this problem partially as malicious users can re-join the network with a new identity if their reputation is too low. By performing a Sybil attack, i.e., joining with multiple seemingly distinct identities, malicious participants can further boost their own reputation. In this paper, we present MarketPalace. MarketPalace relies on a peer-to-peer infrastructure to realize a decentralized market place during trading. Only when registering, users communicate with a central server to verify that they are not Sybils. More concretely, our system leverages self-sovereign identity to detect and undermine repeated joins by the same user. We implemented MarketPalace and demonstrated its feasibility for small regional markets.
Polar Coded Computing: The Role of the Scaling Exponent

We consider the problem of coded distributed computing using polar codes. The average execution time of a coded computing system is related to the error probability for transmission over the binary erasure channel in recent work by Soleymani, Jamali and Mahdavifar, where the performance of binary linear codes is investigated. In this paper, we focus on polar codes and unveil a connection between the average execution time and the scaling exponent μ of the family of codes. In the finite-length characterization of polar codes, the scaling exponent is a key object capturing the speed of convergence to capacity. In particular, we show that (i) the gap between the normalized average execution time of polar codes and that of optimal MDS codes is O(n–1/μ), and (ii) this upper bound can be improved to roughly O(n–1/2) by considering polar codes with large kernels. We conjecture that these bounds could be improved to O(n–2/μ) and O(n–1), respectively, and provide a heuristic argument as well as numerical evidence supporting this view.
Online Actuator Selection and Controller Design for Linear Quadratic Regulation over a Finite Horizon

We study the simultaneous actuator selection and controller design problem for linear quadratic regulation over a ﬁnite horizon, when the system matrices are unknown a priori. We propose an online actuator selection algorithm to solve the problem which speciﬁes both a set of actuators to be utilized and the control policy corresponding to the set of selected actuators. Speciﬁcally, our algorithm is a model based learning algorithm which maintains an estimate of the system matrices using the system trajectories. The algorithm then leverages an algorithm for the multiarmed bandit problem to determine the set of actuators under an actuator selection budget constraint and also identiﬁes the corresponding control policy that minimizes a quadratic cost based on the estimated system matrices. We show that the proposed online actuator selection algorithm yields a sublinear regret.
MonarchNet: Differentiating Monarch Butterflies from Butterflies Species with Similar Phenotypes

In recent years, the monarch butterfly's iconic migration patterns have come under threat from a number of factors, from climate change to pesticide use. To track trends in their populations, scientists as well as citizen scientists must identify individuals accurately. This is uniquely key for the study of monarch butterflies because there exist other species of butterfly, such as viceroy butterflies, that are"look-alikes"(coined by the Convention on International Trade in Endangered Species of Wild Fauna and Flora), having similar phenotypes. To tackle this problem and to aid in more efficient identification, we present MonarchNet, the first comprehensive dataset consisting of butterfly imagery for monarchs and five look-alike species. We train a baseline deep-learning classification model to serve as a tool for differentiating monarch butterflies and its various look-alikes. We seek to contribute to the study of biodiversity and butterfly ecology by providing a novel method for computational classification of these particular butterfly species. The ultimate aim is to help scientists track monarch butterfly population and migration trends in the most precise and efficient manner possible.
Motion Estimation and Compensation in Automotive MIMO SAR

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle’s ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-called autofocus algorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocus requirements in typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets’ localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration.
ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals

Recently, there has been a surge of significant interest on application of Deep Learning (DL) models to autonomously perform hand gesture recognition using surface Electromyogram (sEMG) signals. Many of the existing DL models are, however, designed to be applied on sparse sEMG signals. Furthermore, due to the complex structure of these models, typically, we are faced with memory constraint issues, require large training times and a large number of training samples, and; there is the need to resort to data augmentation and/or transfer learning. In this paper, for the first time (to the best of our knowledge), we investigate and design a Vision Transformer (ViT) based architecture to perform hand gesture recognition from High Density (HD-sEMG) signals. Intuitively speaking, we capitalize on the recent breakthrough role of the transformer architecture in tackling different com-plex problems together with its potential for employing more input parallelization via its attention mechanism. The proposed Vision Transformer-based Hand Gesture Recognition (ViT-HGR) framework can overcome the aforementioned training time problems and can accurately classify a large number of hand gestures from scratch without any need for data augmentation and/or transfer learning. The efficiency of the proposed ViT-HGR framework is evaluated using a recently-released HD-sEMG dataset consisting of 65 isometric hand gestures. Our experiments with 64-sample (31.25 ms) window size yield average test accuracy of 84.62 ± 3.07%, where only 78,210 learnable parameters are utilized in the model. The compact structure of the proposed ViT-based ViT-HGR framework (i.e., having significantly reduced number of trainable parameters) shows great potentials for its practical application for prosthetic control.
Seismic behavior analysis of intake tower on the south bank of Xiaolangdi reservoir

The intake tower on the south bank of Xiaolangdi Reservoir is a big concrete structure with complex shape and boundary conditions.By use of three dimensional finite element method,the static and dynamic analysis of the intake tower are completed for six loading cases respectively and the results of stresses and displacements are presented. Then,the results of finite element method are compared with those of structure mechanics method. Under the earthquake loads, the deformations of intake tower satisfy the requirement of structure design. The maximum internal forces at vulnerable position of the intake tower are great,so some special treatments are needed to keep safety of the intake tower. The results in this paper may be served as the basis of structure design of the intake tower. 
Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics

Algorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI. Training these models requires a large labeled dataset and considerable computational resources, which are not readily available in many domains. Also, it is difficult to explore the internal representations and reasoning mechanisms of these models. As a step towards addressing the underlying knowledge representation, reasoning, and learning challenges, the architecture described in this paper draws inspiration from research in cognitive systems. As a motivating example, we consider an assistive robot trying to reduce clutter in any given scene by reasoning about the occlusion of objects and stability of object configurations in an image of the scene. In this context, our architecture incrementally learns and revises a grounding of the spatial relations between objects and uses this grounding to extract spatial information from input images. Non-monotonic logical reasoning with this information and incomplete commonsense domain knowledge is used to make decisions about stability and occlusion. For images that cannot be processed by such reasoning, regions relevant to the tasks at hand are automatically identified and used to train deep network models to make the desired decisions. Image regions used to train the deep networks are also used to incrementally acquire previously unknown state constraints that are merged with the existing knowledge for subsequent reasoning. Experimental evaluation performed using simulated and real-world images indicates that in comparison with baselines based just on deep networks, our architecture improves reliability of decision making and reduces the effort involved in training data-driven deep network models.