Miracle-dz/UniFAR-FTUs huggingface dataset:
Chromosome aberrations, micronucleus frequency, and catalase concentration in a population chronically exposed to high levels of radon

Abstract Purpose To deepen our knowledge on the effects of high levels of indoor radon exposure, we assessed the frequencies of unstable and stable chromosome aberrations and micronucleus (MN), as well as the concentration of an endogenous antioxidant (catalase, CAT), in blood samples of individuals chronically exposed to high indoor radon concentrations in Indonesia (Tande-Tande sub-village, Mamuju, West Sulawesi). Moreover, we also investigated the occurrence of a radio-adaptive response (RAR) in Tande-Tande sub-village inhabitants using the G2 MN assay. Materials and Methods The frequencies of dicentric (DC), acentric (AF), ring (R), and translocation (Tr) chromosomes in Tande-Tande inhabitants were compared to those in people living in a reference area with low levels of indoor radon levels (Topoyo village, Indonesia). The number of MN per 1000 binucleated cells (BNC) and CAT concentration per total protein was quantified and compared between groups. Lastly, we irradiated (2 Gy) phytohemagglutinin-stimulated samples in vitro and measured the frequency of MN to verify the occurrence of a RAR in Tande-Tande sub-village inhabitants. Results and Conclusion The frequencies of DC, AF, and Tr did not differ between Tande-Tande inhabitants and control subjects (p = 0.350, 0.521, 0.597). The frequency of MN in Tande-Tande inhabitants was significantly lower than that in the control group (p = 0.006). Similarly, CAT concentration in Tande-Tande inhabitants was also significantly lower than that in the control population (p < 0.001). Significant negative correlations were identified for MN number and CAT concentration versus indoor radon concentration, annual effective dose, or cumulative dose both within groups and when all data were analyzed together. Our findings indicate that, despite the high indoor radon levels, Tande-Tande inhabitants are not under oxidative stress, since this group had lower CAT concentration and MN frequency than those in the control group. The negative correlation between MN frequency and indoor radon concentration, annual effective dose, and cumulative dose suggests the occurrence of an RAR phenomenon in Tande-Tande sub-village inhabitants. This interpretation is also supported by the results of the G2 MN assay, which revealed lower MN frequencies after in vitro irradiation of samples from Tande-Tande sub-village inhabitants than those in samples from the control group (p = 0.0069, for cumulative MN frequency; p = 0.0146, for radiation-induced MN only).
HOST-ATS: automatic thumbnail selection with dashboard-controlled ML pipeline and dynamic user survey

We present HOST-ATS, a holistic system for the automatic selection and evaluation of soccer video thumbnails, which is composed of a dashboard-controlled machine learning (ML) pipeline, and a dynamic user survey. The ML pipeline uses logo detection, close-up shot detection, face detection, image quality prediction, and blur detection to automatically select thumbnails from soccer videos in near real-time, and can be configured via a graphical user interface. The web-based dynamic user survey can be employed to qualitatively evaluate the thumbnails selected by the pipeline. The survey is fully configurable and easy to update via continuous integration, allowing for the dynamic aggregation of participant responses to different sets of multimedia assets. We demonstrate the configuration and execution of the ML pipeline via the custom dashboard, and the agile (re-)deployment of the user survey via Firebase and Heroku cloud service integrations, where the audience can interact with configuration parameter updates in real-time. Our experience with HOST-ATS shows that an automatic thumbnail selection system can yield highly attractive highlight clips, and can be used in conjunction with existing soccer broadcast practices in real-time.
Federated learning to understand human emotions via smart clothing: research proposal

This paper contains the research proposal of Mary Pidgeon that was presented at the MMSys 2022 doctoral symposium. Emotion recognition from physiological signals has seen a huge growth in recent decades. Wearables such as smart watches now have sensors to accurately measure physiological signals such as electrocardiography (ECG), blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature (ST). These sensors have also been embedded in textiles. Collaborative body sensor networks (CBSN) have been used to analyse emotion reactions in a social setting from heart rate sensors. Federated learning, a recently proposed machine learning paradigm, protects user's private information while using information from several users to train a global machine learning model. Federated learning has several categorisations based on data partitioning, the privacy mechanisms, machine learning models and methods for solving heterogeneity. In this doctoral thesis, we propose using a smart clothing body sensor network to collect peripheral physiological data while protecting the user's privacy using federated machine learning. We present three primary research questions to address the challenges in emotion prediction, data collection from e-textile sensors and federated (FL) learning.
3DeformR: freehand 3D model editing in virtual environments considering head movements on mobile headsets

3D objects are the primary media in virtual reality environments in immersive cyberspace, also known as the Metaverse. Users, through editing such objects, can communicate with other individuals on mobile headsets. Knowing that the tangible controllers cause the burden to carry such addendum devices, the body-centric interaction techniques, such as hand gestures, get rid of such burdens. However, object editing with hand gestures is usually overlooked. Accordingly, we propose and implement a palm-based virtual embodiment for hand gestural model editing, namely 3DeformR. We employ three optimized hand gestures on bi-harmonic deformation algorithms that enable selecting and editing 3D models in fine granularity. Our evaluation with nine participants considers three interaction techniques (two-handed tangible controller (OMC), a naive implementation of hand gestures (SH), and 3DeformR. Two experimental tasks of planar and spherical objects imply that 3DeformR outperforms SH, in terms of task completion time (~51%) and required actions (~17%). Also, our participants with 3DeformR make significantly better performance than the commercial standard (OMC) - saved task time (~43%) and actions (~3%). Remarkably, the edited objects by 3DeformR show no discernible difference from those with tangible controllers characterised by accurate and responsive detection.
Virtual visits: life-size immersive communication

Elderly people in care homes face a great lack of contact with their families and loved ones. Social isolation and loneliness are detrimental factors for older people in their health condition, cognitive impairment and quality of life. We previously presented a communication system that facilitated high-quality mediated social contact through an Augmented Reality tool with 3D user capture and rendering on an iPad. We have further enhanced this system and propose a new demonstrator that provides a working communication system between the elderly and family members on a life-size display. A complete end-to-end chain architecture is presented, where capture, transmission, and rendering are enhanced to provide a tool for natural interaction and high social presence. Our system is technically ready and well-adapted to meet the needs of elderly users.
Functional modelling and prototyping of electronic integrated kinetic energy harvesters

The aim of developing infinite-life autonomous wireless electronics, powered by the energy of the surrounding environment, drives the research efforts in the field of Energy Harvesting. Electromagnetic and piezoelectric techniques are deemed to be the most attractive technologies for vibrational devices. In the thesis, both these technologies are investigated taking into account the entire energy conversion chain. In the context of the collaboration with the STMicroelectronics, the project of a self-powered Bluetooth step counter embedded in a training shoe has been carried out. A cylindrical device 27 × 16mm including the transducer, the interface circuit, the step-counter electronics and the protective shell, has been developed. Environmental energy extraction occurs exploiting the vibration of a permanent magnet in response to the impact of the shoe on the ground. A self-powered electrical interface performs maximum power transfer through optimal resistive load emulation and load decoupling. The device provides 360 μJ to the load, the 90% of the maximum recoverable energy. The energy requirement is four time less than the provided and the effectiveness of the proposed device is demonstrated also considering the foot-steps variability and the performance spread due to prototypes manufacturing. In the context of the collaboration with the G2Elab of Grenoble and STMicroelectronics, the project of a piezoelectric energy arvester has been carried out. With the aim of exploiting environmental vibrations, an uni-morph piezoelectric cantilever beam 60×25×0.5mm with a proof mass at the free-end has been designed. Numerical results show that electrical interfaces based on SECE and sSSHI techniques allows increasing performance up to the 125% and the 115% of that in case of STD interface. Due to the better performance in terms of harvested power and in terms of electric load decoupling, a self-powered SECE interface has been prototyped. In response to 2 m/s2 56,2 Hz sinusoidal input, experimental power recovery of 0.56mW is achieved demonstrating that the device is compliant with standard low-power electronics requirements.
Swipe along: a measurement study of short video services

Short videos have recently emerged as a popular form of short-duration User Generated Content (UGC) within modern social media. Short video content is generally less than a minute long and predominantly produced in vertical orientation on smartphones. While still fundamentally being streaming, short video delivery is distinctly characterized by the deployment of a mechanism that pre-loads ahead of user request. Background pre-loading aims to eliminate start-up time, which is now prioritized higher in Quality of Experience (QoE) objectives, given that the application design facilitates instant 'swiping' to the next video in a recommended sequence. In this work, we provide a comprehensive comparison of four popular short video services. In particular, we explore content characteristics and evaluate the video quality across resolutions for each service. We next characterize the pre-loading policy adopted by each service. Last, we conduct an experimental study to investigate data consumption and evaluate achieved QoE under different network scenarios and application configurations.
Groundwater quality evaluation using water quality index (WQI) and human health risk (HHR) assessment in Herat aquifer, west Afghanistan

Abstract Numerous political and economic crises, and successive wars in Afghanistan have put safe water resources at risk due to mismanagement in the country. In this study, groundwater quality in the Herat aquifer, western Afghanistan is evaluated by geochemical methods and quality indices. Total of 27 samples were collected and analyzed for nitrate, fluoride and trace elements. Sodium and bicarbonate are the main cation and anion, respectively. Various diagrams revealed that rock weathering and normal ion exchange may controls the major ion chemistry. Granitic or biotite gneiss rocks and leachate from the Herat urban sewage are the main sources of fluoride and nitrate, respectively. Geogenic source of the studied parameters was verified by principal component analysis (PCA). Groundwater is contaminated by F, As and U in the Northwest of the region, and by nitrate almost in the majority of samples, especially in urban areas. Water quality index (WQI) showed poor quality for 29.6% of the samples, and the best quality was observed in Southwest of the area. Also, based on heavy metal evaluation index (HEI), the highest pollution was observed in Northwestern region. Nitrate poses a serious health risk to consumers (particularly in central parts), so that non-carcinogenic risk (HQ) values ranged from 1.1 to 3 in most of the samples, and very high (> 3) non-carcinogenic risk values were observed in 4 sampling sites.
A Framework for Categorising AI Evaluation Instruments

The current and future capabilities of Artificial Intelligence (AI) are typically assessed with an ever increasing number of benchmarks, competitions, tests and evaluation standards, which are meant to work as AI evaluation instruments (EI). These EIs are not only increasing in number, but also in complexity and diversity, making it hard to understand this evaluation landscape in a meaningful way. In this paper we present an approach for categorising EIs using a set of 18 facets , accompanied by a rubric to allow anyone to apply the framework to any existing or new EI. We apply the rubric to 23 EIs in different domains through a team of raters, and analyse how consistent the rubric is and how well it works to distinguish between EIs and map the evaluation landscape in AI.
Analysis and Operation of DePFET X-ray Imaging Detectors

The latest pixel sensor for X-ray imaging spectroscopy is the DePFET. This type of sensor in which each pixel has an integrated amplifier shows excellent energy resolution, low noise readout at high speed, and low power consumption. In the scope of this thesis a data analysis framework for DePFET detectors has been developed. The software was used to investigate and qualify different structural variants of DePFET detectors. Algorithms to examine detector effects and methods to correct them were developed and integrated into the software. The modular design of the software made its adaption to new detector types and analysis methods easy. The software was especially used to qualify prototypes for the Mercury Imaging X-ray Spectrometer (MIXS). The detectors were studied with respect to linearity, charge collection and detection efficiency.
Towards lifelong learning of Recurrent Neural Networks for control design

This paper proposes a method for lifelong learning of Recurrent Neural Networks, such as NNARX, ESN, LSTM, and GRU, to be used as plant models in control system synthesis. The problem is significant because in many practical applications it is required to adapt the model when new information is available and/or the system undergoes changes, without the need to store an increasing amount of data as time proceeds. Indeed, in this context, many problems arise, such as the well known Catastrophic Forgetting and Capacity Saturation ones. We propose an adaptation algorithm inspired by Moving Horizon Estimators, deriving conditions for its convergence. The described method is applied to a simulated chemical plant, already adopted as a challenging benchmark in the existing literature. The main results achieved are discussed.
Resilient Distributed MPC Algorithm for Microgrid Energy Management under Uncertainties

This paper proposes a resilient distributed energy management algorithm able to cope with different types of faults in a DC microgrid system. A distributed optimization method allows to solve the energy management problem without sharing any private data with the network and reducing the computational cost for each agent, with respect to the centralised case. A distributed MPC scheme based on distributed optimization is used to cope with uncertainty that characterizes the microgrid operation. In order to be resilient to faults that limit the amount of power available to consumers, we propose to adaptively store an amount of power in the storage systems to support the loads. A soft constraint on the minimum energy stored in each battery is introduced for feasibility and to cope with persistent faults. The effectiveness of the method is proved by extensive simulation results considering faults on three types of components: renewable generator, distribution grid and communication network.
Energy efficient on-chip optical broadcast with partial-absorption photodiodes

As we enter in the 1000-core era, broadcast-enabled interconnects are needed to synchronize processors and maintain cache coherency. While silicon photonics is a promising technology to deliver high bandwidth communications, there are currently no photonic links enabling energy efficient broadcast. Indeed, existing solutions rely on thermally sensitive microring resonators that require continuous calibration. We propose an on-chip optical link dedicated to broadcast that relies on partial-absorption photodiodes, which allow to evenly distribute the broadcasted signal power to all receivers. Since a single wavelength is used, no ring resonators are required, thus allowing a 39% reduction in the broadcast energy consumption.
Intelligent Wind Farm Control via Grouping-Based Reinforcement Learning

This paper aims to maximize the total power generation for wind farms subject to strong wake effects and stochastic inflow wind speeds. A data-driven control method that only requires the accessible measurements of every turbine in the farm is proposed via deep reinforcement learning (DRL). We employ a grouping strategy to mitigate the high computational complexity induced by DRL and enhance our method's applicability to large-scale wind farms. Based on the levels of aerodynamic interactions among turbines, this grouping strategy divides the whole farm into small sub-groups. Therefore, one can execute DRL on these sub-groups instead of carrying on a complicated learning process for the entire farm. Simulations verify the advantages of the proposed DRL-based wind farm control method over the commonly employed greedy strategy. Results also show that the proposed method can significantly reduce the overall computing cost compared with the direct execution of DRL on the whole wind farm.
Learning-Based Iterative Optimal Control for Unknown Systems Using Gaussian Process Regression

In this paper, we propose an iterative learning-based optimal control strategy for unknown systems. The system model is assumed to be initially unknown and learned by the Gaussian process regression with the historical data collected in the previous iterations. To impose the constant improvement on the control performance and strict constraint satisfaction on the state of the system, we derive a multi-step ahead deterministic bound of the error between the prediction via a learned model and the state of the system, and then use it in the control design. The result from the numerical experiment shows the effectiveness of our method.
Combining Offline and Online Machine Learning to Estimate State of Health of Lithium-Ion Batteries

This article reports a new state of health (SOH) estimation method for lithium-ion batteries using machine learning. Practical problems with cell inconsistency and online implementability are addressed using a proposed individualized estimation scheme that blends a model migration method with ensemble learning. A set of candidate models, based on slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are first trained offline by choosing a single-point feature on the incremental capacity curve as the model input. For online operation, the prediction errors due to cell inconsistency in the target new cell are next mitigated by a proposed modified random forest regression (mRFR) for high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably high SOH estimation accuracy with only a small amount of early data and online measurements are needed for practical operation.
Effects of Prandtl number on mixing process in miscible Rayleigh‐Taylor instability: A lattice Boltzmann study

Purpose – The purpose of this paper is to investigate, numerically, the effects of the Prandtl number on the mixing process in two‐dimensional Rayleigh‐Taylor instability of incompressible and miscible fluids.Design/methodology/approach – The simulations are carried out based on a double‐distribution‐function lattice Boltzmann method in which the Prandtl number can be adjusted.Findings – The simulations reveal that the mixing‐zone grows inversely with increasing Prandtl number, but the processes of Rayleigh‐Taylor instability are nearly identical in terms of a dimensionless time as the Prandtl number ranges from 0.1 to 10. The symmetric property of the mixing‐zone is also studied, which is found to be closely dependent on the symmetry of the initial perturbations.Originality/value – The results here show that the growth of the mixing‐zone is related to the Prandtl number, whereas most previous studies have been focused on the relationship between the growth of the mixing zone and time with a constant Pran.
The energy balance of the solar transition region

It is shown how the observed distribution of the emission measure with temperature can be used to limit the range of energy deposition functions suitable for heating the solar transition region and inner corona. The minimum energy loss solution is considered in view of the work by Hearn (1975) in order to establish further scaling laws between the transition region pressure, the maximum coronal temperature and the parameter giving the absolute value of the emission measure. Also discussed is the absence of a static energy balance at the base of the transition region in terms of measurable atmospheric parameters, and the condition for a static energy balance is given. In addition, the possible role of the emission from He II in stabilizing the atmosphere by providing enhanced radiation loss is considered.
An Analysis of Computational Complexity of Various Implementation Techniques of MMSE Demodulation Algorithm

Currently, the demodulation algorithm optimal for the minimum mean squared error criterion (MMSE algorithm) is increasingly used in communication systems. However, as the dimension of this algorithm increases, it is obviously difficult to implement it in practice. The development of modern communication systems requires an increasingly high dimension of the MMSE algorithm. A number of possible methods for implementing the MMSE algorithm are known, among which those that allow the algorithm itself to be implemented accurately, i.e. without loss in noise immunity, can be distinguished. An analysis of the computational complexity of various embodiments of the MMSE algorithm was carried out: the implementation of the MMSE algorithm in the form of a Kalman filter; implementing the MMSE algorithm using the Frobenius bordering method; implementation of the MMSE algorithm using the Frobenius method with division of dimension in half. As a criterion of computational complexity, the total number of arithmetic operations (multiplications, additions and subtractions, as well as divisions) is used, which is necessary for the exact implementation of the analyzed algorithm. Implementation using the Frobenius bordering method allows you to gain a computational total of about 15% compared to implementation using the Frobenius method with division of dimension in half; and a gain of about 25% over the Kalman filter implementation.