Miracle-dz/UniFAR-FTUs huggingface dataset:
GaAs radiation detectors

This thesis presents the results obtained with radiation detectors made with Schottky barrier contacts to GaAs. Three types of detectors are investigated, namely pads, microstrips and pixellated detectors on both semi-insulating un-doped material as well as on epitaxial material. Tests, devised and carried out by the author, which use a variety of radiation sources to characterise the devices are described. Read out electronics used in conjunction with these detectors are described as well as the tests performed in high energy particle beams at CERN, the European Centre for Nuclear Research. Irradiations of the detectors have also been carried out at CERN, in the Rutherford Laboratory and at a commercial site in France to measure the "radiation hardness" of these detectors. Their suitability for use in the High Energy Physics environment has not been proven due to the progress made in increasing the resistance of detectors made from silicon to the high radiation dose. Initial work on pixellated detectors for HEP applications has turned to the field of X- ray imaging as a natural alternative where GaAs has much to offer. Measurements have been done to evaluate the imaging capabilities of GaAs detectors using read out electronics developed for HEP. A 320 x 240 array of bump bonded pixels each 38 mum square has been tested using a charge integrating readout with results indicating a performance of better than 10 line pairs per mm. Epitaxially grown material has been investigated and shown to perform well as a detection medium. The purest material that has been tested still has a free carrier concentration that is at least an order of magnitude too high. An initial investigation into a novel photonic detector read out scheme is presented. The idea involves the use of reflective optical modulators to readout the charge from the detector element. A modulator was designed and successfully tested in order to demonstrate that this is possible. The integrated detector/modulator did not perform as hoped and alternative directions have been suggested.
Audio-Visual Active Speaker Identification: A comparison of dense image-based features and sparse facial landmark-based features

The field of speaker detection is relatively well researched. Multiple solutions focusing solely on audio or video, or a combination of both exist. On the audio side, a popular feature representation are mel-frequency cepstral coefficients, which are a sparse representation of the audio signal. On the video side, mostly pixel intensities are used, which is not sparse at all. In this paper, we take a look at a sparse video feature representation, namely facial landmarks. We first evaluate what selection of landmarks conveys the most information. Afterwards we propose several neural network architectures trained for audio-visual speaker detection. A comparison both on computational performance and accuracy is shown between the original architecture and architectures utilizing facial landmarks. For the evaluation, we introduce a new dataset to better understand the differences between the pixel and landmark features. The landmark features achieve similar accuracies for a forward oriented head position. There is a small reduction in performance for non-ideal head positions and in the case of occlusions. There is however a significant computational benefit, as there is a complexity reduction of orders two or three of magnitude due to the decreased feature dimensionality. When considering embedded devices, this is a big upside. This way we hope to provide insight and interest in a novel type of active speaker identification models.
SPTA: A Scalable Parallel ILP-Based Track Assignment Algorithm with Two-Stage Partition

Routability has always been a very challenging issue in Very Large Scale Integrated (VLSI) circuit design. The routability is considered in track assignment so that the global routing results can better match the requirements of detailed routing. However, existing heuristic track assignment algorithms are prone to local optimality, which cannot provide the accurate routability estimation. To overcome this limitation, we propose a scalable parallel Integer Linear Programming (ILP)-based track assignment algorithm, called SPTA, which employs a two-stage partition strategy. First, by taking into account both the global and local nets, all wires are assigned to tracks, making full use of the information from the global routing results. Second, an efficient ILP model for track assignment is proposed to minimize the overlap between iroutes1, thus significantly improving routability. Third, a two-stage partition strategy is designed to reduce the runtime. Finally, a panel-subpanel-level parallelism is proposed to further speed up the algorithm without sacrificing the quality of the solutions. Experimental results show that SPTA has a better routability estimation compared with the existing algorithms.
Physical mechanisms of surface wind variability at Incheon Airport

The mechanisms of surface wind variability observed by Aerodrome Meteorological Observation System at Incheon International Airport (IIA), located near the western coast of the Korean Peninsula, are investigated using cyclostationary empirical orthogonal function (CSEOF) analysis. Despite the complex nature of surface wind, as various factors contribute to its variance, distinct features of surface wind variability are separated reasonably into CSEOF modes. The physical interpretation of each mode using the ERA‐Interim reanalysis data is accomplished through regression analysis in CSEOF space. In essence, three atmospheric features dominate the surface wind variability: East Asian monsoon, land–sea breeze, and baroclinic waves. East Asian monsoon, the first mode, explains ~12% of the total variance of the surface wind at IIA. The annual reversal of the monsoon circulation due to the thermal contrast between the Asian continent and the western Pacific is captured as the single most dominant source of surface wind variability at IIA. The second CSEOF mode reveals that zonal circulation induced by land–sea breeze dominates the diurnal variation of the surface wind at IIA, and explains ~8% of the total variance. The largest fraction of variability is associated with baroclinic waves, which is separated into many CSEOF modes due to their irregular phase and wavelength, and the locations of vortex centre. Modes 3–10 are all associated with baroclinic waves and they together explain ~42% of the total variance. Baroclinic waves are amplified as they move over the ocean due to strong turbulent flux from the ocean surface induced by the associated upper tropospheric vortices.
Evaluating the accuracy of satellite-derived evapotranspiration estimates acquired during conditions of water stress

ABSTRACT Surface energy balance models such as the surface energy balance system (SEBS) assume that the influence of soil moisture and biophysical parameters on evapotranspiration (ET), are implicitly encompassed in model input variables such as the land surface temperature. This simplification may generally be suitable for environments in which the available energy constrains ET but may prove to be inadequate during suboptimal environmental conditions. To address this limitation, the SEBSESF approach was proposed to better represent the influence of environmental stress on surface energy fluxes, using an empirically derived calibration factor to adjust the evaporative fraction previously determined in SEBS. SEBSESF was evaluated by comparing the modelled ET estimates against Eddy Covariance ET measurements (ECET) at two sites, with contrasting environmental conditions. Comparisons between ECET and modelled ET indicated that the implementation of SEBSESF resulted in an improved correlation between ECET and modelled ET estimates, as well as an increase in the percentage of modelled ET estimates within an acceptable accuracy range (± 15 to 30%). Overall, the results of the study demonstrate how potential improvements in the mapping of ET within arid and semi-arid environments can be achieved by accounting for environmental stress. This in turn provides an opportunity to better understand and quantify energy and water fluxes, using satellite-based ET estimation approaches.
CASH FLOW ANALYSIS BY THE PRUDENT BANKER'S METHOD, OR DISCOUNTING TURNED ON ITS HEAD

Prudent bankers charge more on loans than they pay on deposits. In the cycle of life insurance products, it often happens that the insurer's cash flow alternates between positive and negative. Figures concerning corporate return on equity (ROE) are nothing short of delusive unless one knows whose equity, the stockholders' (as lenders or investors), or the policyholders' (as depositors), is involved at every step of a series of transactions creating cash flows. Traditional discounting is hazardous to corporate health unless, as this paper demonstrates, it is turned on its head and insurance managers adopt the methods of prudent bankers.
A Systematic Review on Melanoma Skin Cancer Cell Detection

Cancer is a fatal disease. Skin cancer is observed to be the most common of all. Primarily, skin cancer has three types i.e melanoma, basal cell carcinoma, and squamous cell carcinoma. Melanoma proliferates very fast and causes most deaths. However, early diagnosis of melanoma significantly increases the survival rates in patients. These types of malignancies in the skin cells are conventionally identified with a skin biopsy. A biopsy is a painful and intrusive operation. To tackle this challenge, a technologically automated diagnosis of melanoma skin cancer is required. Such technology may also prompt a quicker and more exact finding since the accuracy will not be subjective to the doctors. Approaches like Picture handling and Artificially Intelligent algorithms could enable the identification of prospective clinical variables and outperform existing time-consuming, painful, and less accurate methods. This article reviews different techniques based on their performance evaluation metrics like accuracy, precision, recall/sensitivity and specificity.
A Microcontroller-based and Cost-efficient Computer Numerical Control (CNC) Soldering Station

Soldering is already a hazard as it may cause burns to whoever touches it. The fumes are dangerous and toxic when inhaled. An unskilled solderer most likely will run into health or quality of work problems while soldering. This research aims to design and develop a cost-efficient automated computer numerical- controlled soldering station equipped with temperature controls, an emergency stop button, and a ventilation system capable of vacuuming the soldering fumes out of the system. Circuits made in ExpressPCB can be converted to a g-code file using bCNC and a Python code programmed by the researchers. Then, this g-code was processed by the program, Pronterface, and was uploaded to the Arduino MEGA + RAMPS vl.4 CNC Shield. The g-code was executed by stepper motors and servo motors holding the soldering iron at an angle. Analyzing the results statistically using Minitab software, the prototype was able to match the time of the test subjects proving that it was time efficient. It was also consistent in its accuracy - being on par with the test subjects in their soldering capabilities. The integrated ventilation system of the prototype was able to vacuum out the soldering fumes in the system. It is capable of meeting the standards of proper and quality soldering techniques based on literature reviews and experimentations. Nonetheless, with a final cost of around 20,000 PHP, it was fourteen times (14x) cost-efficient with improved capabilities compared to the commercially available automated soldering station.
Mitigation of Motion Artifacts in Pulse Oximetry through Redundant Sensors

Photoplethysmography (PPG) signals are used in pulse oximetry to calculate peripheral capillary oxygen saturation $(SpO_{2})$ levels. It is well known that the accuracy of the calculated $SpO_{2}$ is susceptible to motion noise. Using field data collected from human subjects during rest and while exhibiting medium to high-intensity physical exercises, we show how sensor redundancy can mitigate motion effects by employing two channels of red and infrared PPG signals. We demonstrate the advantage of redundancy using two $SpO_{2}$ calculation methods. These methods are widely used “Red over Infrared” (RoI) method and the Discrete Saturation Transform (DST) algorithm both, assisted by pre-filtering by a heart-rate tuned comb filter. The data were collected from individuals at rest and while exercising. $SpO_{2}$ levels are calculated for two channels, each using a red and an infrared PPG signal. We integrated the $SpO_{2}$ computed from the two channels with a Kalman filter (KF) and computed the $SpO_{2}$ mean absolute error (MAE) from each channel individually and after integrating the two channels. The $SpO_{2}$ MAEs of each one of the two channels separately were greater (and statistically significant) than the $SpO_{2}$ MAE obtained from the two channels integrated with a KF. We observed that when two channels of PPG (red and infrared) signals were available, then combining $SpO_{2}$ levels obtained from these two channels exhibited a smoother and more reliable estimate of the blood oxygen saturation level as compared the $SpO_{2}$ levels computed from each channel separately. The reason is that if the data for the two channels are collected at sufficient physical distance from one another, the motion artifacts affecting the two channels are uncorrelated. The KF is then able to use past measurements and modeling of the dynamics to attenuate the effect of the motion artifacts.
A Nonlinear Finite Element Heterogeneous Multiscale Method for the Homogenization of Hyperelastic Solids and a Novel Staggered Two-Scale Solution Algorithm

In this paper we address three aspects of nonlinear computational homogenization of elastic solids by two-scale finite element methods. First, we present a nonlinear formulation of the finite element heterogeneous multiscale method FE-HMM in a Lagrangean formulation that covers geometrical nonlinearity and, more general, hyperelasticity. Second, a-priori estimates of FE-HMM, which exist so far only for the fully linear elastic case in solid mechanics, are assessed in the regime of nonlinear elasticity. The measured convergence rates agree fairly well with those of the fully linear regime. Third, we revise the standard solution algorithm of FE$^2$ which is a staggered scheme in terms of a nested loop embedding the full solution of the micro problem into one macro solution iteration step. We demonstrate that suchlike staggered scheme, which is typically realized by a nested two-level Newton algorithm, can safely and efficiently be replaced by direct alternations between micro and macro iterations. The novel algorithmic structure is exemplarily detailed for the proposed nonlinear FE-HMM, its efficiency is substantiated by a considerable speedup in numerical tests.
Existence of probability measure valued jump-diffusions in generalized Wasserstein spaces

We study existence of probability measure valued jump-diffusions described by martingale problems. We develop a simple device that allows us to embed Wasserstein spaces and other similar spaces of probability measures into locally compact spaces where classical existence theory for martingale problems can be applied. The method allows for general dynamics including drift, diffusion, and possibly infinite-activity jumps. We also develop tools for verifying the required conditions on the generator, including the positive maximum principle and certain continuity and growth conditions. To illustrate the abstract results, we consider large particle systems with mean-field interaction and common noise.
Unveiling Elite Developers’ Activities in Open Source Projects

Open source developers, particularly the elite developers who own the administrative privileges for a project, maintain a diverse portfolio of contributing activities. They not only commit source code but also exert significant efforts on other communicative, organizational, and supportive activities. However, almost all prior research focuses on specific activities and fails to analyze elite developers’ activities in a comprehensive way. To bridge this gap, we conduct an empirical study with fine-grained event data from 20 large open source projects hosted on GITHUB. We investigate elite developers’ contributing activities and their impacts on project outcomes. Our analyses reveal three key findings: (1) elite developers participate in a variety of activities, of which technical contributions (e.g., coding) only account for a small proportion; (2) as the project grows, elite developers tend to put more effort into supportive and communicative activities and less effort into coding; and (3) elite developers’ efforts in nontechnical activities are negatively correlated with the project’s outcomes in terms of productivity and quality in general, except for a positive correlation with the bug fix rate (a quality indicator). These results provide an integrated view of elite developers’ activities and can inform an individual’s decision making about effort allocation, which could lead to improved project outcomes. The results also provide implications for supporting these elite developers.
Deconstructing Blockchains: A Comprehensive Survey on Consensus, Membership and Structure

It is no exaggeration to say that since the introduction of Bitcoin, blockchains have become a disruptive technology that has shaken the world. However, the rising popularity of the paradigm has led to a flurry of proposals addressing variations and/or trying to solve problems stemming from the initial specification. This added considerable complexity to the current blockchain ecosystems, amplified by the absence of detail in many accompanying blockchain whitepapers. Through this paper, we set out to explain blockchains in a simple way, taming that complexity through the deconstruction of the blockchain into three simple, critical components common to all known systems: membership selection, consensus mechanism and structure. We propose an evaluation framework with insight into system models, desired properties and analysis criteria, using the decoupled components as criteria. We use this framework to provide clear and intuitive overviews of the design principles behind the analyzed systems and the properties achieved. We hope our effort will help clarifying the current state of blockchain proposals and provide directions to the analysis of future proposals.
There is no Reliable Way to Detect Hacked Ballot-Marking Devices

Election system vendors are marketing ballot-marking devices (BMDs) as a universal system, and some states are deploying them for all voters, not just those who need a BMD to vote independently. Like all devices with CPUs, BMDs can be hacked, misprogrammed, or misconfigured. BMD printout might not reflect what the BMD screen or audio confirmed. If a voter complains that the BMD altered votes, officials have no way to tell whether there was a BMD malfunction, the voter erred, or the voter is attempting to cast doubt on the election. Pre-election logic and accuracy (L&A) tests have little ability to detect outcome-changing problems, in part because BMDs know the time and date, and in part because it is in practice impossible to simulate the full range of voter interactions with the device. _Parallel_ or _live_ tests of BMDs on election day address the first problem but not the second. In practice, neither L&A nor parallel tests can probe all combinations of voter preferences, device settings, ballot language, duration of voter interaction, input and output interfaces, and other variables that could include a enough votes to change election outcomes. Even if less parallel testing sufficed, it would still require extra BMDs, new infrastructure for creating test patterns, new infrastructure for reporting test results, additional polling-place staff, and additional staff training. And if parallel testing discovers an error, the only remedy is to hold a new election: there is no way to reconstruct the correct election result from an untrustworthy paper trail. Minimizing the number of votes cast using BMDs is prudent election administration.
Experimental Analysis of Harvested Energy and Throughput Trade-off in a Realistic SWIPT System

We build a realistic Simultaneous Wireless Information and Power Transfer (SWIPT) prototype and experimentally analyse the harvested energy and throughput trade-off. Both time-switching and power splitting receiver architectures are implemented, and the performance comparison is carried out. Systematic SWIPT transmission signal design methods are also considered and implemented on the prototype. The harvested energy-throughput (E-T) performance with different transmission signal designs, modulation schemes, and receiver architectures are evaluated and compared. The combination of the power splitting receiver architecture and the superposition transmission signal design technique shows significant expansion of the E-T region. The experimental results fully validate the observations predicted from the theoretical signal designs and confirm the benefits of systematic signal designs on the system performance. The observations give important insights on how to design a practical SWIPT system.
Face Mask Detection under the Threat of Covid-19 Virus

For wearing a mask, Covid19 has a new identity. Detecting masked faces with accuracy and efficiency is becoming increasingly important. Face mask recognition is a tough task because of the high occlusions that cause the loss of face in-formation. Furthermore, There aren't many large-scale, masked face with a well-label datasets out there, which makes face mask recognition more difficult. CNN-based deep learning algorithms have achieved significant advances in a variety, one aspect of computer vision is face detection disciplines. We introduce a new cascade structure based on CNN for recognising face masks in this paper, This is made up of three convolutional neural networks that have been successfully built. To finetune our CNN models, we propose a new dataset named “facial mask dataset. ” due to a paucity of face masked training instances. Face masks were tested on a set of faces, we assess our suggested face mask recognition algorithm, and it performs admirably. A new cascade framework based on CNN is proposed for detecting face masks and their types, which comprises of three properly built convolutional neural networks.
Signatures of coupling between spin waves and Dirac fermions in 
YbMnBi2

We present inelastic neutron scattering (INS) measurements of magnetic excitations in YbMnBi$_2$, which reveal features consistent with a direct coupling of magnetic excitations to Dirac fermions. In contrast with the large broadening of magnetic spectra observed in antiferromagnetic metals such as the iron pnictides, here the spin waves exhibit a small but resolvable intrinsic width, consistent with our theoretical analysis. The subtle manifestation of spin-fermion coupling is a consequence of the Dirac nature of the conduction electrons, including the vanishing density of states near the Dirac points. Accounting for the Dirac fermion dispersion specific to \ymb\ leads to particular signatures, such as the nearly wave-vector independent damping observed in the experiment.