Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-supernova Alert System for Super-Kamiokande

In 2020, the Super-Kamiokande (SK) experiment moved to a new stage (SK-Gd) in which gadolinium (Gd) sulfate octahydrate was added to the water in the detector, enhancing the efficiency to detect thermal neutrons and consequently improving the sensitivity to low energy electron anti-neutrinos from inverse beta decay (IBD) interactions. SK-Gd has the potential to provide early alerts of incipient core-collapse supernovae through detection of electron anti-neutrinos from thermal and nuclear processes responsible for the cooling of massive stars before the gravitational collapse of their cores. These pre-supernova neutrinos emitted during the silicon burning phase can exceed the energy threshold for IBD reactions. We present the sensitivity of SK-Gd to pre-supernova stars and the techniques used for the development of a pre-supernova alarm based on the detection of these neutrinos in SK, as well as prospects for future SK-Gd phases with higher concentrations of Gd. For the current SK-Gd phase, high-confidence alerts for Betelgeuse could be issued up to 9 hr in advance of the core collapse itself.
Adversarial Body Shape Search for Legged Robots

We propose an evolutionary computation method based on deep reinforcement learning to determine the vulnerability to adversarial attacks (such as corrosion and defects caused by collisions) on the length and thickness of parts of legged robots. This type of attack changes the robot’s body shape and interferes with walking; we call the attacked body the adversarial body shape. The proposed evolutionary computation method searches adversarial body shape by minimizing the expected cumulative reward earned through walking simulation. To evaluate the effectiveness of the proposed method, we performed experiments with three different legged robots (Walker2d, Ant-v2, and Humanoid-v2) in OpenAI Gym. The experimental results reveal that Walker2d and Ant-v2 are more vulnerable to attack on the length than on the thickness of the body parts, whereas Humanoid-v2 is vulnerable to attack on both the length and thickness. We further identified that the adversarial body shapes break left-right symmetry or shift the center of gravity of the legged robots. This method of finding adversarial body shapes can be used to proactively diagnose the vulnerability of legged robot walking.
Approximate Dynamic Programming for Constrained Linear Systems: A Piecewise Quadratic Approximation Approach

Approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommodation of constraints and stability guarantees, although its computation is sometimes prohibitive. This paper introduces an approach combining the two methodologies to overcome their individual limitations. The predictive control law for constrained linear quadratic regulation (CLQR) problems has been proven to be piecewise affine (PWA) while the value function is piecewise quadratic. We exploit these formal results from MPC to design an ADP method for CLQR problems. A novel convex and piecewise quadratic neural network with a local-global architecture is proposed to provide an accurate approximation of the value function, which is used as the cost-to-go function in the online dynamic programming problem. An efficient decomposition algorithm is developed to speed up the online computation. Rigorous stability analysis of the closed-loop system is conducted for the proposed control scheme under the condition that a good approximation of the value function is achieved. Comparative simulations are carried out to demonstrate the potential of the proposed method in terms of online computation and optimality.
Global temperature projections from a statistical energy balance model using multiple sources of historical data

This paper estimates a two-component energy balance model as a linear state space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of non-stationarity in forcing and the incorporation of multiple data sources for the unobserved processes. We estimate the EBM-SS model using historical datasets at the global level for the period 1955 to 2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under Representative Concentration Pathway scenarios are comparable to those from Coupled Model Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth System Models.
Optical Manipulation of the Rashba Effect in Germanium Quantum Wells

The Rashba effect in Ge/Si0.15Ge0. 85 multiple quantum wells embedded in a p‐i‐n diode is studied through polarization and time‐resolved photoluminescence. In addition to a sizeable redshift arising from the quantum‐confined Stark effect, a threefold enhancement of the circular polarization degree of the direct transition is obtained by increasing the pump power over a 2 kW cm−2 range. This marked variation reflects an efficient modulation of the spin population and is further supported by dedicated investigations of the indirect gap transition. This study demonstrates a viable strategy to engineer the spin‐orbit Hamiltonian through contactless optical excitation and opens the way toward the electro‐optical manipulation of spins in quantum devices based on group‐IV heterostructures.
Survey on Tools and Techniques Detecting Microservice API Patterns

It is well recognized that design patterns improve system development and maintenance in many aspects. While we commonly recognize these patterns in monolithic systems, many patterns emerged for cloud computing, specifically microservices. Unfortunately, while various patterns have been proposed, available quality assessment tools often do not recognize many. This article performs a grey literature review to find and catalog available tools to detect microservice API patterns (MAP). It reasons about mechanisms that can be used to detect these patterns. Furthermore, the results indicate gaps and opportunities for improvements for quality assessment tools. Finally, the reader is provided with a route map to detection techniques that can be used to mine MAPs.
Quantitative Analysis of Community Evolution in Developer Social Networks Around Open Source Software Projects

Understanding the evolution of communities in developer social networks (DSNs) around open source software (OSS) projects can provide valuable insights about the socio-technical process of OSS development. Existing studies show the evolutionary behaviors of social communities can effectively be described using patterns including split, shrink, merge, expand, emerge, and extinct. However, existing pattern-based approaches are limited in supporting quantitative analysis, and are potentially problematic for using the patterns in a mutually exclusive manner when describing community evolution. In this work, we propose that different patterns can occur simultaneously between every pair of communities during the evolution, just in different degrees. Four entropy-based indices are devised to measure the degree of community split, shrink, merge, and expand, respectively, which can provide a comprehensive and quantitative measure of community evolution in DSNs. The indices have properties desirable to quantify community evolution including monotonicity, and bounded maximum and minimum values that correspond to meaningful cases. They can also be combined to describe more patterns such as community emerge and extinct. We conduct experiments with real-world OSS projects to evaluate the validity of the proposed indices. The results suggest the proposed indices can effectively capture community evolution, and are consistent with existing approaches in detecting evolution patterns in DSNs with an accuracy of 94.1\%. The results also show that the indices are useful in predicting OSS team productivity with an accuracy of 0.718. In summary, the proposed approach is among the first to quantify the degree of community evolution with respect to different patterns, which is promising in supporting future research and applications about DSNs and OSS development.
Generating extremely large-volume reionisation simulations

Preparing for a first detection of the 21-cm signal during reionisation by large-scale interferometer experiments requires rigorous testing of the data analysis and reduction pipelines. Validating that these do not erroneously add/remove features mimicking the signal (e.g. from side-lobes or large-scale power leakage) requires simulations extending beyond the primary field-of-view. However, the Murchison Wide Field Array (MWA) with a field-of-view of ∼252 deg.2 would require simulations spanning several Gpcs, which are currently infeasible. To address this, we developed a simplified version of the semi-numerical reionisation simulation code 21cmfast, sacrificing some physical accuracy (linear structure formation) in favour of extremely large volumes. We then constructed a 7.5 Gpc comoving volume specifically tailored to the binned spectral resolution of the MWA (∼1.17 cMpc), required for validating the pipeline used in the 2020 MWA 21-cm power spectrum (PS) upper limits. With this large-volume simulation we then explored: (i) whether smaller volume simulations are biased by missing large-scale modes, (ii) non-Gaussianity in the cosmic variance uncertainty, (iii) biases in the recovered 21-cm PS following foreground wedge avoidance and (iv) the impact of tiling smaller simulations to achieve large volumes. We found: (i) no biases from missing large-scale power, (ii) significant contribution from non-Gaussianity, as expected (iii) a 10–20 per cent over-estimate of the 21-cm PS following wedge mode excision and (iv) tiling smaller simulations under-estimates the large-scale power and cosmic variance.
Flow-Based Spatio-Temporal Structured Prediction of Motion Dynamics

Conditional Normalizing Flows (CNFs) are flexible generative models capable of representing complicated distributions with high dimensionality and large interdimensional correlations, making them appealing for structured output learning. Their effectiveness in modelling multivariates spatio-temporal structured data has yet to be completely investigated. We propose MotionFlow as a novel normalizing flows approach that autoregressively conditions the output distributions on the spatio-temporal input features. It combines deterministic and stochastic representations with CNFs to create a probabilistic neural generative approach that can model the variability seen in high-dimensional structured spatio-temporal data. We specifically propose to use conditional priors to factorize the latent space for the time dependent modeling. We also exploit the use of masked convolutions as autoregressive conditionals in CNFs. As a result, our method is able to define arbitrarily expressive output probability distributions under temporal dynamics in multivariate prediction tasks. We apply our method to different tasks, including trajectory prediction, motion prediction, time series forecasting, and binary segmentation, and demonstrate that our model is able to leverage normalizing flows to learn complicated time dependent conditional distributions.
Personalized Federated Learning with Adaptive Batchnorm for Healthcare

There is a growing interest in applying machine learning techniques to healthcare. Recently, federated learning (FL) is gaining popularity since it allows researchers to train powerful models without compromising data privacy and security. However, the performance of existing FL approaches often deteriorates when encountering non-iid situations where there exist distribution gaps among clients, and few previous efforts focus on personalization in healthcare. In this article, we propose FedAP to tackle domain shifts and then obtain personalized models for local clients. FedAP learns the similarity between clients based on the statistics of the batch normalization layers while preserving the specificity of each client with different local batch normalization. Comprehensive experiments on five healthcare benchmarks demonstrate that FedAP achieves better accuracy compared to state-of-the-art methods (e.g., 10% accuracy improvement for PAMAP2) with faster convergence speed.
Model free endurance markers based on the second derivative of blood lactate curves.

Blood lactate assessment is used regularly by exercise physiologists as a predictor of endurance performance. Typically fingertip blood samples are collected at selected running speeds on a treadmill and a plot of lactate concentration against workload (e.g. treadmill speed) is presented. Several features of the lactate curve have been suggested as markers for endurance. Typically these features, or endurance markers, are used to monitor changes in aerobic fitness, set training regimes and predict endurance performance. Determination of these markers however can be problematic (Weltman, 1995). In order to explore the structure of lactate curves, Functional Data Analysis (FDA) techniques are applied to provide graphical summaries. A new nonparametric endurance marker is then presented which corresponds to the point of maximum acceleration in the lactate curve. Using smoothing splines it is easily calculated by examining the second derivative of the smoothed lactate response. In addition we propose a simple discrete approximation to this marker.
The determination of the relationship between entrepreneurship education and entrepreneurial intentions of students in developing economies

The object of the research is the relationship between entrepreneurship education and the entrepreneurial intention of students in developing economies. The paper aims at the provision of better understanding of premises for policy making in Higher Educational Institutions of developing economies towards enhancing the growth of entrepreneurship. Data was collected from the students of the multi-campus National Open University of Nigeria via an adapted structured questionnaire. The study utilized the non-parametric method of Structural Equation Modelling with the aid of PLS 3 software. The major finding of the study is the existence of a causal relationship between Entrepreneurship Education and Entrepreneurial Intention of the students of Higher Institutions in developing economies that is in line with several studies conducted in other developing economies. It further affirms the key role of entrepreneurship education in inspiring students’ inclination towards entrepreneurship. It recommends, amongst other things, that policymakers of Universities and other Tertiary institutions should provide, in an early stage, a modified mandatory entrepreneurial program structured to shape and develop student’s entrepreneurial Intentions.
Second-order uniformly asymptotic-preserving space-time-ImEx schemes for hyperbolic balance laws with stiff relaxation

We consider hyperbolic systems of conservation laws with relaxation source terms leading to a diffusive asymptotic limit under a parabolic scaling. We introduce a new class of secondorder in time and space numerical schemes, which are uniformly asymptotic preserving schemes. The proposed Implicit-Explicit (ImEx) approach, does not follow the usual path relying on the method of lines, either with multi-step methods or Runge-Kutta methods, or semi-discretized in time equations, but is inspired from the Lax-Wendroff approach with the proper level of implicit treatment of the source term. As a result, it yields a very compact stencil in space and time and we are able to rigorously show that both the second-order accuracy and the stability conditions are independent of the fast scales in the asymptotic regime, including the study of boundary conditions. We provide an original derivation of l 2 and l $\infty$ stability conditions of the scheme that do not deteriorate the second order accuracy without relying on a limiter of any type in the linear case, in particular for shock solutions, and extend such results to the nonlinear case, showing the novelty of the method. The prototype system for the linear case is the hyperbolic heat equation, whereas barotropic Euler equations of gas dynamics with friction are the one for the nonlinear case. The method is also able to yield very accurate steady solutions in the nonlinear case when the relaxation coefficient in the source term depends on space. A thorough numerical assessment of the proposed strategy is provided by investigating smooth solutions, solutions with shocks and solutions leading to a steady state with space dependent relaxation coefficient.
Path Capacity Estimation for Measurement-based Admission Control in Military IP Networks

In this thesis, the performance of a state-of-the-art path capacity estimation algorithm is evaluated in terms of its suitability for being part of a measurement-based admission controller in a military Internet Protocol (IP) network. The strive towards Network Enabled Capability (NEC) in miltary organizations drives the need for interconnecting all the actors taking part in an operation. The resulting network is an IPbased, heterogeneous Wide Area Network (WAN), comprising of a variety of fixed and mobile communication links with different capacities. In military IP networks, Quality of Service (QoS) cannot be guaranteed when there is congestion. This is due to the use of IPsec, which forms a cryptographic boundary between the traffic source and forwarding routers, thus rendering end-to-end resource reservation impossible. This calls for the implementation of a congestion avoidance policy through Measurement-based Admission Control (MBAC). Based on a literature study, the estimation algorithms combining the use of packet-pair dispersion and delay analysis, were found to be the most suited for providing low-intrusive, fast and reliable measurements of the path capacity. One of these algorithms, Ad Hoc Probe, was extensively evaluated in a test bed based on link technologies typically found in military networks. A number of performance limiting factors were identified, including a lack of support for Time Division Multiple Access (TDMA)-based links, restrictions on the number of hops in contention-based Mobile Ad Hoc Networks (MANETs) and a minimum required capacity that made the algorithm unfit for use in networks containing narrowband, low-capacity links.
Vertical Axis Wind Turbines: Current Technologies and Future Trends

This paper, based on the pursuit of scientific articles published and recorded in the last five ye ars (2010-2014) patents on VAWT technology, gives an image of the current situation of the treated techn ology. From data extracted we know: The different models that are working with differen t geometries, distinguishing between Savonius, Darrie us, hybrid of both (D+S), models dedicated to Offshore technology and what can be applied generally (D&S) on both types of VAWT (controllers, electric generator s, materials .).
Challenges of hospital ethics committees: a phenomenological study

Medical ethics committees play an important role in examining and resolving ethical problems in hospitals by developing ethical guidelines and making ethical decisions. This study aimed at investigating the challenges that these committees typically face. This qualitative phenomenological study was conducted in 2020. Data was collected through semi-structured interviews with purposive sampling and participation of 19 ethics committees’ members in Tehran hospitals. Then, data were analyzed by the content analysis method using MAXQDA-10 software. Challenges of hospital ethics committees were classified into three main themes including external factors, intra-organizational factors, and ethics committee structure, in addition to six sub-themes including inadequate supervision, lack of instructions, organizational culture, human resources, nature of the committee, and ineffectiveness of committees’ decisions. Since many challenges are faced by ethics committees, plans should be developed and implemented to fulfill the following purposes: (i) strengthen the position of these committees in hospitals, (ii) continuous supervision over the formation and holding of the committees, (iii) their operation process, (iv) their decision-making process, and (v) process of sharing committees’ decisions with all hospital stakeholders and staff.
An Improved Phase-Shifted Carrier PWM for Modular Multilevel Converters with Redundancy Sub-Modules

In this paper, the PSC PWM method is chosen as the optimal modulation method for a 20MW VSC HVDC, with consideration of the harmonic distortion of the output voltage, the switching frequency, and the control implementation difficulty. In addition, a new PSC PWM method is proposed in order to achieve an easy application and to solve the redundant control problems encountered in the previous PSC PWM method. To verify the proposed PSC PWM method, PSCAD/EMTDC simulations for an 11-level MMC RTDS HILS test and an 11-level MMC prototype converter test were performed. As can be seen from the results of these tests, the proposed PSC PWM method shows good results in an 11-level MMC with redundant sub-modules.
A ride quality evaluation of a semi-active railway vehicle suspension system with MR damper: Railway field tests

This work presents experimental assessment of the improvements to the horizontal ride quality of a railway vehicle equipped with a semi-active magneto-rheological (MR) suspension system. The assessment includes the development of a mathematical model and magnetic circuit analysis of the MR damper, the design and manufacture of MR damper, and field test on the railway. After evaluating the field-dependent damping force characteristics, the conventional passive dampers of the operational railway vehicle are replaced with the MR dampers to evaluate horizontal dynamic characteristics that directly indicates the ride quality of the railway vehicle. Various sensors are installed in the vehicle and a skyhook controller with semi-active condition is implemented to produce an appropriate input current for the generation of the desired damping force. Three periods of testing are undertaken on the railway bridge at 120 km/h and the measured data of acceleration level are recoded and presented. It is demonstrated from the measured results that the vibration can be effectively controlled by the proposed semi-active MR suspension system associated with the skyhook controller. Finally, from the vibration control responses the horizontal ride quality of railway vehicle is evaluated and presented in frequency domain.
Low-profile antenna for mobile Ka-band satellite communications

This paper describes the concept of a low-profile user terminal antenna intended for mobile bi-directional Ka-band satellite communications. The concept addresses a hybrid electronic and mechanical tracking method. The antenna consists of several two-dimensional leaky-wave antenna panels. Each panel is excited by a slotted waveguide feed, composed of a linear array of circularly polarised slots. This structure offers a reconfigurable radiation pattern. As a proof-of-principle, a rectangular antenna panel for the downlink at 20 GHz was manufactured and measured. The paper describes the design of the panel in detail and presents the results of numerical simulation and measurement.