Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechatronics of Hard Disk Drives: RISE Feedback Track Following Control of a R/W Head

This chapter addresses design challenges associated with a servo system of a Hard Disc Drive (HDD). A P-RISE-NN control approach is proposed. The main objective of this novel controller is to enhance the track following in a Hard Disc Drive. Indeed, P-RISE-NN approach involves an optimal gain selection, in a RISE-NN controller. Taking advantage of the prediction feature, the controller is able to anticipate the future behavior of the system. Consequently, it can easily handle various constraints that may be imposed on its variables. Furthermore, the boundedness of the closed-loop signals as well as the convergence of the tracking error to zero are ensured. Comprehensive comparison between the classical RISE-NN and P-RISE-NN are provided through nu-merical simulations in different operating conditions. It is shown that P-RISE-NN can maintain a good control performance in nominal case and in the presence of external disturbances. Besides, the controller is also robust towards uncertainties on the system parameters.
Quantifying semantic salience to investigate contact-induced language change

Contact varieties that emerge in societies where multilingualism is the norm provide an ideal testing ground to study cognitive constraints on language change. One of these constraints is salience, which has been argued to be an important factor in determining whether a feature gets adopted or discarded in a contact variety (Aboh and Ansaldo 2006; Siemund and Kintana 2008). However, the notion of salience is problematic as it is an extremely broad concept causing scholars to each adopt slightly different definitions which are not always applicable cross-linguistically. In this paper, a new approach is put forward that quantifies semantic salience as the relative contribution of an individual word to the overall meaning of the sentence, coined as semantic load. Models that apply principles of distributional and compositional semantics have shown success in capturing meanings of words (Erk 2012). Hence, a recursive neural network (RNN) is employed that incorporates these principles in its architecture, which is then trained on the task of missing word prediction (Le and Zuidema 2014a). Vector representations for words are based on their distributional properties in large text corpora and the sentence 1 The author wishes to thank Phong Le, Sara Veldhoen and Jelle Zuidema (ILLC, University of Amsterdam) for assistance with the development of the computational model. Online Proceedings of UK-CLA Meetings 132 representations are formed during the task of word prediction. With these two types of vector representations it is possible to measure the semantic load of individual words. This paper reports on semantic load as a cognitive constraint on language change by looking at the competition and selection of features with varying degrees of semantic load in data of contact languages. In doing so, it shows that computational models are not only useful in their domain of humancomputer interaction but can also be employed as a tool in linguistic research. Moreover, as the neural network uses distributional and compositional representations of meaning, the results touch upon the cognitive plausibility of representing meaning in this manner.
Fractal and multifractal geometry in quantitative analysis at nanoscale

This study presents a multifractal approach to characterize the structural complexity of 3-D surface roughness of electrodeposited Ni-P (10.65 at.% P) coatings thermally treated at three temperatures (400°C, 500°C and 600°C), obtained with atomic force microscopy (AFM) analysis. 3D surface roughness was studied in air (ex-situ), on square areas of 2.5 μm x 2.5 μm. The results showed that the Ni-P coatings (10.6% P) that were thermally treated at 500 °C possessed the best surface finish.
FathomNet: Building the Dataset

Annotation and organization of collected oceanographic visual data proves to be a significant challenge for the oceanographic community. As the quantity and quality of collected data has increased, manual annotation approaches have become costlier and more intensive. Machine learning techniques, particularly in the area of deep convolutional neural networks (CNNs), have produced significant advances in the task of image classification. The Big Ocean, Big Data project is a joint effort of the oceanographic research community to create a public platform for semi-autonomous video annotation. This paper discusses the construction of FathomNet , an image dataset created with imagery from the Monterey Bay Aquarium Research Institute (MBARI) to train machine learning models for image classification of oceanographic video. The FathomNet dataset currently contains 77100 images, spread over 38 concepts and 3 high-level categories: Benthic, Midwater, and Geology.
Superresolution far-field imaging by coded phase reflectors distributed only along the boundary of synthetic apertures: supplementary material

This document provides supplementary information to “Superresolution far-field imaging by coded phase reflectors distributed only along the boundary of synthetic apertures,” https://doi.org/10.1364/OPTICA.5. 001607. The supplementary material consists of four sections: The first section describes the generation and design of the aperture function. The second section characterizes the obtained result via Structural Similarity and visibility analysis, and illustrates the superiority of SMART (synthetic marginal aperture with revolving telescopes) and PAIS (partial aperture imaging system) over direct imaging. The third section discusses the SMART and PAIS with a large field of view and the final section describes an experimental demonstration with two spatial light modulators and discusses its results.
Characterisation of the microbiome along the gastrointestinal tract of growing turkeys

microbiome is largely understudied, despite its relationship with bird health and growth, and the prevalence of human pathogens such as Campylobacter spp. In this study we investigated the microbiome within the small intestine (SI), caeca (C), large intestine (LI), and cloaca (CL) of turkeys at 6, 10, and 16 weeks of age. Eight turkeys were dissected within each age category and the contents of the SI, C, LI, and CL were harvested. 16S rDNA based QPCR was performed on all samples and samples for the four locations within three birds/age group were sequenced using ion torrent-based sequencing of the 16S rDNA. Sequencing data showed on a genus level, an abundance of Lactobacillus, Streptococcus, and Clostridium XI (38.2, 28.1, and 13.0% respectively) irrespective of location and age. The caeca exhibited the greatest microbiome diversity throughout the development of the turkey. PICRUSt data predicted an array of bacterial function, with most differences being apparent in the caeca of the turkeys as they matured. QPCR revealed that the caeca within 10 week old birds, contained the most Campylobacter spp. Understanding the microbial ecology of the turkey gastrointestinal tract is essential in terms of understanding production efﬁciency and in order to develop novel strategies for targeting Campylobacter spp.
The boomer penalty: excess mortality among baby boomers in Canada and the United States

Studies suggest that, relative to adjacent cohorts, baby boomers in Canada and the United States have experienced a slowdown, or even a deterioration, in mortality improvements. These findings are counterintuitive and surprising since the unprecedented improvements in early-life conditions experienced by baby boomers should have led to declines in morbidity and mortality in later life, as was the case for earlier generations. The present study explores the mechanisms that could have produced this “excess” mortality among the baby boom cohorts in Canada and in three racial/ethnic groups in the United States. Using micro-level mortality data from vital statistics systems, we analyzed the contributions of the causes of death that are likely driving this cohort’s excess mortality, and their dynamics over time. The analyses were done using demographic decomposition, visual, and statistical methods. We found evidence of a higher susceptibility of the trailing edge boomers (those born around 1960) to behavioral causes of death: namely, mortality from drugs, alcohol, HIV/AIDS, hepatitis C, COPD, and suicide. Most of these causes contributed to the all-cause mortality disadvantage of baby boomers through sustained cohort effects that followed the cohorts over time. This finding calls into question the assumption that secular improvements in early life conditions lead to a monotonic decline in cohort mortality rates. Instead, there may be important disruptions in the continuous progress in health and mortality, and it is possible that the baby boom generation represents one such disruption. This insight calls for a rethinking of the mechanisms that drive current age-period-cohort mortality patterns. The mechanisms that can generate the observed cohort disadvantage of baby boomers – such as the higher levels of distress and frustration as well as the riskier attitudes toward drug use and sexual practices that are constituent of the boomer generation identity – are addressed and discussed.
Modeling Security-Critical Applications with UML in the SecureMDD Approach

—Developing security-critical applications is very dif-ﬁcult and the past has shown that many applications turned out to be erroneous after years of usage. For this reason it is desirable to have a sound methodology for developing security-critical applications. We present our approach, called SecureMDD, to model these applications with the uniﬁed modeling language (UML) extended by a UML proﬁle to tailor our models to security applications. We automatically generate a formal speciﬁcation suitable for veriﬁcation as well as an implementation from the model. Therefore we offer a model-driven development method seamlessly integrating semi-formal and formal methods as well as the implementation. This is a signiﬁcant advantage compared to other approaches not dealing with all aspects from abstract models down to code. Based on this approach we can prove security properties on the abstract protocol level as well as the correctness of the protocol implementation in Java with respect to the formal model. In this paper we concentrate on the modeling with UML and some details regarding the transformation of this model into the formal speciﬁcation. We illustrate our approach on an electronic payment system called Mondex [1]. Mondex has become famous for being the target of the ﬁrst ITSEC evaluation of the highest level E6 which requires formal speciﬁcation and veriﬁcation.
A questionable way to lower cholesterol: food supplements containing red yeast rice to be taken only on medical advice-BfR opinion No 003/2020 issued 15 January 2020

Red “yeast” rice (also known as red rice) has its traditional origin in China. Red yeast rice is made by fermenting boiled white rice with a type of mould of the Monascus genus. This produces substances that dye the rice bright red. In East Asia in particular, red yeast rice is therefore used as a food colouring. The fermentation process also leads to the production of compounds that can have pharmacological effects (like medicines) and that may also be harmful to health.
A long-period (P = 61.8 d) M5V dwarf eclipsing a Sun-like star from TESS and NGTS

Science & Technology Facilities Council (STFC) ST/M001962/1 ST/S002642/1 Swiss National Science Foundation (SNSF) Science & Technology Facilities Council (STFC) ST/R000824/1 ST/1L000733/1 ST/P000495/1 ST/N000757/1 STFC via an Ernest Rutherford Fellowship ST/R003726/1 ST/R0384X/1 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1161218 Centro de Astrofisca y Tecnologias Afines (CATA-Basal) Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) PB06 Chilean National Allocation Committee (CNTAC) 155475001865 CONICYT-PFCHA/Doctorado Nacional 21191829 Juan Carlos Torres Fellowship German Research Foundation (DFG) SPP 1992 RA 714/13-1
Radon transforms on homogeneous trees

We study the different horospherical Radon transforms that arise by regarding a homogeneous tree T as a simplicial complex whose simplices are vertices V, edges E or flags F (flags are oriented edges). The ends (infinite geodesic rays starting at a reference vertex) provide a boundary $\Omega$ for the tree. Then the horospheres form a trivial principal fiber bundle with base $\Omega$ and fiber $\mathZ$. There are three such fiber bundles, consisting of horospheres of vertices, edges or flags, but they are isomorphic: however, no isomorphism between these fiber bundles maps special sections to special sections (a special section consists of the set of horospheres through a given vertex, edge or flag). The groups of automorphisms of the fiber bundles contain a subgroup $A$ of parallel shifts, analogous to the Cartan subgroup of a semisimple group. The normalized eigenfunctions of the Laplace operator on T are boundary integrals of complex powers of the Poisson kernel, that is characters of $A$, and are matrix coefficients of representations induced from $A$ in the sense of Mackey, the so-called spherical representations. The vertex-horospherical Radon transform consists of summation over V in each vertex-horosphere, and similarly for edges or flags. We prove inversion formulas for all these Radon transforms, and give applications to harmonic analysis and the Plancherel measure on T. We show via integral geometry that the spherical representations for vertices and edges are equivalent. Also, we define the Radon back-projections and find the inversion operator of each Radon transform by composing it with its back-projection. This gives rise to a convolution operator on T, whose symbol is obtained via the spherical Fourier transform, and its reciprocal is the symbol of the Radon inversion formula.
A micro-macro Markov chain Monte Carlo method for molecular dynamics using reaction coordinate proposals I: direct reconstruction

We introduce a new micro-macro Markov chain Monte Carlo method (mM-MCMC) to sample invariant distributions of molecular dynamics systems that exhibit a time-scale separation between the microscopic (fast) dynamics, and the macroscopic (slow) dynamics of some low-dimensional set of reaction coordinates. The algorithm enhances exploration of the state space in the presence of metastability by allowing larger proposal moves at the macroscopic level, on which a conditional accept-reject procedure is applied. Only when the macroscopic proposal is accepted, the full microscopic state is reconstructed from the newly sampled reaction coordinate value and is subjected to a second accept/reject procedure. The computational gain stems from the fact that most proposals are rejected at the macroscopic level, at low computational cost, while microscopic states, once reconstructed, are almost always accepted. We analytically show convergence and discuss the rate of convergence of the proposed algorithm, and numerically illustrate its efficiency on a number of standard molecular test cases. We also investigate the effect of the choice of different numerical parameters on the efficiency of the resulting mM-MCMC method.
Adapted Center and Scale Prediction: More Stable and More Accurate

Pedestrian detection benefits from deep learning technology and gains rapid development in recent years. Most of detectors follow general object detection frame, i.e. default boxes and two-stage process. Recently, anchor-free and one-stage detectors have been introduced into this area. However, their accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of anchor-free detectors and the accuracy of two-stage ones simultaneously, we propose some adaptations based on a detector, Center and Scale Prediction(CSP). The main contributions of our paper are: (1) We improve the robustness of CSP and make it easier to train. (2) We propose a novel method to predict width, namely compressing width. (3) We achieve the second best performance on CityPersons benchmark, i.e. 9.3% log-average miss rate(MR) on reasonable set, 8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and one-stage detector can still have high accuracy. (4) We explore some capabilities of Switchable Normalization which are not mentioned in its original paper.
Upset Recovery Control for Quadrotors Subjected to a Complete Rotor Failure from Large Initial Disturbances

This study has developed a fault-tolerant controller that is able to recover a quadrotor from arbitrary initial orientations and angular velocities, despite the complete failure of a rotor. This cascaded control method includes a position/altitude controller, an almost-global convergence attitude controller, and a control allocation method based on quadratic programming. As a major novelty, a constraint of undesirable angular velocity is derived and fused into the control allocator, which significantly improves the recovery performance. For validation, we have conducted a set of Monte-Carlo simulation to test the reliability of the proposed method of recovering the quadrotor from arbitrary initial attitude/rate conditions. In addition, real-life flight tests have been performed. The results demonstrate that the post-failure quadrotor can recover after being casually tossed into the air.
Fast Evaluation of Link Prediction by Random Sampling of Unobserved Links

The evaluation of a link prediction algorithm requires to estimate the possibility of the existence of all unobserved links in a network. However, the number of unobserved links grows exponentially with the increase of the number of nodes, which limits link prediction in large networks. In this paper, we propose a new evaluation scheme for link prediction algorithms, i.e., link prediction with random sampling. We use this method to evaluate the performance of twelve link predictors on ten real-world networks of different contexts and scales. The results show that the performance ranking of these algorithms is not affected by randomly sampling a very small part from unobserved links for experiments, whether using AUC or the precision metric. Moreover, this sampling method can reduce the computational complexity for the evaluation of link prediction algorithms from O(n^2) to O(n) in large networks. Our findings show that the proposed scheme is a fast and effective evaluation method.
Alternating Minimization Based Trajectory Generation for Quadrotor Aggressive Flight

With much research has been conducted into trajectory planning for quadrotors, planning with spatial and temporal optimal trajectories in real-time is still challenging. In this letter, we propose a framework for large-scale waypoint-based polynomial trajectory generation, with highlights on its superior computational efficiency and simultaneous spatial-temporal optimality. Exploiting the implicitly decoupled structure of the problem, we conduct alternating minimization between boundary conditions and time durations of trajectory pieces. Algebraic convenience of both sub-problems is leveraged to escape poor local minima and achieve the lowest time consumption. Theoretical analysis for the global/local convergence rate of our method is provided. Moreover, based on polynomial theory, an extremely fast feasibility checker is designed for various kinds of constraints. By incorporating it into our alternating structure, a constrained minimization algorithm is constructed to optimize trajectories on the premise of feasibility. Benchmark evaluation shows that our algorithm outperforms state-of-the-art waypoint-based methods regarding efficiency, optimality, and scalability. The algorithm can be incorporated in a high-level waypoint planner, which can rapidly search over a three-dimensional space for aggressive autonomous flights. The capability of our algorithm is experimentally demonstrated by quadrotor fast flights in a limited space with dense obstacles. We release our implementation as an open-source ros-package.1
Implementation of a Spatial Channel Model for ns-3

The next generation of wireless networks will feature a more flexible radio access design, integrating multiple new technological solutions (e.g., massive Multiple-Input Multiple-Output (MIMO), millimeter waves) to satisfy different verticals and use cases. The performance evaluation of these networks will require more complex models to represent the interactions of different components of the networks accurately. For example, channel models, which are of paramount importance to precisely characterize the behavior of such systems, need to account for multi-antenna systems and new frequency bands. This paper presents the ns-3 implementation of a spatial channel model for the 0.5-100 GHz spectrum, following the 3GPP Technical Report 38.901. The code, designed to be flexible and easily extensible, is integrated in ns-3's antenna, propagation and spectrum models, and offers the support for the investigation of future wireless systems in ns-3.
SummerTime: Variable-length Time Series Summarization with Application to Physical Activity Analysis

SummerTime seeks to summarize global time-series signals and provides a fixed-length, robust representation of the variable-length time series. Many machine learning methods depend on data instances with a fixed number of features. As a result, those methods cannot be directly applied to variable-length time series data. Existing methods such as sliding windows can lose minority local information. Summarization conducted by the SummerTime method will be a fixed-length feature vector which can be used in place of the time series dataset for use with classical machine learning methods. We use Gaussian Mixture models (GMM) over small same-length disjoint windows in the time series to group local data into clusters. The time series’ rate of membership for each cluster will be a feature in the summarization. By making use of variational methods, GMM converges to a more robust mixture, meaning the clusters are more resistant to noise and overfitting. Further, the model is naturally capable of converging to an appropriate cluster count. We validate our method on a challenging real-world dataset, an imbalanced physical activity dataset with a variable-length time series structure. We compare our results to state-of-the-art studies and show high-quality improvement by classifying with only the summarization.
Comparing recurrent and convolutional neural networks for predicting wave propagation

Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering. In this work, we investigate the performance of recurrent and convolutional deep neural network architectures to predict the surface waves. The system is governed by the Saint-Venant equations. We improve on the long-term prediction over previous methods while keeping the inference time at a fraction of numerical simulations. We also show that convolutional networks perform at least as well as recurrent networks in this task. Finally, we assess the generalisation capability of each network by extrapolating in longer time-frames and in different physical settings.