Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a generic sampling system for monitoring the key arthropod pests of table grapes, Vitis vinifera L.

We describe a system developed for monitoring arthropod pests of table grapes in the Hex River Valley, South Africa. The system has wide applicability for monitoring pests at grape-producing localities in the country. Six Vitis vinifera cv. Barlinka and six V. vinifera cv. Dauphine vineyards were used as sampling sites. In each vineyard, 100 vines were inspected bi-weekly, to record damage or the presence of arthropod pests on the cordon, one bunch and one leaf per vine. Sampling precision and operating characteristic (OC) curves were estimated for each of the pests. Using previously determined economic thresholds together with the OC curves, action thresholds were determined for Tetranychus urticae Koch (Prostigmata: Tetranychidae) (11–29% leaf infestation), Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae) (2% cordon infestation), Epichoristodes acerbella (Walker) (Lepidoptera: Tortricidae) and Phlyctinus callosus Boh. (Coleoptera: Curculionidae) (1% bunch damage). This novel system can be used f.
Atomic structure and lifetimes

of Volume 1. - The Term Analysis of Atomic Spectra: Present Status and Remaining Problems. - Recent Configuration Interaction Studies in Atomic Lifetimes. - Oscillator Strengths for Ac I, Sc II and Ti III. - Transition Probabilities for Ionized Atoms. - Correlation Effects and f-Values in the Sodium Sequence. - On the Possibility of Observing Nonexponential Decays in Autoionizing States. - Coulomb Methods in Atomic Transition Probability Calculations. - Lamb Shift in Hydrogenlike Ions. - Hyperfine Quenching of the 23P0 State in Heliumlike Ions. - Anomalies in the Fine and Hyperfine Structure. - Relativistic Contributions to Transition Energies in NiI and CuI Isoelectronic Sequences. - Oscillator Strengths in N, N+,O and O+ Obtained From the First Order Theory of Oscillator Strengths (Fotos). - Lifetimes and Fluorescence Yields of Three-Electron Ions. - Future Directions for Beam-Foil Spectroscopy. - Review of Experimental Lifetimes: Third Period Elements. - Review of Experimental Lifetimes: Fourth Period Elements. - Heavy-Element Beam-Foil Lifetime Measurements and Related Experimental Problems. - Measurements of He I Lifetimes and Fine Structure by a Two-Spectrometer Method. - Mean-Lives of RbII in the Visible and Vacuum Ultraviolet. - Applications of High Resolution Measurements of Optical Lifetimes. - Beam-Gas Studies of Cu II, C1 II, and As II. - Beam-Foil Study of S III - S VI. - The Independent-Electron Model Applied to 100-600 keV Sulfur Beam-Foil Population Functions. - Beam-Foil Spectroscopy at the University of Alberta. - Decay of the 2p3p3S1 Level of O III. - Profiles of the Spectral Lines Near 2363A and 2577A From Foil-Excited He. - Relative Initial Populations of Foil-Excited He I States. - Radio Frequency Spectroscopy with a Fast Atomic Beam. - X-Rays From Foil-Excited Beams at Tandem Energies. - Lifetime Measurement of the 3P1 State of Heliumlike Sulphur. - Radiative Decay and Fine Structure of the 23Po and the 23S1 States of Helium-like Krypton (Kr XXXV). - Radiative Decay of the 23P States of Heliumlike Argon. - Extreme Ultraviolet Spectra of Highly Stripped Silicon Ions. - Beam Foil Spectroscopy of Highly Ionized Fluorine, Silicon and Copper Beams. - Beam-Foil Studies of Nitrogen, Sulfur and Silicon. - Recent Beam-Foil Mean-Life Measurements in Fluorine V-VIII. - Beam-Foil Spectroscopy of Highly-Ionized C, N, O and Ne Atoms at 1 MeV/Nucleon. - Spectroscopy of Heavy Ions Using the Beam-Foil Technique. - Satellite Lines in Highly-Stripped Ions of B,C,N,O, and F. - Doubly-Excited States in B III. - Doubly-Excited States in N V and N VI. - The Atomic Physics Potential of New Accelerators. - Autoionizing States in the Alkalis. - Metastable Autoionizing Quartet- Quintet- and Sextet States in B. - Auger Electron Emission Spectra From Foil and Gas Excited Carbon Beams. - Autoionizing States in Highly Ionized Oxygen, Fluorine, and Silicon.
An end-to-end data-driven optimisation framework for constrained trajectories

Many real-world problems require to optimise trajectories under constraints. Classical approaches are based on optimal control methods but require an exact knowledge of the underlying dynamics, which could be challenging or even out of reach. In this paper, we leverage data-driven approaches to design a new end-to-end framework which is dynamics-free for optimised and realistic trajectories. We first decompose the trajectories on function basis, trading the initial infinite dimension problem on a multivariate functional space for a parameter optimisation problem. A maximum \emph{a posteriori} approach which incorporates information from data is used to obtain a new optimisation problem which is regularised. The penalised term focuses the search on a region centered on data and includes estimated linear constraints in the problem. We apply our data-driven approach to two settings in aeronautics and sailing routes optimisation, yielding commanding results. The developed approach has been implemented in the Python library PyRotor.
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems

Distributed optimization often requires finding the minimum of a global objective function written as a sum of local functions. A group of agents work collectively to minimize the global function. We study a continuous-time decentralized mirror descent algorithm that uses purely local gradient information to converge to the global optimal solution. The algorithm enforces consensus among agents using the idea of integral feedback. Recently, the asymptotic convergence of this algorithm was studied for when the global function is strongly convex but local functions are convex. Using control theoretical tools, in this work, we prove (theoretically) that the algorithm indeed achieves local exponential convergence. We also provide a numerical experiment on a real data-set as a validation of the convergence speed of our algorithm.
On the possibility of probing the flare productivity of an active region in the early stage of emergence

A long-standing problem is to predict the future flare productivity of an active region (AR) when it is on the stage of early emergence. The aim of this study is to probe two parameters of the photospheric magnetic field, both derived during the emergence phase of an AR, and to compare them with the flare productivity of the well-developed AR. The parameters are: (i) the index of the magnetic power spectrum (the slope of the spectrum) on the stage of emergence, and (ii) the flux emergence rate. Analysis of 243 emerging ARs showed that the magnetic power index increases from values typical for quiet-Sun regions to the values typical for mature ARs within a day while the emergence proceeds for several days; frequently, after the increase, the value of the power index undulates around some mean value with fluctuations being several time less than the growth of the power index during the emergence onset. For a subset of 34 flare-productive ARs we found no correlation between the power spectrum index at the stage of emergence and flare index derived from the entire interval of the AR's presence on the disc. At the same time, the flux emergence rate correlates well with the flare index (the Pearson's correlation coefficient is 0.74). We conclude that high flux emergence rate is a necessary condition for an AR to produce strong flares in the future, and so, the flux emergence rate can be used to probe the future flare productivity of the AR.
Structure of human Nav1.5 reveals the fast inactivation-related segments as a mutational hotspot for the long QT syndrome

Significance Dysfunction of Nav1.5, the primary cardiac Nav channel, is associated with multiple arrhythmia syndromes, exemplified by type 3 long QT syndrome (LQT3) and Brugada syndrome (BrS). Establishment of the structure-function relationship and mechanistic understanding of the disease variants will facilitate the development of antiarrhythmic drugs. Here we report the cryo-EM structure of human Nav1.5-E1784K, the most common variant shared by LQT3 and BrS. Structural mapping of 91 LQT3-associated mutations reveal a hotspot that involves the fast inactivation segments. The high density of LQT3 mutation sites in this region can be reasonably interpreted by the “door wedge” model for fast inactivation, which was derived from our previous structural observations and is supported by a wealth of functional characterizations. Nav1.5 is the primary voltage-gated Na+ (Nav) channel in the heart. Mutations of Nav1.5 are associated with various cardiac disorders exemplified by the type 3 long QT syndrome (LQT3) and Brugada syndrome (BrS). E1784K is a common mutation that has been found in both LQT3 and BrS patients. Here we present the cryo-EM structure of the human Nav1.5-E1784K variant at an overall resolution of 3.3 Å. The structure is nearly identical to that of the wild-type human Nav1.5 bound to quinidine. Structural mapping of 91- and 178-point mutations that are respectively associated with LQT3 and BrS reveals a unique distribution pattern for LQT3 mutations. Whereas the BrS mutations spread evenly on the structure, LQT3 mutations are clustered mainly to the segments in repeats III and IV that are involved in gating, voltage-sensing, and particularly inactivation. A mutational hotspot involving the fast inactivation segments is identified and can be mechanistically interpreted by our “door wedge” model for fast inactivation. The structural analysis presented here, with a focus on the impact of mutations on inactivation and late sodium current, establishes a structure-function relationship for the mechanistic understanding of Nav1.5 channelopathies.
CAT: Change-focused Android GUI Testing

Android Apps are frequently updated, every couple of weeks, to keep up with changing user, hardware and business demands. Correctness of App updates is checked through extensive testing. Recent research has proposed tools for automated GUI event generation in Android Apps. These techniques, however, are not efficient at checking App updates as the generated GUI events do not prioritise updates, and instead explore other App behaviours. We address this need in this paper with CAT (Change-focused Android GUI Testing). For App updates, at the source code or GUI level, CAT performs change impact analysis to identify GUI elements affected by the update. CAT then generates GUI event sequences to interact with the affected GUI elements. Our empirical evaluations using 21 publicly available open source and 2 commercial Android Apps demonstrate that CAT is able to automatically identify GUI elements affected by App updates, generate and execute GUI event sequences focusing on change-affected GUI elements. Comparison with two popular GUI event generation tools, DroidBot and DroidMate, revealed that CAT was more effective at interacting with the change-affected GUI elements. Finally, CAT was able to detect previously unknown change-related bugs in two open source Apps. Developers of the commercial Apps found CAT was more effective than their in-house GUI testing tool in interacting with changed elements and faster at detecting seeded bugs.
Multiscale energy budget of inertially driven turbulence in normal and superfluid helium

In this paper we present a novel hydrodynamic experiment using liquid $^4$He. The flow is forced inertially by a canonical oscillating grid using either its normal (He~I) or superfluid (He~II) phase, generating a statistically stationary turbulence. We characterise the turbulent properties of the flow using 2D Lagrangian Particle tracking on hollow glass micro-spheres. As expected for tracer particles, the Voronoi tessellation on particle positions does not show a significant departure from a random Poisson process neither in He~I nor He~II phase. Particles' positions are tracked with high temporal resolution, allowing to resolve velocity fluctuations at integral and inertial scales while properly assessing the noise contribution. Additionally, we differentiate the particles' positions (by convolution with Gaussian kernels) in order to access small scale quantities like acceleration. Using these measured quantities and the formalism of classical Homogeneous Isotropic Turbulence (HIT) to perform an energy budget across scales we extract the energy injection rate at the large scale, the energy flux cascading through inertial scales, down to small scales at which it is dissipated. We found that in such inertially driven turbulence, regardless of the normal or superfluid state of the fluid, estimates of energy at the different scales are compatible with each other and consistent with oscillating grid turbulence results reported for normal fluids in the literature. The largest discrepancy shows up at small scales where the signal to noise ratio is harder to control and where the 2D measurement is contaminated by the 3D nature of the flow. This motivates to focus future experimental projects towards small scales, low noise and 3D measurements.
Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road

Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially lack of ground truth, definition of multiple risk exposures. This study proposes a domain-specific automatic clustering (termed AutoCluster) to self-learn the optimal models for unsupervised risk assessment, which integrates key steps of clustering into an auto-optimisable pipeline, including feature and algorithm selection, hyperparameter auto-tuning. Firstly, based on surrogate conflict measures, a series of risk indicator features are constructed to represent temporal-spatial and kinematical risk exposures. Then, we develop an unsupervised feature selection method to identify the useful features by elimination-based model reliance importance (EMRI). Secondly, we propose balanced Silhouette Index (bSI) to evaluate the internal quality of imbalanced clustering. A loss function is designed that considers the clustering performance in terms of internal quality, inter-cluster variation, and model stability. Thirdly, based on Bayesian optimisation, the algorithm auto-selection and hyperparameter auto-tuning are self-learned to generate the best clustering results. Herein, NGSIM vehicle trajectory data is used for test-bedding. Findings show that AutoCluster is reliable and promising to diagnose multiple distinct risk levels inherent to generalised driving behaviour. We also delve into risk clustering, such as, algorithms heterogeneity, Silhouette analysis, hierarchical clustering flows, etc. Meanwhile, the AutoCluster is also a method for unsupervised data labelling and indicator threshold calibration. Furthermore, AutoCluster is useful to tackle the challenges in imbalanced clustering without ground truth or a priori knowledge.
Self-supervised transfer learning of physiological representations from free-living wearable data

Wearable devices such as smartwatches are becoming increasingly popular tools for objectively monitoring physical activity in free-living conditions. To date, research has primarily focused on the purely supervised task of human activity recognition, demonstrating limited success in inferring high-level health outcomes from low-level signals. Here, we present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels. With a deep neural network, we set HR responses as the supervisory signal for the activity data, leveraging their underlying physiological relationship. In addition, we propose a custom quantile loss function that accounts for the long-tailed HR distribution present in the general population. We evaluate our model in the largest free-living combined-sensing dataset (comprising >280k hours of wrist accelerometer & wearable ECG data). Our contributions are two-fold: i) the pre-training task creates a model that can accurately forecast HR based only on cheap activity sensors, and ii) we leverage the information captured through this task by proposing a simple method to aggregate the learnt latent representations (embeddings) from the window-level to user-level. Notably, we show that the embeddings can generalize in various downstream tasks through transfer learning with linear classifiers, capturing physiologically meaningful, personalized information. For instance, they can be used to predict variables associated with individuals' health, fitness and demographic characteristics (AUC >70), outperforming unsupervised autoencoders and common bio-markers. Overall, we propose the first multimodal self-supervised method for behavioral and physiological data with implications for large-scale health and lifestyle monitoring. Code: https://github.com/sdimi/Step2heart.
Inconsistencies arising from the coupling of galaxy formation sub-grid models to pressure-smoothed particle hydrodynamics

Smoothed particle hydrodynamics (SPH) is a Lagrangian method for solving the fluid equations that is commonplace in astrophysics, prized for its natural adaptivity and stability. The choice of variable to smooth in SPH has been the topic of contention, with smoothed pressure (P-SPH) being introduced to reduce errors at contact discontinuities relative to smoothed density schemes. Smoothed pressure schemes produce excellent results in isolated hydrodynamics tests; in more complex situations however, especially when coupling to the ‘sub-grid’ physics and multiple time-stepping used in many state-of-the-art astrophysics simulations, these schemes produce large force errors that can easily evade detection as they do not manifest as energy non-conservation. Here, two scenarios are evaluated: the injection of energy into the fluid (common for stellar feedback) and radiative cooling. In the former scenario, force and energy conservation errors manifest (of the same order as the injected energy), and in the latter large force errors that change rapidly over a few time-steps lead to instability in the fluid (of the same order as the energy lost to cooling). Potential ways to remedy these issues are explored with solutions generally leading to large increases in computational cost. Schemes using a density-based formulation do not create these instabilities and as such it is recommended that they are preferred over pressure-based solutions when combined with an energy diffusion term to reduce errors at contact discontinuities.
Acoustic Span Embeddings for Multilingual Query-by-Example Search

Query-by-example (QbE) speech search is the task of matching spoken queries to utterances within a search collection. In low-or zero-resource settings, QbE search is often addressed with approaches based on dynamic time warping (DTW). Recent work has found that methods based on acoustic word embeddings (AWEs) can improve both performance and search speed. However, prior work on AWE-based QbE has primarily focused on English data and with single-word queries. In this work, we generalize AWE training to spans of words, producing acoustic span embeddings (ASE), and explore the application of ASE to QbE with arbitrary-length queries in multiple unseen languages. We consider the commonly used setting where we have access to labeled data in other languages (in our case, several low-resource languages) distinct from the unseen test languages. We evaluate our approach on the QUESST 2015 QbE tasks, finding that multilingual ASE-based search is much faster than DTW-based search and outperforms the best previously published results on this task.
HD 63021: Chromospheric Activity and Mass Transfer in a Close Binary

Prompted by X-ray detections from multiple surveys, we investigated the A-type star HD 63021 and found that it is a double-lined spectroscopic binary with highly variable emission associated with the primary star. Analysis of our multiepoch spectroscopic observations, the majority of which were carried out on small-aperture telescopes, indicates a very short orbital period of just 2.9 days and a mass ratio M2/M1 of 0.23. The A1 V star is a slow rotator, with a rotational speed of ∼34 km s−1. Assuming that its mass is 2.3 M⊙, the present-day secondary is an evolved star of ∼0.5 M⊙ that nearly fills its Roche lobe. This secondary star rotates comparatively rapidly at ∼44 km s−1, and we see evidence that it is chromospherically active. Analysis of a photometric light curve from TESS reveals two strong periods, one at the orbital period for the system and another at half the orbital period. These findings suggest that HD 63021 is a close binary system undergoing mass transfer from the secondary star onto the primary star—in all ways like an Algol eclipsing binary system, except without the eclipse. We discuss the system’s mass transfer, which is not steady but seems to run in fits and bursts, and infer the system’s basic physical properties from an orbital parameter study, the Roche lobe geometry, and its extant X-ray emission.
[Pr\é\servation des apprentissages implicites en musique dans le vieillissement normal et la maladie d'Alzheimer]

La robustesse des apprentissages implicites a ete demontree pour des stades debutants du vieillissement normal et pathologique. L’objectif de l’etude est de mettre en evidence des capacites comparables dans le vieillissement (plus de 90 ans) et les stades moderes de la maladie d’Alzheimer (MMSE inferieur a 21). Les participants realisent deux tâches d’apprentissage implicite et une tâche d’apprentissage explicite, toutes trois avec des stimuli musicaux. Cinq groupes experimentaux sont testes : etudiants, personnes âgees de 80 a 86 ans, et de 90 a 96 ans, patients Alzheimer en stade leger, et en stade modere. Les resultats montrent des performances equivalentes dans les deux tâches implicites pour tous les groupes de participants, alors que les personnes normales tres âgees ou a un stade modere de demence d’Alzheimer echouent a la tâche d’apprentissage explicite. Ces resultats sont discutes en fonction de leurs implications cliniques pour de nouvelles perspectives therapeutiques.
Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing

This paper proposes a new model for augmenting algorithms with useful predictions that go beyond worst-case bounds on the algorithm performance. By refining existing models, our model ensures predictions are formally learnable and instance robust. Learnability guarantees that predictions can be efficiently constructed from past data. Instance robustness formally ensures a prediction is robust to modest changes in the problem input. Further, the robustness model ensures two different predictions can be objectively compared, addressing a shortcoming in prior models. This paper establishes the existence of predictions which satisfy these properties. The paper considers online algorithms with predictions for a network flow allocation problem and the restricted assignment makespan minimization problem. For both problems, three key properties are established: existence of useful predictions that give near optimal solutions, robustness of these predictions to errors that smoothly degrade as the underlying problem instance changes, and we prove high quality predictions can be learned from a small sample of prior instances.