Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure

The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations. The ALCD method was designed to minimize human operator time thanks to a manually-supervised active learning method. The trained classifier uses a combination of spectral and multi-temporal information as input features and produces fully-classified images. The ALCD method was validated using visual criteria, consistency checks, and compared to another manually-generated cloud masks, with an overall accuracy above 98%. ALCD was used to create 32 reference cloud masks, on 10 different sites, with different seasons and cloud cover types. These masks were used to validate the cloud and shadow masks produced by three Sentinel-2 Level 2A processors: MAJA, used by the French Space Agency (CNES) to deliver Level 2A products, Sen2Cor, used by the European Space Agency (ESA), and FMask, used by the United States Geological Survey (USGS). The results show that MAJA and FMask perform similarly, with an overall accuracy around 90% (91% for MAJA, 90% for FMask), while Sen2Cor’s overall accuracy is 84%. The reference cloud masks, as well as the ALCD software used to generate them are made available to the Sentinel-2 user community.
Comparison of 1-week and 2-week recall periods for caregiver-reported diarrhoeal illness in children, using nationally representative household surveys

Abstract Background Diarrhoeal outcomes in children are often ascertained using caregiver-reported symptoms, which are subject to a variety of biases and methodological challenges. One source of bias is the time window used for reporting diarrhoeal illness and the ability of caregivers to accurately recall episodes in children. Methods Diarrhoea period prevalence in children under five was determined using two similarly administered, nationally representative household surveys: Performance Monitoring and Accountability 2020 (PMA2020) (1-week recall, N = 14 603) and Demographic and Health Surveys (DHS) (2-week recall, N = 66 717). Countries included in the analysis were the Democratic Republic of the Congo, Ethiopia, Ghana, Kenya and Uganda. Diarrhoea period prevalence estimates were compared and water, sanitation and hygiene risk factors were analysed. Results Childhood diarrhoea prevalence using 1-week recall (PMA2020) pooled across countries was 21.4% [95% confidence interval (CI): 19.9%, 22.9%] versus 16.0% using 2-week recall (DHS) (95% CI: 15.4%, 16.5%). In stratified analyses for all five countries, the number of diarrhoea cases detected was consistently higher using 1-week recall versus 2-week recall. The key risk factors identified in the PMA2020 data that were not associated with diarrhoeal episodes or were attenuated in the DHS data included: the main sanitation classifications for households, disposal method used for child faeces, number of household members and wealth quintiles. Conclusions For nationally representative household surveys assessing childhood diarrhoea period prevalence, a 2-week recall period may underestimate diarrhoea prevalence compared with a 1-week period. The household sanitation facility and practices remain key risk factors for diarrhoeal disease in children under five.
Trifocal Relative Pose from Lines at Points and its Efficient Solution

We present a new minimal problem for relative pose estimation mixing point features with lines incident at points observed in three views and its efficient homotopy continuation solver. We demonstrate the generality of the approach by analyzing and solving an additional problem with mixed point and line correspondences in three views. The minimal problems include correspondences of (i) three points and one line and (ii) three points and two lines through two of the points which is reported and analyzed here for the first time. These are difficult to solve, as they have 216 and - as shown here - 312 solutions, but cover important practical situations when line and point features appear together, e.g., in urban scenes or when observing curves. We demonstrate that even such difficult problems can be solved robustly using a suitable homotopy continuation technique and we provide an implementation optimized for minimal problems that can be integrated into engineering applications. Our simulated and real experiments demonstrate our solvers in the camera geometry computation task in structure from motion. We show that new solvers allow for reconstructing challenging scenes where the standard two-view initialization of structure from motion fails.
Biological Bases of Beauty Revisited: The Effect of Symmetry, Averageness, and Sexual Dimorphism on Female Facial Attractiveness

The factors influencing human female facial attractiveness—symmetry, averageness, and sexual dimorphism—have been extensively studied. However, recent studies, using improved methodologies, have called into question their evolutionary utility and links with life history. The current studies use a range of approaches to quantify how important these factors actually are in perceiving attractiveness, through the use of novel statistical analyses and by addressing methodological weaknesses in the literature. Study One examines how manipulations of symmetry, averageness, femininity, and masculinity affect attractiveness using a two-alternative forced choice task, finding that increased masculinity and also femininity decrease attractiveness, compared to unmanipulated faces. Symmetry and averageness yielded a small and large effect, respectively. Study Two utilises a naturalistic ratings paradigm, finding similar effects of averageness and masculinity as Study One but no effects of symmetry and femininity on attractiveness. Study Three applies geometric face measurements of the factors and a random forest machine learning algorithm to predict perceived attractiveness, finding that shape averageness, dimorphism, and skin texture symmetry are useful features capable of relatively accurate predictions, while shape symmetry is uninformative. However, the factors do not explain as much variance in attractiveness as the literature suggests. The implications for future research on attractiveness are discussed.
Improving Details of Building Façades in Open LiDAR Data Using Ground Images

Recent open data initiatives allow free access to a vast amount of light detection and ranging (LiDAR) data in many cities. However, most open LiDAR data of cities are acquired by airborne scanning, where points on building façades are sparse or even completely missing due to occlusions in the urban environment, leading to the absence of façade details. This paper presents an approach for improving the LiDAR data coverage on building façades by using point cloud generated from ground images. A coarse-to-fine strategy is proposed to fuse these two-point clouds of different sources with very limited overlaps. First, the façade point cloud generated from ground images is leveled by adjusting the facade normal to perpendicular to the upright direction. Then leveling façade point cloud is geolocated by alignment between images GPS data and their structure from motion (SfM) coordinates. Next, a modified coherent point drift algorithm with (surface) normal consistency is proposed to accurately align the façade point cloud to the LiDAR data. The significance of this work resides in the use of 2D overlapping points on the building outlines instead of the limited 3D overlap between the two-point clouds. This way we can still achieve reliable and precise registration under incomplete coverage and ambiguous correspondence. Experiments show that the proposed approach can significantly improve the façade details in open LiDAR data, and achieve 2 to 10 times higher registration accuracy, when compared to classic registration methods.
Analysing the bias in the primal-dual upper bound method for early exercisable derivatives: bounds, estimation and removal

We analyse the primal-dual upper bound method for Bermudan options and prove that its bias is inversely proportional to the number of paths in sub-simulations for a large class of cases. We develop a methodology for estimating and reducing the bias. We present numerical results showing that the new technique is indeed effective.
Measuring light with light-dependent resistors: an easy approach for optics experiments

We entertain the use of light-dependent resistors as a viable option as measuring sensors in optics laboratory experiments or classroom demonstrations. The main advantages of these devices are their essentially very low cost, easy handling and commercial availability, which can make them interesting for instructors with limited resources. Simple calibration procedures were developed indicating a precision of ∼5% for illuminance measurements. Optical experiments were carried out as proof of feasibility for measurements of reflected and transmitted light and its quality results are presented. In particular, the sensor measurements allowed us to verify the angular distribution of a Lambertian reflective material, to observe transmitted and reflected specular light on a glass slab as function of the incoming angle of a light beam, and to estimate glass refractive index with values averaging 1.51 ± 0.06 in satisfactory agreement with the expected 1.52 value.
Combining Electrical Resistivity Tomography and Satellite Images for Improving Evapotranspiration Estimates of Citrus Orchards

An adjusted satellite-based model was proposed with the aim of improving spatially distributed evapotranspiration (ET) estimates under plant water stress conditions. Remote sensing data and near surface geophysics information, using electrical resistivity tomography (ERT), were used in a revised version of the original dual crop coefficient (Kc) FAO-56 approach. Sentinel 2-A imagery were used to compute vegetation indices (VIs) required for spatially estimating ET. The potentiality of the ERT technique was exploited for tracking the soil wetting distribution patterns during and after irrigation phases. The ERT-derived information helped to accurately estimate the wet exposed fraction (few) and therefore the water evaporated from the soil surface into the dual Kc FAO-56 approach. Results, validated by site-specific ET measurements (ETEC) obtained using the eddy covariance (EC) technique, showed that ERT-adjusted ET estimates (ETERT) were considerably reduced (15%) when compared with the original dual Kc FAO-56 approach (ETFAO), soil evaporation overestimation being the main reason for these discrepancies. Nevertheless, ETFAO and ETERT showed overestimations of 64% and 40% compared to ETEC. This is because both approaches determine ET under standard conditions without water limitation, whereas EC is able to determine ET even under soil water deficit conditions. From the comparison between ETEC and ETERT, the water stress coefficient was experimentally derived, reaching a mean value for the irrigation season of 0.74. The obtained results highlight how new technologies for soil water status monitoring can be incorporated for improving ET estimations, particularly under drip irrigation conditions.
Breaking Point: Violence Against Long-Term Care Staff

Direct resident care in long-term care facilities is carried out predominantly by personal support workers and registered practical nurses, the majority of whom are women. They experience physical, verbal, and sexual violence from residents on a regular basis. To explore this widespread problem, fifty-six staff in seven communities in Ontario, Canada, were consulted. They identified such immediate causes of violence as resident fear, confusion, and agitation and such underlying causes as task-driven organization of work, understaffing, inappropriate resident placement, and inadequate time for relational care. They saw violence as symptomatic of an institution that undervalues both its staff and residents. They described how violence affects their own health and well-being—causing injuries, unaddressed emotional trauma, job dissatisfaction, and burnout. They outlined barriers to preventing violence, such as insufficient training and resources, systemic underfunding, lack of recognition of the severity and ubiquity of the phenomenon, and limited public awareness.
Online Optimisation for Online Learning and Control - From No-Regret to Generalised Error Convergence

This paper presents early work aiming at the development of a new framework for the design and analysis of algorithms for online learning based prediction and control. Firstly, we consider the task of predicting values of a function or time series based on incrementally arriving sequences of inputs by utilising online programming. Introducing a generalisation of standard notions of convergence, we derive theoretical guarantees on the asymptotic behaviour of the prediction accuracies when prediction models are updated by a no-external-regret algorithm. We prove generalised learning guarantees for online regression and provide an example of how this can be applied to online learning-based control. We devise a model-reference adaptive controller with novel online performance guarantees on tracking success in the presence of a priori dynamic uncertainty. Our theoretical results are accompanied by illustrations on simple regression and control problems.
Supermassive Black Hole Feedback

Understanding the processes that drive galaxy formation and shape the observed properties of galaxies is one of the most interesting and challenging frontier problems of modern astrophysics. We now know that the evolution of galaxies is critically shaped by the energy injection from accreting supermassive black holes (SMBHs). However, it is unclear how exactly the physics of this feedback process affects galaxy formation and evolution. In particular, a major challenge is unraveling how the energy released near the SMBHs is distributed over nine orders of magnitude in distance throughout galaxies and their immediate environments. The best place to study the impact of SMBH feedback is in the hot atmospheres of massive galaxies, groups, and galaxy clusters, which host the most massive black holes in the Universe, and where we can directly image the impact of black holes on their surroundings. We identify critical questions and potential measurements that will likely transform our understanding of the physics of SMBH feedback and how it shapes galaxies, through detailed measurements of (i) the thermodynamic and velocity fluctuations in the intracluster medium (ICM) as well as (ii) the composition of the bubbles inflated by SMBHs in the centers of galaxy clusters, and their influence on the cluster gas and galaxy growth, using the next generation of high spectral and spatial resolution X-ray and microwave telescopes.
Volatility forecasts and at-the-money implied volatility: a multi-component ARCH approach and its relation to market models

This article explores the relationships between several forecasts for the volatility built from multi-scale linear ARCH processes, and linear market models for the forward variance. This shows that the structures of the forecast equations are identical, but with different dependencies on the forecast horizon. The process equations for the forward variance are induced by the process equations for an ARCH model, but postulated in a market model. In the ARCH case, they are different from the usual diffusive type. The conceptual differences between both approaches and their implication for volatility forecasts are analysed. The volatility forecast is compared with the realized volatility (the volatility that will occur between date t and t + ΔT), and the implied volatility (corresponding to an at-the-money option with expiry at t + ΔT). For the ARCH forecasts, the parameters are set a priori. An empirical analysis across multiple time horizons ΔT shows that a forecast provided by an I-GARCH(1) process (one time scale) does not capture correctly the dynamics of the realized volatility. An I-GARCH(2) process (two time scales, similar to GARCH(1,1)) is better, while a long-memory LM-ARCH process (multiple time scales) replicates correctly the dynamics of the implied and realized volatilities and delivers consistently good forecasts for the realized volatility.
Probing IGM accretion on to faint Lyα emitters at z ∼ 2.8

Observing the signature of accretion from the intergalactic medium (IGM) on to galaxies at z ∼ 3 requires the detection of faint (L ≪ L*) galaxies embedded in a filamentary matrix of low-density ($\rho \lt 100\ \overline{\rho }$), metal-poor gas (Z ∼ 10−2.5 Z⊙) coherent over hundreds of kpc. We study the gaseous environment of three Lyα emitters (LAEs) at z = 2.7 − 2.8, found to be aligned in projection with a background QSO over ∼250 kpc along the slit of a long-slit spectrum. The lack of detection of the LAEs in deep continuum images and the low inferred Lyα luminosities show the LAEs to be intrinsically faint, low-mass galaxies ($L\lesssim 0.1\, L^*$, $M_\mathrm{star}\lesssim 0.1\, M^*$). An echelle spectrum of the QSO reveals strong Lyα absorption within ±200 km s−1 from the LAEs. Our absorption line analysis leads to $\rm{H\,{\small I}}$ column densities in the range of log $N\mathrm{(\rm{H\,{\small I}})}/\mbox{${\rm cm^{-2}}$}=16\! -\! 18$. Associated absorption from ionic metal species $\rm{C\,{\small IV}}$ and $\rm{Si\,{\small IV}}$ constrains the gas metallicities to ∼0.01 solar if the gas is optically thin, and possibly as low as ∼0.001 solar if the gas is optically thick, assuming photoionization equilibrium. While the inferred metallicities are at least a factor of 10 lower than expected metallicities in the interstellar medium (ISM) of these LAEs, they are consistent with the observed chemical enrichment level in the IGM at the same epoch. Total metal abundances and kinematic arguments suggest that these faint galaxies have not been able to affect the properties of their surrounding gas. The projected spatial alignment of the LAEs, together with the kinematic quiescence and correspondence between the LAEs and absorbing gas in velocity space, suggests that these observations probe a possible filamentary structure. Taken together with the blue-dominant Lyα emission line profile of one of the objects, the evidence suggests that the absorbing gas is part of an accretion stream of low-metallicity gas in the IGM.
Book Reviews

This special issue forum on the AIDS Coalition to Unleash Power invites us all to ask, in this ostensibly post-AIDS era, who are we in relation to ACT UP, and where does ACT UP reside in a constellation of political and rhetorical activity about HIV/AIDS? Collectively considered, Jennifer Brier’s Infectious Ideas, Deborah B. Gould’s Moving Politics, and Roger Hallas’s Reframing Bodies offer compelling and occasionally painful responses to these questions. For Hallas, a boyfriend’s sharing of a videotape documenting an ACT UP demonstration galvanizes a broader exploration of AIDS activist video; for Gould, direct and long-term participation as an activist in the Chicago chapter of ACT UP galvanizes interrogation of the ‘‘structures of feeling’’ that circulate through HIV/AIDS activism. From the vantage point of 2009, both
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the spatial structure of the LOBs as well as long short-term memory modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [A. Ntakaris, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Benchmark dataset for mid-price prediction of limit order book data with machine learning methods,” J. Forecasting, vol. 37, no. 8, 852–866, 2018]. In a more realistic setting, we test our model by using one-year market quotes from the London Stock Exchange, and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments that were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a “black box” model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features that translate well to other instruments is an important property of our model, which has many other applications.
Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has heightened local wildfire risk as canopy loss facilitates the conversion from bare to fire-prone grassland. We collected images from NASA satellite-based Earth observations to visualize land cover succession at roughly five-year intervals following a severe, mid-1990’s beetle infestation to the present. We classified these data by vegetation cover type to quantify grassland encroachment patterns over time. Raster band math provided a change detection analysis on the land cover classifications. Results indicate the highest wildfire risk is linked to herbaceous and black spruce land cover types, The resulting land cover change image will give the Kenai National Wildlife Refuge (KENWR) ecologists a better understanding of where forests have converted to grassland since the 1990s. These classifications provided a foundation for us to integrate digital elevation models (DEMs), temperature, and historical fire data into a model using Python for assessing and mapping changes in wildfire risk. Spatial representations of this risk will contribute to a better understanding of ecological trajectories of beetle-affected landscapes, thereby informing management decisions at KENWR.