Miracle-dz/UniFAR-FTUs huggingface dataset:
Human herpesvirus 8 (HHV-8) salivary shedding in mothers and children from Uganda: risk factors and clues about transmission

Background: in Africa, the increase of Kaposi Sar-coma-associated herpesvirus (KSHV/HHV-8) seroprevalence during childhood suggests an horizontal intrafamilial transmission. However, the exact transmission modes are yet unknown, but there is evidence that the virus is intermittently spread through the saliva of seropositive subjects. Fur-thermore, a significant correlation was found between the geographical distribution of bloodsucking arthropods and incidence rates of Kaposi Sarcoma. In this view, key roles can be played by: i) the inflammatory reaction due to the arthropod bites which create a micro-environment favourable to viral replication, and ii) the use of saliva as first-aid medication in several circumstances. According to that, the viral transmission could occur through the application of mothers’ saliva on children’s skin to relieve itching and scratching after a bloodsucking arthropod bite. This work investigat-ed the risk factors involved in HHV-8 transmission based on the “promoter arthropod hypothesis”. Methods: one hundred and thirty questionnaires were administered to Ugandan children (54 from settled and 76 from nomad communities) to evalu-ate the skin inflammatory reaction to the arthropod bites and the frequency of behavioural practices associated with saliva usage. At the same time saliva samples were collected from the children and respective mothers to detect the presence of HHV-8 DNA by Real-Time PCR. Results: we detected HHV-8 DNA in 23.85% of mothers and in 11.54% of children. The use of saliva is significantly more common in settled than in the nomad communities (p<0.001). The statistical analysis showed significant association between: i) skin inflammatory reaction and use of saliva in both groups; ii) environmental factors which increase the presence of bloodsucking arthropods and skin inflammatory reaction. Discussion: from our data we suppose that HHV-8 transmission could occur through non-sexual routes and that the infected saliva applied at child bite site could represent a favourable condition for the acquiring of the infection.
Affective Signals in a Social Media Recommender System

People come to social media to satisfy a variety of needs, such as being informed, entertained and inspired, or connected to their friends and community. Hence, to design a ranking function that gives useful and personalized post recommendations, it would be helpful to be able to predict the affective response a user may have to a post (e.g., entertained, informed, angered). This paper describes the challenges and solutions we developed to apply Affective Computing to social media recommendation systems. We address several types of challenges. First, we devise a taxonomy of affects that was small (for practical purposes) yet covers the important nuances needed for the application. Second, to collect training data for our models, we balance between signals that are already available to us (namely, different types of user engagement) and data we collected through a carefully crafted human annotation effort on 800k posts. We demonstrate that affective response information learned from this dataset improves a module in the recommendation system by more than 8%. Online experimentation also demonstrates statistically significant decreases in surfaced violating content and increases in surfaced content that users find valuable.
An open source package to perform basic and advanced statistical analysis of turbulence data and other complex systems

We present an open-source package that helps the user to perform a basic statistical analysis of a given turbulence data set which we believe to be useful to the entire turbulence community.It can be used to estimate statistical quantities of turbulence such as the spectral density, turbulence intensity, integral length scale, Taylor microscale, Kolmogorov scale and dissipation rate as well as velocity increments, structure functions and their scaling exponents.Different well-known methods available in the literature were selected so that they can be compared.This package also performs an advanced analysis that includes the scale-dependent statistical description of turbulent cascade using the Fokker-Planck equation which, consequently leading to the assessment of integral fluctuation theorems.As a precondition for the stochastic process approach, Markovian properties of the turbulent cascade in scale are tested.This is utilized to estimate the drift and diffusion coefficients of the Fokker-Planck equation.The knowledge of a Fokker-Planck equation allows to determine a total entropy production for each independent cascade trajectory. Estimating total entropy production admits a rigorous law of non-equilibrium stochastic thermodynamics, namely the integral fluctuation theorem, which must be valid if Markov properties hold and the Fokker-Planck equation is correct. This approach to the turbulent cascade process has the potential for a new way to link the statistical description of turbulence, non-equilibrium stochastic thermodynamics, and local turbulent flow structures. Finally, we emphasize that the presented package can be used for the analysis of other data with turbulent-like complexity as well.
Detection and Characterisation of Conductive Objects Using Electromagnetic Induction and a Fluxgate Magnetometer

Eddy currents induced in electrically conductive objects can be used to locate metallic objects as well as to assess the properties of materials non-destructively without physical contact. This technique is useful for material identification, such as measuring conductivity and for discriminating whether a sample is magnetic or non-magnetic. In this study, we carried out experiments and numerical simulations for the evaluation of conductive objects. We investigated the frequency dependence of the secondary magnetic field generated by induced eddy currents when a conductive object is placed in a primary oscillating magnetic field. According to electromagnetic theory, conductive objects have different responses at different frequencies. Using a table-top setup consisting of a fluxgate magnetometer and a primary coil generating a magnetic field with frequency up to 1 kHz, we were able to detect aluminium and steel cylinders using the principle of electromagnetic induction. The experimental results were compared to numerical simulations, with good overall agreement. This technique enables the identification and characterisation of objects using their electrical conductivity and magnetic permeability.
A transient equivalence between Aldous-Broder and Wilson's algorithms and a two-stage framework for generating uniform spanning trees

The Aldous-Broder and Wilson are two well-known algorithms to generate uniform spanning trees (USTs) based on random walks. This work studies their relationship while they construct random trees with the goal of reducing the total time required to build the spanning tree. Using the notion of branches – paths generated by the two algorithms on particular stopping times, we show that the trees built by the two algorithms when running on a complete graph are statistically equivalent on these stopping times. This leads to a hybrid algorithm that can generate uniform spanning trees of complete graphs faster than either of the two algorithms. An eﬃcient two-stage framework is also proposed to explore this hybrid approach beyond complete graphs, showing its feasibility in various examples, including transitive graphs where it requires 25% less time than Wilson to generate a UST.
Stochastic formalism for U(1) gauge fields in axion inflation

We develop the stochastic formalism for U(1) gauge fields that has the Chern-Simons coupling to a rolling pseudo-scalar field during inflation. The Langevin equations for the physical electromagnetic fields are derived and the analytic solutions are studied. Using numerical simulation we demonstrate that the electromagnetic fields averaged over the Hubble scale continuously change their direction and their amplitudes fluctuate around the analytically obtained expectation values. Though the isotropy is spontaneously broken by picking up a particular local Hubble patch, each Hubble patch is understood independent and the isotropy is conserved globally by averaging all the Hubble patches.
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets

Permutation invariant neural networks are a promising tool for making predictions from sets. However, we show that existing permutation invariant architectures, Deep Sets and Set Transformer, can suffer from vanishing or exploding gradients when they are deep. Additionally, layer norm, the normalization of choice in Set Transformer, can hurt performance by removing information useful for prediction. To address these issues, we introduce the "clean path principle" for equivariant residual connections and develop set norm (sn), a normalization tailored for sets. With these, we build Deep Sets++ and Set Transformer++, models that reach high depths with better or comparable performance than their original counterparts on a diverse suite of tasks. We additionally introduce Flow-RBC, a new single-cell dataset and real-world application of permutation invariant prediction. We open-source our data and code here: https://github.com/rajesh-lab/deep_permutation_invariant.
Advancing descriptor search in materials science: feature engineering and selection strategies

A main goal of data-driven materials research is to find optimal low-dimensional descriptors, allowing us to predict a physical property, and to interpret them in a human-understandable way. In this work, we advance methods to identify descriptors out of a large pool of candidate features by means of compressed sensing. To this extent, we develop schemes for engineering appropriate candidate features that are based on simple basic properties of building blocks that constitute the materials and that are able to represent a multi-component system by scalar numbers. Cross-validation based feature-selection methods are developed for identifying the most relevant features, thereby focusing on high generalizability. We apply our approaches to an ab initio dataset of ternary group-IV compounds to obtain a set of descriptors for predicting lattice constants and energies of mixing. In particular, we introduce simple complexity measures in terms of involved algebraic operations as well as the amount of utilized basic properties.
Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images

Ophthalmic images may contain identical-looking pathologies that can cause failure in automated techniques to distinguish different retinal degenerative diseases. Additionally, reliance on large annotated datasets and lack of knowledge distillation can restrict ML-based clinical support systems’ deploy-ment in real-world environments. To improve the robustness and transferability of knowledge, an enhanced feature-learning module is required to extract mean-ingful spatial representations from the retinal subspace. Such a module, if used effectively, can detect unique disease traits and differentiate the severity of such retinal degenerative pathologies. In this work, we propose a robust disease detection architecture with three learning heads, i) A supervised encoder for retinal disease classiﬁcation, ii) An unsupervised decoder for the reconstruction of disease-speciﬁc spatial information, and iii) A novel representation learning module for learning the similarity between encoder-decoder feature and enhancing the accuracy of the model. Our experimental results on two publicly available OCT datasets illustrate that the proposed model outperforms existing state-of-the-art models in terms of accuracy, interpretability, and robustness for out-of-distribution retinal disease detection.
Méthodes algébriques et analytiques pour la simplification et la commande de systèmes bilinéaires à deux dynamiques

Apres la presentation de diverses methodes permettant de mettre en evidence un modele bilineaire a plusieurs dynamiques, l'application de la methode des perturbations singulieres conduit a des modeles reduits decouples. Plusieurs techniques sont proposees pour les rendre bilineaires. L'utilisation de ces modeles reduits permet de generer des commandes quasi-optimales simplifiees. Dans ce but, dans le cadre plus general des systemes non-lineaires analytiques a commande affine, on construit un retour d'etat non lineaire dont l'expression est analytiquement determinee. Cette methode, ainsi que les principes singulierement perturbes, sont utilises dans le cas particulier des systemes bilineaires a deux dynamiques
On Codimension one Holomorphic Distributions on Compact Toric Orbifolds

The number of singularities, counted with multiplicity, of a generic codimension one holomorphic distribution on a compact toric orbifold is determined. As a consequence, we give the classification of regular distributions on rational normal scrolls and weighted projective spaces. Under certain conditions, we also prove that the singular set of a codimension one holomorphic foliation on a toric orbifold admits at least one irreducible component of codimension two, and we give a Darboux-Jouanolou type integrability theorem for codimension one holomorphic foliations. We illustrate our results with several examples.
Towards a Maturity Model for Systematic Literature Review Process

Background : Systematic literature review (SLR) has been widely adopted to synthesize evidence in a reliable and unbiased manner. The process of conducting SLR is rigorous and well-known; however, most SLR have not followed this process systematically, leading to various problems, including poor documentation, lack of quality, and difﬁculty to be reproduced. These problems are added to the inherent difﬁculties in conducting SLR, especially regarding effort and time consumption. Several practices (including methods, techniques, and tools) have already been experimented with to mitigate such problems. Still, it is unclear which practices should be prioritized to reduce such problems and difﬁculties. Aims : The main goal of this paper is to contribute to improving the SLR process being adopted by the community. For that, we introduce the idea of the Maturity Model for SLR process (MM4SLR). Method : We systematically examined the literature searching for practices destined to support or improve the SLR process. After collecting 84 practices, we analyzed, grouped, and synthesized them into 46 key practices further distributed into 15 speciﬁc goals. Inspired by the maturity model for software development process, we also propose ﬁve process areas and an initial version of MM4SLR containing four maturity levels. Results : MM4SLR provides a new view on those practices that could be progressively adopted to improve the SLR process. MM4SLR could deﬁne a pragmatic pathway that guides the adoption of those practices, which the of who conducts SLR. Conclusion process is very complex and We that ways to select and the various existing necessary. We conclude that a long SLR process still exists. planned, monitored, controlled, reviewed, and evaluated for the adherence to the process descriptions and must be in accordance with organization policies; (iii) Deﬁned - processes are well-characterized, understood, and described in standards, procedures, tools, and methods; (iv) Quantitatively Managed - organization and projects establish quantitative objectives for quality and process performance and use them as criteria to manage their projects; and (v) Optimizing processes are continuously improved based on a quantitative understanding of business objectives and performance needs.
Self-stabilizing positron acceleration in a plasma column

Plasma accelerators sustain extreme ﬁeld gradients, and potentially enable future compact linear colliders. Although tremendous progress has been achieved in accelerating electron beams in a plasma accelerator, positron acceleration with collider-relevant parameters is challenging. A recently proposed positron acceleration scheme relying on the wake generated by an electron drive beam in a plasma column has been shown to be able to accelerate positron witness beams with low emittance and low energy spread. However, since this scheme relies on cylindrical symmetry, it is possibly prone to transverse instabilities that could lead, ultimately, to beam break-up. In this article, we show that the witness beam itself is subject to various damping mechanisms and, therefore, this positron acceleration scheme is inherently stable towards misalignment of the drive and witness beams. This enables stable, high-quality plasma-based positron acceleration.
Towards a consistent model of the hot quadruple system HD 93206 = QZ Carinæ. II. N-body model

1 Charles University, Faculty of Mathematics and Physics, Institute of Astronomy, V Holešovičkách 2, CZ-18000 Prague, Czech Republic 2 Department of Physics, High Point University, One University Way, High Point, NC 27268, USA 3 Dr. Karl Remeis-Observatory & ECAP, Astronomical Institute, Friedrich-Alexander-University Erlangen-Nuremberg, Sternwartstr. 7, 96049 Bamberg, Germany 4 Astronomisches Institut, Ruhr-Universität Bochum, Universitätsstr. 150, 44801 Bochum, Germany 5 Instituto de Astronomı́a, Universidad Católica del Norte, Avenida Angamos 0610, Antofagasta, Chile 6 Instituto de Astronomia, Geofı́sica e Ciencias Atmosféricas, Universidade de São Paulo, Rua do Matão 1226, Cidade Universitária, 05508-900 São Paulo, SP, Brazil 7 Auckland Observatory, PO Box 24180, Royal Oak, Auckland, New Zealand 8 Variable Stars South, P O Box 173, Awanui, New Zealand, 0451 9 Variable Stars South, Congarinni Observatory, Congarinni, NSW, Australia 2447 10 Variable Stars South, and Astronomical Society of Southern Africa, Henley Observatory, Henley on Klip, Gautenburg, South Africa 11 AAVSO, 106 Hawking Pond Road, Center Harbor, NH03226, USA 12 Mirranook Observatory, Boorolong Rd Armidale, NSW, 2350, Australia 13 Hvar Observatory, Faculty of Geodesy, Zagreb University, Kačićeva 26, 10000 Zagreb, Croatia 14 Eukaryotic Pathogens Innovation Center, Clemson University, Clemson, South Carolina 29634, USA
Path Integral Methods with Stochastic Control Barrier Functions

Safe control designs for robotic systems remain challenging because of the difficulties of explicitly solving optimal control with nonlinear dynamics perturbed by stochastic noise. However, recent technological advances in computing devices enable online optimization or sampling-based methods to solve control problems. For example, Control Barrier Functions (CBFs) have been proposed to numerically solve convex optimization problems that ensure the control input to stay in the safe set. Model Predictive Path Integral (MPPI) control uses forward sampling of stochastic differential equations to solve optimal control problems online. Both control algorithms are widely used for nonlinear systems because they avoid calculating the derivatives of the nonlinear dynamic functions. In this paper, we use Stochastic Control Barrier Functions (SCBFs) constraints to limit sample regions in the sampling-based algorithm, ensuring safety in a probabilistic sense and improving sample efficiency with a stochastic differential equation. We also show that our algorithm needs fewer samples than the original MPPI algorithm does by providing a sampling complexity analysis.
When Satellites Work as Eavesdroppers

This paper considers satellite eavesdroppers in uplink satellite communication systems where the eavesdroppers are randomly distributed at arbitrary altitudes according to homogeneous binomial point processes and attempt to overhear signals that a ground terminal transmits to a serving satellite. Non-colluding eavesdropping satellites are assumed, i.e., they do not cooperate with each other, so that their received signals are not combined but are decoded individually. Directional beamforming with two types of antennas: fixed- and steerable-beam antennas, is adopted at the eavesdropping satellites. The possible distribution cases for the eavesdropping satellites and the distributions of the distances between the terminal and the satellites are analyzed. The distributions of the signal-to-noise ratios (SNRs) at both the serving satellite and the most detrimental eavesdropping satellite are derived as closed-form expressions. The ergodic and outage secrecy capacities of the systems are derived with the secrecy outage probability using the SNR distributions. Simpler approximate expressions for the secrecy performance are obtained based on the Poisson limit theorem, and asymptotic analyses are also carried out in the high-SNR regime. Monte-Carlo simulations verify the analytical results for the secrecy performance. The analytical results are expected to be used to evaluate the secrecy performance and design secure satellite constellations by considering the impact of potential threats from malicious satellite eavesdroppers.
Cartesian closed coreflective subcategories of topological spaces determined by monotone convergence classes

We introduce the notion of an operation P and a P -determined space. It is shown that a category is a coreﬂective full subcategory of Top if and only if it is equal to Top P for some idempotent and consistent operation P , where Top P is the category of all P -determined spaces. As concrete examples of P -determined spaces, several classes of topological spaces determined by monotone convergence classes are investigated in detail. By the tool of C -generated spaces, it is shown uniformly that categories of these examples of P -determined spaces are all cartesian closed. Moreover, the exponential objects and categorical products of some categories in domain theory are shown to be closely related to those of DTop , the category of directed spaces. a class of pairs of convergent nets and limits. We call P an operation if it determines a class P X of pairs of convergent nets and limits for every topological space X . A new ﬁner topology on X , denoted by P ( X ), can be deﬁned through P X . We call a topological space X a P -determined space if P ( X ) is equal to the topology of X . We show that for any idempotent and consistent operation P , Top P is a coreﬂective subcategory of Top and vice versa, where Top is the category of all topological spaces and Top P is the category of all P -determined spaces.
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation

We propose a framework for robust and efficient training of Dense Object Nets (DON) [1] with a focus on industrial multi-object robot manipulation scenarios. DON is a popular approach to obtain dense, view-invariant object descriptors, which can be used for a multitude of downstream tasks in robot manipulation, such as, pose estimation, state representation for control, etc. However, the original work [1] focused training on singulated objects, with limited results on instance-specific, multi-object applications. Additionally, a complex data collection pipeline, including 3D reconstruction and mask annotation of each object, is required for training. In this paper, we further improve the efficacy of DON with a simplified data collection and training regime, that consistently yields higher precision and enables robust tracking of keypoints with less data requirements. In particular, we focus on training with multi-object data instead of singulated objects, combined with a well-chosen augmentation scheme. We additionally propose an alternative loss formulation to the original pixel wise formulation that offers better results and is less sensitive to hyperparameters. Finally, we demonstrate the robustness and accuracy of our proposed framework on a real-world robotic grasping task.