Miracle-dz/UniFAR-FTUs huggingface dataset:
Qualitative and quantitative evaluation of COVID-19 outbreak severity with the use of meta-projections based on Richards' curve parameters

Researchers have shown that even simple empirical models stemming from biological growth modeling have the potential to provide useful information on the development and severity of ongoing epidemics since they can be employed as tools for carrying out projections on the size of the affected population, timing of turning points, as well as best- and worst-case scenarios. Nevertheless, they commonly exhibit considerable sensitivity to some input parameters' variance which results in large fluctuations in the generated projections, thus rendering predictions difficult and even risky. In this work we examine a novel meta-projections-based approach which allows us to evaluate the model's current trends and assess whether generated projections are at a transient or stable state. Meta-projections can be extracted from graphs of successive estimations of model's parameters and resulting projections, over a sequence of days being gradually added to the employed model. In other words, projections are carried out on truncated time series of cumulative numbers of confirmed cases with increased lengths at each successive evaluation. This allows us to trace the values of model parameters over a certain period of time and examine their trends which may converge to specific values for settled-growth cases or exhibit a changing or even an erratic behavior for cases that undergo epidemiological transitions and/or are inappropriately described by the current model instance(s). We have computed meta-projections and compared our findings for countries at different stages of the epidemic with stable or unstable behaviors and increasing or decreasing numbers of confirmed cases. Our results indicate that meta-projections can aid researchers in assessing the appropriateness of their relevant models and in effect decrease the uncertainty in their estimations of an epidemic's severity and development.
Semi-Lexical Languages - A Formal Basis for Unifying Machine Learning and Symbolic Reasoning in Computer Vision

Human vision is able to compensate imperfections in sensory inputs from the real world by reasoning based on prior knowledge about the world. Machine learning has had a significant impact on computer vision due to its inherent ability in handling imprecision, but the absence of a reasoning framework based on domain knowledge limits its ability to interpret complex scenarios. We propose semi-lexical languages as a formal basis for dealing with imperfect tokens provided by the real world. The power of machine learning is used to map the imperfect tokens into the alphabet of the language and symbolic reasoning is used to determine the membership of input in the language. Semi-lexical languages also have bindings that prevent the variations in which a semi-lexical token is interpreted in different parts of the input, thereby leaning on deduction to enhance the quality of recognition of individual tokens. We present case studies that demonstrate the advantage of using such a framework over pure machine learning and pure symbolic methods.
Infrared interferometry to spatially and spectrally resolve jets in X-ray binaries

Infrared interferometry is a new frontier for precision ground-based observing, with new instrumentation achieving milliarcsecond (mas) spatial resolutions for faint sources, along with astrometry on the order of 10 microarcseconds (μas). This technique has already led to breakthroughs in the observations of the supermassive black hole at the Galactic centre and its orbiting stars, active galactic nucleus, and exo-planets, and can be employed for studying X-ray binaries (XRBs), microquasars in particular. Beyond constraining the orbital parameters of the system using the centroid wobble and spatially resolving jet discrete ejections on mas scales, we also propose a novel method to discern between the various components contributing to the infrared bands: accretion disc, jets, and companion star. We demonstrate that the GRAVITY instrument on the Very Large Telescope Interferometer should be able to detect a centroid shift in a number of sources, opening a new avenue of exploration for the myriad of transients expected to be discovered in the coming decade of radio all-sky surveys. We also present the first proof-of-concept GRAVITY observation of a low-mass XRB transient, MAXI J1820+070, to search for extended jets on mas scales. We place the tightest constraints yet via direct imaging on the size of the infrared emitting region of the compact jet in a hard state XRB.
Why integral equations should be used instead of differential equations to describe the dynamics of epidemics

It is of vital importance to understand and track the dynamics of rapidly unfolding epidemics. The health and economic consequences of the current COVID-19 pandemic provide a poignant case. Here we point out that since they are based on differential equations, the most widely used models of epidemic spread are plagued by an approximation that is not justified in the case of the current COVID-19 pandemic. Taking the example of data from New York City, we show that currently used models significantly underestimate the initial basic reproduction number ($R_0$). The correct description, based on integral equations, can be implemented in most of the reported models and it much more accurately accounts for the dynamics of the epidemic after sharp changes in $R_0$ due to restrictive public congregation measures. It also provides a novel way to determine the incubation period, and most importantly, as we demonstrate for several countries, this method allows an accurate monitoring of $R_0$ and thus a fine-tuning of any restrictive measures. Integral equation based models do not only provide the conceptually correct description, they also have more predictive power than differential equation based models, therefore we do not see any reason for using the latter.
A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis

Although deep learning models like CNNs have achieved a great success in medical image analysis, small-sized medical datasets remain to be the major bottleneck in this area. To address this problem, researchers start looking for external information beyond the current available medical datasets. Traditional approaches generally leverage the information from natural images. More recent works utilize the domain knowledge from medical doctors, by letting networks either resemble how they are trained, mimic their diagnostic patterns, or focus on the features or areas they particular pay attention to. In this survey, we summarize the current progress on introducing medical domain knowledge in deep learning models for various tasks like disease diagnosis, lesion, organ and abnormality detection, lesion and organ segmentation. For each type of task, we systematically categorize different kinds of medical domain knowledge that have been utilized and the corresponding integrating methods. We end with a summary of challenges, open problems, and directions for future research.
Identifying a Higgsino-like NLSP in the context of a keV-scale gravitino LSP

The presence of a Higgsino-like neutralino next-to-lightest sparticle (NLSP) and a keV scale gravitino ($\widetilde{G}$) LSP opens up new decay modes of the NLSP, mainly to a Higgs/$Z$ boson and the LSP. Besides, a keV-scale gravitino as a warm dark matter candidate salvages a relatively-light Higgsino-like NLSP from dark matter constraints. We focus on the prospects of observing at least one b jet and two opposite sign leptons with a large missing transverse momenta ($\geq 1 b$ $+$ $ \ell^+\ell^-+\not{\! \! E}_T$) signal at the LHC. A distinguishing feature of this scenario is the production of longitudinal $Z$ bosons in neutralino decays, unlike in the case of gauginolike neutralinos, where the $Z$ is mostly transverse. The polarisation information of the parent $Z$ boson gets reflected in the angular distributions of the decay leptons and in some other variables derived therefrom.
Modelling architectures of parametric weighted component-based systems.

The design of complex software systems usually lies in multiple coordinating components with an unknown number of instances. For such systems a main challenge is modelling efficiently their architecture that determines the topology and the interaction principles among the components. To achieve well-founded design there is need to address the quantitative aspects of software architectures. In this paper we study the modelling problem of software architectures applied on parametric weighted component-based systems where the parameter is the number of instances of each component. For this, we introduce a weighted first-order extended interaction logic over a commutative semiring in order to serve as a modelling language for parametric quantitative architectures. We prove that the equivalence problem of formulas of that logic is decidable in the class (of subsemirings) of skew fields. Moreover, we show that our weighted logic can efficiently describe well-known parametric architectures with quantitative characteristics.
Controlling propagation of spatial coherence for enhanced imaging through scattering media

It is known that a spatially partially coherent light field produces better imaging contrast compared to a spatially coherent field and that the contrast increases as the spatial coherence length of the field becomes smaller. The transverse spatial coherence length of most spatially partially coherent fields increases upon propagation. As a result, the field produces progressively decreasing image quality at subsequent transverse planes. By controlling the propagation of spatial coherence, we demonstrate enhanced image quality at different transverse planes along the propagation direction through a scattering medium. Using a source with propagation-invariant spatial coherence function, we report experimental observations of imaging different transverse planes with equal contrast over a significant distance. Furthermore, we generate a spatially partially coherent source that can be tailored to have minimum-possible transverse coherence area at the plane of the object to be imaged, and using this source, we demonstrate imaging spatially separated transverse planes with maximum possible image contrast.
The evolutionary status of chemically peculiar eclipsing binary star DV Boo

Eclipsing binary systems are unique stellar objects to examine and understand the stellar evolution and the formation. Thanks to these systems, the fundamental stellar parameters (mass, radius) can be obtained very precisely. The existence of metallic-line (Am) stars in binaries is noticeably common. However, the known number of Am stars in eclipsing binaries is less. The Am stars in eclipsing binaries are extremely useful to deeply investigate the properties of Am stars, as the eclipsing binaries are the only tool to directly derive the fundamental stellar parameters. Additionally, the atmospheric parameters and the metallicities of the binary components could be obtained by a detailed spectroscopic study. Therefore, in this study, we present a comprehensive photometric and spectroscopic analysis of the eclipsing binary system DV\,Boo which has a possible Am component. The fundamental stellar parameters were determined by the analysis of radial velocity and photometric light curves. The atmospheric parameters of both binary components of DV\,Boo were derived using the disentangled spectra. The chemical abundance analysis was carried out as well. As a result, we showed that the primary component illustrates a typical Am star chemical abundance distribution. The fundamental stellar parameters of the binary components were also obtained with an accuracy of <1\% for masses and $<$3\% for radii. The evolutionary status of DV\,Boo was examined using the precisely obtained stellar parameters. The age of the system was found to be 1.00 +- 0.08\,Gyr.
From Theory to Behaviour: Towards a General Model of Engagement

Engagement is a fuzzy concept. In the present work we operationalize engagement mechanistically by linking it directly to human behaviour and show that the construct of engagement can be used for shaping and interpreting data-driven methods. First we outline a formal framework for engagement modelling. Second we expanded on our previous work on theory-inspired data-driven approaches to better model the engagement process by proposing a new modelling technique, the Melchoir Model. Third, we illustrate how, through model comparison and inspection, we can link machine-learned models and underlying theoretical frameworks. Finally we discuss our results in light of a theory-driven hypothesis and highlight potential application of our work in industry.
Quandle coloring quivers of links using dihedral quandles

Cho and Nelson introduced the notion of a quandle coloring quiver, which is a quiver-valued link invariant. This invariant is in general a stronger link invariant than the quandle coloring number. In this paper, we study quandle coloring quiver using dihedral quandle. We show that when we use a dihedral quandle of prime order, the quandle coloring quivers are equivalent to the quandle coloring numbers. We also discuss shadow versions of their invariants.
TPNet: Trajectory Proposal Network for Motion Prediction

Making accurate motion prediction of the surrounding traffic agents such as pedestrians, vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction methods have attempted to learn to directly regress the exact future position or its distribution from massive amount of trajectory data. However, it remains difficult for these methods to provide multimodal predictions as well as integrate physical constraints such as traffic rules and movable areas. In this work we propose a novel two-stage motion prediction framework, Trajectory Proposal Network (TPNet). TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals which meets the physical constraints. By steering the proposal generation process, safe and multimodal predictions are realized. Thus this framework effectively mitigates the complexity of motion prediction problem while ensuring the multimodal output. Experiments on four large-scale trajectory prediction datasets, i.e. the ETH, UCY, Apollo and Argoverse datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively.
Simply transitive NIL-affine actions of solvable Lie groups

Abstract Every simply connected and connected solvable Lie group 𝐺 admits a simply transitive action on a nilpotent Lie group 𝐻 via affine transformations. Although the existence is guaranteed, not much is known about which Lie groups 𝐺 can act simply transitively on which Lie groups 𝐻. So far, the focus was mainly on the case where 𝐺 is also nilpotent, leading to a characterization depending only on the corresponding Lie algebras and related to the notion of post-Lie algebra structures. This paper studies two different aspects of this problem. First, we give a method to check whether a given action ρ:G→Aff⁡(H)\rho\colon G\to\operatorname{Aff}(H) is simply transitive by looking only at the induced morphism φ:g→aff⁡(h)\varphi\colon\mathfrak{g}\to\operatorname{aff}(\mathfrak{h}) between the corresponding Lie algebras. Secondly, we show how to check whether a given solvable Lie group 𝐺 acts simply transitively on a given nilpotent Lie group 𝐻, again by studying properties of the corresponding Lie algebras. The main tool for both methods is the semisimple splitting of a solvable Lie algebra and its relation to the algebraic hull, which we also define on the level of Lie algebras. As an application, we give a full description of the possibilities for simply transitive actions up to dimension 4.
Caractéristiques hydrauliques, régulation stomatique et efficience d'utilisation de l'eau de quatre espèces de conifères méditerranéens (Cedrus atlantica, Cupressus sempervirens, Pinus halepensis et Pinus nigra)

Les objectifs de ce travail etaient l'analyse des caracteristiques hydrauliques, de la regulation stomatique et de l'efficience d'utilisation de l'eau (Wi) de 4 coniferes mediterraneens presentant differents degres de resistance a la secheresse. Des plants de 3 ans, cultives en serre, presentent une large gamme de conductivites hydrauliques et de vulnerabilite a la cavitation du xyleme, allant d'une espece vulnerable (P. Nigra) a des especes peu vulnerables (C. Atlantica, C. Sempervirens et P. Halepensis). Pendant un dessechement du sol, la fermeture totale des stomates intervient a un potentiel hydrique du xyleme proche du seuil de cavitation dans les tiges, dependant de l'espece. Ces resultats confirment le role des stomates dans la limitation du developpement de l'embolie. Entre especes, des differences de [delta]13C et de Wi intrinseque existent, le classement reste stable dans differentes conditions experimentales mais aucune relation interspecifique n'a ete montree entre ces deux parametres. Wi intrinseque est faiblement correle a la conductance hydraulique specifique des plants. D'apres nos resultats, un modele integre de fonctionnement hydrique a ete propose.
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts

We study question answering over a dynamic textual environment. Although neural network models achieve impressive accuracy via learning from input-output examples, they rarely leverage various types of knowledge and are generally not interpretable. In this work, we propose a graph-based approach, where a heterogeneous graph is automatically built with factual knowledge of the context, temporal knowledge of the past states, and logical knowledge that combines human-curated knowledge bases and rule bases. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner. Experimental results on a benchmark dataset show that the injection of various types of knowledge improves a strong neural network baseline. An additional benefit of our approach is that the graph itself naturally serves as a rational behind the decision making.
Randomized spectral co-clustering for large-scale directed networks

Directed networks are generally used to represent asymmetric relationships among units. Co-clustering aims to cluster the senders and receivers of directed networks simultaneously. In particular, the well-known spectral clustering algorithm could be modified as the spectral co-clustering to co-cluster directed networks. However, large-scale networks pose computational challenge to it. In this paper, we leverage randomized sketching techniques to accelerate the spectral co-clustering algorithms in order to co-cluster large-scale directed networks more efficiently. Specifically, we derive two series of randomized spectral co-clustering algorithms, one is random-projection-based and the other is random-sampling-based. Theoretically, we analyze the resulting algorithms under two generative models\textendash the \emph{stochastic co-block model} and the \emph{degree corrected stochastic co-block model}. The approximation error rates and misclustering error rates of proposed two randomized spectral co-clustering algorithms are established, which indicate better bounds than the state-of-the-art results of co-clustering literature. Numerically, we conduct simulations to support our theoretical results and test the efficiency of the algorithms on real networks with up to tens of millions of nodes.
GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization

Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3D points can be recovered using planar constraints without triangulation. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. We validate our algorithm on the collected chessboard dataset against baseline implementations and present extensive analysis. Experimental results show that our method outperforms the ne-tuned baselines in both accuracy and real-time.
Charm-meson triangle singularity in 
e+e−
 annihilation into 
D*0D¯0+γ

We calculate the cross section for ${e}^{+}{e}^{\ensuremath{-}}$ annihilation into ${D}^{*0}{\overline{D}}^{0}+\ensuremath{\gamma}$ at center-of-mass energies near the ${D}^{*0}{\overline{D}}^{*0}$ threshold under the assumption that $X(3872)$ is a weakly bound charm-meson molecule. The Dalitz plot has a ${\overline{D}}^{*0}$ resonance band in the squared invariant mass $t$ of ${\overline{D}}^{0}\ensuremath{\gamma}$. In the limit as the decay width of the ${D}^{*0}$ goes to 0, the Dalitz plot also has a narrow band in the squared invariant mass $u$ of ${D}^{*0}{\overline{D}}^{0}$ from a charm-meson triangle singularity. At the physical value of the ${D}^{*0}$ width, the narrow band reduces to a shoulder. Thus, the triangle singularity cannot be observed directly as a peak in a differential cross section as a function of $u$. It may however be observed indirectly as a local minimum in the $t$ distribution for events with $u$ below the triangle singularity. The minimum is produced by the Schmid cancellation between triangle loop diagrams and a tree diagram. The observation of this minimum would support the identification of $X(3872)$ as a weakly bound charm-meson molecule.
Social and Emotional Skills Training with Embodied Moxie

We present a therapeutic framework, namely STAR Framework, that leverages established and evidence-based therapeutic strategies delivered by the Embodied Moxie, an animate companion to support children with mental behavioral developmental disorders (MBDDs). This therapeutic framework jointly with Moxie aims to provide an engaging, safe, and secure environment for children aged five to ten years old. Moxie delivers content informed by therapeutic strategies including but not limited to naturalistic Applied Behavior Analysis, graded cueing, and Cognitive Behavior Therapy. Leveraging multimodal input from a camera and microphones, Moxie is uniquely positioned to be a first-hand witness of a child's progress and struggles alike. Moxie measures skills captured in state-of-the-art assessment scales, such as the Social Responsiveness Scale and Social Skill Improvement Scale, and augments those measures with quantitatively measured behavior skills, such as eye contact and language skills. While preliminary, the present study (N=12) also provides evidence that a six-week intervention using the STAR Framework and Moxie had significant impact on the children's abilities. We present our research in detail and provide an overview of the STAR Framework and all related components, such as Moxie and the companion app for parents.