Miracle-dz/UniFAR-FTUs huggingface dataset:
Hard X-Ray Emission from an Activated Flux Rope and Subsequent Evolution of an Eruptive Long-duration Solar Flare

In this paper, we present a comprehensive study of the evolutionary phases of a major M6.6 long duration event with special emphasize on its pre-flare phase. The event occurred in NOAA 12371 on 2015 June 22. A remarkable aspect of the event was an active pre-flare phase lasting for about an hour during which a hot EUV coronal channel was in the build-up stage and displayed cospatial hard X-ray (HXR) emission up to energies of 25 keV. This is the first evidence of the HXR coronal channel. The coronal magnetic field configuration based on nonlinear-force-free-field modeling clearly exhibited a magnetic flux rope (MFR) oriented along the polarity inversion line (PIL) and cospatial with the coronal channel. We observed significant changes in the AR’s photospheric magnetic field during an extended period of ≈42 hr in the form of rotation of sunspots, moving magnetic features, and flux cancellation along the PIL. Prior to the flare onset, the MFR underwent a slow rise phase (≈14 km s−1) for ≈12 minutes, which we attribute to the faster build-up and activation of the MFR by tether-cutting reconnection occurring at multiple locations along the MFR itself. The sudden transition in the kinematic evolution of the MFR from the phase of slow to fast rise (≈109 km s−1 with acceleration ≈110 m s−2) precisely divides the pre-flare and impulsive phase of the flare, which points toward the feedback process between the early dynamics of the eruption and the strength of the flare magnetic reconnection.
Long-range magnetic order in the ${\tilde S}=1/2$ triangular lattice antiferromagnet KCeS$_2$

Recently, several putative quantum spin liquid (QSL) states were discovered in ${\tilde S} = 1/2$ rare-earth based triangular-lattice antiferromagnets (TLAF) with the delafossite structure. A way to clarify the origin of the QSL state in these systems is to identify ways to tune them from the putative QSL state towards long-range magnetic order. Here, we introduce the Ce-based TLAF KCeS$_2$ and show via low-temperature specific heat and $\mu$SR investigations that it yields magnetic order below $T_{\mathrm N} = 0.38$ K despite the same delafossite structure. We identify a well separated ${\tilde S} = 1/2$ ground state for KCeS$_2$ from inelastic neutron scattering and embedded-cluster quantum chemical calculations. Magnetization and electron spin resonance measurements on single crystals indicate a strong easy-plane $g$~factor anisotropy, in agreement with the ab initio calculations. Finally, our specific-heat studies reveal an in-plane anisotropy of the magnetic field-temperature phase diagram which may indicate anisotropic magnetic interactions in KCeS$_2$.
Application of the screened Coulomb potential to fit the DA-type variable star HS 0507 + 0434B

We use \texttt{WDEC} to evolve grids of DAV star models adopting the element diffusion scheme with pure and screened Coulomb potential effect. The core compositions are thermonuclear burning results which are derived from \texttt{MESA}. \texttt{MESA} yields composition profiles that the version of WDEC used in this work could not accommodate (most notably the presence of helium in the core of the model). According to the theory of rotational splitting, Fu et al. identified 6 triplets for a DAV star HS 0507+0434B based on 206\,h photometric data. The grids of DAV star models are used to fit the 6 reliable $m$ = 0 modes. When adopting the screened Coulomb potential, we obtain a best fitting model of log($M_{\rm He}/M_{\rm *}$) = -3.0, log($M_{\rm H}/M_{\rm *}$) = -6.1, $T_{\rm eff}$ = 11790\,K, $M_{\rm *}$ = 0.625\,$M_{\odot}$, log$g$ = 8.066, and $\sigma_{RMS}$ = 2.08\,s. Compared with adopting the pure Coulomb potential, the value of $\sigma_{RMS}$ is improved by 34\%. The study may provide a new method for the research of mode trapping properties.
Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond

Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP). With the burst of deep neural networks and the evolution of contextualized language models (CLMs), the research of MRC has experienced two significant breakthroughs. MRC and CLM, as a phenomenon, have a great impact on the NLP community. In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with a particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches. We propose a full-view categorization and new taxonomies on these topics. The primary views we have arrived at are that 1) MRC boosts the progress from language processing to understanding; 2) the rapid improvement of MRC systems greatly benefits from the development of CLMs; 3) the theme of MRC is gradually moving from shallow text matching to cognitive reasoning.
Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors

In recent years, considerable progress has been made for the task of rigid object pose estimation from a single RGB-image, but achieving robustness to partial occlusions remains a challenging problem. Pose refinement via rendering has shown promise in order to achieve improved results, in particular, when data is scarce. In this paper we focus our attention on pose refinement, and show how to push the state-of-the-art further in the case of partial occlusions. The proposed pose refinement method leverages on a simplified learning task, where a CNN is trained to estimate the reprojection error between an observed and a rendered image. We experiment by training on purely synthetic data as well as a mixture of synthetic and real data. Current state-of-the-art results are outperformed for two out of three metrics on the Occlusion LINEMOD benchmark, while performing on-par for the final metric.
Chemical Changes Accompanying Abscission in Coleus Blumei

VON MOHL (13) in i86o was the first to announce that previous to the fall of the leaf there is formed near the base of the petiole a definite separation layer in which abscission always occurs by the separation of the cells from each other with their walls still intact. The xylem tubes not being included in this separation layer are finally ruptured mechanically, and the leaf falls. He also called attention to the fact that abscission and the formation of a protective tissue are two very distinct processes, and that the latter process might either precede or follow the former. WIESNER (I7) in I87I confirmed the observation of VON MOHL in the main, and formulated the theory that the dissolution of the intercellular substance of the cells of the separation layer is caused by the action of organic acids developed in the leaves. In i886 MOLISCH (14) suggested that a gum ferment might be the cause of this dissolution process. Two years later MANGIN (ii), upon his discovery of the pectic nature of the middle lamella of cell walls in plants, indirectly advanced the knowledge of abscission. Since this discovery the abscission process has generally been referred to as a dissolution of the pectose and calcium pectate of the middle lamella. LLOYD (8, 9), assuming the organic acid theory of WIESNER, speaks of the process as a hydrolysis of these pectic compounds, and later (ii) of cellulose also. The anatomical workers disagree somewhat on the amount of the cell wall altered during abscission. LEE (7) in i9ii reported the disappearance of the middle lamella only, and two years later HANNIG (6) reported the same condition in the abscission of flowers, with the exception of a species of Mirabilis and of Oxybaphus, in which the entire cell wall disappeared. On the other hand, TIsON
The Unstoppable Rise of Computational Linguistics in Deep Learning

In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of variable binding and its instantiation in attention-based models, and argue that Transformer is not a sequence model but an induced-structure model. This perspective leads to predictions of the challenges facing research in deep learning architectures for natural language understanding.
Novelty search makes evolvability inevitable

Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to solve. The estimation of evolvability is not straight-forward and is generally too expensive to be directly used as selective pressure in the evolutionary process. Indirectly promoting evolvability as a side effect of other easier and faster to compute selection pressures would thus be advantageous. In an unbounded behavior space, it has already been shown that evolvable individuals naturally appear and tend to be selected as they are more likely to invade empty behavior niches. Evolvability is thus a natural byproduct of the search in this context. However, practical agents and environments often impose limits on the reachable behavior space. How do these boundaries impact evolvability? In this context, can evolvability still be promoted without explicitly rewarding it? We show that Novelty Search implicitly creates a pressure for high evolvability even in bounded behavior spaces, and explore the reasons for such a behavior. More precisely we show that, throughout the search, the dynamic evaluation of novelty rewards individuals which are very mobile in the behavior space, which in turn promotes evolvability.
A Model of Changes in the Composition of Binary Mixtures of Subterranean Clover Strains

A relatively simple deterministic model is presented which predicts the long-term changes in plant densities of subterranean clover strains when grown as binary mixtures in continuously grazed swards. The model combines life-cycle parameters, based on measurements in monocultures, together with the 'k' parameters of the de Wit replacement series model for mixtures during the seed production phase. Field data, for the most part under grazing conditions, were collected from the CSIRO Experiment Station at Bakers Hill, W.A. The principal strain pairs of clover were Dwalganup/Daliak, Yarloop/ Seaton Park and Seaton Park/Midland B. Beginning with 50/50 mixtures, our results indicate that Daliak dominates the first mixture fairly rapidly; that Seaton Park dominates the second mixture, but more slowly; and that the two strains in the Seaton Park/Midland B mixture may coexist for long periods (up to c. 100 years). Results for some other mixtures, viz. Dwalganup/Northam, Daliak/ Dinninup and Geraldton/Dinninup are presented. The effects of intervention of a cereal crop in the pasture system are considered briefly. The findings are discussed in relation to density dependence, constant v. variable environments, importance of seed characters, genetic changes, and also long-term coexistence.
Finite-Blocklength and Error-Exponent Analyses for LDPC Codes in Point-to-Point and Multiple Access Communication

This paper applies error-exponent and dispersionstyle analyses to derive finite-blocklength achievability bounds for low-density parity-check (LDPC) codes over the point-to-point channel (PPC) and multiple access channel (MAC). The errorexponent analysis applies Gallager’s error exponent to bound achievable symmetrical and asymmetrical rates in the MAC. The dispersion-style analysis begins with a generalization of the random coding union (RCU) bound from random code ensembles with i.i. d. codewords to random code ensembles in which codewords may be statistically dependent; this generalization is useful since the codewords of random linear codes such as LDPC codes are dependent. Application of the RCU bound yields finiteblocklength error bounds and asymptotic achievability results for both i.i. d. random codes and LDPC codes. For discrete, memoryless channels, these results show that LDPC codes achieve first- and second-order performance that is optimal for the PPC and identical to the best prior results for the MAC.
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience.
Thermally driven disc winds as a mechanism for X-ray irradiation heating in black hole X-ray binaries: the case study of GX339–4

X-ray irradiation heating of accretion discs in black hole X-ray binaries (BHXBs) plays a key role in regulating their outburst cycles. However, despite decades of theoretical and observational efforts, the physical mechanism(s) responsible for irradiating these discs remains largely unknown. We have built an observation-based methodology to estimate the strength of irradiation of BHXB discs by combining multiwavelength X-ray and optical/infrared (OIR) data throughout transient outbursts. We apply this to ∼15 yr of activity in the Galactic BHXB GX339–4. Our findings suggest that the irradiation heating required by the optical data is large in this system. Direct illumination of the outer disc does not produce sufficient irradiation, but this should also produce a thermal-radiative wind which adds to the irradiation heating by scattering flux down on to the disc. However, analytic estimates of X-ray illumination from scattering in the wind are still not sufficient to produce the observed heating, even in combination with direct illumination. Either the analytic thermal-radiative wind models are underestimating the effect of the wind, or there are additional scattering mechanisms at work, such as magnetically driven outflows, acting to increase the OIR flux. While wind-driven irradiation is likely a common feature among long-period BHXBs, fully understanding the driving mechanism(s) behind such a wind will require radiation hydrodynamic simulations.
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation

Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document. In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory. In this paper, we propose to create the document memory with some anticipated responses in mind. This is achieved using a teacher-student framework. The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information. The student learns to construct a response-anticipated document memory from the first two sources, and teacher’s insight on memory creation. Empirical results show that our model outperforms the previous state-of-the-art for the CbR task.
Pollen storages in nests of bees of the genera Partamona, Scaura and Trigona (Hymenoptera, Apidae)

Bees and angiosperms established a mutualistic relationship along the evolutionary time. The aim of this study is to contribute for the understanding of this relation analyzing pollen stored by stingless bees colonies distributed along the Rio Negro. Fourteen species of Meliponini from the genera Partamona, Scaura, and Trigona were studied with regard to the content of pollen pots. The pollen material was removed from the pollen pots, homogenized, and prepared according to the usual acetolysis technique. The overlap of the trophic niche and the grouping of species by similarity of niches was calculated. The identification revealed 78 pollen types belonging to 36 families, being 37 types attractive and 16 considered as promoters of a temporary specialization event. With the results, it was possible to indicate a list of important plants for meliponiculture in the Amazon.
An asymptotic result of conditional logistic regression estimator

Abstract In cluster-specific studies, ordinary logistic regression and conditional logistic regression for binary outcomes provide maximum likelihood estimator (MLE) and conditional maximum likelihood estimator (CMLE), respectively. In this paper, we show that CMLE is approaching to MLE asymptotically when each individual data point is replicated infinitely many times. Our theoretical derivation is based on the observation that a term appearing in the conditional average log-likelihood function is the coefficient of a polynomial, and hence can be transformed to a complex integral by Cauchy’s differentiation formula. The asymptotic analysis of the complex integral can then be performed using the classical method of steepest descent. Our result implies that CMLE can be biased if individual weights are multiplied with a constant, and that we should be cautious when assigning weights to cluster-specific studies.
The Forest Fire Model: The Subtleties of Criticality and Scale Invariance

Amongst the numerous models introduced with SOC, the Forest Fire Model (FFM) is particularly attractive for its close relationship to stochastic spreading, which is central to the study of systems as diverse as epidemics, rumors, or indeed, fires. However, since its introduction, the nature of the model's scale invariance has been controversial, and the lack of scaling observed in many studies diminished its theoretical attractiveness. In this study, we analyse the behavior of the tree density, the average cluster size and the largest cluster and show that the model could be of high practical relevance for the activation dynamics seen in brain and rain studies. From this perspective, its peculiar scaling properties should be regarded as an asset rather than a limitation.
Recognition of Potential COVID-19 Drug Treatments through the Study of Existing Protein–Drug and Protein–Protein Structures: An Analysis of Kinetically Active Residues

We report the results of our in silico study of approved drugs as potential treatments for COVID-19. The study is based on the analysis of normal modes of proteins. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). We applied the tools we developed and standard tools used in the structural biology community. Our results indicate that small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic sites over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.
Rotating axion boson stars

We construct and study rotating axion boson stars (RABSs). These are the spinning generalisations of the spherical gravitating solitons recently introduced in [1]. RABSs are asymptotically flat, stationary, axially symmetric, everywhere regular solutions of the Einstein-Klein-Gordon theory, in the presence of a periodic scalar potential arising in models of axion-like particles. The potential is characterised by two parameters: the mass of the scalar field ma and the decay constant fa. We present an overview of the solution space, for different values of fa, and analyse some of their phenomenological properties. For large decay constants the solutions become identical to the standard spinning mini boson stars. For small decay constants, on the other hand, the solutions develop distinctive features. In particular, we analyse their compactness, the emergence of ergoregions, light rings and the distribution of stable and unstable equatorial timelike circular orbits, including the innermost stable circular orbit. We also establish the occurrence of violations of the strong energy condition for physical observers, for some RABSs. We observe some analogy between RABSs and spinning gravitating Q-balls.
The Variable and Non-variable X-Ray Absorbers in Compton-thin Type II Active Galactic Nuclei

We have conducted an extensive X-ray spectral variability study of a sample of 20 Compton-thin type II galaxies using broadband spectra from XMM-Newton, Chandra, and Suzaku. The aim is to study the variability of the neutral intrinsic X-ray obscuration along the line of sight and investigate the properties and location of the dominant component of the X-ray-obscuring gas. The observations are sensitive to absorption columns of ∼ 1020.5–24 cm−2 of fully and partially covering neutral and/or lowly ionized gas on timescales spanning days to well over a decade. We detected variability in the column density of the full-covering absorber in 7/20 sources, on timescales of months to years, indicating a component of compact-scale X-ray-obscuring gas lying along the line of sight of each of these objects. Our results imply that torus models incorporating clouds or over-dense regions should account for line-of-sight column densities as low as ∼a few ×1021 cm−2. However, 13/20 sources yielded no detection of significant variability in the full-covering obscurer, with upper limits of ΔNH spanning 1021–23 cm−2. The dominant absorbing media in these systems could be distant, such as kiloparsec-scale dusty structures associated with the host galaxy, or a homogeneous medium along the line of sight. Thus, we find that overall, strong variability in full-covering obscurers is not highly prevalent in Compton-thin type IIs, at least for our sample, in contrast to previous results in the literature. Finally, 11/20 sources required a partial-covering, obscuring component in all or some of their observations, consistent with clumpy near-Compton-thick compact-scale gas.