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We report commissioning observations of the Si X 1430 nm solar coronal line observed coronagraphically with the Cryogenic Near-Infrared Spectropolarimeter (Cryo-NIRSP) at the National Science Foundation's Daniel K. Inouye Solar Telescope (DKIST). These are the first known spatially resolved observations of this spectral line, which has strong potential as a coronal magnetic field diagnostic. The observations target a complex active region located on the solar northeast limb on 4 March 2022. We present a first analysis of this data, which extracts the spectral line properties through a careful treatment of the variable atmospheric transmission that is known to impact this spectral window. Rastered images are created and compared with EUV observations from the SDO/AIA instrument. A method for estimating the electron density from the Si X observations is then demonstrated that makes use of the forbidden line's density-sensitive emissivity and an emission-measure analysis of the SDO/AIA bandpass observations. In addition, we derive an effective temperature and non-thermal line width across the region. This study informs the calibration approaches required for more routine observations of this promising diagnostic line., Comment: 12 pages, 9 figures, Accepted for publication in ApJ

Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline integrated into the ReaderBench platform designed to simplify the process of training AES models by automating both feature extraction and architecture tuning for any multilingual dataset uploaded by the user. The dataset must contain a list of texts, each with potentially multiple annotations, either scores or labels. The platform includes traditional ML models relying on linguistic features and a hybrid approach combining Transformer-based architectures with the previous features. Our method was evaluated on three publicly available datasets in three different languages (English, Portuguese, and French) and compared with the best currently published results on these datasets. Our automated approach achieved comparable results to state-of-the-art models on two datasets, while it obtained the best performance on the third corpus in Portuguese. [This is the online first version of an article published in "International Journal of Artificial Intelligence in Education."]

In light of the growing impact of disinformation on social, economic, and political landscapes, accurate and efficient identification methods are increasingly critical. This paper introduces HyperGraphDis, a novel approach for detecting disinformation on Twitter that employs a hypergraph-based representation to capture (i) the intricate social structures arising from retweet cascades, (ii) relational features among users, and (iii) semantic and topical nuances. Evaluated on four Twitter datasets -- focusing on the 2016 U.S. Presidential election and the COVID-19 pandemic -- HyperGraphDis outperforms existing methods in both accuracy and computational efficiency, underscoring its effectiveness and scalability for tackling the challenges posed by disinformation dissemination. HyperGraphDis displays exceptional performance on a COVID-19-related dataset, achieving an impressive F1 score (weighted) of approximately 89.5%. This result represents a notable improvement of around 4% compared to the other state-of-the-art methods. Additionally, significant enhancements in computation time are observed for both model training and inference. In terms of model training, completion times are accelerated by a factor ranging from 2.3 to 7.6 compared to the second-best method across the four datasets. Similarly, during inference, computation times are 1.3 to 6.8 times faster than the state-of-the-art., Comment: Please cite the ICWSM'24 version of this article

Conspiracy theories have become a prominent and concerning aspect of online discourse, posing challenges to information integrity and societal trust. As such, we address conspiracy theory detection as proposed by the ACTI @ EVALITA 2023 shared task. The combination of pre-trained sentence Transformer models and data augmentation techniques enabled us to secure first place in the final leaderboard of both sub-tasks. Our methodology attained F1 scores of 85.71% in the binary classification and 91.23% for the fine-grained conspiracy topic classification, surpassing other competing systems.

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of Machine Learning. Detecting sunquakes is a daunting task for human operators and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine learning representation methods for sunquake detection using AutoEncoders, Contrastive Learning, Object Detection and recurrent techniques, which we enhance by introducing several custom domain-specific data augmentation transformations. We address the main challenges of the automated sunquake detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations., Comment: Solar Physics accepted for publication, 44 total pages, 9 appendix pages, 21 figures, 6 tables

This paper identifies spectral lines from X-ray to infrared wavelengths which are optimally suited to measuring vector magnetic fields as high as possible in the solar atmosphere. Instrumental and Earth's atmospheric properties, as well as solar abundances, atmospheric properties and elementary atomic physics are considered without bias towards particular wavelengths or diagnostic techniques. While narrowly-focused investigations of individual lines have been reported in detail, no assessment of the comparative merits of all lines has ever been published. Although in the UV, on balance the Mg+ h and k lines near 2800 Angstroms are optimally suited to polarimetry of plasma near the base of the solar corona. This result was unanticipated, given that longer-wavelength lines offer greater sensitivity to the Zeeman effect. While these lines sample optical depths photosphere to the coronal base, we argue that cores of multiple spectral lines provide a far more discriminating probe of magnetic structure as a function of optical depth than the core and inner wings of a strong line. Thus, together with many chromospheric lines of Fe+ between 2585 and the h line at 2803 Angstrom, this UV region promises new discoveries concerning how the magnetic fields emerge, heat, and accelerate plasma as they battle to dominate the force and energy balance within the poorly-understood chromosphere., Comment: Accepted for publication in the Astrophysical Journal. 12 pages, 2 figures, and 1 table

We present observations of recurrent active region coronal jets and derive their thermal and non-thermal properties, by studying the physical properties of the plasma simultaneously at the base footpoint, and along the outflow of jets. The sample of analyzed solar jets were observed by SDO-AIA in Extreme Ultraviolet and by RHESSI in the X-Ray domain. The main thermal plasma physical parameters: temperature, density, energy flux contributions, etc. are calculated using multiple inversion techniques to obtain the differential emission measure from extreme-ultraviolet filtergrams. The underlying models are assessed, and their limitations and applicability are scrutinized. Complementarily, we perform source reconstruction and spectral analysis of higher energy X-Ray observations to further assess the thermal structure and identify non-thermal plasma emission properties. We discuss a peculiar penumbral magnetic reconnection site, which we previously identified as a ``Coronal Geyser''. Evidence supporting cool and hot thermal emission, and non-thermal emission, is presented for a subset of geyser jets. These active region jets are found to be energetically stronger than their polar counterparts, but we find their potential influence on heliospheric energetics and dynamics to be limited. We scrutinize whether the geyser does fit the non-thermal erupting microflare picture, finding that our observations at peak flaring times can only be explained by a combination of thermal and non-thermal emission models. This analysis of geysers provides new information and observational constraints applicable to theoretical modeling of solar jets., Comment: ApJ accepted for publication. 35 pages, 9 figures and 3 tables

Coronal mass ejections (CMEs) are the most geoeffective space weather phenomena, being associated with large geomagnetic storms, having the potential to cause disturbances to telecommunication, satellite network disruptions, power grid damages and failures. Thus, considering these storms' potential effects on human activities, accurate forecasts of the geoeffectiveness of CMEs are paramount. This work focuses on experimenting with different machine learning methods trained on white-light coronagraph datasets of close to sun CMEs, to estimate whether such a newly erupting ejection has the potential to induce geomagnetic activity. We developed binary classification models using logistic regression, K-Nearest Neighbors, Support Vector Machines, feed forward artificial neural networks, as well as ensemble models. At this time, we limited our forecast to exclusively use solar onset parameters, to ensure extended warning times. We discuss the main challenges of this task, namely the extreme imbalance between the number of geoeffective and ineffective events in our dataset, along with their numerous similarities and the limited number of available variables. We show that even in such conditions, adequate hit rates can be achieved with these models., Comment: 25 pages, 7 figures, and 10 tables, The Astrophysical Journal, In Press

In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub, Comment: Semeval 2022, Task 5 submission 8 pages, 3 figures

We present CLEDB, a "single point inversion" algorithm for inferring magnetic parameters using I,Q,U, and V Stokes parameters of forbidden magnetic dipole lines formed in the solar corona. We select lines of interest and construct databases of Stokes parameters for combinations of plasma thermal and magnetic configurations. The size and complexity of such databases are drastically reduced by taking advantage of symmetries. Using wavelength-integrated line profiles, each of which might be decomposed beforehand into several line-of-sight components, we search for nearest matches to observed Stokes parameters computed for the elongation corresponding to the observed region. The method is intended to be applied to two or more lines observed simultaneously. The solutions initially yield magnetic orientation, thermal properties, and the spatial position of the emitting plasma in three dimensions. Multiple possible solutions for each observation are returned, including irreducible degeneracies, where usually sets of two solutions are compatible with the two input I,Q,U, and V measurements. In solving for the scattering geometry, this method avoids an additional degeneracy pointed out by Dima & Schad (2020). The magnetic field strength is separately derived from the simple ratio of observed to database Stokes V data, after the thermal properties and scattering geometry solutions have been determined., Comment: Solar Physics, accepted for publication. 19 pages, 7 Figures, and 3 Tables

The infrared solar spectrum contains a wealth of physical data about the Sun and is being explored using modern detectors and technology with new ground-based solar telescopes. One such instrument will be the ground-based Cryogenic Near-IR Spectro-Polarimeter of the Daniel K. Inouye Solar Telescope that will be capable of sensitive imaging of the faint infrared solar coronal spectra with full Stokes I, Q, U, and V polarization states. Highly ionized magnetic dipole emission lines have been observed in galaxies and the solar corona. Quantifying the accuracy of spectral inversion procedures requires a precise spectroscopic calibration of observations. A careful interpretation of the spectra around prominent magnetic dipole lines is essential for deriving physical parameters, and particularly, for quantifying the off-limb solar coronal observations from DKIST. In this work, we aim to provide an analysis of the spectral regions around the infrared coronal emission lines of Fe XIII 1074.68 nm, Fe XIII 1079.79 nm, Si X 1430.10 nm, and Si IX 3934.34 nm, aligning with the goal of identifying solar photospheric and telluric lines that will help facilitate production of reliable inversions and data products from four sets of solar coronal observations. The outputs will be integrated in the processing pipeline to produce Level-2 science-ready data that will be made available to DKIST observers.

We study the Cebyshev-Halley methods applied to the family of polynomials $f_{n,c}(z)=z^n+c$, for $n\ge 2$ and $c\in \mathbb{C}^{*}$. We prove the existence of parameters such that the immediate basins of attraction corresponding to the roots of unity are infinitely connected. We also prove that, for $n \ge 2$, the corresponding dynamical plane contains a connected component of the Julia set, which is a quasiconformal deformation of the Julia set of the map obtained by applying Newton's method to $f_{n,-1}$.

As recent events have demonstrated, disinformation spread through social networks can have dire political, economic and social consequences. Detecting disinformation must inevitably rely on the structure of the network, on users particularities and on event occurrence patterns. We present a graph data structure, which we denote as a meta-graph, that combines underlying users' relational event information, as well as semantic and topical modeling. We detail the construction of an example meta-graph using Twitter data covering the 2016 US election campaign and then compare the detection of disinformation at cascade level, using well-known graph neural network algorithms, to the same algorithms applied on the meta-graph nodes. The comparison shows a consistent 3%-4% improvement in accuracy when using the meta-graph, over all considered algorithms, compared to basic cascade classification, and a further 1% increase when topic modeling and sentiment analysis are considered. We carry out the same experiment on two other datasets, HealthRelease and HealthStory, part of the FakeHealth dataset repository, with consistent results. Finally, we discuss further advantages of our approach, such as the ability to augment the graph structure using external data sources, the ease with which multiple meta-graphs can be combined as well as a comparison of our method to other graph-based disinformation detection frameworks.

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.

The real-world impact of polarization and toxicity in the online sphere marked the end of 2020 and the beginning of this year in a negative way. Semeval-2021, Task 5 - Toxic Spans Detection is based on a novel annotation of a subset of the Jigsaw Unintended Bias dataset and is the first language toxicity detection task dedicated to identifying the toxicity-level spans. For this task, participants had to automatically detect character spans in short comments that render the message as toxic. Our model considers applying Virtual Adversarial Training in a semi-supervised setting during the fine-tuning process of several Transformer-based models (i.e., BERT and RoBERTa), in combination with Conditional Random Fields. Our approach leads to performance improvements and more robust models, enabling us to achieve an F1-score of 65.73% in the official submission and an F1-score of 66.13% after further tuning during post-evaluation.

Prompted by a recent paper by Dima and Schad, we re-consider the problem of inferring magnetic properties of the corona using polarimetric observations of magnetic dipole (M1) lines. Dima and Schad point to a potential source of degeneracy in a formalism developed by Plowman, which under some circumstances can lead to the solution being under-determined. Here we clarify the nature of the problem. Its resolution lies in solving for the scattering geometry using the elongation of the observed region of the corona. We discuss some conceptual problems that arise when casting the problem for inversion in the observer's reference frame, and satisfactorily resolve difficulties identified by Plowman, Dima and Schad., Comment: 7 pages 1 figure. In press for ApJ

In this paper we study the connectivity of Fatou components for maps in a large family of singular perturbations. We prove that, for some parameters inside the family, the dynamical planes for the corresponding maps present Fatou components of arbitrarily large connectivity and we determine precisely these connectivities. In particular, these results extend the ones obtained in [Can17, Can18].

Manipulative and misleading news have become a commodity for some online news outlets and these news have gained a significant impact on the global mindset of people. Propaganda is a frequently employed manipulation method having as goal to influence readers by spreading ideas meant to distort or manipulate their opinions. This paper describes our participation in the SemEval-2020, Task 11: Detection of Propaganda Techniques in News Articles competition. Our approach considers specializing a pre-trained BERT model on propagandistic and hyperpartisan news articles, enabling it to create more adequate representations for the two subtasks, namely propaganda Span Identification (SI) and propaganda Technique Classification (TC). Our proposed system achieved a F1-score of 46.060% in subtask SI, ranking 5th in the leaderboard from 36 teams and a micro-averaged F1 score of 54.302% for subtask TC, ranking 19th from 32 teams.

Solar active region jets are small-scale collimated plasma eruptions that are triggered from magnetic sites embedded in sunspot penumbral regions. Multiple trigger mechanisms for recurrent jets are under debate. Vector magnetic field data from SDO-HMI observations are used to analyze a prolific photospheric configuration, identified in extreme ultraviolet observations as a `Coronal Geyser', that triggered a set of at least 10 recurrent solar active region jets. We focus on interpreting the magnetic fields of small-scale flaring sites aiming to understand the processes that govern recurrent jet eruptions. We perform a custom reprocessing of the SDO-HMI products, including disambiguation and uncertainty estimation. We scrutinized the configuration and dynamics of the photospheric magnetic structures. The magnetic configuration is described via the analysis of the photospheric magnetic vertical fields, to identify the process is responsible for driving the jet eruptions. We report that the two widely debated magnetic trigger processes, namely magnetic flux cancellation and magnetic flux emergence, appear to be responsible on a case by case basis for generating each eruption in our set. We find that 4 of 10 jets were due to flux cancellation while the rest were clearly not, and were more likely due to flux emergence., Comment: 19 pages, 6 figure, and 1 table. The Astrophysical journal, In press

Spatio-temporal information is used for driving a plethora of intelligent transportation, smart-city, and crowd-sensing applications. Since data is now considered a valuable production factor, data marketplaces have appeared to help individuals and enterprises bring it to market to satisfy the ever-growing demand. In such marketplaces, several sources may need to combine their data in order to meet the requirements of different applications. In this paper we study the problem of estimating the relative value of different spatio-temporal datasets combined in wholesale and retail marketplaces for the purpose of predicting demand in metropolitan areas. Using as case studies large datasets of taxi rides from Chicago and New York, we ask questions such as "When does it make sense for different taxi companies to combine their data?", and "How should different companies be compensated for the data that they share?". We then turn our attention to the even harder problem of establishing the relative value of the data brought to retail marketplaces by individual drivers. Overall, we show that simplistic but popular approaches for estimating the relative value of data, such as using volume, or the ``leave-one-out'' heuristic, are inaccurate. Instead, more complex notions of value from economics and game-theory, such as the Shapley value need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. Applying the Shapley value to large datasets from many sources is, of course, computationally challenging. We resort to structured sampling and manage to compute accurately the importance of thousands of data sources. We show that the relative value of the data held by different taxi companies and drivers may differ substantially, and that its relative ranking may change from district to district within a metropolitan area.

Many companies use Excel spreadsheets to keep stock records and to calculate process-specific data. These spreadsheets are often hard to understand and track. And if the user does not protect them, there is a risk that the user randomly changes or erase formulas. The paper focuses on the stocks of products used in a blending process with a known recipe. Developing an application that can bring this data in a centralized form and that can assist the operator in decide is a necessity. When a programmer implements an application that uses data from plants he needs to consider one fundamental aspect as reading real-time data from the process. The real-time stock analysis application takes into account all the above elements. The application is easy to use by an operator in the command room of installation because of the planning algorithms integrated into it. The algorithms proposed and implemented in this paper have well-defined goals: identifying the ingredients needed to achieve the blending process for required quantities, determine the quantities of the finished product that can be made with the existing ingredients and determine the optimum quantities of the finished product. The application implemented in C# intensively uses these algorithms and gives the user the ability to build the result step by step., Comment: Accepted for presentation at 23rd International Conference on System Theory, Control and Computing (ICSTCC 2019), October 9-11, 2019, Sinaia, Romania

Data acquisition is an important process in the functioning of any control system. Usually, the acquired signal is analogic, representing a continuous physical measure, and it should be processed in a digital system based on an analog to digital converter (ADC) and a microcontroller. The ADC provides the converted value in ADC units, but the system and its operator need the value expressed in physical units. In this paper we propose a novel design solution for the scaling module, which is a key component of a digital measurement system. The scaling module refers to fitting the sensor result of a variable number of bits depending on the ADC resolution into physical units. A general method for scaling is proposed and a SageMath script is presented for obtaining easily the scaling function. In the last part of the paper, the proposed method is validated in a case study, by calculus, and it is implemented on a low-cost development system in order to create a wireless sensor node., Comment: Paper accepted for ICSTCC 2019 - 23rd International Conference on System Theory, Control and Computing, October 9-11, 2019, Sinaia, Romania

The idea of paying people for their data is increasingly seen as a promising direction for resolving privacy debates, improving the quality of online data, and even offering an alternative to labor-based compensation in a future dominated by automation and self-operating machines. In this paper we demonstrate how a Human-Centric Data Economy would compensate the users of an online streaming service. We borrow the notion of the Shapley value from cooperative game theory to define what a fair compensation for each user should be for movie scores offered to the recommender system of the service. Since determining the Shapley value exactly is computationally inefficient in the general case, we derive faster alternatives using clustering, dimensionality reduction, and partial information. We apply our algorithms to a movie recommendation data set and demonstrate that different users may have a vastly different value for the service. We also analyze the reasons that some movie ratings may be more valuable than others and discuss the consequences for compensating users fairly., Comment: 10 pages, 7 figures

Coronal Jets are transitory small-scale eruptions omnipresent in solar observations. Active regions jets produce significant perturbations on the ambient solar atmosphere and are believed to be generated by microflare reconnection. Multiple sets of recurrent jets are identified in extreme-ultraviolet filter imaging. In this work we analyze the long time-scale recurrence of coronal jets originating from a unique footpoint structure observed in the lower corona. We report the detection of penumbral magnetic structures in the lower corona. These structures, that we entitled "Coronal Geysers", persist through multiple reconnection events that trigger recurrent jets in a quasi-periodical trend. Recurrent jet eruptions have been associated with Type-III radio bursts that are manifestations of traveling non-thermal electron beams.We examine the assumed link, as the coronal sources of interplanetary Type-III bursts are still open for debate. We scrutinized the hypothesized association by temporally correlating a statistically significant sample of six Geyser structures, that released at least 50 recurrent jets, with correspondent Type-III radio bursts detected in the interplanetary medium. Data analysis of these phenomena provides new information towards understanding of small-scale reconnection, non-thermal electron beam acceleration, and energy release. We find that the penumbral Geyser-like flaring structures produce recurring jets. They can be long-lived, quasi-stable, and act as coronal sources for Type-III bursts and implicitly for upwards accelerated electron beams., Comment: 18 pages, 5 figures and 1 table. APJ Accepted

Control systems behavior can be analyzed taking into account a large number of parameters: performances, reliability, availability, security. Each control system presents various security vulnerabilities that affect in lower or higher measure its functioning. In this paper the authors present a method to assess the impact of security issues on the systems availability. A fuzzy model for estimating the availability of the system based on the security level and achieved availability coefficient (depending on MTBF and MTR) is developed and described. The results of the fuzzy inference system (FIS) are presented in the last section of the paper., Comment: Paper accepted for 2016 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI 2016) Proceedings, Ploiesti, Romania

Nontrivial connectivity has allowed the training of very deep networks by addressing the problem of vanishing gradients and offering a more efficient method of reusing parameters. In this paper we make a comparison between residual networks, densely-connected networks and highway networks on an image classification task. Next, we show that these methodologies can easily be deployed into automatic speech recognition and provide significant improvements to existing models., Comment: Accepted at the ML4Audio workshop at the NIPS 2017

We consider the observational basis for the belief that flare ribbons in the chromosphere result from energy transport from the overlying corona. We study ribbons of small flares using magnetic as well as intensity data from the Hinode, SDO and IRIS missions. While most ribbons appear connected to the corona, and they over-lie regions of significant vertical magnetic field, we examine one ribbon with no clear evidence for such connections. Evolving horizontal magnetic fields seen with Hinode suggest that reconnection with pre-existing fields below the corona can explain the data. The identification of just one, albeit small, ribbon, with no apparent connection to the corona, leads us to conclude that at least two mechanisms are responsible for the heating that leads to flare ribbon emission.

We address the question of whether or not global entanglement of a quantum state can be inferred from local properties. Specifically, we are interested in genuinely multiparticle entangled states whose two-body marginals are all separable, but where the entanglement can be proven using knowledge of a subset of the marginals only. Using an iteration of semidefinite programs we prove that for any possible marginal configuration up to six particles multiqubit states with the desired properties can be found. We then present a method to construct states with the same properties for more particles in higher dimensions., Comment: 9 pages, 5 figures, v2: final version

As part of the EURONEAR project, almost 70,000 mosaic Suprime-Cam images taken between 1999 and 2013 were data mined for about 9,800 near Earth asteroids (NEAs) known by 2013 May. Using our PRECOVERY server and the "Find Subaru CCD" tool, we scrutinized 4,186 candidate CCD images possibly holding 518 NEAs. We found 113 NEAs as faint as V<25 magnitude, their positions being measured in 589 images using Astrometrica, then reported to the Minor Planet Center. Among them, 18 objects represent encounters of previously single opposition NEAs, their orbital arcs being extended by up to 10 years. In the second part of this work we searched for unknown NEAs in 78 sequences (780 CCD fields) of 4-5 mosaic images selected from the same Suprime-Cam archive and totaling 16.6 sq.deg, with the aim to assess the faint NEA distribution observable with an 8-m class survey. A total of 2,018 moving objects were measured, from which we identified 18 better NEA candidates. Using the R_c filter in good weather conditions, mostly dark time and sky directions slightly biased towards the ecliptic, at least one NEA could be discovered in every 1 sq.deg surveyed., Comment: Accepted for publication in Astronomical Nachrichten (7 Jan 2017)

Solid-density plasmas driven by intense x-ray free-electron laser (XFEL) radiation are seeded by sources of non-thermal photoelectrons and Auger electrons that ionize and heat the target via collisions. Simulation codes that are commonly used to model such plasmas, such as collisional-radiative (CR) codes, typically assume a Maxwellian distribution and thus instantaneous thermalization of the source electrons. In this study, we present a detailed description and initial applications of a collisional particle-in-cell code, PICLS, that has been extended with a self-consistent radiation transport model and Monte-Carlo models for photoionization and KLL Auger ionization, enabling the fully kinetic simulation of XFEL-driven plasmas. The code is used to simulate two experiments previously performed at the Linac Coherent Light Source investigating XFEL-driven solid-density Al plasmas. It is shown that PICLS-simulated pulse transmissions using the Ecker-Kr\"oll continuum-lowering model agree much better with measurements than do simulations using the Stewart-Pyatt model. Good quantitative agreement is also found between the time-dependent PICLS results and those of analogous simulations by the CR code SCFLY, which was used in the analysis of the experiments to accurately reproduce the observed K{\alpha} emissions and pulse transmissions. Finally, it is shown that the effects of the non-thermal electrons are negligible for the conditions of the particular experiments under investigation., Comment: 12 pages, 11 figures

Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research.

Systems of neutral kaons can be used to observe entanglement and the violation of Bell inequalities. The decay of these particles poses some problems, however, and recently an effective formalism for treating such systems has been derived. We generalize this formalism and make it applicable to other quantum systems that can be made to behave in a similar manner. As examples, we discuss two possible implementations of the generalized formalism using trapped ions such as $^{171}$Yb or $^{172}$Yb, which may be used to simulate kaonic behavior in a quantum optical system., Comment: 11 pages, 20 figures

This paper outlines the necessity for the availability, accessibility, and expansion of atomic physics data and analysis tools for the meaningful interpretation of spectroscopic and polarimetric observations. As we move towards observing the Sun at higher spatio-temporal resolutions, and near-continuously at a range of wavelengths, it becomes critical to develop the appropriate atomic data and physics tools to facilitate scientific progress. We recommend the continued improvement and expansion of current databases to support the development of optically-thick/radiative transfer models, evaluate non-thermal and non-equilibrium ionization effects, and quantify uncertainties in atomic and molecular values. A critical longterm goal will require extending and strengthening collaborations across the atomic, solar/heliospheric, and laboratory plasma physics communities through the participation and training of early career scientists. We also recommend establishing funding for a centralized atomic physics resource made up of a comprehensive and user-oriented atomic database and modeling framework.

The target of this work is to investigate the physical nature of polar jets in the solar corona and their possible contribution to coronal heating and solar wind flow based on the analysis of X-ray images acquired by the Hinode XRT telescope. We estimate the different forms of energy associated with many of these small-scale eruptions, in particular the kinetic energy and enthalpy. Two Hinode XRT campaign datasets focusing on the two polar coronal holes were selected to analyze the physical properties of coronal jets; the analyzed data were acquired using a series of three XRT filters. Typical kinematical properties (e.g., length, thickness, lifetime, ejection rate, and velocity) of 18 jets are evaluated from the observed sequences, thus providing information on their possible contribution to the fast solar wind flux escaping from coronal holes. Electron temperatures and densities of polar-jet plasmas are also estimated using ratios of the intensities observed in different filters. We find that the largest amount of energy eventually provided to the corona is thermal. The energy due to waves may also be significant, but its value is comparatively uncertain. The kinetic energy is lower than thermal energy, while other forms of energy are comparatively low. Lesser and fainter events seem to be hotter, thus the total contribution by polar jets to the coronal heating could have been underestimated so far. The kinetic energy flux is usually around three times smaller than the enthalpy counterpart, implying that this energy is converted into plasma heating more than in plasma acceleration. This result suggests that the majority of polar jets are most likely not escaping from the Sun and that only cooler ejections could possibly have enough kinetic energy to contribute to the total solar wind flow., Comment: 21 pages, 10 figures, Submitted and accepted for publishing in Astronomy and Astrophysics journal

The COronal Solar Magnetism Observatory (COSMO) will make the first synoptic, simultaneous measurements of magnetic and plasma properties of the global solar atmosphere, filling crucial gaps in our understanding of the drivers of solar eruptions and the evolution of the coronal magnetic field on time scales from minutes to decades. - COSMO uniquely addresses critical Heliophysics science. With an unparalleled combination of large field of view and high magnetic sensitivity, the 1.5m COSMO Large Coronagraph (LC) opens a new window on coronal magnetism on global scales. Along with K-Coronagraph (K-Cor) middle-corona observations and the Chromosphere and Prominence Magnetometer (ChroMag) observations of the photosphere and chromosphere, these capabilities enable researchers to finally answer crucial questions about solar eruptions, coronal heating/solar wind acceleration, and the solar dynamo. - COSMO is mature. K-Cor has been operating at the Mauna Loa Solar Observatory (MLSO) since 2013 and ChroMag is soon to be deployed. Also at MLSO, the 20cm Upgraded Coronal Multichannel Polarimeter (UCoMP) is proving the power of global coronal spectropolarimetry and whetting the community’s appetite for the unprecedented sensitivity of the LC. - COSMO is low risk. A recent development: the NSF-funded COSMO Site and Design Advancement (COSADA) is a three-year effort currently underway that reduces risk through site selection and final design of the LC. - COSMO has broad community support. The fact that COSMO fills a critical gap in our observational capabilities was recognized in the last Solar and Space Physics Decadal Survey. COSMO builds on the legacy and thriving user base of the MLSO, which has provided global synoptic solar observations to the community for over sixty years. - COSMO is complementary to other solar telescopes. The breakthrough observations obtained by COSMO will not be provided by any other current or proposed observatory, and will enhance the value of other ground- and space-based Heliophysics assets.

Synopsis: The mechanism for the release of stored magnetic energy in solar eruptions remains a major unsolved problem of Heliophysics. Choosing between triggers requires knowledge of pre-eruptive magnetic fields (B). Although linear polarization in visible/infrared (VIR) coronal emission lines reveals intriguing clues about coronal mass ejection (CME) precursor topology, small telescope apertures limit current capability for measuring vector field. Current coronal observations are insufficient to diagnose 3D coronal magnetic fields in CME precursors. Coronal cavities are the ideal candidates for CME precursor studies. BLOS in cavities is a direct measure of stored magnetic free energy, and the presence and location of topological X-points (reconnection locations) and O-points (circulation of B about axis) distinguish predictions of the flux rope-torus-instability and sheared-arcade-breakout CME models. - Finding: Coronal cavities are ubiquitous throughout the solar cycle, erupt as CMEs, and have coronal polarimetric signatures that distinguish between model predictions. - Recommendation: Coronal cavities should be targeted in a comprehensive multiwavelength study of CME precursors & associated eruption-driving mechanisms. Large ground-based VIR telescopes can measure both coronal magnetic field topology and strength through a combination of the saturated Hanle and BLOS -sensitive Zeeman effects. - Finding: The 4m Daniel K. Inouye Solar Telescope (DKIST) and proposed 1.5m Coronal Solar Magnetism Observatory Large Coronagraph (COSMO-LC) make a major leap forward in VIR coronal sensitivity, enabling measurement of vector B. - Recommendation: A dedicated coronal synoptic telescope with a large field-of-view (i.e., COSMO-LC) is needed to identify the dominant CME driving mechanisms, by measuring coronal magnetic fields from precursor state into eruption. In the UV, a new opportunity has arisen to make use of the strong H I Lyman-α coronal line, which obtains a measurement of B that is independent of the Zeeman effect. - Finding: Small-telescope spectropolarimetric capability in the unsaturated Hanle regime provides an independent and complementary coronal magnetic diagnostic to large ground-based telescope measurements. - Recommendation: The proposed 12-cm Coronal Lyman-α Resonance Observatory (CLARO) spectropolarimetric coronagraph demonstrates a path forward for space-based observations of the coronal magnetic field that should be incorporated into future missions away from the Sun-Earth line (e.g., COMPLETE).

We report follow-up observations of 477 program Near-Earth Asteroids (NEAs) using nine telescopes of the EURONEAR network having apertures between 0.3 and 4.2 m. Adding these NEAs to our previous results we now count 739 program NEAs followed-up by the EURONEAR network since 2006. The targets were selected using EURONEAR planning tools focusing on high priority objects. Analyzing the resulting orbital improvements suggests astrometric follow-up is most important days to weeks after discovery, with recovery at a new opposition also valuable. Additionally we observed 40 survey fields spanning three nights covering 11 sq. degrees near opposition, using the Wide Field Camera on the 2.5m Isaac Newton Telescope (INT), resulting in 104 discovered main belt asteroids (MBAs) and another 626 unknown one-night objects. These fields, plus program NEA fields from the INT and from the wide field MOSAIC II camera on the Blanco 4m telescope, generated around 12,000 observations of 2,000 minor planets (mostly MBAs) observed in 34 square degrees. We identify Near Earth Object (NEO) candidates among the unknown (single night) objects using three selection criteria. Testing these criteria on the (known) program NEAs shows the best selection methods are our epsilon-miu model which checks solar elongation and sky motion and the MPC's NEO rating tool. Our new data show that on average 0.5 NEO candidates per square degree should be observable in a 2m-class survey (in agreement with past results), while an average of 2.7 NEO candidates per square degree should be observable in a 4m-class survey (although our Blanco statistics were affected by clouds). At opposition just over 100 MBAs (1.6 unknown to every 1 known) per square degree are detectable to R=22 in a 2m survey based on the INT data, while our two best ecliptic Blanco fields away from opposition lead to 135 MBAs (2 unknown to every 1 known) to R=23., Comment: Published in Planetary and Space Sciences (Sep 2013)

The ESO/MPG WFI and the INT WFC wide field archives comprising 330,000 images were mined to search for serendipitous encounters of known Near Earth Asteroids (NEAs) and Potentially Hazardous Asteroids (PHAs). A total of 152 asteroids (44 PHAs and 108 other NEAs) were identified using the PRECOVERY software, their astrometry being measured on 761 images and sent to the Minor Planet Centre. Both recoveries and precoveries were reported, including prolonged orbital arcs for 18 precovered objects and 10 recoveries. We analyze all new opposition data by comparing the orbits fitted before and after including our contributions. We conclude the paper presenting Mega-Precovery, a new online service focused on data mining of many instrument archives simultaneously for one or a few given asteroids. A total of 28 instrument archives have been made available for mining using this tool, adding together about 2.5 million images forming the Mega-Archive., Comment: Accepted for publication in Astronomische Nachrichten (Sep 2012)

This paper discusses the development of self-determination skills by students with disabilities and the role of the professional in promoting these skills. It begins by providing a list of 25 elements of self-determination and then explains the importance of promoting the development of self-advocacy groups and other endeavors initiated by and for individuals with disabilities. Basic leadership characteristics are provided, along with secondary leadership characteristics. Self-determination and leadership skills are compared, and barriers to leadership for individuals with developmental disabilities are identified. The paper discusses "truths" about leadership for people with disabilities, and provides a self-determination paradigm. Professionals are urged to provide people with disabilities with skills and information to express self-determination and the opportunity to obtain leadership positions, and to reform systems to provide greater opportunities for choice and self-direction. (CR)

This paper discusses the development of the Basic Skills Model, a theoretical framework that has enabled North Carolina educators to propose to the Department of Public Instruction the development of a curriculum for high school juniors and seniors with disabilities. The curriculum is designed to enable students to gain the necessary skills to cope with various unexpected, difficult situations and promote independent and self-determined behavior. The main outline of the curriculum is presented and includes the development of skills in the following areas: (1) self-knowledge; (2) assertiveness training; (3) problem solving skills; (4) self-advocacy skills; and (5) leadership skills. Current trends in the field of developmental disabilities are discussed and 25 elements of self-determination are listed. Appendices also include a list of personal outcome measures, the skills model, North Carolina graduation and course of study requirements, and principles of the occupational course of study on self-advocacy and problem solving. (CR)

This paper describes the "Problem Solving for Life" training program which trains adolescents and adults with mental retardation in skills for solving social problems. The program requires group participants to solve social problems by practicing two prerequisite skills (relaxation and positive self-statements) and four problem solving steps: (1) identification/definition of the problem; (2) identification of alternative solutions; (3) choice of the best solution; and (4) implementation of the chosen solution. Key principles underlying the program are group learning, active learning, use of natural reinforcers, repetition, relaxation, role playing, identifying emotions, visual prompts, individualized instruction, and group rules. The program also stresses transfer and maintenance of learned skills through such procedures as overlearning, stimulus variability, delayed reinforcement, fading prompts, and mediated generalization. Evaluation using verbal and role-playing measures found gains in self-knowledge, assertiveness, self-advocacy, problem solving, decision making, responsibility, determining what is important, evaluating outcomes, adjusting performance, communicating and listening, and internal locus of control. (Contains 12 references.) (DB)

It was found that as a result of ultrasonic treatment fir wood (Abies alba Mill) shows the effects of homogenization, and the modulus of elasticity for static flexure drops on an average by 50% for 45 min of treatment. The drop is more pronounced for test pieces with 8 to 12 annual rings. Study of microscopic sections disclosed that early wood with one annual ring undergoes the most powerful effect.