Miracle-dz/UniFAR-FTUs huggingface dataset:
A VOEvent-based automatic trigger system for the Murchison Widefield Array

Abstract The Murchison Widefield Array (MWA) is an electronically steered low-frequency (<300 MHz) radio interferometer, with a ‘slew’ time less than 8 s. Low-frequency (∼100 MHz) radio telescopes are ideally suited for rapid response follow-up of transients due to their large field of view, the inverted spectrum of coherent emission, and the fact that the dispersion delay between a 1 GHz and 100 MHz pulse is on the order of 1–10 min for dispersion measures of 100–2000 pc/cm3. The MWA has previously been used to provide fast follow-up for transient events including gamma-ray bursts (GRBs), fast radio bursts (FRBs), and gravitational waves, using systems that respond to gamma-ray coordinates network packet-based notifications. We describe a system for automatically triggering MWA observations of such events, based on Virtual Observatory Event standard triggers, which is more flexible, capable, and accurate than previous systems. The system can respond to external multi-messenger triggers, which makes it well-suited to searching for prompt coherent radio emission from GRBs, the study of FRBs and gravitational waves, single pulse studies of pulsars, and rapid follow-up of high-energy superflares from flare stars. The new triggering system has the capability to trigger observations in both the regular correlator mode (limited to ≥0.5 s integrations) and using the Voltage Capture System (VCS, 0.1 ms integration) of the MWA and represents a new mode of operation for the MWA. The upgraded standard correlator triggering capability has been in use since MWA observing semester 2018B (July–Dec 2018), and the VCS and buffered mode triggers will become available for observing in a future semester.
Emergent naming of resources in a foraging robot swarm

We investigate the emergence of language convention within a swarm of robots foraging in an open environment from two identical resources. While foraging, the swarm needs to explore and decide which resource to exploit, moving through complex transitory dynamics towards different possible equilibria, such as, selection of a single resource or spread across the two. Our point of interest is the understanding of possible correlations between the emergent, evolving, task-induced interaction network and the language dynamics. In particular, our goal is to determine whether the dynamics of the interaction network are sufficient to determine emergent naming conventions that represent features of the task execution (e.g., choice of one or the other resource) and of the environment, In other words, we look for an emergent vocabulary that is both complete (a word for each resource) and correct (no misnomer) for as long as each resource is relevant to the swarm. In this study, robots are playing two variants of the minimal language game. The classic one, where words are created when needed, and a new variant we introduce in this article: the spatial minimal naming game, where the creation of words is linked with the discovery of resources by exploring robots. We end the article by proposing a proof of concept extension of the spatial minimal naming game that assures the completeness and correctness of the swarms vocabulary.
Propagation of Alfvén Waves in the Expanding Solar Wind with the Fast–Slow Stream Interaction

We carry out two-dimensional magnetohydrodynamic simulations of an ensemble of Alfvénic fluctuations propagating in a structured, expanding solar wind including the presence of fast and slow solar wind streams. Using an appropriate expanding box model, the simulations incorporate the effects of fast–slow stream shear and compression and rarefaction self-consistently. We investigate the radial and longitudinal evolution of the cross helicity, the total and residual energies and the power spectra of outward and inward Alfvénic fluctuations. The stream interaction is found to strongly affect the radial evolution of Alfvénic turbulence. The total energy in the Alfvén waves is depleted within the velocity shear regions, accompanied by the decrease of the normalized cross helicity. The presence of stream compression facilitates this process. Residual energy fluctuates around zero due to the correlation and de-correlation between the inward/outward waves but no net growth or decrease of the residual energy is observed. The radial power spectra of the inward/outward Alfvén waves show significant longitudinal variations. Kolmogorov-like spectra are developed only inside the fast and slow streams and when both the compression and shear are present. On the other hand, the spectra along the longitudinal direction show clear Kolmogorov-like inertial ranges in all cases.
Four-Dimension Cross Constellations with Gray Mapping

Recently a four-dimension (4D) cross constellation has been proposed, where a 4D-vector is drawn from two $(3\times 4^m)$-ary QAM constellations, in an effort to reduce the peak-to-average-power ratio (PAPR). We construct a bits-to-signal mapping and prove that it is a Gray mapping. Simulation results show that the proposed modulation scheme is effective in reducing the PAPR while providing better error performance than existing 4D modulation schemes.
Observation of electric-dipole transitions in the laser-cooling candidate 
Th−
 and its application for cooling antiprotons

Despite the fact that the laser cooling method is a well-established technique to obtain ultra-cold neutral atoms and atomic cations, it has so far never been applied to atomic anions due to the lack of suitable electric-dipole transitions. Efforts of more than a decade currently has La$^-$ as the only promising candidate for laser cooling. Our previous work [Tang et al., Phys. Rev. Lett. 123, 203002(2019)] showed that Th$^-$ is also a potential candidate. Here we report on a combination of experimental and theoretical studies to determine the relevant transition frequencies, transition rates, and branching ratios in Th$^-$. The resonant frequency of the laser cooling transition is determined to be $\nu/c$ = 4118.0 (10) cm$^{-1}$. The transition rate is calculated as A=1.17x10^4 s$^{-1}$. The branching fraction to dark states is very small, 1.47x10$^{-10}$, thus this represents an ideal closed cycle for laser cooling. Since Th has zero nuclear spin, it is an excellent candidate to be used to sympathetically cool antiprotons in a Penning trap.
ortisol and induced cognitive fatigue : Effects on memory activation n healthy males lissa

We investigated the relationship between individual differences in acute fatigue and endogenous cortisol changes elicited by the sustained performance of cognitively demanding tasks (fatigue condition). Healthy males provided salivary cortisol measurements and subjective fatigue ratings, and were scanned (functional magnetic resonance imaging) during memory encoding and recognition tasks in fatigue and control conditions. A group of 15 ‘responders’ showed significantly higher cortisol levels in the fatigue condition than 12 ‘non-responders’. Responders showed higher subjective fatigue and reduced encoding and recognition activation than non-responders in the fatigue condition. An interaction in activation
PREDICTION OF VIABILITY WITH EARLY ELECTROCARDIOGRAPHIC FINDINGS AFTER ACUTE MYOCARDIAL INFARCTION. AN ELECTROCARDIOGRAPHIC SUBSTUDY OF THE VIAMI-TRIAL

Objectives: This study investigated early electrocardiographic characteristics after acute myocardial infarction (AMI) to evaluate their predictive value for myocardial viability with the intention to develop an ECG prediction model. Background: In the VIAMI-trial we demonstrated a beneficial effect of early in-hospital stenting of the infarct-related coronary artery in patients with viability in the infarct-area after AMI. Low dose dobutamine echocardiography (LDDE) is a time-consuming test with a less than 100% yield due to poor acoustic windows. It would be even more practical when simple electrocardiographic measures could help clinicians in the selection of patients with viability. Therefore, we used the electrocardiogram for detecting myocardial viability early after AMI. Methods: We retrospectively investigated 285 patients who were initially included in the VIAMI-trial. Eventually, 213 patients with proven viability and 72 patients without viability had well evaluable electrocardiograms. The number of Q waves, ST segment deviation/resolution and T wave direction on the pre-discharge ECG were evaluated. A risk model was developed with these independent predictors for viability. The performance of the model was tested using the Area Under the Receiver Operating Characteristic (ROC), the Hosmer & Lemeshow goodness-of-fit test and the explained variance. Coefficients were converted to easy to use risk scores that were further validated for clinical use by measures of sensitivity, specificity and positive and negative predictive values. Results: The number of pathological Q waves, persistent ST-segment elevation with positive or negative T waves on discharge ECG were all strong independent negative predictors for the present of myocardial viability with an area under the curve of 73% (95% CI 66-80%). The goodness-of-fit of the model was good and the explained variance was 17%. The risk scores indicated that especially in the low and high risk values, our model is useful in clinical practice. For the intermediate range alternative testing should be recommended. Conclusion: The number of Q waves and persistent ST-segment elevation (with positive or negative T waves) on the discharge ECG are independent negative predictors for the present of myocardial viability. The developed model for predicting myocardial viability appears useful in clinical practice. Especially in the high and low range scores.
Hard Lefschetz properties and distribution of spectra in singularity theory and Ehrhart theory

We discuss the distribution of the spectrum at infinity of a convenient and nondegenerate Laurent polynomial (singularity side) and the distribution of the Newton spectrum of a polytope (Ehrhart theory side). To this end, we study a hard Lefschetz property for Laurent polynomials and for polytopes and we give combinatorial criteria for this property to be true. This provides informations about a conjecture by Katzarkov-Kontsevitch-Pantev.
Learn to Explain Efficiently via Neural Logic Inductive Learning

The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain problem in the scope of inductive logic programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data. In experiments, compared with the state-of-the-art methods, we find NLIL can search for rules that are x10 times longer while remaining x3 times faster. We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.
High loading of polygenic risk for ADHD in those with comorbid aggression

245 Manuscript excluding abstract & references: 3331 Tables: 1 Figures: 1 Appendices: 1 High loading of polygenic risk for ADHD in those with comorbid aggression Marian L. Hamshere, Ph.D.* 1 & Kate Langley, Ph.D.* 1 , Joanna Martin 1 BSc (Hons), Sharifah Shameem Agha 1 MSc, Evangelia Stergiakouli, Ph.D. 1 , Richard JL Anney, Ph.D. 2 , Jan Buitelaar, M.D. 3 , Stephen V Faraone, Ph.D. 4 , KlausPeter Lesch, M.D. 5 , Benjamin M
RSS-Based Indoor Localization Using Belief Function Theory

Received signal strength (RSS) is a simple and low-cost method of localization in wireless sensor networks (WSNs) and is of significant interest in ambient intelligence technologies. However, RSS-based indoor localization poses important challenges due to the intrinsic characteristics of RSS measurements. This paper proposes a localization approach that accounts for the imperfection of RSS measurements and the reliability of RSS sources to estimate the target node position in an indoor WSN environment. Non-Gaussian probability density functions are used to model RSS deviations more realistically in the context of indoor environments. In addition, the proposed approach uses the Dempster–Shafer theory to represent and combine separate pieces of information (evidence) provided by more or less reliable or conflicting RSS sources (anchor nodes) on the same hypotheses regarding the target node position. Experiments conducted in two different indoor environments demonstrate the effectiveness of the proposed approach in terms of its accuracy, robustness, and computation time and its superiority compared with state-of-the-art methods. Note to Practitioners—This paper was motivated by the problem of indoor localization in the context of ambient intelligence applications. The localization technique proposed in this paper exploits RSS measurements to estimate the target node position. This technology is very attractive to system designers, due to its simplicity and low cost. This paper also suggests a new approach using, on the one hand, the belief function theory to represent and manage the imperfection of RSS measurements and the reliability of the RSS sources, and, on the other hand, a more realistic modeling of the variability of RSS measurements due to interference and attenuation phenomena that strongly affect signal propagation in indoor environments. Experimental results obtained in two different indoor environments (a residential apartment and a laboratory) are provided to demonstrate the effectiveness of the proposed approach and its superiority compared to state-of-the-art localization techniques.
Boosting Local Causal Discovery in High-Dimensional Expression Data

We study the performance of Local Causal Discovery (LCD) [5], a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expression data. We construct practical estimators specific to the high-dimensional regime. Inspired by the ICP algorithm [13], we use an optional preselection method and two different statistical tests. Empirically, the resulting LCD estimator is seen to closely approach the accuracy of ICP, the state-of-the-art method, while it is algorithmically simpler and computationally more efficient.
Convex and Non-convex Approaches for Statistical Inference with Noisy Labels

We study the problem of estimation and testing in logistic regression with class-conditional noise in the observed labels, which has an important implication in the Positive-Unlabeled (PU) learning setting. With the key observation that the label noise problem belongs to a special sub-class of generalized linear models (GLM), we discuss convex and non-convex approaches that address this problem. A non-convex approach based on the maximum likelihood estimation produces an estimator with several optimal properties, but a convex approach has an obvious advantage in optimization. We demonstrate that in the low-dimensional setting, both estimators are consistent and asymptotically normal, where the asymptotic variance of the non-convex estimator is smaller than the convex counterpart. We also quantify the efficiency gap which provides insight into when the two methods are comparable. In the high-dimensional setting, we show that both estimation procedures achieve l2-consistency at the minimax optimal s log p/n rates under mild conditions. Finally, we propose an inference procedure using a de-biasing approach. To the best of our knowledge, this is the first work providing point estimation guarantees and hypothesis testing results for GLMs with non-canonical link functions, which is of independent interest. We validate our theoretical findings through simulations and a real-data example.
A Conservative Finite Element ALE Scheme for Mass-Conserving Reaction-Diffusion Equations on Evolving Two-Dimensional Domains

Mass-conservative reaction-diffusion systems have recently been proposed as a general framework to describe intracellular pattern formation. These systems have been used to model the conformational switching of proteins as they cycle from an inactive state in the cell cytoplasm, to an active state at the cell membrane. The active state then acts as input to downstream effectors. The paradigm of activation by recruitment to the membrane underpins a range of biological pathways - including G-protein signalling, growth control through Ras and PI 3-kinase, and cell polarity through Rac and Rho; all activate their targets by recruiting them from the cytoplasm to the membrane. Global mass conservation lies at the heart of these models reflecting the property that the total number of active and inactive forms, and targets, remains constant. Here we present a conservative arbitrary Lagrangian Eulerian (ALE) finite element method for the approximate solution of systems of bulk-surface reaction-diffusion equations on an evolving two-dimensional domain. Fundamental to the success of the method is the robust generation of bulk and surface meshes. For this purpose, we use a moving mesh partial differential equation (MMPDE) approach. Global conservation of the fully discrete finite element solution is established independently of the ALE velocity field and the time step size. The developed method is applied to model problems with known analytical solutions; these experiments indicate that the method is second-order accurate and globally conservative. The method is further applied to a model of a single cell migrating in the presence of an external chemotactic signal.
Modeling and digital predistortion of wireless radio frequency transmitters

In current telecommunication systems, the main concern when developing the radio frequency transmitter is power efficiency. This type of design generally leads to a highly nonlinear transmission characteristic, mainly due to the radio frequency power amplifier. This nonlinear transmission severely distorts the information envelope, leading to spectral regrowth, out-of-band distortion. To correct this problem a nonlinear compensation process is employed. For this application, digital predistortion is generally favored for its flexibility and accuracy. Digital predistortion is mostly applied in a blind manner, using brute force until the desired compensation is achieved. Because of this, when the method fails, as it has in gallium nitride based power amplifiers, it is difficult to modify the system to achieve the desired results. To understand and design robust predistortion systems, it is both necessary to have knowledge of the power amplifiers’ behavior, on one hand, and understand the limitations and relations between the digital models and these behaviors, on the other. To do this, this thesis explores and describes these relationships, granting support to the digital predistortion model choice, it further develops new predistortion models based on the physics of the transistors’ behaviors, and it proposes methods for the characterization of radio frequency power amplifiers.
Castration Effects on the Expression of Kisspeptin and Rf-Amide Related Peptide-3 and their Co-Expression with Oestrogen Receptor a in the Ram Hypothalamus

Our objective of the study was to effect of probiotics on the immune response of Hanwoo heifers. Lipopolysaccharide (LPS) challenge was used for investigating physiological response of dietary probiotics. A completely random design was used (4 pens; 2pens/treatment; 5 heifers/pen). After the cattle fed probiotic for 5 months, 16 heifers were transported and acclimated to environmentally controlled chambers. Heifers were fitted with indwelling jugular catheters prior to 24 hours of the LPS challenge. Blood samples were collected at 30-min intervals from -1 to 6 h (0 h; 1μg/kg BW of LPS from Escherichia coli O111:B4). Glucose, non-esterified fatty acid (NEFA), albumin, triglyceride, total protein, phosphorus concentrations, plasma CBC (WBC, RBC, Platelet, Neutrophils, Lymphocytes, Eosinophils, Basophil, Hemoglobin), and pro-inflammatory cytokines (TNFα, IL6, IL1b) were determined from blood samples. Response to the LPS challenge over time was analyzed by ANOVA with the MIXED procedure of SAS. Overall ADG and serum compositions did not differ between probiotic or control diet for 5 months (P >0.05). Pre-LPS NEFA concentration did not differ (P >0.05), but probiotic treated heifers was decreased at 2 hours after LPS challenge. NEFA concentration was decreased at 2 hours after LPS challenge in probiotic treated group (P <0.05). Serum triglyceride was peaked at 0.5 h after LPS challenge in of probiotic treated heifers (P <0.05). There was no difference at CBC test between treatment preand post LPS challenge except red blood cell (RBC). Plasma RBC concentration was increased from 0.5h to 3h post-LPS challenge in probiotic treated heifers. These data suggest that probiotic diet did not directly altered immune response to Hanwoo heifers but indirectly regulated lipid metabolism of Hanwoo heifers at the LPS challenge.
The Normal Neutrino Mass Hierarchy is Exactly What We Need

The preference of the normal neutrino mass hierarchy from the recent cosmological constraints and the global fits of neutrino oscillation experiments does not seem like a wise choice at first glance since it obscures the neutrinoless double beta decay and hence the Majorana nature of neutrinos. Contrary to this naive expectation, we point out that the actual situation is the opposite. Choosing the normal mass hierarchy opens up the possibility of determining the solar octant and simultaneously measuring the two Majorana CP phases. If the neutrino mass hierarchy is normal, the funnel region would completely disappear if the solar mixing angle lives in the higher octant. With a typical $\mathcal O(\mbox{meV})$ sensitivity on the effective mass $|m_{ee}|$, the neutrinoless double beta decay experiment can tell if the funnel region really exists and hence if the solar octant takes the higher octant. With the sensitivity further improved to sub-meV, the two Majorana CP phases can be simultaneously determined. We need the normal neutrino mass hierarchy with very good reasons.
Study on Data Selection Method of Historical Operation Data for Large Scale Power System

A data selection method based on similarity measurement and support vector machine (SVM) is proposed. At first, the critical clearing time (CCT) is used as the class label, and features which are strongly correlated with the class label will be extracted. Secondly, a SVM classifier is trained on the initial training instances with extracted features, and the instance which is misclassified will be removed. Thirdly, the concept of the most similar instance pair is proposed, which two instances with the minimum distance are selected, and then removes the eligible instances which is noisy and redundant instances. The proposed method which can simultaneously prune data in horizontal and vertical directions is tested by online historical data of an actual large scale power system. Experimental results demonstrate that more than 70% features and 30% instances are reduced, and the accuracy and storage reduction are also improved. This method can be well used with the good performance in large scale power system.