Miracle-dz/UniFAR-FTUs huggingface dataset:
Isotopic ratios in the red giant component of the recurrent nova T Coronae Borealis

We report the determination of abundances and isotopic ratios for C, O and Si in the photosphere of the red giant component of the recurrent nova T Coronae Borealis from new 2.284--2.402 $\mu$m and 3.985--4.155 $\mu$m spectroscopy. Abundances and isotopic ratios in the photosphere may be affected by (i) processes in the red giant interior which are brought to the surface during dredge-up, (ii) contamination of the red giant, either during the common envelope phase of the binary evolution or by material synthesised in recurrent nova eruptions, or a combination of the two. We find that the abundances of C, O and Si are reasonably consistent with the expected composition of a red giant after first dredge-up, as is the $^{16}$O/$^{17}$O ratio. The $^{28}$Si/$^{29}$Si ratio is found to be $8.6\pm3.0$, and that for $^{28}$Si/$^{30}$Si is $21.5\pm3.0$. The $^{12}$C/$^{13}$C ratio ($10\pm2$) is somewhat lower than expected for first dredge-up. The $^{16}$O/$^{18}$O ratio ($41\pm3$) is highly inconsistent with that expected either from red giant evolution ($\sim550$) or from contamination of the red giant by the products of a nova thermonuclear runaway. In particular the C and O isotopic ratios taken in combination are a puzzle. We urge confirmation of our results using spectroscopy at high resolution. We also encourage a thorough theoretical study of the effects on the secondary star in a recurrent nova system of contamination by ejecta having anomalous abundances and isotopic ratios.
Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems

Inferring behavior model of a running software system is quite useful for several automated software engineering tasks, such as program comprehension, anomaly detection, and testing. Most existing dynamic model inference techniques are white-box, i.e., they require source code to be instrumented to get run-time traces. However, in many systems, instrumenting the entire source code is not possible (e.g., when using black-box third-party libraries) or might be very costly. Unfortunately, most black-box techniques that detect states over time are either univariate, or make assumptions on the data distribution, or have limited power for learning over a long period of past behavior. To overcome the above issues, in this paper, we propose a hybrid deep neural network that accepts as input a set of time series, one per input/output signal of the system, and applies a set of convolutional and recurrent layers to learn the non-linear correlations between signals and the patterns, over time. We have applied our approach on a real UAV auto-pilot solution from our industry partner with half a million lines of C code. We ran 888 random recent system-level test cases and inferred states, over time. Our comparison with several traditional time series change point detection techniques showed that our approach improves their performance by up to 102%, in terms of finding state change points, measured by F1 score. We also showed that our state classification algorithm provides on average 90.45% F1 score, which improves traditional classification algorithms by up to 17%.
Ionized calcium, 25-hydroxyvitamin D, and parathyroid hormone in children with steroid-sensitive nephrotic syndrome

Background The chief complications of nephrotic syndrome (NS) are infections and thromboembolic events. Others include hypovolemic crisis, acute renal failure, and hormonal and mineral alterations. Objective The aim was to study the level of serum ionized calcium during the active stage and after remission in steroid-sensitive nephrotic syndrome (SSNS) patients and 25-hydroxyvitamin D [25(OH)D] and parathyroid hormone (PTH) during the active stage of the disease. Patients and methods The study was conducted on two groups. Twenty children with the first episode of SSNS were investigated and compared with 20 healthy children as a control group. Serum ionized calcium, serum 25(OH)D, PTH, phosphorus, and alkaline phosphatase were measured during the active stage of the disease and serum ionized calcium was repeated after remission. Results Children with active SSNS had low ionized calcium, low-serum 25 (OH)D levels, with high PTH and high phosphorus and low alkaline phosphatase levels compared with the control. All patients had 25(OH)D deficiency, of which, 80% were severely deficient. Both serum ionized calcium and 25(OH)D had a significant negative correlation with PTH (r=−0.655, P=0.002 and r=−0.575, P=0.008, respectively). Serum ionized calcium was negatively correlated to spot protein/creatinine ratio in urine (r=− 0.565, P=0.009). Levels of serum ionized calcium during the active stage of the disease were markedly lower than that after remission. However, both were significantly lower than the control group. Conclusion Children with SSNS are at risk of vitamin D deficiency and hypocalcemia, therefore, further research will be needed to prove the need for vitamin D supplementation to prevent the occurrence of possible complications.
A provably entropy stable subcell shock capturing approach for high order split form DG

The main result in this paper is a provably entropy stable shock capturing approach for the high order entropy stable Discontinuous Galerkin Spectral Element Method based on a hybrid blending with a subcell low order variant. Lead by theoretical findings presented in Fisher and Carpenter, where they show how to rewrite a high order summation-by-parts operator into an equivalent conservative finite difference form, it is possible to design a low order scheme directly with the Legendre-Gauss-Lobatto nodes that is compatible to the discrete entropy analysis used for the proof of the entropy stable DGSEM. We are able to design a hybrid low order/high order discretisation where it is possible to seamlessly blend between the two approaches, while still being provably entropy stable. With tensor products and careful design of the low order scheme on curved elements, we are able to extend the approach to three spatial dimensions on unstructured curvilinear hexahedral meshes. We validate our theoretical findings and demonstrate convergence order for smooth problems, conservation of the primary quantities and discrete entropy stability for an arbitrary blending on curvilinear grids. In practical simulations, we connect the blending factor to a local troubled element indicator that provides the control of the amount of low order dissipation injected into the high order scheme. We extended the shock indicator of Persson and Peraire to our provably stable hybrid scheme. We describe our indicator in detail and demonstrate its robustness in combination with the hybrid scheme, as it is possible to compute all the different test cases without changing the indicator. The test cases include e.g. the double Mach reflection setup, forward and backward facing steps with shock Mach numbers up to 100.
Relativistic accretion disc reflection in AGN X-ray spectra at z = 0.5–4: a study of four Chandra Deep Fields

We confirm that the spectra are best fit by a model containing two Compton reflection components, one from distant material, and the other displaying relativistic broadening, most likely from the inner accretion disk. The degree of relativistic broadening indicates a preference for high black hole spin, but the reflection is weaker than that expected for a flat disk illuminated by a point source. We investigate the Compton reflection signatures as a function of luminosity, redshift and obscuration, confirming an X-ray Baldwin effect for both the narrow and broad components of the iron line. Anti-correlations are also seen with redshift and obscuring column density, but are difficult to disentangle from the Baldwin effect. Our methodology is able to extract information from multiple spectra with low signal-to-noise ratio, and can be applied to future data sets such as eROSITA. We show using simulations, however, that it is necessary to apply an appropriate signal-to-noise ratio cut to the samples to ensure the spectra add useful information.
Data-driven filtering for linear systems using Set Membership multistep predictors

This paper presents a novel data-driven, direct filtering approach for unknown linear time-invariant systems affected by unknown-but-bounded measurement noise. The proposed technique combines independent multistep prediction models, identified resorting to the Set Membership framework, to refine a set that is guaranteed to contain the true system output. The filtered output is then computed as the central value in such a set. By doing so, the method achieves an accurate output filtering and provides tight and minimal error bounds with respect to the true system output. To attain these results, the online solution of linear programs is required. A modified filtering approach with lower online computational cost is also presented, obtained by moving the solution of the optimization problems to an offline preliminary phase, at the cost of larger accuracy bounds. The performance of the proposed approaches are evaluated and compared with those of standard model-based filtering techniques in a numerical example.
Expiratory variability index is associated with asthma risk, wheeze and lung function in infants with recurrent respiratory symptoms

Analysis of tidal breathing flow–volume (TBFV) curves is a convenient method to assess lung function in infants. We have shown that impedance pneumography (IP) is a valid method to assess overnight TBFV profiles in young children [1] and infants [2] and that certain specific curve shapes are related to symptom presentation and asthma risk [3]. We have also shown that the reduced variability of TBFV curves may be an indicator of airway obstruction in children [4–6]. However, in infants, it is unknown whether overnight TBFV variability is associated with respiratory symptoms or asthma risk. Impedance pneumography enables the measurement of the expiratory variability index (EVI) at home during a night's sleep in infants with recurrent respiratory symptoms. EVI is associated with asthma risk, symptoms and lung function. https://bit.ly/2PF2cx8
Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning

Reinforcement learning is a framework to optimize an agent’s policy using rewards that are revealed by the system as a response to an action. In its standard form, reinforcement learning involves a single agent that uses its policy to accomplish a specific task. These methods require large amounts of reward samples to achieve good performance, and may not generalize well when the task is modified, even if the new task is related. In this paper we are interested in a collaborative scheme in which multiple policies are optimized jointly. To this end, we we introduce cross-learning, in which policies are trained for related tasks in separate environments, and they are constrained to be close to one another. Two properties make our new approach attractive: (i) it produces a multi-task central policy that can be used as a starting point to adapt quickly to one of the tasks trained for, and (ii) as in meta-learning, it adapts to environments related but different to those seen during training. We focus on policies belonging to reproducing kernel Hilbert spaces for which we bound the distance between the task-specific policies and the cross-learned policy. To solve the resulting optimization problem, we resort to a projected policy gradient algorithm and prove that it converges to a near-optimal solution with high probability. We evaluate our methodology with a navigation example in which an agent moves through environments with obstacles of multiple shapes and avoids obstacles not trained for.
Learning to Profile: User Meta-Profile Network for Few-Shot Learning

Meta-learning approaches have shown great success in solving challenging knowledge transfer and fast adaptation problems with few samples in vision and language domains. However, few studies discuss the practice of meta-learning for large-scale industrial applications, e.g., representation learning for e-commerce platform users. Although e-commerce companies have spent many efforts on learning accurate and expressive representations to provide a better user experience, we argue that such efforts cannot be stopped at this step. In addition to learning a strong profile of user behaviors, the challenging question about how to effectively transfer the learned representation and quickly adapt the learning process to the subsequent learning tasks or applications is raised simultaneously. This paper introduces the contributions that we made to address these challenges from three aspects. 1) Meta-learning model: In the context of representation learning with e-commerce user behavior data, we propose a meta-learning framework called the Meta-Profile Network, which extends the ideas of matching network and relation network for knowledge transfer and fast adaptation; 2) Encoding strategy: To keep high fidelity of large-scale long-term sequential behavior data, we propose a time-heatmap encoding strategy that allows the model to encode data effectively; 3) Deep network architecture: A multi-modal model combined with multi-task learning architecture is utilized to address the cross-domain knowledge learning and insufficient label problems. Moreover, we argue that an industrial model should not only have good performance in terms of accuracy, but also have better robustness and uncertainty performance under extreme conditions. We evaluate the performance of our model with extensive control experiments in various extreme scenarios, i.e. out-of-distribution detection, data insufficiency and class imbalance scenarios. The Meta-Profile Network shows significant improvement in the model performance when compared to baseline models.
Effect of base-N to dress-N ratio on water and nitrogen utilization,growth of summer maize in North China PlainIGrowth,development and water use efficiency of summer maize

Under low N application rate [180 kg(N)/ha],maize(Zhengdan958) was used to study the effect of base-N to dress-N ratios(RBTD) at 1∶2,1∶1 and 2∶1 on growth,development and water use efficiency (WUE) of summer maize in North China Plain.Results show increased leaf number,plant height and yield of summer maize under N fertilization,and that the increment gets larger with increased dress-N to totalN ratio(RDTT). Base-N has beneficial effects on leaf area expansion,while dress-N has beneficial effects on leaf area maintenance in filling stage.Coarse protein and free amino acid contents in maize grains increase with N fertilization and increased RDTT.The starch content of 1∶2 RBTD treatment increases,while those of the other two treatments drop.WUE heightens with increasing N application,and higher percentages of dressN are proved to be advantageous to heightened WUE.Because precipitation mainly occurs in the sowing to jointing stage of summer maize in the North China Plain,it is concluded that less base-N application and RBTD ratio of 1∶2 are the best dosage for agronomic and quality traits amelioration,heightening yield and WUE of summer maize.
#ISIS vs #ActionCountersTerrorism: A Computational Analysis of Extremist and Counter-extremist Twitter Narratives

The rapid expansion of cyberspace has greatly facilitated the strategic shift of traditional crimes to online platforms. This has included malicious actors, such as extremist organisations, making use of online networks to disseminate propaganda and incite violence through radicalising individuals. In this article, we seek to advance current research by exploring how supporters of extremist organisations craft and disseminate their content, and how posts from counter-extremism agencies compare to them. In particular, this study will apply computational techniques to analyse the narratives of various pro-extremist and counter-extremist Twitter accounts, and investigate how the psychological motivation behind the messages compares between pro-ISIS and counter-extremism narratives. Our findings show that pro-extremist accounts often use different strategies to disseminate content (such as the types of hashtags used) when compared to counter-extremist accounts across different types of organisations, including accounts of governments and NGOs. Through this study, we provide unique insights into both extremist and counter-extremist narratives on social media platforms. Furthermore, we define several avenues for discussion regarding the extent to which counter-messaging may be effective at diminishing the online influence of extremist and other criminal organisations. Cybercrime, online radicalisation, counterextremism, social media analysis, Twitter, cyber-criminals.
DeepFake Detection Based on the Discrepancy Between the Face and its Context

We propose a method for detecting face swapping and other identity manipulations in single images. Face swapping methods, such as DeepFake, manipulate the face region, aiming to adjust the face to the appearance of its context, while leaving the context unchanged. We show that this modus operandi produces discrepancies between the two regions. These discrepancies offer exploitable telltale signs of manipulation. Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context (e.g., hair, ears, neck). We describe a method which uses the recognition signals from our two networks to detect such discrepancies, providing a complementary detection signal that improves conventional real vs. fake classifiers commonly used for detecting fake images. Our method achieves state of the art results on the FaceForensics++, Celeb-DF-v2, and DFDC benchmarks for face manipulation detection, and even generalizes to detect fakes produced by unseen methods.
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events

As a vital topic in media content interpretation, video anomaly detection (VAD) has made fruitful progress via deep neural network (DNN). However, existing methods usually follow a reconstruction or frame prediction routine. They suffer from two gaps: (1) They cannot localize video activities in a both precise and comprehensive manner. (2) They lack sufficient abilities to utilize high-level semantics and temporal context information. Inspired by frequently-used cloze test in language study, we propose a brand-new VAD solution named Video Event Completion (VEC) to bridge gaps above: First, we propose a novel pipeline to achieve both precise and comprehensive enclosure of video activities. Appearance and motion are exploited as mutually complimentary cues to localize regions of interest (RoIs). A normalized spatio-temporal cube (STC) is built from each RoI as a video event, which lays the foundation of VEC and serves as a basic processing unit. Second, we encourage DNN to capture high-level semantics by solving a visual cloze test. To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE). The DNN learns to restore the original video event from the IE by inferring the missing patch. Third, to incorporate richer motion dynamics, another DNN is trained to infer erased patches' optical flow. Finally, two ensemble strategies using different types of IE and modalities are proposed to boost VAD performance, so as to fully exploit the temporal context and modality information for VAD. VEC can consistently outperform state-of-the-art methods by a notable margin (typically 1.5%-5% AUROC) on commonly-used VAD benchmarks. Our codes and results can be verified at github.com/yuguangnudt/VEC_VAD
The effect of environment on Type Ia supernovae in the Dark Energy Survey three-year cosmological sample

Analyses of Type Ia supernovae (SNe Ia) have found puzzling correlations between their standardized luminosities and host galaxy properties: SNe Ia in high-mass, passive hosts appear brighter than those in lower mass, star-forming hosts. We examine the host galaxies of SNe Ia in the Dark Energy Survey 3-yr spectroscopically confirmed cosmological sample, obtaining photometry in a series of ‘local’ apertures centred on the SN, and for the global host galaxy. We study the differences in these host galaxy properties, such as stellar mass and rest-frame U − R colours, and their correlations with SN Ia parameters including Hubble residuals. We find all Hubble residual steps to be >3σ in significance, both for splitting at the traditional environmental property sample median and for the step of maximum significance. For stellar mass, we find a maximal local step of 0.098 ± 0.018 mag; ∼0.03 mag greater than the largest global stellar mass step in our sample (0.070 ± 0.017 mag). When splitting at the sample median, differences between local and global U − R steps are small, both ∼0.08 mag, but are more significant than the global stellar mass step (0.057 ± 0.017 mag). We split the data into sub-samples based on SN Ia light-curve parameters: stretch (x1) and colour (c), finding that redder objects (c > 0) have larger Hubble residual steps, for both stellar mass and U − R, for both local and global measurements, of ∼0.14 mag. Additionally, the bluer (star-forming) local environments host a more homogeneous SN Ia sample, with local U − R rms scatter as low as 0.084 ± 0.017 mag for blue (c < 0) SNe Ia in locally blue U − R environments.
Non-unitary dynamics of Sachdev-Ye-Kitaev chain

We construct a series of one-dimensional non-unitary dynamics consisting of both unitary and imaginary evolutions based on the Sachdev-Ye-Kitaev model. Starting from a short-range entangled state, we analyze the entanglement dynamics using the path integral formalism in the large N limit. Among all the results that we obtain, two of them are particularly interesting: (1) By varying the strength of the imaginary evolution, the interacting model exhibits a first order phase transition from the highly entangled volume law phase to an area law phase; (2) The one-dimensional free fermion model displays an extensive critical regime with emergent two-dimensional conformal symmetry.
Light Verbs are Just Regular Verbs

Light verbs constructions like *give a sigh* and *take a shower* have always been analyzed as involving a process of complex predicate formation, where the verb and its complement combine to form a single predicate. In contrast, I argue that light verb constructions are just regular verb-complement combinations. The verb has its usual argument structure and interpretation (which is often quite unspecified), while its complement has the same interpretation it has elsewhere. We only need two things to account for light verb constructions: the fact that NPs can be eventive; and control into NPs. Both of these are needed anyway, outside of light verb constructions. We can therefore dispense with *light verb* as a grammatical category, and do without processes of complex predicate formation in our models of grammar. This working paper is available in University of Pennsylvania Working Papers in Linguistics: http://repository.upenn. edu/pwpl/ vol22/iss1/7 Light Verbs are Just Regular Verbs