Miracle-dz/UniFAR-FTUs huggingface dataset:
Photoproductions of 
f1(1285)
 and 
η′(958)
 from the analysis of CLAS data with the Primakoff effect at high energies

We investigate photoproduction of axial vector meson $f_1(1285)$ based on the CLAS experiment $\gamma p\to pf_1(1285)\to p\eta\pi^+\pi^-$ using the Regge model for $\rho^0+\omega$ exchanges. The combined analysis of $\gamma p\to p\eta'\to p\eta\pi^+\pi^-$ reaction including $\eta(1295)$ is accompanied to evaluate the decay mode $\eta(1295)\to \eta\pi^+\pi^-$ from the potential overlap with the $f_1$ reaction. The predominance of $\rho^0(770)$ exchange over the $\omega(782)$ with the coupling constant $\gamma\rho^0f_1$ extracted from the CLAS experiment leads to a good description of the measured cross sections for $f_1$, while the scale of the cross section is corrected by the branching fraction $\eta'\to\eta\pi^+\pi^-$, and $f_1\to\eta\pi^+\pi^-$, respectively. As an extension to study the nonmesonic production mechanism, the Primakoff effect by the virtual photon exchange is investigated in the exclusive reaction $\gamma p\to pf_1$ up to $\sqrt{s}\approx50$ GeV, finding that it plays a similar role to the Pomeron in the vector meson photoproduction at high energies. Such an unique role of the virtual photon exchange similar to the $f_1$ case is identified up to $\sqrt{s}\approx250$ GeV in the $\gamma p\to p\pi^0$ and $\gamma p\to p\eta$ as well as $\gamma p\to p\eta'$ reaction.
Adma: A Flexible Loss Function for Neural Networks

Highly increased interest in Artificial Neural Networks (ANNs) have resulted in impressively wide-ranging improvements in its structure. In this work, we come up with the idea that instead of static plugins that the currently available loss functions are, they should by default be flexible in nature. A flexible loss function can be a more insightful navigator for neural networks leading to higher convergence rates and therefore reaching the optimum accuracy more quickly. The insights to help decide the degree of flexibility can be derived from the complexity of ANNs, the data distribution, selection of hyper-parameters and so on. In the wake of this, we introduce a novel flexible loss function for neural networks. The function is shown to characterize a range of fundamentally unique properties from which, much of the properties of other loss functions are only a subset and varying the flexibility parameter in the function allows it to emulate the loss curves and the learning behavior of prevalent static loss functions. The extensive experimentation performed with the loss function demonstrates that it is able to give state-of-the-art performance on selected data sets. Thus, in all the idea of flexibility itself and the proposed function built upon it carry the potential to open to a new interesting chapter in deep learning research.
Bar effect on gas-phase abundance gradients. I. Data sample and chemical abundances

Studies of gas-phase radial metallicity profiles in spirals published in the last decade have diminished the importance of galactic bars as agents that mix and flatten the profiles, contradicting results obtained in the 1990s. We have collected a large sample of 2831 published HII region emission-line fluxes in 51 nearby galaxies, including objects both with and without the presence of a bar, with the aim of revisiting the issue of whether bars affect the radial metal distribution in spirals. In this first paper of a series of two, we present the galaxy and the HII region samples. The methodology is homogeneous for the whole data sample and includes the derivation of HII region chemical abundances, structural parameters of bars and discs, galactocentric distances, and radial abundance profiles. We have obtained O/H and N/O abundance ratios from the Te-based (direct) method for a sub-sample of 610 regions, and from a variety of strong-line methods for the whole HII region sample. The strong-line methods have been evaluated in relation to the Te-based one from both a comparison of the derived O/H and N/O abundances for individual HII regions, and a comparison of the abundance gradients derived from both methodologies. The median value and the standard deviation of the gradient distributions depend on the abundance method, and those based on the O3N2 indicator tend to flatten the steepest profiles, reducing the range of observed gradients. A detailed analysis and discussion of the derived O/H and N/O radial abundance gradients and y-intercepts for barred and unbarred galaxies is presented in the companion Paper II. The whole HII region catalogue including emission-line fluxes, positions and derived abundances is made publicly available on the CDS VizieR facility, together with the radial abundance gradients for all galaxies.
Abstract Interpretation in Formal Argumentation: with a Galois Connection for Abstract Dialectical Frameworks and May-Must Argumentation (First Report)

Labelling-based formal argumentation relies on labelling functions that typically assign one of 3 labels to indicate either acceptance, rejection, or else undecided-to-be-either, to each argument. While a classical labelling-based approach applies globally uniform conditions as to how an argument is to be labelled, they can be determined more locally per argument. Abstract dialectical frameworks (ADF) is a well-known argumentation formalism that belongs to this category, offering a greater labelling flexibility. As the size of an argumentation increases in the numbers of arguments and argument-to-argument relations, however, it becomes increasingly more costly to check whether a labelling function satisfies those local conditions or even whether the conditions are as per the intention of those who had specified them. Some compromise is thus required for reasoning about a larger argumentation. In this context, there is a more recently proposed formalism of may-must argumentation (MMA) that enforces still local but more abstract labelling conditions. We identify how they link to each other in this work. We prove that there is a Galois connection between them, in which ADF is a concretisation of MMA and MMA is an abstraction of ADF. We explore the consequence of abstract interpretation at play in formal argumentation, demonstrating a sound reasoning about the judgement of acceptability/rejectability in ADF from within MMA. As far as we are aware, there is seldom any work that incorporates abstract interpretation into formal argumentation in the literature, and, in the stated context, this work is the first to demonstrate its use and relevance.
Estimation of Stochastic Volatility Models Using Optimized Filtering Algorithms

In this paper, we describe and implement two recursive ﬁltering algorithms, the optimized particle ﬁlter, and the Viterbi algorithm, which allow the joint estimation of states and parameters of continuous-time stochastic volatility models, such as the Cox Ingersoll Ross and Heston model. In practice, good parameter estimates are required so that the models are able to generate accurate forecasts. To achieve the objectives the proposed algorithms were implemented using daily empirical data from the time series of the S & P 500 returns of the stock exchange index. The proposed methodology facilitates computational calculations of the marginal likelihood of states and allows the reconstruction of unknown states in a suitable way, and reliable estimation of the parameters. To measure the quality of estimation of the algorithms, we used the square root of the mean square error and relative deviation standard as measures of goodness of ﬁt. The estimated errors are insigniﬁcant for the analyzed data and the two models considered. We also calculated the execution times of the algorithms, demonstrating that the Viterbi algorithm has less execution time than the optimized particle ﬁlter.
Decision-making in driver-automation shared control: A review and perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driverʼ s abilities to control. The human driver, as an essential agent in the driver-vehicle shared control systems, should be precisely modeled regarding their cognitive processes, control strategies, and decision-making processes. The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans. Many open-ended questions arise, such as what proper role of human drivers should act in a shared control scheme? How to make an intelligent decision capable of balancing the benefits of agents in shared control systems? Due to the advent of these attentions and questions, it is desirable to present a survey on the decision making between human drivers and highly automated vehicles, to understand their architectures, human driver modeling, and interaction strategies under the driver-vehicle shared schemes. Finally, we give a further discussion on the key future challenges and opportunities. They are likely to shape new potential research directions.
An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability

Background: Developers spend a lot of their time on understanding source code. Static code analysis tools can draw attention to code that is difficult for developers to understand. However, most of the findings are based on non-validated metrics, which can lead to confusion and code that is hard to understand not being identified. Aims: In this work, we validate a metric called Cognitive Complexity which was explicitly designed to measure code understandability and which is already widely used due to its integration in well-known static code analysis tools. Method: We conducted a systematic literature search to obtain data sets from studies which measured code understandability. This way we obtained about 24,000 understandability evaluations of 427 code snippets. We calculated the correlations of these measurements with the corresponding metric values and statistically summarized the correlation coefficients through a meta-analysis. Results: Cognitive Complexity positively correlates with comprehension time and subjective ratings of understandability. The metric showed mixed results for the correlation with the correctness of comprehension tasks and with physiological measures. Conclusions: It is the first validated and solely code-based metric which is able to reflect at least some aspects of code understandability. Moreover, due to its methodology, this work shows that code understanding is currently measured in many different ways, which we also do not know how they are related. This makes it difficult to compare the results of individual studies as well as to develop a metric that measures code understanding in all its facets.
Maximizing the Minimum Achievable Secrecy Rate for a Two-User Gaussian Weak Interference Channel

This paper is a study of the Gaussian weak interference channel in which two single-antenna sources aim to send their messages to legitimate destinations such that all messages are kept confidential from non-intended receivers. Under the Gaussian codebook assumption, the problem of secrecy rate balancing with the objective of exploring the optimal power allocation policy at the sources in an attempt to maximize the minimum achievable secrecy rate is investigated, assuming each source is subject to a transmit power constraint. It is shown that at the optimal point, the two secrecy rates are equal; hence, the problem is abstracted to maximize the secrecy rate associated with one destination, while the other is restricted to having the same secrecy rate. The optimum secrecy rate associated with the investigated max–min problem is analytically derived, leading to the solution of the secrecy rate balancing problem. An additional scenario is considered in which sources can transmit artificial noise in addition to their signals. In this case, the max–min problem is solved and a suboptimal solution is provided. Numerical results show that the proposed solution is near-optimal and can be used as a suitable alternative.
The importance of small-sized copepods in a frontal area of the Aegean Sea

Distribution, production and grazing of the copepod community were investigated in the northern Aegean Sea, which is characterized by a permanent thermohaline front. Cruises were conducted along a transect crossing the frontal area during spring and late summer. Biomass and production of autotrophs were measured by size fractionation and heterotrophic nanoflagellates and ciliates were also studied. Copepod biomass, production and grazing impact on the phytoplankton and ciliate populations were estimated. The copepod community was sampled with a 45 mm net to include the smallest species and their developmental stages. The size, structure and distribution of the phytoplankton imply that most carbon was fixed by picoplankton during both seasons and throughout the study area. The partitioning of carbon among the different plankton compartments was not a broadbased pyramid and the biomass of heterotrophs was higher than that of autotrophs, except in the non-frontal region during spring. Copepod biomass was substantially higher in the frontal area. Our results showed that the small-sized copepods (calanoids and cyclopoids) dominated in terms of biomass and production, but also had a greater influence on the efficiency of the trophic coupling between the primary producers and the protozooplankton than the larger species, stressing their importance in the northern Aegean Sea and the Eastern Mediterranean in general.
FiSSA at SemEval-2020 Task 9: Fine-tuned for Feelings

In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data in the SemEval-2020 Task 9. We investigate performance of various pre-trained Transformer models by using different fine-tuning strategies. We explore both monolingual and multilingual models with the standard fine-tuning method. Additionally, we propose a custom model that we fine-tune in two steps: once with a language modeling objective, and once with a task-specific objective. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score with 0.537 on development data and 0.739 on test data. With this score, our team jupitter placed tenth overall in the competition.
Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems

We study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact.
All-optical density downramp injection in electron-driven plasma wakefield accelerators

The FACET E-210 plasma wakefield acceleration experiment was built and operated with support from UCLA (US Department of Energy (DOE) contract no. DESC0009914), RadiaBeam Technologies (DOE contract no. DE-SC0009533), and the FACET E200 team and DOE under contract no. DE-AC02-76SF00515. B.H. , P.S. , A.S. , F.A. H. , T.H. , A.B. were supported by the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (NeXource, ERC Grant agreement No. 865877). The work was supported by STFC ST/S006214/1 PWFAFEL, EPSRC (grant no. EP/N028694/1). D.L. B. acknowledges support from the US DOE Office of High Energy Physics under award no. DE-SC0013855. J.R. C. acknowledges support from the National Science Foundation under award no. PHY 1734281. M.D.L acknowledges support from the US DOE Office of High Energy Physics under award no. DE-SC0017906. This work used computational resources of the National Energy Research Scientific Computing Center, which is supported by DOE DE-AC02-05CH11231, and of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.
A Nonparametric Test of Dependence Based on Ensemble of Decision Trees

In this paper, a robust non-parametric measure of statistical dependence, or correlation, between two random variables is presented. The proposed coefficient is a permutation-like statistic that quantifies how much the observed sample S_n : {(X_i , Y_i), i = 1 . n} is discriminable from the permutated sample ^S_nn : {(X_i , Y_j), i, j = 1 . n}, where the two variables are independent. The extent of discriminability is determined using the predictions for the, interchangeable, leave-out sample from training an aggregate of decision trees to discriminate between the two samples without materializing the permutated sample. The proposed coefficient is computationally efficient, interpretable, invariant to monotonic transformations, and has a well-approximated distribution under independence. Empirical results show the proposed method to have a high power for detecting complex relationships from noisy data.
User-driven Analysis of Longitudinal Health Data with Hidden Markov Models for Clinical Insights

A goal of clinical researchers is to understand the progression of a disease through a set of biomarkers. Researchers often conduct observational studies, where they collect numerous samples from selected subjects throughout multiple years. Hidden Markov Models (HMMs) can be applied to discover latent states and their transition probabilities over time. However, it is challenging for clinical researchers to interpret the outcomes and to gain insights about the disease. Thus, this demo introduces an interactive visualization system called DPVis, which was designed to help researchers to interactively explore HMM outcomes. The demo provides guidelines of how to implement the clinician-in-the-loop approach for analyzing longitudinal, observational health data with visual analytics.
Monte-Carlo Tree Search as Regularized Policy Optimization

The combination of Monte-Carlo tree search (MCTS) with deep reinforcement learning has led to significant advances in artificial intelligence. However, AlphaZero, the current state-of-the-art MCTS algorithm, still relies on handcrafted heuristics that are only partially understood. In this paper, we show that AlphaZero's search heuristics, along with other common ones such as UCT, are an approximation to the solution of a specific regularized policy optimization problem. With this insight, we propose a variant of AlphaZero which uses the exact solution to this policy optimization problem, and show experimentally that it reliably outperforms the original algorithm in multiple domains.
News Shocks and Asset Price Volatility in General Equilibrium

This paper studies equity price volatility in general equilibrium with news shocks about future productivity and monetary policy. As West (1998) shows, in a partial equilibrium present discounted value model, news about the future cash flow reduces asset price volatility. This paper shows that introducing news shocks in canonical dynamic stochastic general equilibrium model may not reduce asset price volatility under plausible parameter assumptions. This is because, in general equilibrium, the asset cash flow itself may be affected by the introduction of new shocks. In addition, it is shown that neglecting to account for policy news shocks (e. g. , policy announcements) can potentially bias empirical estimates of the impact of monetary policy shocks on asset prices.
The effect of nutrition on the reproductive performance of first-litter sows 2. Protein and energy intakes during lactation

A factorial experiment involving high (53·1 to 59·5 MJ) and low (25·4 to 27·1 MJ) daily intakes of digestible energy, and high (550 to 745 g) and low (302 to 318 g) daily intakes of crude protein during lactation, was conducted with 68 first-litter sows. Average lactation length was 32 days, and average backfat changes during lactation were +0·3, −1·4, −7·2 and −5·4 mm for the high energy/high protein, high energy/low protein, low energy/high protein and low energy/low protein combinations, respectively. The corresponding mean live-weight losses during lactation were 3·9, 32·5, 29·8 and 35·8 kg. Within 8 days of weaning, more sows receiving high intakes of both energy and protein during lactation exhibited oestrus (0·88 v. 0·53, x2 = 6·7, P < 0·01) than sows whose energy and/or protein intake throughout lactation had been restricted. Nitrogen balances of sows were determined between 18 and 22 days after farrowing. Sows receiving high intakes of both energy and protein during lactation were in positive nitrogen balance, whereas the nitrogen balances of sows on the other three dietary treatments during lactation were similar to each other but negative. Ovulation rate, subsequent litter size and embryo mortality were not significantly affected by energy or protein intake during lactation.
Species richness and distribution of understorey bryophytes in different forest types in Colombian Amazonia

Abstract The first bryophyte survey results from Colombian Amazonia are presented. Bryophyte species, differentiated into mosses and liverworts, and further into four life-form classes, were sampled in 0.1-ha plots. These plots were distributed over four landscape units in the middle Caquetá area: floodplains, swamps, terra firme forests and white-sand areas. The total numbers of bryophyte species in the units were 50, 45, 45 and 32, respectively. The plots in floodplains and swamps were richer in moss species than the terra firme and white-sand plots, suggesting that coexistence of many moss species is favoured by high humidity. Moss species with fan life-forms preferred floodplains. On the other hand, liverwort species richness was highest in white-sand plots, which suggests that light incidence controls liverwort species-richness, perhaps more than humidity. All plots from the floodplain of the Caquetá River differed remarkably in species composition (of both mosses and liverworts) from the other landscape units. This may be due to the unique properties of this varzea system where, during yearly flooding events, soil, dead logs and stems are covered with a fresh layer of nutrient-rich fine silt, enhancing the surface for colonization and improving the conditions for productive bryophyte growth compared with elsewhere in the middle Caquetá area.
V-ATPase a3-d2 and a3-B2 Subunit Interaction in Osteoclasts are Viable Targets for Anti-resorptive Therapeutics

For bone resorption, vacuolar-type H+-ATPases (V-ATPases) on the plasma membranes of osteoclasts acidifies the extracellular millieu adjacent to the bone surface. The V-ATPase a3 and d2 subunits are enriched in osteoclasts. B2 subunit is also expressed on the osteoclast plasma membrane. Disruption of genes encoding subunits a3 and d2 impairs bone resorption. In this study, we have shown an interaction between the a3-B2 and a3-d2 subunits. Luteolin and KM91104 were found to be effective inhibitors of the a3-d2 and a3-B2 interactions respectively. Secondary assays revealed luteolin and KM91104 were not toxic to cells, did not affect osteoclastogenesis yet inhibited bone resorption. Furthermore luteolin did not affect V-ATPase subunit formation or assembly. Inhibitors of osteoclast resorption that do not affect osteoclast viability, preserve osteoclast–osteoblast signalling are desirable than existing anti-resorptives. Therefore, V-ATPase a3–d2 and a3-B2 interactions are viable targets for anti-resorptive therapeutics for osteoporosis.