Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for Continuous Gravitational Waves from the Central Compact Objects in Supernova Remnants Cassiopeia A, Vela Jr., and G347.3–0.5

We perform a sub-threshold follow-up search for continuous nearly monochromatic gravitational waves from the central compact objects associated with the supernova remnants (SNRs) Vela Jr., Cassiopeia A, and SNR G347.3−0.5. Across the three targets, we investigate the most promising ≈10,000 combinations of gravitational-wave frequency and frequency derivative values, based on the results from an Einstein@Home search of the LIGO O1 observing run data, dedicated to these objects. The selection threshold is set so that a signal could be confirmed using the newly released O2 run LIGO data. In order to achieve best sensitivity we perform two separate follow-up searches, on two distinct stretches of the O2 data. Only one candidate survives the first O2 follow-up investigation, associated with the central compact object in SNR G347.3−0.5, but it is not conclusively confirmed. In order to assess a possible astrophysical origin we use archival X-ray observations and search for amplitude modulations of a pulsed signal at the putative rotation frequency of the neutron star and its harmonics. This is the first extensive electromagnetic follow-up of a continuous gravitational-wave candidate performed to date. No significant associated signal is identified. New X-ray observations contemporaneous with the LIGO O3 run will enable a more sensitive search for an electromagnetic counterpart. A focused gravitational-wave search in O3 data based on the parameters provided here should be easily able to shed light on the nature of this outlier. Noise investigations on the LIGO instruments could also reveal the presence of a coherent contamination.
Cyberbullying Detection With Fairness Constraints

Cyberbullying is a widespread adverse phenomenon among online social interactions in today’s digital society. While numerous computational studies focus on enhancing the cyberbullying detection performance of machine learning algorithms, proposed models tend to carry and reinforce unintended social biases. In this study, we try to answer the research question of “Can we mitigate the unintended bias of cyberbullying detection models by guiding the model training with fairness constraints? ” For this purpose, we propose a model training scheme that can employ fairness constraints and validate our approach with different datasets. We demonstrate that various types of unintended biases can be successfully mitigated without impairing the model quality. We believe our work contributes to the pursuit of unbiased, transparent, and ethical machine learning solutions for cyber-social health.
Resistance to Common Bacterial Blight amongPhaseolus Species and Common Bean Improvement

Common bean (Phaseolus vulgaris L.) is highly susceptible to common bacterial blight (CBB), caused by Xanthomonas campestris pv. phaseoli (Smith) Dye. High levels of cultivar resistance would minimize yield losses, reduce bactericide use and production costs, and facilitate integrated disease-and-crop management and the production and distribution of pathogen-free seed. We aimed to (i) assess the levels of CBB resistance of different Phaseolus species in the tropics, (ii) determine the CBB reaction of resistant cultivars and advanced breeding lines, and (iii) report on CBB resistance of lines developed from P. vulgaris × P. acutifolius (tepary bean) hybridization and gene pyramiding at CIAT. Between 1994 and 1998, we evaluated, in the field, 162 accessions of tepary, scarlet runner (P. coccineus), lima (P. lunatus), and common beans, 119 CBB-resistant cultivars and advanced breeding lines of common bean, and six lines recently developed by interspecific hybridization and gene pyramiding. For inoculation, we used aspersion, surgical blades, and/or multiple needles. The highest levels (scores of 1.2-2.0) of CBB resistance were found in P. acutifolius accessions, G40029 and G40156, followed by P. lunatus (scores of 4.2-6.2), P. coccineus (scores of 4.8-5.5), and P. vulgaris (scores of 4.5-6.4). Resistance available in P. coccineus and P. vulgaris landraces has already been transferred to common bean. But resistance transferred from P. acutifolius was much lower (scores of 3.8-4.5) than those available. Gene pyramiding produced lines with high CBB resistance (scores of 1.5-2.4), and is thus, a suitable method for developing CBB-resistant cultivars of different market classes.
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition

Skeleton-based action recognition has attracted increasing attention due to its strong adaptability to dynamic circumstances and potential for broad applications such as autonomous and anonymous surveillance. With the help of deep learning techniques, it has also witnessed substantial progress and currently achieved around 90\% accuracy in benign environment. On the other hand, research on the vulnerability of skeleton-based action recognition under different adversarial settings remains scant, which may raise security concerns about deploying such techniques into real-world systems. However, filling this research gap is challenging due to the unique physical constraints of skeletons and human actions. In this paper, we attempt to conduct a thorough study towards understanding the adversarial vulnerability of skeleton-based action recognition. We first formulate generation of adversarial skeleton actions as a constrained optimization problem by representing or approximating the physiological and physical constraints with mathematical formulations. Since the primal optimization problem with equality constraints is intractable, we propose to solve it by optimizing its unconstrained dual problem using ADMM. We then specify an efficient plug-in defense, inspired by recent theories and empirical observations, against the adversarial skeleton actions. Extensive evaluations demonstrate the effectiveness of the attack and defense method under different settings.
Named Entity Recognition as Dependency Parsing

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Manning, 2009). In this paper, we use ideas from graph-based dependency parsing to provide our model a global view on the input via a biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of start and end tokens in a sentence which we use to explore all spans, so that the model is able to predict named entities accurately. We show that the model works well for both nested and flat NER through evaluation on 8 corpora and achieving SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points.
ECoPANN: A Framework for Estimating Cosmological Parameters Using Artificial Neural Networks

In this work, we present a new method to estimate cosmological parameters accurately based on the artificial neural network (ANN), and a code called ECoPANN (Estimating Cosmological Parameters with ANN) is developed to achieve parameter inference. We test the ANN method by estimating the basic parameters of the concordance cosmological model using the simulated temperature power spectrum of the cosmic microwave background (CMB). The results show that the ANN performs excellently on best-fit values and errors of parameters, as well as correlations between parameters when compared with that of the Markov Chain Monte Carlo (MCMC) method. Besides, for a well-trained ANN model, it is capable of estimating parameters for multiple experiments that have different precisions, which can greatly reduce the consumption of time and computing resources for parameter inference. Furthermore, we extend the ANN to a multibranch network to achieve a joint constraint on parameters. We test the multibranch network using the simulated temperature and polarization power spectra of the CMB, Type Ia supernovae, and baryon acoustic oscillations and almost obtain the same results as the MCMC method. Therefore, we propose that the ANN can provide an alternative way to accurately and quickly estimate cosmological parameters, and ECoPANN can be applied to the research of cosmology and even other broader scientific fields.
Pair production of dark particles in meson decays

Rare decays of K and B mesons provide a powerful probe of dark sectors with light new particles. We show that the pair production of O(100 MeV) dark states can be probed with the decays of KL mesons, owing to the enhanced two-body kinematics, KL→X1X2 or X2X2. If either of these two particles is unstable, e.g., X2→X1π0, X2→X1γ or X1,2→γγ, such decays could easily mimic KL→π0νν¯ signatures, while not being ruled out by the decays of charged kaons. We construct explicit models that have enhanced KL decay signatures, and are constrained by the results of the KOTO experiment. We note that recently reported excess events can also be accommodated while satisfying all other constraints (B decays, colliders, beam dumps). These models are based on the extensions of the gauge and/or scalar sector of the theory. The lightest of X1,2 particles, if stable, could constitute the entirety of dark matter.
Stability of approximate group actions: uniform and probabilistic

We prove that every uniform approximate homomorphism from a discrete amenable group into a symmetric group is uniformly close to a homomorphism into a slightly larger symmetric group. That is, amenable groups are uniformly flexibly stable in permutations. This answers affirmatively a question of Kun and Thom and a slight variation of a question of Lubotzky. We also give a negative answer to Lubotzky's original question by showing that the group $\mathbb{Z}$ is not uniformly strictly stable. Furthermore, we show that $\text{SL}_{r}(\mathbb{Z})$, $r\geq3$, is uniformly flexibly stable, but the free group $F_{r}$, $r\geq 2$, is not. We define and investigate a probabilistic variant of uniform stability that has an application to property testing.
A precision calculation of relic neutrino decoupling

We study the distortions of equilibrium spectra of relic neutrinos due to the interactions with electrons, positrons, and neutrinos in the early Universe. We solve the integro-differential kinetic equations for the neutrino density matrix, including three-flavor oscillations and finite temperature corrections from QED up to the next-to-leading order O(e3) for the first time. In addition, the equivalent kinetic equations in the mass basis of neutrinos are directly solved, and we numerically evaluate the distortions of the neutrino spectra in the mass basis as well, which can be easily extrapolated into those for non-relativistic neutrinos in the current Universe. In both bases, we find the same value of the effective number of neutrinos, Neff=3.044, which parameterizes the total neutrino energy density. The estimated error for the value of Neff due to the numerical calculations and the choice of neutrino mixing parameters would be at most 0.0005.
MixML: A Unified Analysis of Weakly Consistent Parallel Learning

Parallelism is a ubiquitous method for accelerating machine learning algorithms. However, theoretical analysis of parallel learning is usually done in an algorithm- and protocol-specific setting, giving little insight about how changes in the structure of communication could affect convergence. In this paper we propose MixML, a general framework for analyzing convergence of weakly consistent parallel machine learning. Our framework includes: (1) a unified way of modeling the communication process among parallel workers; (2) a new parameter, the mixing time tmix, that quantifies how the communication process affects convergence; and (3) a principled way of converting a convergence proof for a sequential algorithm into one for a parallel version that depends only on tmix. We show MixML recovers and improves on known convergence bounds for asynchronous and/or decentralized versions of many algorithms, includingSGD and AMSGrad. Our experiments substantiate the theory and show the dependency of convergence on the underlying mixing time.
Modeling the heterogeneous disease-behavior-information dynamics during epidemics

The transmission of infectious diseases depends on the social networks among people and the protections that people have taken before being exposed to the disease. Mass media is playing a key role in making the public aware of the disease and its transmissibility and severity. Motivated by the importance of heterogeneous risk perception in the population to response to infectious disease outbreaks, we propose a heterogeneous three-layer network model, namely the Susceptible-Exposed-Infectious-Recovered Unaware-Aware-Protected (SEIR-UAP) model, where people's vulnerability to the disease is influenced by the processes of awareness information diffusion, preventive behavior change and infectious disease transmission. We found that (a) the awareness of the disease plays the central role in preventing disease outbreak; (b) we need a reasonable ratio of "over-reacting" nodes to effectively control the disease outbreak; (c) diseases with a longer incubation period and a higher recovery rate are easier to control because the processes of information diffusion and behavior change can help people prepare for the upcoming exposure to the disease; (d) it is more difficult to control the disease with asymptomatic cases. The results provide evidence that mass media should not play down the transmissibility and severity of diseases, so that the public can become aware of the disease as soon as possible.
Differentiable Greedy Submodular Maximization: Guarantees, Gradient Estimators, and Applications

Motivated by, e.g., sensitivity analysis and end-to-end learning, the demand for differentiable optimization algorithms has been significantly increasing. In this paper, we establish a theoretically guaranteed versatile framework that makes the greedy algorithm for monotone submodular function maximization differentiable. We smooth the greedy algorithm via randomization, and prove that it almost recovers original approximation guarantees in expectation for the cases of cardinality and $\kappa$-extensible system constrains. We also show how to efficiently compute unbiased gradient estimators of any expected output-dependent quantities. We demonstrate the usefulness of our framework by instantiating it for various applications.
Eficiência agronômica de adubos nitrogenados em soqueira de cana-de-açúcar colhida sem queima

The objective of this work was to evaluate the agronomic efficiency of the nitrogen sources urea (U), ammonium nitrate (AN), ammonium sulfate (AS), and ammonium chloride (AC) on green harvesting sugarcane ratoon, as well as the residual effect of these sources on crop productivity in the subsequent crop year. A randomized block design was used with four replicates. Treatments consisted of applying 120 kg ha -1 of N fertilizer on a Typic Hapludox covered by straw residues resulting from the harvest without burning of the first ratoon. A control treatment was included with no N fertilizer. To evaluate the residual effect of the nitrogen fertilizers, 450 kg ha -1 of a 20‑5‑19 formulation was applied to soil after sugarcane ratoon harvest. The highest production of stalks and sugar, in the 2006/2007 season, was obtained with the source AS. In the 2007/2008 harvest season, no residual effect of N fertilization was observed and there was a productivity decrease in the AC treatment in comparison to the control. The AC source, at a dose of 120 kg ha -1 N in the fertilization of the second ratoon, is agronomically as efficient as the other N sources used, but shows less efficiency in the subsequent crop year.
Divergência genética entre acessos de taro

Avaliou-se a variabilidade existente entre 36 acessos de taro do Banco de Germoplasma de Hortalicas da Universidade Federal de Vicosa em componentes do rendimento, visando a identificacao de acessos produtivos e divergentes para serem utilizados em programas de melhoramento. O experimento foi conduzido na horta de pesquisas da UFV, de 19/09/2000 a 13/06/2001. Utilizou-se o delineamento experimental blocos casualizados, com cinco repeticoes. A parcela foi composta de quatro fileiras de 4 m de comprimento, espacadas por 1,0 m e, entre plantas, 0,5 m, totalizando 32 plantas. Avaliou-se a produtividade de rizomas comerciaveis/planta; peso medio de rizomas comerciaveis; numero de rizomas comerciaveis/planta; produtividades de rizomas-mae, filho grande, filho medio, filho pequeno e refugo/planta. Os dados foram submetidos as analises por variaveis canonicas e de agrupamento pelo metodo de Tocher, adotando a distância generalizada de Mahalanobis (D2) como estimativa da similaridade genetica. Os acessos foram separados em seis grupos, sendo que 80,56% dos mesmos constituiram um unico grupo. Os acessos BGH 5916, BGH 6137 e BGH 6298 destacaram-se pelo elevado potencial agronomico e pela divergencia genetica, o que os qualifica como promissores para serem utilizados em programas de melhoramento. As caracteristicas com maior contribuicao relativa para a divergencia genetica foram produtividade de rizomas filho grande/planta (42,50%), produtividade de rizomas filho pequeno/planta (24,67%) e produtividade de rizomas comerciaveis/planta (16,95%).
The Effect of Thyme Extract on Growth Performance, Digestive Organ Weights and Serum Lipoproteins of Broilers Fed Wheat-Based Diets

The effect of the thyme extract and wheat form of the diet on performance, digestive organ weights and serum lipoproteins was studied in 320 male day old (ROSS 308) chicks. From 1 to 42 d of age, 8 diets arranged factorial with 4 thyme extract level (0.0, 0.2, 0.4 and 0.6%) and 2 wheat forms (ground vs whole) was used. Each treatment was replicated 4 times (10 birds per replicate). Thyme extract had no significant effect on body weight gain, feed intake and feed conversion ratio irrespective of wheat form used over whole trial period (1-42 d). From 1 to 21 d of age, broilers fed the whole wheat diet grew faster than the broilers fed ground wheat based diet (609.06 vs 573.34 g). Relative abdominal fat and gizzard weight was significantly higher (P<0.05) in birds fed the whole wheat diets. Two-way interaction between thyme extract and wheat form were observed for total cholesterol, LDL-cholesterol, HDL-cholesterol and very-low-density lipoprotein (VLDL) in the experiment. The results showed that the extract had no effect on performance of broilers irrespective of wheat form used. Relative gizzard weight was higher (P<0.05) in birds fed the whole wheat diets, but an increase in abdominal fat should be taken into account for carcass quality and processing. The predominant effect of thyme extract on lowering lipid levels in broilers fed on wheat based diets was on triglyceride rather than on cholesterol concentrations.
Stimulation hormonale, in vivo, de l'ovaire d'anguille européenne au stade jaune

The immature ovary of yellow eel was stimulated by a long-term treatment with a gonadotropin II rich extract of carp pituitary. Ovarian follicles of treated yellow eels showed an accumulation of lipidic vacuoles in the oocyte cytoplasm and a thickened follicular envelope (in this regard, they resembled the ovarian follicles of previously studied silver eels). The treatment also resulted in a significant but limited increase in the gonadosomatic ratio and mean follicle size (these values were much lower than those measured in silver eels). No change in pigmentation or general morphology was observed. Thus, under our experimental conditions, a gonadotropic stimulation of the yellow eel leads to some but not the totality of the ovarian modifications which occur physiologically during silvering.
Noise reduction in gravitational-wave data via deep learning

With the advent of gravitational wave astronomy, techniques to extend the reach of gravitational wave detectors are desired. In addition to the stellar-mass black hole and neutron star mergers already detected, many more are below the surface of the noise, available for detection if the noise is reduced enough. Our method (DeepClean) applies machine learning algorithms to gravitational wave detector data and data from on-site sensors monitoring the instrument to reduce the noise in the time-series due to instrumental artifacts and environmental contamination. This framework is generic enough to subtract linear, non-linear, and non-stationary coupling mechanisms. It may also provide handles in learning about the mechanisms which are not currently understood to be limiting detector sensitivities. The robustness of the noise reduction technique in its ability to efficiently remove noise with no unintended effects on gravitational-wave signals is also addressed through software signal injection and parameter estimation of the recovered signal. It is shown that the optimal SNR ratio of the injected signal is enhanced by $\sim 21.6\%$ and the recovered parameters are consistent with the injected set. We present the performance of this algorithm on linear and non-linear noise sources and discuss its impact on astrophysical searches by gravitational wave detectors.
Brief communication. Characterization and chromosomal distribution of a novel satellite DNA sequence of Japanese quail (Coturnix coturnix japonica

A novel satellite DNA sequence of Japanese quail (Coturnix coturnix japonica) was isolated from genomic DNA digested with restriction endonuclease, Bg/II. Sequence analysis of three different-size clones revealed the presence of a tandem array of a GC-rich 41 bp repeated element. This sequence was localized by fluorescence in situ hybridization (FISH) primarily to microchromosomes of Japanese quail (2n = 78); approximately 50 of the 66 microchromosomes showed positive signals, although hybridization signals were also detected on chromosomes 4 and W. This satellite DNA did not cross-hybridize with genomic DNA of chicken (Gallus gallus) and Chinese painted quail (Excalfactoria chinensis) under moderately stringent conditions, suggesting that this class of repetitive DNA sequences was species specific and fairly divergent in Galliformes species.
Disorder-induced quantum phase transitions in three-dimensional second-order topological insulators

Disorder effects on three-dimensional second-order topological insulators (3DSOTIs) are investigated numerically and analytically. The study is based on a tight-binding Hamiltonian for non-interacting electrons on a cubic lattice with a reflection symmetry that supports a 3DSOTI in the absence of disorder. Interestingly, unlike the disorder effects on a topological trivial system that can only be either a diffusive metal (DM) or an Anderson insulator (AI), disorders can sequentially induce four phases of 3DSOTIs, three-dimensional first-order topologicalinsulators (3DFOTIs), DMs and AIs. At a weak disorder when the on-site random potential of strength $W$ is below a low critical value $W_{c1}$ at which the gap of surface states closes while the bulk sates are still gapped, the system is a disordered 3DSOTI characterized by a constant density of states and a quantized integer conductance of $e^2/h$ through its chiral hinge states. The gap of the bulk states closes at a higher critical disorder $W_{c2}$, and the system is a disordered 3DFOTI in a lower intermediate disorder between $W_{c1}$ and $W_{c2}$ in which electron conduction is through the topological surface states. The system becomes a DM in a higher intermediate disorder between $W_{c2}$ and $W_{c3}$ above which the states at the Fermi level are localized. It undergoes a normal three-dimension metal-to-insulator transition at $W_{c3}$ and becomes the conventional AI for $W>W_{c3}$. The self-consistent Born approximation allows one to see how the density of bulk states and the Dirac mass are modified by the on-site disorders.