Miracle-dz/UniFAR-FTUs huggingface dataset:
Interferometric Monitoring of Gamma-Ray Bright AGNs: OJ 287

We present the results of simultaneous multifrequency imaging observations at 22, 43, 86, and 129 GHz of OJ 287. We used the Korean Very Long Baseline Interferometry Network as part of the Interferometric Monitoring of Gamma-ray Bright active galactic nuclei (iMOGABA). The iMOGABA observations were performed during 31 epochs from 2013 January 16 to 2016 December 28. We also used 15 GHz OVRO and 225 GHz SMA flux density data. We analyzed four flux enhancements in the light curves. The estimated timescales of three flux enhancements were similar with timescales of ∼50 days at two frequencies. A fourth flux enhancement had a variability timescale approximately twice as long. We found that 225 GHz enhancements led the 15 GHz enhancements by a range of 7–30 days in the time delay analysis. We found the fractional variability did not change with frequency between 43 and 86 GHz. We could reliably measure the turnover frequency, , of the core of the source in three epochs. This was measured to be in a range from 27 to 50 GHz and a flux density at the turnover frequency, , ranging from 3 to 6 Jy. The derived SSA magnetic fields, , are in a range from 0.157 ± 0.104 to 0.255 ± 0.146 mG. We estimated the equipartition magnetic field strengths to be in a range from 0.95 ± 0.15 to 1.93 ± 0.30 mG. The equipartition magnetic field strengths are up to a factor of 10 higher than the values of . We conclude that the downstream jet may be more particle energy dominated.
A revised view of the Canis Major stellar overdensity with DECam and Gaia: new evidence of a stellar warp of blue stars

We present DECam imaging combined with Gaia DR2 data to study the Canis Major overdensity. The presence of the so-called Blue Plume stars in a low-pollution area of the color-magnitude diagram allows us to derive the distance and proper motions of this stellar feature along the line of sight of its hypothetical core. The stellar overdensity extends on a large area of the sky at low Galactic latitudes, below the plane, and between 230$^\circ < \ell < 255^\circ$. According to the orbit derived for Canis Major, it presents an on-plane rotation around the Milky Way. Moreover, additional overdensities of Blue Plume stars are found around the plane and across the Galaxy, proving that these objects are not only associated with that structure. The spatial distribution of these stars, derived using Gaia astrometric data, confirms that the detection of the Canis Major overdensity results more from the warped structure of the Milky Way disk than from the accretion of a dwarf galaxy.
Flavor ratios of astrophysical neutrinos interacting with stochastic gravitational waves having arbitrary spectra

We study the evolution and oscillations of fixed massive neutrinos interacting with stochastic gravitational waves (GWs). The energy spectrum of these GWs is Gaussian, with the correlator of the amplitudes being arbitrary. We derive the equation for the density matrix for flavor neutrinos in this case. In the two flavors approximation, this equation can be solved analytically. We find the numerical solution for the density matrix in the general case of three neutrino flavors. We consider merging binary black holes as sources of stochastic GWs with realistic spectra. Both normal and inverted mass orderings are analyzed. We discuss the relaxation of the neutrino fluxes in stochastic GWs emitted mainly by supermassive black holes. In this situation, we obtain the range of energies and the propagation lengths for which the relaxation process is the most efficient. We discuss the application of our results for the observation of fluxes of astrophysical neutrinos.
Expression of HSP90, HSP70 mRNA and Change of Plasma Cortisol and Glucose During Water Temperature Rising in Freshwater Adapted Black Porgy, Acanthopagrus schlegeli

The objective of the present study was to investigate the expression of heat shock protein 90 (HSP90) and 70 (HSP70) mRNA as cellular stress responses, the levels of plasma cortisol with glucose as neuro-endocrine stress responses during water temperature rising in freshwater adapted black porgy, Acanthopagrus schlegeli. A cDNA fragment of 891 (HSP90) and 465 (HSP70) bp was cloned from black porgy testis by Reverse transcription-polymerase chain reaction (RT-PCR) with primers designed from the conserved regions of other teleost. The PCR product of HSP90 showed very high homology to red seabream (99%), rainbow trout (95%), Atlantic salmon (94%), zebrafish (94%) HSP90, HSP70 of black porgy was also highly similar to those of rainbow trout (96%), silver seabream (95%), zebrafish (95%) HSP70. Water temperature rising () induced elevation of HSP90 mRNA in black porgy gonad, liver, brain, intestine and kidney, whereas it resulted in an induction of the HSP70 mRNA expression in gonad only. Plasma cortisol levels increased significantly at in the fish compared to those at . Glucose levels of the fish showed a tendency of co-increase with cortisol during water temperature rising. These results suggest that increased HSP90 mRNA in liver with plasma cortisol following heat shock may be related to increasing glucose for homeostasis in this species.
Approximation, characterization, and continuity of multivariate monotonic regression functions

We deal with monotonic regression of multivariate functions [Formula: see text] on a compact rectangular domain [Formula: see text] in [Formula: see text], where monotonicity is understood in a generalized sense: as isotonicity in some coordinate directions and antitonicity in some other coordinate directions. As usual, the monotonic regression of a given function [Formula: see text] is the monotonic function [Formula: see text] that has the smallest (weighted) mean-squared distance from [Formula: see text]. We establish a simple general approach to compute monotonic regression functions: namely, we show that the monotonic regression [Formula: see text] of a given function [Formula: see text] can be approximated arbitrarily well — with simple bounds on the approximation error in both the [Formula: see text]-norm and the [Formula: see text]-norm — by the monotonic regression [Formula: see text] of grid-constant functions [Formula: see text]. monotonic regression algorithms. We also establish the continuity of the monotonic regression [Formula: see text] of a continuous function [Formula: see text] along with an explicit averaging formula for [Formula: see text]. And finally, we deal with generalized monotonic regression where the mean-squared distance from standard monotonic regression is replaced by more complex distance measures which arise, for instance, in maximum smoothed likelihood estimation. We will see that the solution of such generalized monotonic regression problems is simply given by the standard monotonic regression [Formula: see text].
Invasion into remnant instability: a case study of front dynamics

We study the invasion of an unstable state by a propagating front in a peculiar but generic situation where the invasion process exhibits a remnant instability. Here, remnant instability refers to the fact that the spatially constant invaded state is linearly unstable in any exponentially weighted space in a frame moving with the linear invasion speed. Our main result is the nonlinear asymptotic stability of the selected invasion front for a prototypical model coupling spatio-temporal oscillations and monotone dynamics. We establish stability through a decomposition of the perturbation into two pieces: one that is bounded in the weighted space and a second that is unbounded in the weighted space but which converges uniformly to zero in the unweighted space at an exponential rate. Interestingly, long-time numerical simulations reveal an apparent instability in some cases. We exhibit how this instability is caused by round-off errors that introduce linear resonant coupling of otherwise non-resonant linear modes, and we determine the accelerated invasion speed.
Heterogeneous coefficients, control variables, and identification of treatment effects

Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the generalized propensity scores (Imbens, 2000) are bounded away from zero with probability one, a simple identification condition is that their sum be bounded away from one with probability one. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.
Detection of AI-Synthesized Speech Using Cepstral & Bispectral Statistics

Digital technology has made possible unimaginable applications come true. It seems exciting to have a handful of tools for easy editing and manipulation, but it raises alarming concerns that can propagate as speech clones, duplicates, or maybe deep fakes. Validating the authenticity of a speech is one of the primary problems of digital audio forensics. We propose an approach to distinguish human speech from AI synthesized speech exploiting the Bi-spectral and Cepstral analysis. Higher-order statistics have less correlation for human speech in comparison to a synthesized speech. Also, Cepstral analysis revealed a durable power component in human speech that is missing for a synthesized speech. We integrate both these analyses and propose a model to detect AI synthesized speech.
Scalable Prediction of Information Cascades over Arbitrary Time Horizons

We consider the problem of predicting popularity of social network content over arbitrary time horizons, given information about the content's initial popularity growth and other content features. These predictions are useful for various applications, including early detection of potentially harmful viral content in online social networks. The prediction task is difficult because view events are the result of complicated interactions between resharing, feed ranking algorithms, and organic features of the content popularity and network structure. We propose a model for predicting an information cascade size at arbitrary time horizons based on Hawkes point processes with exponentially decaying intensity. The model relies on using a point predictor of the information cascade size at a reference time horizon and a point predictor for the effective growth exponent, capturing the timescale of the information cascade spread. This results in a highly scalable method for prediction of cumulative number of views of a content item over arbitrary future time horizons. We analyzed a large collection of public page content on Facebook over a two-month period. We found that content view rates exhibit complex temporal patterns and follow an exponential decay over time horizons spanning several days. Analysis of the accuracy of our proposed prediction method shows that a single model can achieve high performance over a wide range of horizons.
Policy Gradient Reinforcement Learning for Policy Represented by Fuzzy Rules: Application to Simulations of Speed Control of an Automobile

A method of a fusion of fuzzy inference and policy gradient reinforcement learning has been proposed that directly learns, as maximizes the expected value of the reward per episode, parameters in a policy function represented by fuzzy rules with weights. A study has applied this method to a task of speed control of an automobile and has obtained correct policies, some of which control speed of the automobile appropriately but many others generate inappropriate vibration of speed. In general, the policy is not desirable that causes sudden time change or vibration in the output value, and there would be many cases where the policy giving smooth time change in the output value is desirable. In this paper, we propose a fusion method using the objective function, that introduces defuzzification with the center of gravity model weighted stochastically and a constraint term for smoothness of time change, as an improvement measure in order to suppress sudden change of the output value of the fuzzy controller. Then we show the learning rule in the fusion, and also consider the effect by reward functions on the fluctuation of the output value. As experimental results of an application of our method on speed control of an automobile, it was confirmed that the proposed method has the effect of suppressing the undesirable fluctuation in time-series of the output value. Moreover, it was also showed that the difference between reward functions might adversely affect the results of learning.
Matter trispectrum: theoretical modelling and comparison to N-body simulations

The power spectrum has long been the workhorse summary statistics for large-scale structure cosmological analyses. However, gravitational non-linear evolution moves precious cosmological information from the two-point statistics (such as the power spectrum) to higher-order correlations. Moreover, information about the primordial non-Gaussian signal lies also in higher-order correlations. Without tapping into these, that information remains hidden. While the three-point function (or the bispectrum), even if not extensively, has been studied and applied to data, there has been only limited discussion about the four point/trispectrum. This is because the high-dimensionality of the statistics (in real space a skew-quadrilateral has 6 degrees of freedom), and the high number of skew-quadrilaterals, make the trispectrum numerically and algorithmically very challenging. Here we address this challenge by studying the i-trispectrum, an integrated trispectrum that only depends on four k-modes moduli. We model and measure the matter i-trispectrum from a set of 5000 QUIJOTE N-body simulations both in real and redshift space, finding good agreement between simulations outputs and model up to mildly non-linear scales. Using the power spectrum, bispectrum and i-trispectrum joint data-vector covariance matrix estimated from the simulations, we begin to quantify the added-value provided by the i-trispectrum. In particular, we forecast the i-trispectrum improvements on constraints on the local primordial non-Gaussianity amplitude parameters fnl and gnl. For example, using the full joint data-vector, we forecast fnl constraints up to two times (∼32%) smaller in real (redshift) space than those obtained without i-trispectrum.
Reconstruction of attosecond beating by interference of two-photon transitions in the bulk of solids

The reconstruction of attosecond beating by interference of two-photon transitions (RABBIT) is one of the most widely used techniques for resolving ultrafast electronic dynamics in atomic and molecular systems. As it relies on the interference of photo-electrons in vacuum, similar interference has never been contemplated in the bulk of crystals. Using accurate numerical simulations in a realistic system, here we show that the interference of two-photon transitions can be recorded directly in the bulk of solids and read out with standard angle-resolved photo-emission spectroscopy. The phase of the RABBIT beating in the photoelectron spectra coming from the bulk of solids is sensitive to the relative phase of the Berry connection between bands and it experiences a shift of π as one of the quantum paths crosses a band. For resonant interband transitions, the amplitude of the RABBIT oscillation decays as the pump and probe pulses are separated in time due to electronic decoherence, providing a simple interferometric method to extract dephasing times.
Reproductive performance of female Antarctic fur seals: the influence of age, breeding experience, environmental variation and individual quality

1. The reproductive performance of female Antarctic fur seals was examined in relation to age, reproductive experience and environmental variation over 10 consecutive years (1983-92) at Bird Island, South Georgia. 2. The age at which females first gave birth varied from 3 to 6 years; over 90% of these females were 3 or 4 years of age. We found no evidence to suggest that age at primiparity had significant effects on subsequent reproduction; however, 3-year-old primiparae were less likely to be seen in subsequent years than 4-year-old primparae which may indicate a cost, in terms of survival, for females that first give birth at an early age. 3. Age-specific reproductive rates increased rapidly from ages 2 to 6 years, reached a peak of 0.80 at 7-9 years, remained above 0.75 until 11 years and then began to decline with increasing age. 4. The mean duration of foraging trips in the current year (which was used as a measure of the availability of food resources) consistently improved models of the likelihood of pupping and of weaning success. When these trips were long (indicating reduced local food resources), females returned to the breeding beaches later, fewer females pupped, they gave birth to lighter pups and weaning success was reduced. 5. The reproductive performance of older, experienced Antarctic fur seals was greater than that of younger, inexperienced animals because they had higher natality rates, gave birth to heavier pups earlier in the season, had greater weaning success and were more likely to pup the next season.
KILT: a Benchmark for Knowledge Intensive Language Tasks

Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure. To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the re-use of components, as well as accelerating research into task-agnostic memory architectures. We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text. KILT data and code are available at https://github.com/facebookresearch/KILT.
Propagation of nonlinear waves in multi-component pair plasmas and electron–positron–ion plasmas

The propagation of small amplitude stationary profile nonlinear solitary waves in a pair plasma is investigated by employing the reductive perturbation technique via the well-known Korteweg–de Vries (KdV) and modified KdV (mKdV) equations. This study tends to derive the exact form of nonlinear solutions and study their characteristics. Two distinct pair-ion species of opposite polarity and the same mass are considered in addition to a massive charged background species that is assumed to be stationary, and given the frequency scale of interest within the pair-ion context, the third species is thought of as a background defect (e.g., charged dust) component. On the opposite hand, the model conjointly applies formally to electron–positron–ion plasmas if one neglects electron–positron annihilation. A parametric analysis is carried out, with regard to the impact of the dusty plasma composition (background number density), species temperature(s), and background species. It is seen that distinguishable solitary profiles are observed for KdV and mKdV equations. The results are connected in pair-ion (fullerene) experiments and potentially in astrophysical environments of Halley’s comet and pulsar magnetosphere as well.
The Dynamics, Destruction, and Survival of Supernova-formed Dust Grains

Observations have demonstrated that supernovae efficiently produce dust. This is consistent with the hypothesis that supernovae and asymptotic giant branch stars are the primary producers of dust in the universe. However, there has been a longstanding question of how much of the dust detected in the interiors of young supernova remnants can escape into the interstellar medium. We present new hydrodynamical calculations of the evolution of dust grains that were formed in dense ejecta clumps within a Cas A–like remnant. We follow the dynamics of the grains as they decouple from the gas after their clump is hit by the reverse shock. They are subsequently subject to destruction by thermal and kinetic sputtering as they traverse the remnant. Grains that are large enough (∼0.25 μm for silicates and ∼0.1 μm for carbonaceous grains) escape into the interstellar medium while smaller grains get trapped and destroyed. However, grains that reach the interstellar medium still have high velocities, and are subject to further destruction as they are slowed down. We find that for initial grain size distributions that include large (∼0.25–0.5 μm) grains, 10%–20% of silicate grains can survive, while 30–50% of carbonaceous grains survive even when the initial size distribution cuts off at smaller (0.25 μm) sizes. For a 19 M⊙ star similar to the progenitor of Cas A, up to 0.1 M⊙ of dust can survive if the dust grains formed are large. Thus we show that supernovae under the right conditions can be significant sources of interstellar dust.
A Haystack Full of Needles: Scalable Detection of IoT Devices in the Wild

Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large-scale coordinated global attacks disrupting large service providers. Thus, an important first step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled flow statistics. In particular, it is challenging for an ISP to efficiently and effectively track and trace activity from IoT devices deployed by its millions of subscribers---all with sampled network data. In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our findings indicate that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network flow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is effective for providing network analytics, it also highlights significant privacy consequences.