Miracle-dz/UniFAR-FTUs huggingface dataset:
Defining and measuring abstinence in clinical trials of smoking cessation interventions: An updated review.

BACKGROUND Changes in tobacco products, use patterns, and assessment technology in the last 15 years led the SRNT Treatment Research Network to call for an update to the 2003 SRNT recommendations for assessing abstinence in clinical trials of smoking cessation interventions. METHODS The SRNT Treatment Research Network convened a group of investigators with decades of experience in conducting tobacco treatment clinical trials. To arrive at the updated recommendations, the authors reviewed the recommendations of the prior SRNT Workgroup as well as current literature. Ten additional experts in the field provided feedback on this paper and these recommendations. RESULTS With respect to defining abstinence, the authors recommend: 1) continuing to use the definition of no use of combustible tobacco products (regardless of use of non-combustible tobacco products [e.g., snus] and alternative products [e.g., e-cigarettes]); and collecting additional data to permit alternate abstinence definitions: 2) no use of combustible or smokeless tobacco products, and 3) no use of combustible or smokeless tobacco products or alternative products, as appropriate for the research question being addressed. The authors also recommend reporting point-prevalence and prolonged abstinence at multiple time points (end of treatment, ≥3 months after the end of treatment, and ≥6 months post-quit or post-treatment initiation). CONCLUSIONS Defining abstinence requires specification of which products a user must abstain from using, the type of abstinence (i.e., point-prevalence, continuous), and the duration of abstinence. These recommendations are intended to serve as guidelines for investigators as they collect the necessary data to accurately describe participants' abstinence during smoking cessation clinical trials. IMPLICATIONS This paper provides updated recommendations for defining abstinence in the context of smoking cessation treatment clinical trials.
Minimal constraints for maximum caliber analysis of dissipative steady-state systems.

Maximum caliber (Max Cal) is purported to be a general variational principle for nonequilibrium statistical physics. But recently, Jack and Evans [J. Stat. Mech. : Theory Exp. (2016) 0933051742-546810.1088/1742-5468/2016/09/093305] and Maes [Non-Dissipative Effects in Nonequilibrium Systems (Springer, New York, 2018)] have raised concerns about how Max Cal handles dissipative processes. Here, we show that the problem does not lie in Max Cal; the problem is in the use of insufficient constraints. We also present an exactly solvable single-particle model of dissipation, valid far from equilibrium, and its solution by maximum caliber. The model illustrates how the influx and efflux of work and heat into a flowing system alters the distribution of trajectories. Maximum caliber is a viable principle for dissipative systems.
Clues on Arp 142: The spiral–elliptical merger

Nearby merging pairs are unique laboratories in which one can study the gravitational effects on the individual interacting components. In this manuscript, we report the characterization of selected H ii regions along the peculiar galaxy NGC 2936, member of the galaxy pair Arp 142, an E+S interaction, known as ‘The Penguin’. Using Gemini South spectroscopy, we have derived a high enhancement of the global star formation rate (SFR) = 35.9 M⊙ yr−1 probably stimulated by the interaction. Star-forming regions on this galaxy display oxygen abundances that are consistent with solar metallicities. The current data set does not allow us to conclude any clear scenario for NGC 2936. Diagnostic diagrams suggest that the central region of NGC 2936 is ionized by active galactic nucleus (AGN) activity and the eastern tidal plume in NGC 2936 is experiencing a burst of star formation, which may be triggered by the gas compression due to the interaction event with its elliptical companion galaxy: NGC 2937. The ionization mechanism of these sources is consistent with shock models of low velocities of 200–300 km s −1. The isophotal analysis shows tidal features on NGC 2937: at inner radii non-concentric (or off-centring) isophotes, and at large radii, a faint excess of the surface brightness profile with respect to de Vaucouleurs law. By comparing the radial velocity profiles and morphological characteristics of Arp 142 with a library of numerical simulations, we conclude that the current stage of the system would be about 50 ± 25 Myr after the first pericentre passage.
Openness of K-semistability for Fano varieties

In this paper, we prove the openness of K-semistability in families of log Fano pairs by showing that the stability threshold is a constructible function on the fibers. We also prove that any special test configuration arises from a log canonical place of a bounded complement and establish properties of any minimizer of the stability threshold.
Random Forest Estimation of the Ordered Choice Model

In econometrics so-called ordered choice models are popular when interest is in the estimation of the probabilities of particular values of categorical outcome variables with an inherent ordering, conditional on covariates. In this paper we develop a new machine learning estimator based on the random forest algorithm for such models without imposing any distributional assumptions. The proposed Ordered Forest estimator provides a flexible estimation method of the conditional choice probabilities that can naturally deal with nonlinearities in the data, while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference thereof and thus providing the same output as classical econometric estimators based on ordered logit or probit models. An extensive simulation study examines the finite sample properties of the Ordered Forest and reveals its good predictive performance, particularly in settings with multicollinearity among the predictors and nonlinear functional forms. An empirical application further illustrates the estimation of the marginal effects and their standard errors and demonstrates the advantages of the flexible estimation compared to a parametric benchmark model.
Hydrodynamics of Active Defects: From Order to Chaos to Defect Ordering

Topological defects play a prominent role in the physics of two-dimensional materials. When driven out of equilibrium in active nematics, disclinations can acquire spontaneous self-propulsion and drive self-sustained flows upon proliferation. Here we construct a general hydrodynamic theory for a two-dimensional active nematic interrupted by a large number of such defects. Our equations describe the flows and spatio-temporal defect chaos characterizing active turbulence, even close to the defect unbinding transition. At high activity, nonequilibrium torques combined with many-body screening cause the active disclinations to spontaneously break rotational symmetry forming a collectively moving defect ordered polar liquid. By recognizing defects as the relevant quasiparticle excitations, we construct a comprehensive phase diagram for two-dimensional active nematics. Using our hydrodynamic approach, we additionally show that activity gradients can act like "electric fields", driving the sorting of topological charge. This demonstrates the versatility of our continuum model and its relevance for quantifying the use of spatially inhomogeneous activity for controlling active flows and for the fabrication of active devices with targeted transport capabilities.
Exploring a new definition of the green valley and its implications

The distribution of galaxies on a colour–magnitude diagram reveals a bimodality, featuring a passively evolving red sequence and a star-forming blue cloud. The region between these two, the green valley (GV), represents a fundamental transition where quenching processes operate. We exploit an alternative definition of the GV using the 4000 Å break strength, an indicator that is more resilient than colour to dust attenuation. We compare and contrast our GV definition with the traditional one, based on dust-corrected colour, making use of data from the Sloan Digital Sky Survey. Our GV selection – that does not need a dust correction and thus does not carry the inherent systematics – reveals very similar trends regarding nebular activity (star formation, AGN, quiescence) to the standard dust-corrected 0.1(g − r). By use of high-SNR stacked spectra of the quiescent GV subsample, we derive the simple stellar population (SSP) age difference across the GV, a rough proxy of the quenching time-scale (Δt). We obtain an increasing trend with velocity dispersion (σ), from Δt ∼ 1.5 Gyr at σ = 100 km s−1, up to 3.5 Gyr at σ = 200 km s−1, followed by a rapid decrease in the most massive GV galaxies (Δt ∼ 1 Gyr at σ = 250 km s−1), suggesting two different modes of quenching, or the presence of an additional channel (rejuvenation).
Massive stars in the young cluster VVV CL074

Context. The evolution of massive stars is not fully constrained. Studies of young massive clusters hosting various populations of massive stars can help refine our understanding of the life and fate of massive stars. Aims. In this context, our goal is to study the massive stellar content of the young massive cluster VVV CL074. Methods. We obtained K-band spectroscopy of the brightest cluster members in order to identify the massive star population. We also determined the stellar properties of the cluster’s massive stars to better quantify the evolutionary sequences linking different types of massive stars. We collected integral field spectroscopy of selected fields in the cluster VVV CL074 with SINFONI on the ESO/VLT. We performed a spectral classification based on the K-band spectra and comparison to infrared spectral atlases. We determined the stellar parameters of the massive stars from analysis with atmosphere models computed with the code CMFGEN. Results. We uncover a population of 25 early-type (OB and Wolf–Rayet) stars, 19 being newly discovered by our observations out of which 15 are likely cluster members. The cluster’s spectrophotometric distance is 10.2 ± 1.6 kpc, placing it close to the intersection of the galactic bar and the Norma arm, beyond the galactic center. This makes VVV CL074 one the farthest young massive clusters identified so far. Among the massive stars population, three objects are Wolf–Rayet stars, the remaining are O and B stars. From the Hertzsprung–Russell diagram we find that most stars have an age between 3 and 6 Myr according to the Geneva evolutionary tracks. WN8 and WC8-9 stars are the descendants of stars with initial masses between 40 and 60 M⊙. The massive star population of VVV CL074 is very similar to that of the cluster DBS2003-179 and to a lesser extent to that of the Quintuplet cluster, indicating the same age. The central cluster of the Galaxy is ∼3 Myr older. From the comparison of the massive stars populations in these four clusters, one concludes that galactic stars with an initial mass in the range 40–60 M⊙ likely go through a WN8-9 phase.
Reflection geometries in absorbed and unabsorbed AGN

Context. The hard X-ray emission of active galactic nuclei (AGN), and in particular, the reflection component, is shaped by the innermost and outer regions of the galactic nucleus. Aims. Our main goal is to investigate the variation of the Compton hump amongst a population of sources and correlate it with other spectral properties to constrain the source geometry. Methods. We studied the NuSTAR hard X-ray spectra of a sample of 83 AGN and performed a detailed spectral analysis of each of them. Based on their spectral shape, we divided the sample into five categories and also studied their stacked spectra. Results. We found a stronger reflection in mildly obscured sources, which verifies the results reported in previous works. In addition, the reflection behaviour, and probably origin, varies with absorption. The accretion disc seems to be the main reflector in unabsorbed sources. A clumpy torus seems to produce most of the reflection in obscured sources. The filling factor of the clouds surrounding the active nucleus is a key parameter that drives the appearance of AGN. Finally, we found that the Fe line and the Compton hump are roughly correlated, as expected.
Pressure-induced superconductivity in SnSb2Te4

Here we firstly report the pressure-induced superconductivity in phase change materials SnSb2Te4. Single crystals of SnSb2Te4 were grown using a conventional melting-down method. The resistance under pressure was measured using an originally designed diamond anvil cell with boron-doped diamond electrodes. The temperature dependence of the resistance under different pressures has been measured up to 32.6 GPa. The superconducting transition of SnSb2Te4 appeared at 2.1 K () under 8.1 GPa, which was further increased with applied pressure to a maximum onset transition temperature 7.4 K under 32.6 GPa.
Implementation and performance of Barnes-hut n-body algorithm on extreme-scale heterogeneous many-core architectures

In this paper, we report the implementation and measured performance of our extreme-scale whole planetary ring simulation code on Sunway TaihuLight and two PEZY-SC2 systems: Shoubu System B and Gyoukou. The numerical algorithm is the parallel Barnes-Hut tree algorithm, which has been used in many large-scale astrophysical particle-based simulations. Our implementation is based on our FDPS framework. However, the extremely large numbers of cores of the systems used (10 M on TaihuLight and 16 M on Gyoukou) and their relatively poor memory and network bandwidth pose new challenges. We describe the new algorithms introduced to achieve high efficiency on machines with low memory bandwidth. The measured performance is 47.9, 10.6 PF, and 1.01PF on TaihuLight, Gyoukou and Shoubu System B (efficiency 40%, 23.5% and 35.5%). The current code is developed for the simulation of planetary rings, but most of the new algorithms are useful for other simulations, and are now available in the FDPS framework.
Properties of the donor impurity band in mixed valence insulators

In traditional semiconductors with large effective Bohr radius, an electron donor creates a hydrogen-like bound state just below the conduction band edge. The properties of the impurity band arising from such hydrogenic impurities have been studied extensively during the last 70 years. In this paper we consider whether a similar bound state and a similar impurity band can exist in mixed-valence insulators, where the gap arises at low temperature due to strong electron-electron interactions. We find that the structure of the hybridized conduction band leads to an unusual bound state that can be described using the physics of the one-dimensional hydrogen atom. The properties of the resulting impurity band are also modified in a number of ways relative to the traditional semiconductor case; most notably, the impurity band can hold a much larger concentration without inducing an insulator-to-metal transition. We estimate the critical doping associated with this transition, and then proceed to calculate the dc and ac conductivities and the specific heat. We discuss our results in light of recent measurements on the mixed-valence insualtor SmB$_6$, and find them to be consistent with the experiments.
A model for suspension of clusters of particle pairs

In this paper, we consider N clusters of pairs of particles sedimenting in a viscous fluid. The particles are assumed to be rigid spheres and inertia of both particles and fluid are neglected. The distance between each two particles forming the cluster is comparable to their radii 1/N while the minimal distance between the pairs is of order N−1/2. We show that, at the mesoscopic level, the dynamics are modelled using a transport-Stokes equation describing the time evolution of the position x and orientation ξ of the clusters. Under the additional assumption that the minimal distance is of order N−1/3, we investigate the case where the orientation of each cluster is initially correlated to its position. In this case, a local existence and uniqueness result for the limit model is provided.
Chemical composition of the outer halo globular cluster Palomar 15

Globular clusters (GCs) in the outer Milky Way halo are important tracers of the assembly history of our Galaxy. Only a few of these objects show spreads in heavier elements beyond the canonical light-element variations that have essentially been found throughout the entire Galactic GC system, suggesting a more complex origin and evolution of these objects. Here, we present the first abundance analysis of three red giants in the remote (RGC = 38 kpc) outer halo GC Palomar 15, based on medium-resolution spectra obtained with the Keck/ESI instrument. Our results ascertain a low iron abundance of −1.94 ± 0.06 dex with no evidence of any significant abundance spreads, although this is based on low number statistics. Overall, abundance ratios of 16 species were measured, including carbon, Na, Al, α-peak (Mg,Si,Ca,Ti) and Fe-peak (Sc,V,Cr,Fe,Co,Ni) elements, and the three neutron-capture elements Sr, Ba, and Eu. The majority of abundances are compatible with those of halo field stars and those found in other GCs in the outer and inner halos at similar metallicity. Pal 15 is enhanced to [Mg/Fe] = 0.45 dex, while other α-elements, Ca and Ti, are lower by 0.3 dex. Taking Mg as a representative for [α/Fe], and coupled with the lack of any significant spread in any of the studied elements we conclude that Pal 15 is typical of the outer halo, as is bolstered by its chemical similarity to the benchmark outer halo cluster NGC 7492. One star shows evidence of elevated Na and Al abundances, hinting at the presence of multiple stellar populations in this cluster.
Learning Latent Dynamics for Partially-Observed Chaotic Systems

This paper addresses the data-driven identification of latent representations of partially observed dynamical systems, i.e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications and long-term asymptotic patterns. Whereas state-of-the-art data-driven approaches rely in general on delay embeddings and linear decompositions of the underlying operators, we introduce a framework based on the data-driven identification of an augmented state-space model using a neural-network-based representation. For a given training dataset, it amounts to jointly reconstructing the latent states and learning an ordinary differential equation representation in this space. Through numerical experiments, we demonstrate the relevance of the proposed framework with respect to state-of-the-art approaches in terms of short-term forecasting errors and long-term behavior. We further discuss how the proposed framework relates to the Koopman operator theory and Takens' embedding theorem.
KdV charges in TTbar theories and new models with super-Hagedorn behavior

Two-dimensional CFTs and integrable models have an infinite set of conserved KdV higher spin currents. These currents can be argued to remain conserved under the T\bar{T}TT‾ deformation and its generalizations. We determine the flow equations the KdV charges obey under the T\bar{T}TT‾ deformation: they behave as probes “riding the Burgers flow” of the energy eigenvalues. We also study a Lorentz-breaking T_{s+1}\bar{T}Ts+1T‾ deformation built from a KdV current and the stress tensor, and find a super-Hagedorn growth of the density of states.
An enriched mixture model for functional clustering

There is an increasingly rich literature about Bayesian nonparametric models for clustering functional observations. However, most of the recent proposals rely on infinite-dimensional characterizations that might lead to overly complex cluster solutions. In addition, while prior knowledge about the functional shapes is typically available, its practical exploitation might be a difficult modeling task. Motivated by an application in e-commerce, we propose a novel enriched Dirichlet mixture model for functional data. Our proposal accommodates the incorporation of functional constraints while bounding the model complexity. To clarify the underlying partition mechanism, we characterize the prior process through a Polya urn scheme. These features lead to a very interpretable clustering method compared to available techniques. To overcome computational bottlenecks, we employ a variational Bayes approximation for tractable posterior inference.
Online Mixed-Integer Optimization in Milliseconds

We propose a method to approximate the solution of online mixed-integer optimization (MIO) problems at very high speed using machine learning. By exploiting the repetitive nature of online optimization, we can greatly speed up the solution time. Our approach encodes the optimal solution into a small amount of information denoted as strategy using the voice of optimization framework. In this way, the core part of the optimization routine becomes a multiclass classification problem that can be solved very quickly. In this work, we extend that framework to real-time and high-speed applications focusing on parametric mixed-integer quadratic optimization. We propose an extremely fast online optimization method consisting of a feedforward neural network evaluation and a linear system solution where the matrix has already been factorized. Therefore, this online approach does not require any solver or iterative algorithm. We show the speed of the proposed method both in terms of total computations required and measured execution time. We estimate the number of floating point operations required to completely recover the optimal solution as a function of the problem dimensions. Compared with state-of-the-art MIO routines, the online running time of our method is very predictable and can be lower than a single matrix factorization time. We benchmark our method against the state-of-the-art solver Gurobi obtaining up to two to three orders of magnitude speedups on examples from fuel cell energy management, sparse portfolio optimization, and motion planning with obstacle avoidance. Summary of Contribution: We propose a technique to approximate the solution of online optimization problems at high speed using machine learning. By exploiting the repetitive nature of online optimization, we learn the mapping between the key problem parameters and an encoding of the optimal solution to greatly speed up the solution time. This allows us to significantly improve the computation time and resources needed to solve online mixed-integer optimization problems. We obtain a simple method with a very low computing time variance, which is crucial in online settings.
Avoiding Observability Singularities in Output Feedback Bilinear Systems

Control-affine output systems generically present observability singularities, i.e. inputs that make the system unobservable. This proves to be a difficulty in the context of output feedback stabilization, where this issue is usually discarded by uniform observability assumptions for state feedback stabilizable systems. Focusing on state feedback stabilizable bilinear control systems with linear output, we use a transversality approach to provide perturbations of the stabilizing state feedback law, in order to make our system observable in any time even in the presence of singular inputs.