Miracle-dz/UniFAR-FTUs huggingface dataset:
A reinforcement learning approach to rare trajectory sampling

Very often when studying non-equilibrium systems one is interested in analysing dynamical behaviour that occurs with very low probability, so called rare events. In practice, since rare events are by definition atypical, they are often difficult to access in a statistically significant way. What are required are strategies to ‘make rare events typical’ so that they can be generated on demand. Here we present such a general approach to adaptively construct a dynamics that efficiently samples atypical events. We do so by exploiting the methods of reinforcement learning (RL), which refers to the set of machine learning techniques aimed at finding the optimal behaviour to maximise a reward associated with the dynamics. We consider the general perspective of dynamical trajectory ensembles, whereby rare events are described in terms of ensemble reweighting. By minimising the distance between a reweighted ensemble and that of a suitably parametrised controlled dynamics we arrive at a set of methods similar to those of RL to numerically approximate the optimal dynamics that realises the rare behaviour of interest. As simple illustrations we consider in detail the problem of excursions of a random walker, for the case of rare events with a finite time horizon; and the problem of a studying current statistics of a particle hopping in a ring geometry, for the case of an infinite time horizon. We discuss natural extensions of the ideas presented here, including to continuous-time Markov systems, first passage time problems and non-Markovian dynamics.
Attitude of Finnish timber buyers towards implementation of a forest computer visualisation

Timber buyers’ opinions as regards forest computer visualisation are studied. The results indicated that timber buyers are still rather conventional in their computer use since they mainly use only the information system of their own company. The majority of the buyers perceived computer visualisation to be slightly useful for their work, or they had no opinion concerning the usefulness of it. One third of the buyers considered computer visualisation to be a quality factor for timber trade, and the majority were willing to use it if a program is going to adopt by their company. In analysis, different personal characteristics were found for three timber buyer groups: qualifi ed, neutral and reluctant. Qualifi ed buyers were the largest one, about half of the buyers belonging to this group. The greatest barriers to adopting a positive attitude to computer visualisation turned out to be weak computer skills and a general lack of interest in computer use. On the other side, it was found that organisational factors did not infl uence the buyers’ computer skills or attitudes towards visualisation. The results of this study can be utilised by timber buying organisation in ensuring the successful adoption of a new computer system.
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms: Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO). Specifically, we investigate the consequences of "code-level optimizations:" algorithm augmentations found only in implementations or described as auxiliary details to the core algorithm. Seemingly of secondary importance, such optimizations turn out to have a major impact on agent behavior. Our results show that they (a) are responsible for most of PPO's gain in cumulative reward over TRPO, and (b) fundamentally change how RL methods function. These insights show the difficulty and importance of attributing performance gains in deep reinforcement learning. Code for reproducing our results is available at this https URL .
The Race between Technological Progress and Female Advancement: Changes in Gender and Skill Premia in OECD Countries

In recent decades, the male-female wage gap has fallen, while the skilled-unskilled wage gap has risen in advanced countries. The rate of decline in the gender wage gap has tended to be greater for unskilled than skilled workers, while the rate of increase in the skill wage gap has tended to be greater for male than female workers. To account for these trends, we develop an aggregate production function extended to allow for gender-specific capital-skill complementarity, and estimate it using shift-hare instruments and cross-country panel data from OECD countries. We confirm that information and communication technology (ICT) equipment is not only more complementary to skilled than unskilled workers but also more complementary to female than male workers. Our results show that changes in gender and skill premia are the outcome of the race between progress in ICT and advances in female educational attainment and employment.
Statistics of solar wind electron breakpoint energies using machine learning techniques

Solar wind electron velocity distributions at 1 au consist of a thermal “core” population and two suprathermal populations: “halo” and “strahl”. The core and halo are quasi-isotropic, whereas the strahl typically travels radially outwards along the parallel or anti-parallel direction with respect to the interplanetary magnetic field. Using Cluster-PEACE data, we analyse energy and pitch angle distributions and use machine learning techniques to provide robust classifications of these solar wind populations. Initially, we used unsupervised algorithms to classify halo and strahl differential energy flux distributions to allow us to calculate relative number densities, which are of the same order as previous results. Subsequently, we applied unsupervised algorithms to phase space density distributions over ten years to study the variation of halo and strahl breakpoint energies with solar wind parameters. In our statistical study, we find both halo and strahl suprathermal breakpoint energies display a significant increase with core temperature, with the halo exhibiting a more positive correlation than the strahl. We conclude low energy strahl electrons are scattering into the core at perpendicular pitch angles. This increases the number of Coulomb collisions and extends the perpendicular core population to higher energies, resulting in a larger difference between halo and strahl breakpoint energies at higher core temperatures. Statistically, the locations of both suprathermal breakpoint energies decrease with increasing solar wind speed. In the case of halo breakpoint energy, we observe two distinct profiles above and below 500 km s−1. We relate this to the difference in origin of fast and slow solar wind.
Storage of Energy in Constrained Non-Equilibrium Systems

We study a quantity T defined as the energy U, stored in non-equilibrium steady states (NESS) over its value in equilibrium U0, ΔU=U−U0 divided by the heat flow JU going out of the system. A recent study suggests that T is minimized in steady states (Phys.Rev.E.99, 042118 (2019)). We evaluate this hypothesis using an ideal gas system with three methods of energy delivery: from a uniformly distributed energy source, from an external heat flow through the surface, and from an external matter flow. By introducing internal constraints into the system, we determine T with and without constraints and find that T is the smallest for unconstrained NESS. We find that the form of the internal energy in the studied NESS follows U=U0∗f(JU). In this context, we discuss natural variables for NESS, define the embedded energy (an analog of Helmholtz free energy for NESS), and provide its interpretation.
Dark Energy with Phantom Crossing and the H0 Tension

We investigate the possibility of phantom crossing in the dark energy sector and the solution for the Hubble tension between early and late universe observations. We use robust combinations of different cosmological observations, namely the Cosmic Microwave Background (CMB), local measurement of Hubble constant (H0), Baryon Acoustic Oscillation (BAO) and SnIa for this purpose. For a combination of CMB+BAO data that is related to early universe physics, phantom crossing in the dark energy sector was confirmed at a 95% confidence level and we obtained the constraint H0=71.0−3.8+2.9 km/s/Mpc at a 68% confidence level, which is in perfect agreement with the local measurement by Riess et al. We show that constraints from different combinations of data are consistent with each other and all of them are consistent with phantom crossing in the dark energy sector. For the combination of all data considered, we obtained the constraint H0=70.25±0.78 km/s/Mpc at a 68% confidence level and the phantom crossing happening at the scale factor am=0.851−0.031+0.048 at a 68% confidence level.
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities.Distinct from other scenarios (e.g., social networking or content sharing) which recommend a single item (e.g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference. However, present works focus mainly on one of the requirements and only consider either user-outfit or outfit-item relationships, thereby easily leading to suboptimal representations and limiting the performance. In this work, we unify two tasks, fashion compatibility modeling and personalized outfit recommendation. Towards this end, we develop a new framework, Hierarchical Fashion Graph Network(HFGN), to model relationships among users, items, and outfits simultaneously. In particular, we construct a hierarchical structure upon user-outfit interactions and outfit-item mappings. We then get inspirations from recent graph neural networks, and employ the embedding propagation on such hierarchical graph, so as to aggregate item information into an outfit representation, and then refine a user's representation via his/her historical outfits. Furthermore, we jointly train these two tasks to optimize these representations. To demonstrate the effectiveness of HFGN, we conduct extensive experiments on a benchmark dataset, and HFGN achieves significant improvements over the state-of-the-art compatibility matching models like NGNN and outfit recommenders like FHN.
Simulation study on the effects of diffractive collisions on the prediction of the observables in ultra-high-energy cosmic-ray experiments

The mass composition of ultra-high-energy cosmic rays is important for understanding their origin. Owing to our limited knowledge of the hadronic interaction, the interpretations of the mass composition from observations have several open problems, such as the inconsistent interpretations of $\langle X_{\mathrm{max}}\rangle $ and $\langle X_{\mathrm{max}}^{\mu}\rangle $ and the large difference between the predictions by the hadronic interaction models. Diffractive collision is one of the proposed sources of the uncertainty. In this paper, we discuss the effect of the detailed characteristics of diffractive collisions on the observables of ultra-high-energy cosmic-ray experiments by focusing on three detailed characteristics. These are the cross-sectional fractions of different collision types, diffractive-mass spectrum, and diffractive-mass-dependent particle productions. We demonstrated that the current level of the uncertainty in the cross-sectional fraction can affect 8.9$\mathrm{g/cm^2}$ of $\langle X_{\mathrm{max}}\rangle $ and 9.4$\mathrm{g/cm^2}$ of $\langle X_{\mathrm{max}}^{\mu}\rangle $, whereas the other details of the diffractive collisions exhibit relatively minor effects.
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

This paper studies a central issue in modern reinforcement learning, the sample efficiency, and makes progress toward solving an idealistic scenario that assumes access to a generative model or a simulator. Despite a large number of prior works tackling this problem, a complete picture of the trade-offs between sample complexity and statistical accuracy has yet to be determined. In particular, all prior results suffer from a severe sample size barrier in the sense that their claimed statistical guarantees hold only when the sample size exceeds some enormous threshold. The current paper overcomes this barrier and fully settles this problem; more specifically, we establish the minimax optimality of the model-based approach for any given target accuracy level. To the best of our knowledge, this work delivers the first minimax-optimal guarantees that accommodate the entire range of sample sizes (beyond which finding a meaningful policy is information theoretically infeasible).
Steady-state bifurcations for three-cell networks with asymmetric inputs

We consider codimension-one steady-state bifurcations for coupled cell networks with identical cells and asymmetric inputs. We obtain general results both for networks with any number of cells and any number of asymmetric inputs covering in particular networks with three cells. These results rely on the eigenvalue structure and the existence, or not, of synchrony subspaces. We describe the lattices of synchrony subspaces annotated with the network eigenfunctions for networks with three cells. Applying the previous results, we classify the synchrony-breaking bifurcations that can occur for three-cell minimal networks with one, two or six asymmetric inputs.
A probabilistic approach to phase calibration – I. Effects of source structure on fringe-fitting

We propose a probabilistic framework for performing simultaneous estimation of source structure and fringe-fitting parameters in very long baseline interferometry (VLBI) observations. As a first step, we demonstrate this technique through the analysis of synthetic short-duration Event Horizon Telescope observations of various geometric source models at 230 GHz, in the presence of baseline-dependent thermal noise. We perform Bayesian parameter estimation and model selection between the different source models to obtain reliable uncertainty estimates and correlations between various source and fringe-fitting related model parameters. We also compare the Bayesian posteriors with those obtained using widely used VLBI data reduction packages such as casa and aips, by fringe-fitting 200 Monte Carlo simulations of each source model with different noise realizations, to obtain distributions of the maximum a posteriori estimates. We find that, in the presence of resolved asymmetric source structure and a given array geometry, the traditional practice of fringe-fitting with a point source model yields appreciable offsets in the estimated phase residuals, potentially biasing or limiting the dynamic range of the starting model used for self-calibration. Simultaneously estimating the source structure earlier in the calibration process with formal uncertainties improves the precision and accuracy of fringe-fitting and establishes the potential of the available data, especially when there is little prior information. We also note the potential applications of this method to astrometry and geodesy for specific science cases and the planned improvements to the computational performance and analyses of more complex source distributions.
Opinion Dynamics on Discourse Sheaves

We introduce a novel class of Laplacians and diffusion dynamics on discourse sheaves as a model for network dynamics, with application to opinion dynamics on social networks. These sheaves are algebraic data structures tethered to a network (or more general space) that can represent various modes of communication, including selective opinion modulation and lying. After introducing the sheaf model, we develop a sheaf Laplacian in this context and show how to evolve both opinions and communications with diffusion dynamics over the network. Issues of controllability, reachability, bounded confidence, and harmonic extension are addressed using this framework.
Continuous variation in reproductive strategy as an adaptive response to population density in the moth Plodia interpunctella

Despite being variable, reproductive environments may be predictable. Because of this variability, individuals may maximize fitness through phenotypic plasticity. Here, I describe how males of the moth Plodia interpunctella are sensitive to population density during larval development (controlling for confounding effects of diet), and tailor a strategy which best suits the predicted reproductive environment. Such a strategy may be adaptive for this semelparous species, where all resources for reproduction are accrued during larval development and adults have a brief reproductive stage. Population density influences female mating pattern: at high densities, females mate more frequently, thus generating greater risks of sperm competition. Males reared at higher densities take longer to develop, despite achieving the same body mass. Adults from high densities have relatively larger abdomens and testes, produce greater numbers of sperm and live for shorter periods. At lower densities development time is reduced, and adult males have relatively larger heads and thoraxes, relatively smaller testes, ejaculate fewer sperm and live for longer periods. These results suggest that developing male P. interpunctella detect signals which reflect larval population structure, and hence eventual mating pattern. Males at low densities anticipate rare mate encounter and low risks of sperm competition, and therefore appear to invest in competence for migration and mate-searching. Males at higher densities anticipate frequent mate encounter and high risks of sperm competition, and therefore appear to invest in competence for mating and sperm competition. Such flexible male responses represent a ‘reaction norm’ where male strategies vary as a continuous function of the predicted reproductive environment’s signal.
Self-play reinforcement learning for video transmission

Video transmission services adopt adaptive algorithms to ensure users' demands. Existing techniques are often optimized and evaluated by a function that linearly combines several weighted metrics. Nevertheless, we observe that the given function fails to describe the requirement accurately. Thus, such proposed methods might eventually violate the original needs. To eliminate this concern, we propose Zwei, a self-play reinforcement learning algorithm for video transmission tasks. Zwei aims to update the policy by straightforwardly utilizing the actual requirement. Technically, Zwei samples a number of trajectories from the same starting point, and instantly estimates the win rate w.r. t the competition outcome. Here the competition result represents which trajectory is closer to the assigned requirement. Subsequently, Zwei optimizes the strategy by maximizing the win rate. To build Zwei, we develop simulation environments, design adequate neural network models, and invent training methods for dealing with different requirements on various video transmission scenarios. Trace-driven analysis over two representative tasks demonstrates that Zwei optimizes itself according to the assigned requirement faithfully, outperforming the state-of-the-art methods under all considered scenarios.
Fracton-elasticity duality of two-dimensional superfluid vortex crystals: defect interactions and quantum melting

Employing the fracton-elastic duality, we develop a low-energy effective theory of a zero-temperature vortex crystal in a two-dimensional bosonic superfluid which naturally incorporates crystalline topological defects. We extract static interactions between these defects and investigate several continuous quantum transitions triggered by the Higgs condensation of vortex vacancies/interstitials and dislocations. We propose that the quantum melting of the vortex crystal towards the hexatic or smectic phase may occur via a pair of continuous transitions separated by an intermediate vortex supersolid phase.
Spectroscopic and photometric periods of six ultracompact accreting binaries

Ultracompact accreting binary systems each consist of a stellar remnant accreting helium-enriched material from a compact donor star. Such binaries include two related sub-classes, AM CVn-type binaries and helium cataclysmic variables, in both of which the central star is a white dwarf. We present a spectroscopic and photometric study of six accreting binaries with orbital periods in the range of 40--70 min, including phase-resolved VLT spectroscopy and high-speed ULTRACAM photometry. Four of these are AM CVn systems and two are helium cataclysmic variables. For four of these binaries we are able to identify orbital periods (of which three are spectroscopic). SDSS J1505+0659 has an orbital period of 67.8 min, significantly longer than previously believed, and longer than any other known AM CVn binary. We identify a WISE infrared excess in SDSS J1505+0659 that we believe to be the first direct detection of an AM CVn donor star in a non-direct impacting binary. The mass ratio of SDSS J1505+0659 is consistent with a white dwarf donor. CRTS J1028-0819 has an orbital period of 52.1 min, the shortest period of any helium cataclysmic variable. MOA 2010-BLG-087 is co-aligned with a K-class star that dominates its spectrum. ASASSN-14ei and ASASSN-14mv both show a remarkable number of echo outbursts following superoutbursts (13 and 10 echo outbursts respectively). ASASSN-14ei shows an increased outburst rate over the years following its superoutburst, perhaps resulting from an increased accretion rate.
Young children who intervene in peer conflicts in multicultural child care centers

In emotionally uncertain situations — as in conflicts of high intensity — young children rely on their teachers, and teachers often intervene in rows. However, child interventions in peer conflicts are also common. In this paper we discuss child interventions in peer conflicts by 2- and 3-year old children at Dutch child care centers. When children are nearby the conflict the probability of an intervention is 0.29. Support of one of the opponents is the most common intervention behavior. The probability of an intervention becomes higher when the teacher is out of sight and the intervening child is playing with one of the opponents. Older children intervene more often than younger children, and Moroccan and Antillean children intervene more often than Dutch children. The findings are related to the 'Relationship Model' of conflict management of de Waal, theories of cognitive development, and of cultural differences in education.
Is deeper better? It depends on locality of relevant features

It has been recognized that a heavily overparameterized artificial neural network exhibits surprisingly good generalization performance in various machine-learning tasks. Recent theoretical studies have made attempts to unveil the mystery of the overparameterization. In most of those previous works, the overparameterization is achieved by increasing the width of the network, while the effect of increasing the depth has been less well understood. In this work, we investigate the effect of increasing the depth within an overparameterized regime. To gain an insight into the advantage of depth, we introduce local and global labels as abstract but simple classification rules. It turns out that the locality of the relevant feature for a given classification rule plays an important role; our experimental results suggest that deeper is better for local labels, whereas shallower is better for global labels. We also compare the results of finite networks with those of the neural tangent kernel (NTK), which is equivalent to an infinitely wide network with a proper initialization and an infinitesimal learning rate. It is shown that the NTK does not correctly capture the depth dependence of the generalization performance, which indicates the importance of the feature learning, rather than the lazy learning.