Miracle-dz/UniFAR-FTUs huggingface dataset:
Image-to-Image Translation with Multi-Path Consistency Regularization

Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from a source domain to a target domain as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images; the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation from source domain to target domain and indirect translation from source domain to an auxiliary domain to target domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-to-face translation, paint-to-photo translation, and de-raining/de-noising translation.
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series

Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data. To address these challenges, we propose (1) a continuous-time version of the Gated Recurrent Unit, building upon the recent Neural Ordinary Differential Equations (Chen et al., 2018), and (2) a Bayesian update network that processes the sporadic observations. We bring these two ideas together in our GRU-ODE-Bayes method. We then demonstrate that the proposed method encodes a continuity prior for the latent process and that it can exactly represent the Fokker-Planck dynamics of complex processes driven by a multidimensional stochastic differential equation. Additionally, empirical evaluation shows that our method outperforms the state of the art on both synthetic data and real-world data with applications in healthcare and climate forecast. What is more, the continuity prior is shown to be well suited for low number of samples settings.
Fast Optimal Energy Management With Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles

In this letter we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy management problem considering both power split and engine on/off decisions. The solution of a convex relaxation of the problem is used to initialize the optimization, which is necessarily nonconvex, and whilst only local convergence can be guaranteed, it is demonstrated that the algorithm will terminate with the optimal power split for the given engine switching sequence. The algorithm is compared in simulation against a charge-depleting/charge-sustaining (CDCS) strategy and dynamic programming (DP) using real world driver behaviour data, and it is demonstrated that the algorithm achieves 90% of the fuel savings obtained using DP with a 3000-fold reduction in computational time.
Neutron star binary orbits in their host potential: effect on early r-process enrichment

Coalescing neutron star binary (NSB) systems are primary candidates for r-process enrichment of galaxies. The recent detection of r-process elements in ultrafaint dwarf (UFD) galaxies and the abundances measured in classical dwarfs challenges the NSB merger scenario both in terms of coalescence time-scales and merger locations. In this paper, we focus on the dynamics of NSBs in the gravitational potentials of different types of host galaxies and on its impact on the subsequent galactic enrichment. We find that, for a ∼t−1 delay time distribution, even when receiving a low kick (∼10 km s−1) from the second supernova explosion, in shallow dwarf galaxy potentials NSBs tend to merge with a large off-set from the host galaxy. This results in a significant geometrical dilution of the amount of produced r-process elements that fall back and pollute the host galaxy gas reservoir. The combination of dilution and small number statistics produces a large scatter in the expected r-process enrichment within a single UFD or classical dwarf galaxy. Comparison between our results and observed europium abundances reveals a tension that even a systematic choice of optimistic parameters in our models cannot release. Such a discrepancy could point to the need of additional r-process production sites that suffer less severe dilution or to a population of extremely fast merging binaries.
Self-similar breakup of polymeric threads as described by the Oldroyd-B model

When a drop of fluid containing long, flexible polymers breaks up, it forms threads of almost constant thickness, whose size decreases exponentially in time. Using an Oldroyd-B fluid as a model, we show that the thread profile, rescaled by the thread thickness, converges to a similarity solution. Using the correspondence between viscoelastic fluids and nonlinear elasticity, we derive similarity equations for the full three-dimensional axisymmetric flow field in the limit that the viscosity of the solvent fluid can be neglected. Deriving a conservation law along the thread, we can calculate the stress inside the thread from a measurement of the thread thickness. The explicit form of the velocity and stress fields can be deduced from a solution of the similarity equations. Results are validated by detailed comparison with numerical simulations.
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication

We study the problem of regret minimization for distributed bandits learning, in which $M$ agents work collaboratively to minimize their total regret under the coordination of a central server. Our goal is to design communication protocols with near-optimal regret and little communication cost, which is measured by the total amount of transmitted data. For distributed multi-armed bandits, we propose a protocol with near-optimal regret and only $O(M\log(MK))$ communication cost, where $K$ is the number of arms. The communication cost is independent of the time horizon $T$, has only logarithmic dependence on the number of arms, and matches the lower bound except for a logarithmic factor. For distributed $d$-dimensional linear bandits, we propose a protocol that achieves near-optimal regret and has communication cost of order $\tilde{O}(Md)$, which has only logarithmic dependence on $T$.
The evolution of brightest cluster galaxies in the nearby Universe – I. Colours and stellar masses from the Sloan Digital Sky Survey and Wide Infrared Survey Explorer

We present a study of the evolution of brightest cluster galaxies (BCGs) in a sample of clusters at $0.05 \leq z<0.35$ from the SDSS and WISE with halo masses in the range $6 \times 10^{13}M_\odot$ (massive groups) - $10^{15.5}M_\odot$ (Coma-like clusters). We analyse optical and infrared colours and stellar masses of BCGs as a function of the mass of their host haloes. We find that BCGs are mostly red and quiescent galaxies and that a minority ($\sim 9$\%) of them are star-forming. We find that the optical $g-r$ colours are consistent with those of red sequence galaxies at the same redshifts; however, we detect the presence of a tail of blue and mostly star-forming BCGs preferentially located in low-mass clusters and groups. Although the blue tail is dominated by star-forming galaxies, we find that star-forming BCGs may also have red $g-r$ colours, indicating dust-enshrouded star formation. The fraction of star-forming BCGs increases with redshift and decreases with cluster mass and BCG stellar mass. We find that cool-core clusters host both star-forming and quiescent BCGs; however, non cool-core clusters are dominated by quiescent BCGs. Star formation appears thus as the result of processes that depend on stellar mass, cluster mass and cooling state of the intra-cluster medium. Our results suggest no significant stellar mass growth at $z<0.35$, supporting the notion that BCGs had accreted most of their mass by $z = 0.35$. Overall we find a low (1\%) AGN fraction detected at IR wavelengths.
Adversarial Imitation Learning from Incomplete Demonstrations

Imitation learning targets deriving a mapping from states to actions, a.k. a. policy, from expert demonstrations. Existing methods for imitation learning typically require any actions in the demonstrations to be fully available, which is hard to ensure in real applications. Though algorithms for learning with unobservable actions have been proposed, they focus solely on state information and over- look the fact that the action sequence could still be partially available and provide useful information for policy deriving. In this paper, we propose a novel algorithm called Action-Guided Adversarial Imitation Learning (AGAIL) that learns a pol- icy from demonstrations with incomplete action sequences, i.e., incomplete demonstrations. The core idea of AGAIL is to separate demonstrations into state and action trajectories, and train a policy with state trajectories while using actions as auxiliary information to guide the training whenever applicable. Built upon the Generative Adversarial Imitation Learning, AGAIL has three components: a generator, a discriminator, and a guide. The generator learns a policy with rewards provided by the discriminator, which tries to distinguish state distributions between demonstrations and samples generated by the policy. The guide provides additional rewards to the generator when demonstrated actions for specific states are available. We com- pare AGAIL to other methods on benchmark tasks and show that AGAIL consistently delivers com- parable performance to the state-of-the-art methods even when the action sequence in demonstrations is only partially available.
Towards Coherent and Cohesive Long-form Text Generation

Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called ‘negative-critical sequence training’, which is proposed to eliminate the need of training a separate critic for estimating ‘baseline’. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline – recurrent attention-based bidirectional MLE-trained neural language model.
Carbon cycling and interior evolution of water-covered plate tectonics and stagnant-lid planets

Aims. The long-term carbon cycle for planets with a surface entirely covered by oceans works differently from that of the present-day Earth because inefficient erosion leads to a strong dependence of the weathering rate on the rate of volcanism. In this paper, we investigate the long-term carbon cycle for these planets throughout their evolution. Methods. We built box models of the long-term carbon cycle based on CO2 degassing, seafloor-weathering, metamorphic decarbonation, and ingassing and coupled them with thermal evolution models of plate tectonics and stagnant-lid planets. Results. The assumed relationship between the seafloor-weathering rate and the atmospheric CO2 or the surface temperature strongly influences the climate evolution for both tectonic regimes. For a planet with plate tectonics, the atmospheric CO2 partial pressure is characterized by an equilibrium between ingassing and degassing and depends on the temperature gradient in subduction zones affecting the stability of carbonates. For a stagnant lid planet, partial melting and degassing are always accompanied by decarbonation, such that the combined carbon content of the crust and atmosphere increases with time. While the initial mantle temperature on planets with plate tectonics only affects the early evolution, it influences the evolution of the surface temperature of stagnant-lid planets for much longer. Conclusions. For both tectonic regimes, mantle cooling results in a decreasing atmospheric CO2 partial pressure. For a planet with plate tectonics this is caused by an increasing fraction of subduction zones that avoid crustal decarbonation, and for stagnant-lid planets this is caused by an increasing decarbonation depth. This mechanism may partly compensate for the increase of the surface temperature due to increasing solar luminosity with time, and thereby contribute to keeping planets habitable in the long-term.
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation

A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.
SLM aided noninvasive imaging through thin scattering layers.

We propose and demonstrate a new imaging technique to noninvasively see through scattering layers with the aid of a spatial light modulator (SLM). A relay system projects the incoherent light pattern emitting from the scattering layer onto the SLM. Two coded phase masks are displayed, one after another, on the SLM to modulate the projected scattered field. Two corresponding intensity patterns are recorded by a digital camera, and subtracted one from the other in the computer to obtain a bipolar matrix. A modified phase retrieval algorithm is used to retrieve the object information from this bipolar matrix.
Dispersion of waves in two and three-dimensional periodic media

We consider the propagation of acoustic time-harmonic waves in homogeneous media containing periodic lattices of spherical or cylindrical inclusions. It is assumed that the wavelength has the order of the periods of the lattice while the radius a of inclusions is small. A new approach is suggested to derive the complete asymptotic expansions of the dispersion relations in two- and three-dimensional cases as and first several terms of the expansions are evaluated explicitly. Our method is based on the reduction of the original singularly perturbed (by inclusions) problem to the regular one. The Dirichlet, Neumann, and transmission boundary conditions are considered. In the former case, we estimate the cutoff wavelength supported by the periodic medium in two and three dimensions. The effective wave speed is obtained as a function of the wave frequency, the filling fraction of the inclusions, and the physical properties of the constituents of the mixture. Dependence of the asymptotic formulas obtained in the paper on geometric and material parameters is illustrated by graphs.
Mixed Precision Training With 8-bit Floating Point

Reduced precision computation for deep neural networks is one of the key areas addressing the widening compute gap driven by an exponential growth in model size. In recent years, deep learning training has largely migrated to 16-bit precision, with significant gains in performance and energy efficiency. However, attempts to train DNNs at 8-bit precision have met with significant challenges because of the higher precision and dynamic range requirements of back-propagation. In this paper, we propose a method to train deep neural networks using 8-bit floating point representation for weights, activations, errors, and gradients. In addition to reducing compute precision, we also reduced the precision requirements for the master copy of weights from 32-bit to 16-bit. We demonstrate state-of-the-art accuracy across multiple data sets (imagenet-1K, WMT16) and a broader set of workloads (Resnet-18/34/50, GNMT, Transformer) than previously reported. We propose an enhanced loss scaling method to augment the reduced subnormal range of 8-bit floating point for improved error propagation. We also examine the impact of quantization noise on generalization and propose a stochastic rounding technique to address gradient noise. As a result of applying all these techniques, we report slightly higher validation accuracy compared to full precision baseline.
Optical signature of the pressure-induced dimerization in the honeycomb iridate 
α−Li2IrO3

We studied the effect of external pressure on the electrodynamic properties of $\ensuremath{\alpha}\ensuremath{-}{\mathrm{Li}}_{2}{\mathrm{IrO}}_{3}$ single crystals in the frequency range of the phonon modes and the Ir $d\ensuremath{-}d$ transitions. The abrupt hardening of several phonon modes under pressure supports the onset of the dimerized phase at the critical pressure ${P}_{c}=3.8\phantom{\rule{0.16em}{0ex}}\mathrm{GPa}$. With increasing pressure an overall decrease in spectral weight of the Ir $d\ensuremath{-}d$ transitions is found up to ${P}_{c}$. Above ${P}_{c}$, the local (on-site) $d\ensuremath{-}d$ excitations gain spectral weight with increasing pressure, which hints at a pressure-induced increase in the octahedral distortions. The nonlocal (intersite) Ir $d\ensuremath{-}d$ transitions show a monotonic blueshift and decrease in spectral weight. The changes observed for the nonlocal excitations are most prominent well above ${P}_{c}$, namely, for pressures $\ensuremath{\ge}12$ GPa, and only small changes occur for pressures close to ${P}_{c}$. The profile of the optical conductivity at high pressures ($\ensuremath{\sim}20$ GPa) appears to be indicative of the dimerized state in iridates.
MQLV: Modified Q-Learning for Vasicek Model

In a reinforcement learning approach, an optimal value function is learned across a set of actions, or decisions, that leads to a set of states giving different rewards, with the objective to maximize the overall reward. A policy assigns to each state-action pairs an expected return. We call an optimal policy a policy for which the value function is optimal. QLBS, Q-Learner in the Black-Scholes(-Merton) Worlds, applies the reinforcement learning concepts, and noticeably, the popular Q-learning algorithm, to the financial stochastic model described by Black, Scholes and Merton. However, QLBS is specifically optimized for the geometric Brownian motion and the pricing of vanilla options. Consequently, it suffers from the traditional over-estimation of the Q-values reflected by an over-estimation of the vanilla option prices. Furthermore, its range of application is limited to vanilla option pricing within the financial markets. We propose MQLV, Modified Q-Learner for the Vasicek model, a new reinforcement learning approach that limits the Q-values over-estimation observed in QLBS and extends the simulation to mean reverting stochastic diffusion processes. Additionally, MQLV uses a digital function to estimate the future probability of an event, thus widening the scope of the financial application to any other domain involving time series. Our experiments underline the potential of MQLV on generated Monte Carlo simulations, particularly representative of the retail banking time series. In particular, MQLV is able to determine the optimal policy of money management based on the aggregated financial transactions of the clients, unlocking new frontiers to establish personalized credit card limits or loans. Finally, MQLV is the first methodology compatible with the Vasicek model capable of an event probability estimation targeting simulation of event probabilities in retail banking.
An Asymptotic Derivation of a Single Particle Model with Electrolyte

The standard continuum model of a lithium-ion battery, the Doyle-Fuller-Newman (DFN) model, is computationally expensive to solve. Typically simpler models, such as the single particle model (SPM), are used to provide insight for control purposes. Recently, there has been a move to extend the SPM to include electrolyte effects, which increase the accuracy and range of applicability. However, these extended models are derived in an ad-hoc manner, which leaves open the possibility that important terms may have been neglected, resulting in the model not being as accurate as possible. In this paper, we provide a systematic asymptotic derivation of both the SPM and a correction term that accounts for the behaviour in the electrolyte. Firstly, this allows us to quantify the error in the reduced model in terms of ratios of key parameters in the model, from which the range of applicable operating conditions can be determined. Secondly, in comparing our model with the ad-hoc models from the literature, we show that previous models have neglected a key set of terms. In particular, we make the crucial distinction between writing the terminal voltage in pointwise and electrode-averaged form, which allows us to gain additional accuracy whilst maintaining the same computational complexity as the existing models.
A far-UV survey of three hot, metal-polluted white dwarf stars: WD0455−282, WD0621−376, and WD2211−495

Using newly obtained high-resolution data (R ∼ 1 × 105) from the Hubble Space Telescope, and archival UV data from the Far Ultraviolet Spectroscopic Explorer, we have conducted a detailed UV survey of the three hot, metal-polluted white dwarfs WD0455−282, WD0621−376, and WD2211−495. Using bespoke model atmospheres, we measured Teff, log g, and photospheric abundances for these stars. In conjunction with data from Gaia, we measured masses, radii, and gravitational redshift velocities for our sample of objects. We compared the measured photospheric abundances with those predicted by radiative levitation theory, and found that the observed Si abundances in all three white dwarfs, and the observed Fe abundances in WD0621−376 and WD2211−495, were larger than those predicted by an order of magnitude. These findings imply not only an external origin for the metals, but also ongoing accretion, as the metals not supported by radiative levitation would sink on extremely short time-scales. We measured the radial velocities of several absorption features along the line of sight to the three objects in our sample, allowing us to determine the velocities of the photospheric and interstellar components along the line of sight for each star. Interestingly, we made detections of circumstellar absorption along the line of sight to WD0455−282 with three velocity components. To our knowledge, this is the first such detection of multicomponent circumstellar absorption along the line of sight to a white dwarf.