Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy

This paper proposes a method for learning a trajectory-conditioned policy to imitate diverse demonstrations from the agent's own past experiences. We demonstrate that such self-imitation drives exploration in diverse directions and increases the chance of finding a globally optimal solution in reinforcement learning problems, especially when the reward is sparse and deceptive. Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various sparse-reward reinforcement learning tasks with local optima. In particular, we report a state-of-the-art score of more than 25,000 points on Montezuma's Revenge without using expert demonstrations or resetting to arbitrary states.
The method of Eneström and Phragmén for parliamentary elections by means of approval voting

We study a method for proportional representation that was proposed at the turn from the nineteenth to the twentieth century by Gustav Enestrom and Edvard Phragmen. Like Phragmen's better-known iterative minimax method, it is assumed that the voters express themselves by means of approval voting. In contrast to the iterative minimax method, however, here one starts by fixing a quota, i.e. the number of votes that give the right to a seat. As a matter of fact, the method of Enestrom and Phragmen can be seen as an extension of the method of largest remainders from closed lists to open lists, or also as an adaptation of the single transferable vote to approval rather than preferential voting. The properties of this method are studied and compared with those of other methods of the same kind.
On the relationship between variational inference and adaptive importance sampling

The importance weighted autoencoder (IWAE) (Burda et al., 2016) and reweighted wake-sleep (RWS) algorithm (Bornschein and Bengio, 2015) are popular approaches which employ multiple samples to achieve bias reductions compared to standard variational methods. However, their relationship has hitherto been unclear. We introduce a simple, unified framework for multi-sample variational inference termed adaptive importance sampling for learning (AISLE) and show that it admits IWAE and RWS as special cases. Through a principled application of a variance-reduction technique from Tucker et al. (2019), we also show that the sticking-the-landing (STL) gradient from Roeder et al. (2017), which previously lacked theoretical justification, can be recovered as a special case of RWS (and hence of AISLE). In particular, this indicates that the breakdown of RWS -- but not of STL -- observed in Tucker et al. (2019) may not be attributable to the lack of a joint objective for the generative-model and inference-network parameters as previously conjectured. Finally, we argue that our adaptive-importance-sampling interpretation of variational inference leads to more natural and principled extensions to sequential Monte Carlo methods than the IWAE-type multi-sample objective interpretation.
Dark matter and H i in ultra-diffuse galaxy UGC 2162

Our GMRT (Giant Metrewave Radio Telescope) H i observations of the ultra-diffuse galaxy (UDG) UGC 2162, projected ∼ 300 kpc from the centre of the M 77 group, reveal it to a have an extended H i disc ($R_{\rm H\,{\small I}}/R_{25}$ ∼ 3.3) with a moderate rotational velocity (Vrot ∼ 31 km s−1). This Vrotis in line with that of dwarf galaxies with similar H i mass. We estimate an Mdyn of ∼1.14 × 109 M⊙ within the galaxy’s $R_ \rm {H\,{\small I}}$ ∼ 5.2 kpc. Additionally, our estimates of M200 for the galaxy from NFW models are in the range of 5.0–8.8 × 1010 M⊙. Comparing UGC 2162 to samples of UDGs with H i detections show it to have amongst the smallest Re with its MH i/M* being distinctly higher and g – icolour slightly bluer than typical values in those samples. We also compared H i and dark matter (DM) halo properties of UGC 2162 with dwarf galaxies in the LITTLE THINGS sample and find its DM halo mass and profile are within the range expected for a dwarf galaxy. While we were unable to determine the origin of the galaxy’s present-day optical form from our study, its normal H i rotation velocity in relation to its H i mass, H i morphology, environment, and dwarf mass DM halo ruled out some of the proposed ultra-diffuse galaxy formation scenarios for this galaxy.
Active Asteroid (6478) Gault: A Blue Q-type Surface below the Dust?

We present near-infrared spectroscopy of the sporadically active asteroid (6478) Gault collected on the 3 m NASA/Infrared Telescope Facility observatory in late 2019 March/early April. Long-exposure imaging with the 0.5 m Near Earth Environment Monitoring T05 telescope and previously published data simultaneously monitored the asteroid activity, providing context for our measurements. We confirm that Gault is a silicate-rich (Q- or S-type) object likely linked to the (25) Phocaea collisional family. The asteroid exhibits substantial spectral variability over the 0.75–2.45 μm wavelength range, from unusual blue (s′ = −13.5 ± 1.1% μm−1) to typical red (s′ = +9.1 ± 1.2% μm−1) spectral slope, that does not seem to correlate with activity. Spectral comparisons with samples of ordinary chondrite meteorites suggest that the blue color relates to the partial loss of the asteroid dust regolith, exposing a fresh, dust-free material at its surface. The existence of asteroids rotating close to rotational break-up limit and having similar spectral properties as Gault further supports this interpretation. Future spectroscopic observations of Gault, when the tails dissipate, will help further testing of our proposed hypothesis.
Nearly coeval intermediate-age Milky Way star clusters at very different dynamics evolutionary stages

We report astrophysical properties of 12 Milky Way open clusters located beyond a 2 kpc circle around the Sun by using deep optical photometry. We estimated their age and metallicities on the basis of a maximum likelihood approach using astrometric members determined from Gaia DR2 data. The studied clusters turned out to be of intermediate-age (0.8–4.0 Gyr), with metallicities spanning the range [Fe/H] ∼ −0.5–+0.1 dex, and distributed within the general observed trend of the Milky Way disc radial and perpendicular metallicity gradients. As far as we are aware, these are the first metal abundance estimates derived for these clusters so far. From the constructed stellar density radial profiles and cluster mass functions we obtained a variety of structural and internal dynamics evolution parameters. They show that while the innermost cluster regions would seem to be mainly shaped according to the respective internal dynamics evolutionary stages, the outermost ones would seem to be slightly more sensitive to the Milky Way tidal field. The nearly coeval studied clusters are experiencing different levels of two-body relaxation following star evaporation; those at more advanced stages being more compact objects. Likewise, we found that the more important the Milky way tides, the larger the Jacobi volume occupied by the clusters, irrespective of their actual sizes and internal dynamics evolutionary stages.
From the quantum geometry of Fuchsian systems to conformal blocks of W-algebras

We consider the moduli space of holomorphic principal bundles for reductive Lie groups over Riemann surfaces (possibly with boundaries) and equipped with meromorphic connections. We associate to this space a point-wise notion of quantum spectral curve whose generalized periods define a new set of moduli. We define homology cycles and differential forms of the quantum spectral curve, allowing to derive quantum analogs of the form-cycle duality and Riemann bilinear identities of classical geometry. A tau-function is introduced for this system in the form of a theta-series and in such a way that the variations of its coefficients with respect to moduli, isomonodromic or not, can be computed as quantum period integrals. This lays new grounds to relate our study to that of integrable hierarchies, isomonodromic deformation of meromorphic connections and non-perturbative topological string theory. In turn, we define amplitudes on the quantum spectral curve which have an interpretation in conformal field theory when the Lie algebra is assumed to be simply-laced: they coincide with correlation functions involving twisted chiral fields of an affine Lie algebra at level one. The singularities at the punctures are interpreted as primary fields of the associated Casimir W-algebra. The amplitudes are moreover related by W-constraints, so-called loop equations, allowing one to compute recursively a certain asymptotic expansion of the tau-function, namely the one corresponding both to the heavy-charge regime of conformal field theory and to the weak-coupling regime of topological string theory.
The theta-dependent Yang-Mills theory at finite temperature in a holographic description

The theta-dependent gauge theories can be studied by using holographic duality through string theory on certain spacetimes. Via this correspondence we consider a stack of $N_{0}$ dynamical D0-branes as D-instantons in the background sourced by $N_{c}$ coincident non-extreme black D4-branes. According to the gauge-gravity duality this D0-D4 brane system corresponds to Yang-Mills theory with a theta angle at finite temperature. We solve the IIA supergravity action by taking account into a sufficiently small backreaction of the D-instantons and obtain an analytical solution for our D0-D4-brane configuration. Then the dual theory in the large $N_{c}$ limit can be holographically investigated with the gravity solution. In the dual field theory, we find the coupling constant exhibits the property of asymptotic freedom as it is expected in QCD. The contribution of the theta-dependence to the free energy gets suppressed at high temperature which is basically consistent with the calculation by using the Yang-Mills instanton. The topological susceptibility in the large $N_{c}$ limit vanishes and this behavior remarkably agrees with the implications from the simulation results at finite temperature. Besides we finally find a geometrical interpretation of the theta-dependence in this holographic system.
An Observational Study for Grain Dynamics in the AS 209 Disk with Submillimeter Polarization

We present an 870 μm Atacama Large Millimeter Array polarization observation toward the Class II protoplanetary disk around AS 209, which has concentric, multiple gaps and rings. We successfully detect the polarized emission and find that the polarization orientations and fractions have distinct characteristics between the inner and outer regions. In the inner region, the polarization orientations are parallel to the minor axis of the disk, which is consistent with the self-scattering model. The mean polarization fraction in the region is ∼0.2%, which is lower than the expected value when the grains have the maximum polarization efficiency, which corresponds to λ/2π ∼ 140 μm in grain radius. In the outer region, we detect ∼1.0% polarization and find that the polarization orientations are almost in the azimuthal directions. Moreover, the polarization orientations have systematic angular deviations from the azimuthal directions with Δθ ∼ 4. °5 ± 1. °6. The pattern is consistent with a model in which radially drifting dust grains are aligned by the gas flow against the dust grains. We consider possible scenarios of the grain dynamics at the AS 209 ring that can reproduce the polarization pattern. However, the directions of the observed angular deviations are opposite to what is predicted based on the fact that the disk rotates clockwise. This raises a question regarding our understanding of the alignment processes and/or grain dynamics in protoplanetary disks.
Synthetic Epileptic Brain Activities Using Generative Adversarial Networks

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-threatening. Modern systems monitoring electroencephalography (EEG) signals are being currently developed with the view to detect epileptic seizures in order to alert caregivers and reduce the impact of seizures on patients' quality of life. Such seizure detection systems employ state-of-the-art machine learning algorithms that require a considerably large amount of labeled personal data for training. However, acquiring EEG signals of epileptic seizures is a costly and time-consuming process for medical experts and patients, currently requiring in-hospital recordings in specialized units. In this work, we generate synthetic seizure-like brain electrical activities, i.e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for recorded data. First, we train a Generative Adversarial Network (GAN) with data from 30 epilepsy patients. Then, we generate synthetic personalized training sets for new, unseen patients, which overall yield higher detection performance than the real-data training sets. We demonstrate our results using the datasets from the EPILEPSIAE Project, one of the world's largest public databases for seizure detection.
Counterfactual Learning from Logs for Improved Ranking of E-Commerce Products

Improved search quality enhances users' satisfaction, which directly impacts sales growth of an E-Commerce (E-Com) platform. Learning to Rank (LTR) algorithms require relevance judgments on products for learning. In real commercial scenarios, getting such judgments poses an immense challenge in application of LTR algorithms. In the literature, it is proposed to employ user feedback signals such as clicks, orders etc to generate relevance judgments. It is done by aggregating the logged data and calculating click rate, order rate etc of products, for each query in the logs. In this paper, we advocate counterfactual risk minimization (CRM) approach which circumvents the need of such data pre-processing and is better suited for learning from logged data, i.e. contextual bandit feedback. Due to unavailability of public E-Com LTR dataset, we provide Mercateo dataset from our E-Com platform. This dataset contains information of queries from real users, actions taken by the policy running on the system, probability of these actions and feedback of users on those actions. Our commercial dataset contains more than 10 million click log entries and 1 million order logs from a catalogue of about 3.5 million products and 3000 queries. To the best of our knowledge, this is the first work which examines effectiveness of CRM approach in learning ranking model from real-world logged data. Our empirical evaluation shows that CRM approach is able to learn directly from logged contextual-bandit feedback. Our method outperforms full-information loss on deep neural network model as well as traditional ranking models like LambdaMART. These findings have significant implications for improving the quality of search in E-Com platforms.
A Distributed Control Problem for a Fractional Tumor Growth Model

In this paper, we study the distributed optimal control of a system of three evolutionary equations involving fractional powers of three self-adjoint, monotone, unbounded linear operators having compact resolvents. The system is a generalization of a Cahn–Hilliard type phase field system modeling tumor growth that has been proposed by Hawkins–Daarud, van der Zee and Oden. The aim of the control process, which could be realized by either administering a drug or monitoring the nutrition, is to keep the tumor cell fraction under control while avoiding possible harm for the patient. In contrast to previous studies, in which the occurring unbounded operators governing the diffusional regimes were all given by the Laplacian with zero Neumann boundary conditions, the operators may in our case be different; more generally, we consider systems with fractional powers of the type that were studied in a recent work by the present authors. In our analysis, we show the Fréchet differentiability of the associated control-to-state operator, establish the existence of solutions to the associated adjoint system, and derive the first-order necessary conditions of optimality for a cost functional of tracking type.
Microsecond Electro-Optic Switching of Nematic Liquid Crystals with Giant Dielectric Anisotropy

Nematic liquid crystals exhibit a fast optical response when the applied electric field modifies the degree of order but does not change the direction of molecular orientation. The effect requires a relatively high electric field, on the order of 10^8 V/m for a field induced birefringence change of 0.01. To address this detrimental issue, this work explores electrically induced modification of the order parameter in a material with a giant dielectric anisotropy of +200. A relatively weak field 3X10^7 V/m causes a significant change of birefringence, by about 0.04. Both switching on and off times are ~10 microseconds. The effect is called a microsecond electrically modified order parameter (MEMOP) and can be used in electro-optical devices, such as fast electro-optic shutters, phase modulators, and beam-steerers that require microsecond response times.
AdaNN: Adaptive Neural Network-Based Equalizer via Online Semi-Supervised Learning

The demand for high speed data transmission has increased rapidly, leading to advanced optical communication techniques. In the past few years, multiple equalizers based on neural network (NN) have been proposed to recover signal from nonlinear distortions. However, previous experiments mainly focused on achieving low bit error rate (BER) on certain dataset with an offline-trained NN, neglecting the generalization ability of NN-based equalizer when the properties of optical link change. The development of efficient online training scheme is urgently needed. In this article, we’ve proposed an adaptive online training scheme for NN-based equalizer. Our scheme can fine-tune NN parameters with only received signals rather than original symbols as reference. Once the system enters online stage, no labeled training sequence needs to be transmitted. By introducing data augmentation and virtual adversarial training, the convergence speed has been accelerated by 4.5 times, compared with decision-directed self-training. The proposed adaptive NN-based equalizer is called “AdaNN”. Its BER has been evaluated under two scenarios: a 56 Gb/s PAM4-modulated VCSEL-MMF optical link (100-m), and a 32 Gbaud 16QAM-modulated Nyquist-WDM system (960-km SSMF). In our experiments, with the help of AdaNN, BER values can be quickly stabilized below 1e-3 after receiving $\text{10}^\text{5}$ unlabeled symbols. AdaNN shows great performance improvement compared with non-adaptive NN and conventional MLSE.
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision

The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled. In this work, we introduce a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model. To the best of our knowledge, this is the first method that exploits graph-based semi-supervised learning for X-ray data classification. Furthermore, we introduce a new multi-class classification functional with carefully selected class priors which allows for a smooth solution that strengthens the synergy between the limited number of labels and the huge amount of unlabelled data. We demonstrate, through a set of numerical and visual experiments, that our method produces highly competitive results on the ChestX-ray14 data set whilst drastically reducing the need for annotated data.
A Fine-Grained Spectral Perspective on Neural Networks

Are neural networks biased toward simple functions? Does depth always help learn more complex features? Is training the last layer of a network as good as training all layers? How to set the range for learning rate tuning? These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective. We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*. Roughly, the CK and the NTK tell us respectively "what a network looks like at initialization" and "what a network looks like during and after training. " Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks. By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks. We derive fast algorithms for computing the spectra of CK and NTK when the data is uniformly distributed over the boolean cube, and show this spectra is the same in high dimensions when data is drawn from isotropic Gaussian or uniformly over the sphere. Code replicating our results is available at this http URL.
Switchable Normalization for Learning-to-Normalize Deep Representation

We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see Fig. 1 ). Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g., 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, MegaFace and Kinetics. Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer? (b) What impacts the choices of normalizers? (c) Do different tasks and datasets prefer different normalizers? We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN has been released at https://github.com/switchablenorms.
Progressive Image Inpainting with Full-Resolution Residual Network

Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes. However, they fail to generate plausible textures inside damaged area because there lacks surrounding information. A progressive inpainting approach would be advantageous for eliminating central blurriness, i.e., restoring well and then updating masks. In this paper, we propose full-resolution residual network (FRRN) to fill irregular holes, which is proved to be effective for progressive image inpainting. We show that well-designed residual architecture facilitates feature integration and texture prediction. Additionally, to guarantee completion quality during progressive inpainting, we adopt N Blocks, One Dilation strategy, which assigns several residual blocks for one dilation step. Correspondingly, a step loss function is applied to improve the performance of intermediate restorations. The experimental results demonstrate that the proposed FRRN framework for image inpainting is much better than previous methods both quantitatively and qualitatively.