Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Learning of Probabilistic Movement Primitives

A Probabilistic Movement Primitive (ProMP) defines a distribution over trajectories with an associated feedback policy. ProMPs are typically initialized from human demonstrations and achieve task generalization through probabilistic operations. However, there is currently no principled guidance in the literature to determine how many demonstrations a teacher should provide and what constitutes a “good” demonstration for promoting generalization. In this paper, we present an active learning approach to learning a library of ProMPs capable of task generalization over a given space. We utilize uncertainty sampling techniques to generate a task instance for which a teacher should provide a demonstration. The provided demonstration is incorporated into an existing ProMP if possible, or a new ProMP is created from the demonstration if it is determined that it is too dissimilar from existing demonstrations. We provide a qualitative comparison between common active learning metrics; motivated by this comparison we present a novel uncertainty sampling approach named “Greatest Mahalanobis Distance. ” We perform grasping experiments on a real KUKA robot and show our novel active learning measure achieves better task generalization with fewer demonstrations than a random sampling over the space.
Unusual Formation of Point-Defect Complexes in the Ultrawide-Band-Gap Semiconductor 
β−Ga2O3

Author(s): Johnson, JM; Chen, Z; Varley, JB; Jackson, CM; Farzana, E; Zhang, Z; Arehart, AR; Huang, HL; Genc, A; Ringel, SA; Van De Walle, CG; Muller, DA; Hwang, J | Abstract: © 2019 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/" Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Understanding the unique properties of ultra-wide band gap semiconductors requires detailed information about the exact nature of point defects and their role in determining the properties. Here, we report the first direct microscopic observation of an unusual formation of point defect complexes within the atomic-scale structure of β-Ga2O3 using high resolution scanning transmission electron microscopy (STEM). Each complex involves one cation interstitial atom paired with two cation vacancies. These divacancy-interstitial complexes correlate directly with structures obtained by density functional theory, which predicts them to be compensating acceptors in β-Ga2O3. This prediction is confirmed by a comparison between STEM data and deep level optical spectroscopy results, which reveals that these complexes correspond to a deep trap within the band gap, and that the development of the complexes is facilitated by Sn doping through increased vacancy concentration. These findings provide new insight on this emerging material's unique response to the incorporation of impurities that can critically influence their properties.
Ludii as a Competition Platform

Ludii is a general game system being developed as part of the ERC-funded Digital Ludeme Project (DLP). While its primary aim is to model, play, and analyse the full range of traditional strategy games, Ludii also has the potential to support a wide range of AI research topics and competitions. This paper describes some of the future competitions and challenges that we intend to run using the Ludii system, highlighting some of its most important aspects that can potentially lead to many algorithm improvements and new avenues of research. We compare and contrast our proposed competition motivations, goals and frameworks against those of existing general game playing competitions, addressing the strengths and weaknesses of each platform.
ALMA Band 3 polarimetric follow-up of a complete sample of faint PACO sources

We present Atacama Large Millimetre/submillimiter Array (ALMA) high sensitivity ($\sigma _\mathrm{ P} \simeq 0.4\,$ mJy) polarimetric observations at $97.5\,$ GHz (Band 3) of a complete sample of 32 extragalactic radio sources drawn from the faint Planck–ATCA Co-eval Observations (PACO) sample (b < −75°, compact sources brighter than $200\,$ mJy at $20\,$ GHz). We achieved a detection rate of $~97\, {\rm per\, cent}$ at $3\, \sigma$ (only 1 non-detection). We complement these observations with new Australia Telescope Compact Array (ATCA) data between 2.1 and $35\,$GHz obtained within a few months and with data published in earlier papers from our collaboration. Adding the co-eval GaLactic and Extragalactic All-sky Murchison widefield array (GLEAM) survey detections between $70\,$ and $230\,$ MHz for our sources, we present spectra over more than 3 decades in frequency in total intensity and over about 1.7 decades in polarization. The spectra of our sources are smooth over the whole frequency range, with no sign of dust emission from the host galaxy at mm wavelengths or of a sharp high frequency decline due, for example, to electron ageing. We do however find indications of multiple emitting components and present a classification based on the number of detected components. We analyse the polarization fraction behaviour and distributions up to $97\,$ GHz for different source classes. Source counts in polarization are presented at $95\,$ GHz.
Towards a Better Understanding and Regularization of GAN Training Dynamics

Generative adversarial networks (GANs) are notoriously difficult to train and the reasons underlying their (non-)convergence behaviors are still not completely understood. By first considering a simple yet representative GAN example, we mathematically analyze its local convergence behavior in a non-asymptotic way. Furthermore, the analysis is extended to general GANs under certain assumptions. We find that in order to ensure a good convergence rate, two factors of the Jacobian in the GAN training dynamics should be simultaneously avoided, which are (i) the Phase Factor, i.e., the Jacobian has complex eigenvalues with a large imaginary-to-real ratio, and (ii) the Conditioning Factor, i.e., the Jacobian is ill-conditioned. Previous methods of regularizing the Jacobian can only alleviate one of these two factors, while making the other more severe. Thus we propose a new JAcobian REgularization (JARE) for GANs, which simultaneously addresses both factors by construction. Finally, we conduct experiments that confirm our theoretical analysis and demonstrate the advantages of JARE over previous methods in stabilizing GANs.
Deep Conversational Recommender in Travel

When traveling to a foreign country, we are often in dire need of an intelligent conversational agent to provide instant and informative responses to our various queries. However, to build such a travel agent is non-trivial. First of all, travel naturally involves several sub-tasks such as hotel reservation, restaurant recommendation and taxi booking etc, which invokes the need for global topic control. Secondly, the agent should consider various constraints like price or distance given by the user to recommend an appropriate venue. In this paper, we present a Deep Conversational Recommender (DCR) and apply to travel. It augments the sequence-to-sequence (seq2seq) models with a neural latent topic component to better guide response generation and make the training easier. To consider the various constraints for venue recommendation, we leverage a graph convolutional network (GCN) based approach to capture the relationships between different venues and the match between venue and dialog context. For response generation, we combine the topic-based component with the idea of pointer networks, which allows us to effectively incorporate recommendation results. We perform extensive evaluation on a multi-turn task-oriented dialog dataset in travel domain and the results show that our method achieves superior performance as compared to a wide range of baselines.
Detecting Spiky Corruption in Markov Decision Processes

Current reinforcement learning methods fail if the reward function is imperfect, i.e. if the agent observes reward different from what it actually receives. We study this problem within the formalism of Corrupt Reward Markov Decision Processes (CRMDPs). We show that if the reward corruption in a CRMDP is sufficiently "spiky", the environment is solvable. We fully characterize the regret bound of a Spiky CRMDP, and introduce an algorithm that is able to detect its corrupt states. We show that this algorithm can be used to learn the optimal policy with any common reinforcement learning algorithm. Finally, we investigate our algorithm in a pair of simple gridworld environments, finding that our algorithm can detect the corrupt states and learn the optimal policy despite the corruption.
Global weighted gradient estimates for nonlinear p-Laplacian type elliptic equations and its application

We obtain the global weighted $W^{1,p}$ estimates for weak solutions of nonlinear elliptic equations over Reifenberg flat domains. Where nonlinearity $A(x,z,\xi)$ is assumed to be local uniform continuous in $z$ and have small BMO semi-norm in $x$. Moreover, we derive Besov regularity for solutions of a class of special harmonic equations by making use of $W^{1,p}$ estimate. Keywords: global weighted $W^{1,p}$ estimates; quasilinear equations; Besov regularity
Schottky anomaly of deSitter black holes

The interplay of black hole and cosmological horizons introduces distinctive thermodynamic behavior for deSitter black holes, including well-known upper bounds for the mass and entropy. We point to a new such feature, a Schottky peak in the heat capacity of Schwarzschild–deSitter (SdS) black holes. With this behavior in mind, we explore statistical models for the underlying quantum degrees of freedom of SdS holes. While a simple two-state spin model gives Schottky behavior, in order to capture the non-equilibrium nature of the SdS system we consider a system with a large number of non-interacting spins. We examine to what extent constrained states of this system reproduce the thermodynamic properties of the black hole. We also review results of a recent study of particle production in SdS spacetimes in light of the Schottky anomaly and our spin models.
Self-gravitating disks in binary systems: an SPH approach

The study of the stability of massive gaseous disks around a star in a nonisolated context is a difficult task and becomes even more complicated for disks that are hosted by binary systems. The role of self-gravity is thought to be significant when the ratio of the disk-to-star mass is non-negligible. To solve these problems, we implemented, tested, and applied our own smoothed particle hydrodynamics (SPH) algorithm. The code (named GaSPH) passed various quality tests and shows good performances, and it can therefore be reliably applied to the study of disks around stars when self-gravity needs to be accounted for. We here introduce and describe the algorithm, including some performance and stability tests. This paper is the first part of a series of studies in which self-gravitating disks in binary systems are let evolve in larger environments such as open clusters.
Bayesian Nonparametric Boolean Factor Models

We build upon probabilistic models for Boolean Matrix and Boolean Tensor factorisation that have recently been shown to solve these problems with unprecedented accuracy and to enable posterior inference to scale to Billions of observation. Here, we lift the restriction of a pre-specified number of latent dimensions by introducing an Indian Buffet Process prior over factor matrices. Not only does the full factor-conditional take a computationally convenient form due to the logical dependencies in the model, but also the posterior over the number of non-zero latent dimensions is remarkably simple. It amounts to counting the number false and true negative predictions, whereas positive predictions can be ignored. This constitutes a very transparent example of sampling-based posterior inference with an IBP prior and, importantly, lets us maintain extremely efficient inference. We discuss applications to simulated data, as well as to a real world data matrix with 6 Million entries.
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks

Training convolutional neural networks for image classification tasks usually causes information loss. Although most of the time the information is redundant with respect to the target task, there are still cases where discriminative information is also discarded. For example, if images that belong to the same category have multiple correlated features, the model may only learn a subset of the features and ignore the rest. This may not be a problem unless the classification in the test set highly relies on the ignored features. We argue that the discard of the correlated discriminative information is partially caused by the fact that the minimization of the classification loss doesn't ensure to learn all discriminative information but only the most discriminative information given the training set. To address this problem, we propose an information flow maximization (IFM) loss as a regularization term to find the discriminative correlated features. With less information loss the classifier can make predictions based on more informative features. We validate our method on the shiftedMNIST dataset and show the effectiveness of IFM loss in learning representative and discriminative features.
Propagation of chaos for mean field rough differential equations

We address propagation of chaos for large systems of rough differential equations associated with random rough differential equations of mean field type $$ dX_t = V(X_t,\mathcal{L}(X_t))dt + F(X_t,\mathcal{L}(X_t))dW_t $$ where $W$ is a random rough path and $\mathcal{L}(X_t)$ is the law of $X_t$. We prove propagation of chaos, and provide also an explicit optimal convergence rate. The analysis is based upon the tools we developed in our companion paper [1] for solving mean field rough differential equations and in particular upon a corresponding version of the Ito-Lyons continuity theorem. The rate of convergence is obtained by a coupling argument developed first by Sznitman for particle systems with Brownian inputs.
Analyzing Utility of Visual Context in Multimodal Speech Recognition Under Noisy Conditions

Multimodal learning allows us to leverage information from multiple sources (visual, acoustic and text), similar to our experience of the real world. However, it is currently unclear to what extent auxiliary modalities improve performance over unimodal models, and under what circumstances the auxiliary modalities are useful. We examine the utility of the auxiliary visual context in Multimodal Automatic Speech Recognition in adversarial settings, where we deprive the models from partial audio signal during inference time. Our experiments show that while MMASR models show significant gains over traditional speech-to-text architectures (upto 4.2% WER improvements), they do not incorporate visual information when the audio signal has been corrupted. This shows that current methods of integrating the visual modality do not improve model robustness to noise, and we need better visually grounded adaptation techniques.
Modeling Tabular data using Conditional GAN

Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous columns. Continuous columns may have multiple modes whereas discrete columns are sometimes imbalanced making the modeling difficult. Existing statistical and deep neural network models fail to properly model this type of data. We design TGAN, which uses a conditional generative adversarial network to address these challenges. To aid in a fair and thorough comparison, we design a benchmark with 7 simulated and 8 real datasets and several Bayesian network baselines. TGAN outperforms Bayesian methods on most of the real datasets whereas other deep learning methods could not.
Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

Digital predistortion is the process of using digital signal processing to correct nonlinearities caused by the analog RF front-end of a wireless transmitter. These nonlinearities contribute to adjacent channel leakage, degrade the error vector magnitude of transmitted signals, and often force the transmitter to reduce its transmission power into a more linear but less power-efficient region of the device. Most predistortion techniques are based on polynomial models with an indirect learning architecture which have been shown to be overly sensitive to noise. In this work, we use neural network based predistortion with a novel neural network training method that avoids the indirect learning architecture and that shows significant improvements in both the adjacent channel leakage ratio and error vector magnitude. Moreover, we show that, by using a neural network based predistorter, we are able to achieve a 42% reduction in latency and 9.6% increase in throughput on an FPGA accelerator with 15% fewer multiplications per sample when compared to a similarly performing memory-polynomial implementation.
A Review-Driven Neural Model for Sequential Recommendation

Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction. In contrast, there has been few attempt to enlist the semantic signals covered by user reviews for the task of collaborative filtering. In this paper, we propose a novel review-driven neural sequential recommendation model (named RNS) by considering user's intrinsic preference (long-term) and sequential patterns (short-term). In detail, RNS is devised to encode each user or item with the aspect-aware representations extracted from the reviews. Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level. Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models.
Runaway climate cooling of ocean planets in the habitable zone: a consequence of seafloor weathering enhanced by melting of high-pressure ice

Terrestrial planets covered globally with thick oceans (termed ocean planets) in the habitable zone were previously inferred to have extremely hot climates in most cases. This is because H2O high-pressure (HP) ice on the seafloor prevents chemical weathering and, thus, removal of atmospheric CO2. Previous studies, however, ignored melting of the HP ice and horizontal variation in heat flux from oceanic crusts. Here, we examine whether high heat fluxes near the mid-ocean ridge melt the HP ice and thereby remove atmospheric CO2. We develop integrated climate models of an Earth-size ocean planet with plate tectonics for different ocean masses, which include the effects of HP ice melting, seafloor weathering, and the carbonate–silicate geochemical carbon cycle. We find that the heat flux near the mid-ocean ridge is high enough to melt the ice, enabling seafloor weathering. In contrast to the previous theoretical prediction, we show that climates of terrestrial planets with massive oceans lapse into extremely cold ones (or snowball states) with CO2-poor atmospheres. Such extremely cold climates are achieved mainly because the HP ice melting fixes seafloor temperature at the melting temperature, thereby keeping a high weathering flux regardless of surface temperature. We estimate that ocean planets with oceans several tens of the Earth’s ocean mass no longer maintain temperate climates. These results suggest that terrestrial planets with extremely cold climates exist even in the habitable zone beyond the Solar system, given the frequency of water-rich planets predicted by planet formation theories.
Injection of Deformable Capsules in a Reservoir: A Systematic Analysis

Motivated by red blood cell dynamics and injectable capsules for drug delivery, in this paper, a computational study of capsule ejection from a narrow channel into a reservoir is undertaken for a combination of varying deformable capsule sizes and channel dimensions. A mass-spring membrane model is coupled to an Immersed Boundary–Lattice Boltzmann model solver. The aim of the present work is the description of the capsules’ motion, deformation and the response of the fluid due to the complex particles’ dynamics. The interactions between the capsules affect the local velocity field and are responsible for the dynamics observed. Capsule membrane deformability is also seen to affect inter-capsule interaction. We observe that the train of three particles locally homogenises the velocity field and the leading capsule travels faster than the other two trailing capsules. Variations in the size of reservoir do not seem to be relevant, while the ratio of capsule diameter to channel diameter as well as the ratio of capsule diameter to inter-capsule spacing play a major role. This flow set-up has not been covered in the literature, and consequently we focus on describing capsule motion, membrane deformation and fluid dynamics, as a preliminary investigation in this field.
Fast Radio Bursts from Magnetars Born in Binary Neutron Star Mergers and Accretion Induced Collapse

Recently born magnetars are promising candidates for the engines powering fast radio bursts (FRBs). The focus thus far has been placed on millisecond magnetars born in rare core-collapse explosions, motivated by the star-forming dwarf host galaxy of the repeating FRB 121102, which is remarkably similar to the hosts of superluminous supernovae and long gamma-ray bursts. However, long-lived magnetars may also be created in binary neutron star (BNS) mergers, in the small subset of cases with a sufficiently low total mass for the remnant to avoid collapse to a black hole, or in the accretion-induced collapse (AIC) of a white dwarf. A BNS or AIC FRB channel will be characterized by distinct host galaxy and spatial offset distributions which we show are consistent with the recently reported FRB 180924, localized by the Australian Square Kilometre Array Pathfinder to a massive quiescent host galaxy with an offset of about 1.4 effective radii. Using models calibrated to FRB 121102, we make predictions for the dispersion measure, rotation measure, and persistent radio emission from magnetar FRB sources born in BNS mergers or AIC, and show these are consistent with upper limits from FRB 180924. Depending on the rate of AIC, and the fraction of BNS mergers leaving long-lived stable magnetars, the birth rate of repeating FRB sources associated with older stellar populations could be comparable to that of the core-collapse channel. We also discuss potential differences in the repetition properties of these channels, as a result of differences in the characteristic masses and magnetic fields of the magnetars.