Miracle-dz/UniFAR-FTUs huggingface dataset:
Solvable Model of a Generic Driven Mixture of Trapped Bose–Einstein Condensates and Properties of a Many-Boson Floquet State at the Limit of an Infinite Number of Particles

A solvable model of a periodically driven trapped mixture of Bose–Einstein condensates, consisting of N1 interacting bosons of mass m1 driven by a force of amplitude fL,1 and N2 interacting bosons of mass m2 driven by a force of amplitude fL,2, is presented. The model generalizes the harmonic-interaction model for mixtures to the time-dependent domain. The resulting many-particle ground Floquet wavefunction and quasienergy, as well as the time-dependent densities and reduced density matrices, are prescribed explicitly and analyzed at the many-body and mean-field levels of theory for finite systems and at the limit of an infinite number of particles. We prove that the time-dependent densities per particle are given at the limit of an infinite number of particles by their respective mean-field quantities, and that the time-dependent reduced one-particle and two-particle density matrices per particle of the driven mixture are 100% condensed. Interestingly, the quasienergy per particle does not coincide with the mean-field value at this limit, unless the relative center-of-mass coordinate of the two Bose–Einstein condensates is not activated by the driving forces fL,1 and fL,2. As an application, we investigate the imprinting of angular momentum and its fluctuations when steering a Bose–Einstein condensate by an interacting bosonic impurity and the resulting modes of rotations. Whereas the expectation values per particle of the angular-momentum operator for the many-body and mean-field solutions coincide at the limit of an infinite number of particles, the respective fluctuations can differ substantially. The results are analyzed in terms of the transformation properties of the angular-momentum operator under translations and boosts, and as a function of the interactions between the particles. Implications are briefly discussed.
Investigation on a Doubly-Averaged Model for the Molniya Satellites Orbits.

The aim of this work is to investigate the lunisolar perturbations affecting the long-term dynamics of a Molniya satellite. Some numerical experiments on the doubly-averaged model, including the expansion of the lunisolar disturbing functions up to the third order, are carried out in order to detect the terms dominating the long-term evolution. The analysis focuses on the following significant indicators: the amplitude of the harmonic coefficients, the periods of the arguments involved and, in particular, the ratio between the amplitudes and the corresponding frequency. The results show that the second-order lunisolar perturbation gives the dominant contribution to the long-term dynamics. The second part of this work aims to study the resonant regions associated to the dominant terms identified so far by using both the ideal resonance model and an alternative approach. The results obtained show when the standard method does not catch the main features of the dynamical structure of the resonant regions. Finally, the maximum overlapping region is identified in the proximity of the Molniya orbital environment.
Thermal transport in 
TT¯
-deformed conformal field theories: From integrability to holography

In this paper we consider the energy and momentum transport in (1+1)-dimension conformal field theories (CFTs) that are deformed by an irrelevant operator $T\bar{T}$, using the integrability based generalized hydrodynamics, and holography. The two complementary methods allow us to study the energy and momentum transport after the in-homogeneous quench, derive the exact non-equilibrium steady states and calculate the Drude weights and the diffusion constants. Our analysis reveals that both the energy and the momentum Drude weights satisfy universal formulae regardless of the underlying conformal field theory. These fundamental physical insights have important consequences for our understanding of the $T\bar{T}$-deformed CFTs. First of all, they provide the first check of the $T\bar{T}$-deformed $\mathrm{AdS}_3$/$\mathrm{CFT}_2$ correspondence from the dynamical standpoint. And secondly, we are able to identify a remarkable connection between the $T\bar{T}$-deformed CFTs and reversible cellular automata.
Fundamental limitations to key distillation from Gaussian states with Gaussian operations

We establish fundamental upper bounds on the amount of secret key that can be extracted from continuous variable quantum Gaussian states by using only local Gaussian operations, local classical processing, and public communication. For one-way communication, we prove that the key is bounded by the Renyi-$2$ Gaussian entanglement of formation $E_{F,2}^{\mathrm{\scriptscriptstyle G}}$, with the inequality being saturated for pure Gaussian states. The same is true if two-way public communication is allowed but Alice and Bob employ protocols that start with destructive local Gaussian measurements. In the most general setting of two-way communication and arbitrary interactive protocols, we argue that $2 E_{F,2}^{\mathrm{\scriptscriptstyle G}}$ is still a bound on the extractable key, although we conjecture that the factor of $2$ is superfluous. Finally, for a wide class of Gaussian states that includes all two-mode states, we prove a recently proposed conjecture on the equality between $E_{F,2}^{\mathrm{\scriptscriptstyle G}}$ and the Gaussian intrinsic entanglement, thus endowing both measures with a more solid operational meaning.
"What, not how": Solving an under-actuated insertion task from scratch

Robot manipulation requires a complex set of skills that need to be carefully combined and coordinated to solve a task. Yet, most ReinforcementLearning (RL) approaches in robotics study tasks which actually consist only of a single manipulation skill, such as grasping an object or inserting a pre-grasped object. As a result the skill ('how' to solve the task) but not the actual goal of a complete manipulation ('what' to solve) is specified. In contrast, we study a complex manipulation goal that requires an agent to learn and combine diverse manipulation skills. We propose a challenging, highly under-actuated peg-in-hole task with a free, rotational asymmetrical peg, requiring a broad range of manipulation skills. While correct peg (re-)orientation is a requirement for successful insertion, there is no reward associated with it. Hence an agent needs to understand this pre-condition and learn the skill to fulfil it. The final insertion reward is sparse, allowing freedom in the solution and leading to complex emerging behaviour not envisioned during the task design. We tackle the problem in a multi-task RL framework using Scheduled Auxiliary Control (SAC-X) combined with Regularized Hierarchical Policy Optimization (RHPO) which successfully solves the task in simulation and from scratch on a single robot where data is severely limited.
Tilde at WMT 2020: News Task Systems

This paper describes Tilde’s submission to the WMT2020 shared task on news translation for both directions of the English-Polish language pair in both the constrained and the unconstrained tracks. We follow our submissions form the previous years and build our baseline systems to be morphologically motivated sub-word unit-based Transformer base models that we train using the Marian machine translation toolkit. Additionally, we experiment with different parallel and monolingual data selection schemes, as well as sampled back-translation. Our final models are ensembles of Transformer base and Transformer big models which feature right-to-left re-ranking.
Collisionless sound of bosonic superfluids in lower dimensions

The superfluidity of low-temperature bosons is well established in the collisional regime. In the collisionless regime, however, the presence of superfluidity is not yet fully clarified, in particular in lower spatial dimensions. Here we compare the Vlasov-Landau equation, which does not take into account the superfluid nature of the bosonic system, with the Andreev-Khalatnikov equations, which instead explicitly contain a superfluid velocity. We show that recent experimental data of the sound mode in a two-dimensional collisionless Bose gas of $^{87}$Rb atoms are in good agreement with both theories but the sound damping is better reproduced by the Andreev-Khalatnikov equations below the Berezinskii-Kosterlitz-Thouless critical temperature $T_c$ while above $T_c$ the comparison is not conclusive. For one dimensional bosonic fluids, where experimental data are not yet available, we find larger differences between the sound velocities predicted by the two transport theories and, also in this case, the existence of a superfluid velocity reduces the sound damping.
On the robustness of kernel-based pairwise learning

It is shown that many results on the statistical robustness of kernel-based pairwise learning can be derived under basically no assumptions on the input and output spaces. In particular neither moment conditions on the conditional distribution of Y given X = x nor the boundedness of the output space is needed. We obtain results on the existence and boundedness of the influence function and show qualitative robustness of the kernel-based estimator. The present paper generalizes results by Christmann and Zhou (2016) by allowing the prediction function to take two arguments and can thus be applied in a variety of situations such as ranking.
Deep Autofocus for Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) requires precise positional and environmental information to produce well-focused output during the image reconstruction step. However, errors in these measurements are commonly present resulting in defocused imagery. To overcome these issues, an \emph{autofocus} algorithm is employed as a post-processing step after image reconstruction for the purpose of improving image quality using the image content itself. These algorithms are usually iterative and metric-based in that they seek to optimize an image sharpness metric. In this letter, we demonstrate the potential of machine learning, specifically deep learning, to address the autofocus problem. We formulate the problem as a self-supervised, phase error estimation task using a deep network we call Deep Autofocus. Our formulation has the advantages of being non-iterative (and thus fast) and not requiring ground truth focused-defocused images pairs as often required by other deblurring deep learning methods. We compare our technique against a set of common sharpness metrics optimized using gradient descent over a real-world dataset. Our results demonstrate Deep Autofocus can produce imagery that is perceptually as good as benchmark iterative techniques but at a substantially lower computational cost. We conclude that our proposed Deep Autofocus can provide a more favorable cost-quality trade-off than state-of-the-art alternatives with significant potential of future research.
What can we learn from gradients?

Recent work (\cite{zhu2019deep}) has shown that it is possible to reconstruct the input (image) from the gradient of a neural network. In this paper, our aim is to better understand the limits to reconstruction and to speed up image reconstruction by imposing prior image information and improved initialization. Firstly, we show that for the \textbf{non-linear} neural network, gradient-based reconstruction approximates to solving a high-dimension \textbf{linear} equations for both fully-connected neural network and convolutional neural network. Exploring the theoretical limits of input reconstruction, we show that a fully-connected neural network with a \textbf{one} hidden node is enough to reconstruct a \textbf{single} input image, regardless of the number of nodes in the output layer. Then we generalize this result to a gradient averaged over mini-batches of size B. In this case, the full mini-batch can be reconstructed in a fully-connected network if the number of hidden units exceeds B. For a convolutional neural network, the required number of filters in the first convolutional layer again is decided by the batch size B, however, in this case, input width d and the width after filter $d^{'}$ also play the role $h=(\frac{d}{d^{'}})^2BC$, where C is channel number of input. Finally, we validate and underpin our theoretical analysis on bio-medical data (fMRI, ECG signals, and cell images) and on benchmark data (MNIST, CIFAR100, and face images).
Discovery and reconstitution of the secoiridoid pathway of Catharanthus roseus

Terpene indole alkaloids (TIAs) are important plant-produced secondary metabolites for humans, because of their anti-cancer properties. The production of TIAs still fully relies on extraction from medicinal plants like Catharanthus roseus, which only contains extreme low amounts of these compounds and new ways need to be found to efficiently produce these anticancer drugs at low cost. The common precursor for TIAs is strictosidine and in my PhD project I tried to produce strictosidine in fast-growing tobacco by transferring the genes of the whole biosynthesis pathway into tobacco. At the onset of my project 6 out of the presumed 12 genes of the pathway in C. roseus had not been discovered yet. My thesis tells the story of discovery and characterization of the missing genes and reconstruction of the full strictosidine pathway in tobacco. 1
On the Optimality and Convergence Properties of the Learning Model Predictive Controller

In this technical note we analyse the performance improvement and optimality properties of the Learning Model Predictive Control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop trajectories are used to update the control policy for the next execution of the control task. We show that, when a Linear Independence Constraint Qualification (LICQ) condition holds, the LMPC scheme guarantees strict iterative performance improvement and optimality, meaning that the closed-loop cost evaluated over the entire task converges asymptotically to the optimal cost of the infinite-horizon control problem. Compared to previous works this sufficient LICQ condition can be easily checked, it holds for a larger class of systems and it can be used to adaptively select the prediction horizon of the controller, as demonstrated by a numerical example.
Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

Abstract The past few decades have seen the burgeoning of wide-field, high-cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy; however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO) whose primary science objective is the optical follow-up of gravitational wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near-real time to fully exploit the time domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this ‘off-the-shelf’ pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to subpixel levels, and that measured L-band photometries are accurate to $\sim50$ mmag at $m_L\sim16$ and $\sim200$ mmag at $m_L\sim18$. These values compare favourably to those obtained using GOTO’s primary, in-house pipeline, gotophoto, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic ‘obs package’ that others can build upon, should they wish to use the LSST Science Pipelines to process data from other facilities.
Gravitational waves from mountains in newly born millisecond magnetars

In this paper we study the spin-evolution and gravitational-wave luminosity of a newly born millisecond magnetar, formed either after the collapse of a massive star or after the merger of two neutron stars. In both cases we consider the effect of fallback accretion, and consider the evolution of the system due to the different torques acting on the star, namely the spin up torque due to accretion and spin-down torques due to magnetic dipole radiation, neutrino emission, and gravitational wave emission linked to the formation of a `mountain' on the accretion poles. Initially the spin period is mostly affected by the dipole radiation, but at later times accretion spin the star up rapidly. We find that a magnetar formed after the collapse of a massive star can accrete up to 1 M_{\odot} , and survive on the order of 50 s before collapsing to a black hole. The gravitational wave strain, for an object located at 1 Mpc, is h_c \sim 10^{-23} at kHz frequencies, making this a potential target for next generation ground based detectors. A magnetar formed after a binary neutron star merger, on the other hand, accretes at the most 0.2 M_{\odot}, and emits gravitational waves with a lower maximum strain of the order of h_c \sim 10^{-24} , but also survives for much longer times, and may possibly be associated with the X-ray plateau observed in the light curve of a number of short gamma-ray burst.
Iteratively reweighted greedy set cover

We empirically analyze a simple heuristic for large sparse set cover problems. It uses the weighted greedy algorithm as a basic building block. By multiplicative updates of the weights attached to the elements, the greedy solution is iteratively improved. The implementation of this algorithm is trivial and the algorithm is essentially free of parameters that would require tuning. More iterations can only improve the solution. This set of features makes the approach attractive for practical problems.
DeviceTTS: A Small-Footprint, Fast, Stable Network for On-Device Text-to-Speech

With the number of smart devices increasing, the demand for on-device text-to-speech (TTS) increases rapidly. In recent years, many prominent End-to-End TTS methods have been proposed, and have greatly improved the quality of synthesized speech. However, to ensure the qualified speech, most TTS systems depend on large and complex neural network models, and it's hard to deploy these TTS systems on-device. In this paper, a small-footprint, fast, stable network for on-device TTS is proposed, named as DeviceTTS. DeviceTTS makes use of a duration predictor as a bridge between encoder and decoder so as to avoid the problem of words skipping and repeating in Tacotron. As we all know, model size is a key factor for on-device TTS. For DeviceTTS, Deep Feedforward Sequential Memory Network (DFSMN) is used as the basic component. Moreover, to speed up inference, mix-resolution decoder is proposed for balance the inference speed and speech quality. Experiences are done with WORLD and LPCNet vocoder. Finally, with only 1.4 million model parameters and 0.099 GFLOPS, DeviceTTS achieves comparable performance with Tacotron and FastSpeech. As far as we know, the DeviceTTS can meet the needs of most of the devices in practical application.
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control

In many reinforcement learning (RL) problems, it takes some time until a taken action by the agent reaches its maximum effect on the environment and consequently the agent receives the reward corresponding to that action by a delay called action-effect delay. Such delays reduce the performance of the learning algorithm and increase the computational costs, as the reinforcement learning agent values the immediate rewards more than the future reward that is more related to the taken action. This paper addresses this issue by introducing an applicable enhanced Q-learning method in which at the beginning of the learning phase, the agent takes a single action and builds a function that reflects the environments response to that action, called the reflexive $\gamma$ - function. During the training phase, the agent utilizes the created reflexive $\gamma$- function to update the Q-values. We have applied the developed method to a structural control problem in which the goal of the agent is to reduce the vibrations of a building subjected to earthquake excitations with a specified delay. Seismic control problems are considered as a complex task in structural engineering because of the stochastic and unpredictable nature of earthquakes and the complex behavior of the structure. Three scenarios are presented to study the effects of zero, medium, and long action-effect delays and the performance of the Enhanced method is compared to the standard Q-learning method. Both RL methods use neural network to learn to estimate the state-action value function that is used to control the structure. The results show that the enhanced method significantly outperforms the performance of the original method in all cases, and also improves the stability of the algorithm in dealing with action-effect delays.
Exploring complex networks with the ICON R package

We introduce ICON, an R package that contains 1075 complex network datasets in a standard edgelist format. All provided datasets have associated citations and have been indexed by the Colorado Index of Complex Networks - also referred to as ICON. In addition to supplying a large and diverse corpus of useful real-world networks, ICON also implements an S3 generic to work with the network and ggnetwork R packages for network analysis and visualization, respectively. Sample code in this report also demonstrates how ICON can be used in conjunction with the igraph package. Currently, the Comprehensive R Archive Network hosts ICON v0.4. 0. We hope that ICON will serve as a standard corpus for complex network research and prevent redundant work that would be otherwise necessary by individual research groups. The open source code for ICON and for this reproducible report can be found at this https URL.
SMuRF: a novel tool to identify genomic regions enriched for somatic point mutations

Motivation Single Nucleotide Variants (SNVs), including somatic point mutations and Single Nucleotide Polymorphisms (SNPs), in noncoding cis-regulatory elements (CREs) can affect gene regulation and lead to disease development (Zhou et al., 2016; Zhang et al., 2014). Others have previously developed methods to identify important clusters of somatic point mutations based on proximity (Weinhold et al., 2014) or the enrichment of inherited risk-SNPs at CREs (Ahmed et al., 2017). Here, we present SMuRF (Significantly Mutated Region Finder), a user-friendly command-line tool to identify these significantly mutated regions from user-defined genomic intervals and SNVs. Results SMuRF identified 72 significantly mutated CREs in liver cancer, including known mutated gene promoters as well as previously unreported regions. Availability The source code for SMuRF is open-source and freely available on GitHub (https://github.com/LupienLabOrganization/SMuRF) under the GNU GPLv3 license. SMuRF is implemented in Bash and R; it runs on any platform with Bash (≥4.1.2), R (≥3.3.0) and BEDTools (≥2.26.0). It requires the following R packages: GenomicRanges, gtools, gplots, ggplot2, data.table, psych, and dplyr. Supplementary Information Supplementary information available at Bioinformatics online. Contact paul.guilhamon@uhnresearch.ca; mlupien@uhnres.utoronto. ca