Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Text Style Transfer Based on Deep Learning

Text style transfer is a hot issue in recent natural language processing,which mainly studies the text to adapt to different specific situations, audiences and purposes by making some changes. The style of the text usually includes many aspects such as morphology, grammar, emotion, complexity, fluency, tense, tone and so on. In the traditional text style transfer model, the text style is generally relied on by experts knowledge and hand-designed rules, but with the application of deep learning in the field of natural language processing, the text style transfer method based on deep learning Started to be heavily researched. In recent years, text style transfer is becoming a hot issue in natural language processing research. This article summarizes the research on the text style transfer model based on deep learning in recent years, and summarizes, analyzes and compares the main research directions and progress. In addition, the article also introduces public data sets and evaluation indicators commonly used for text style transfer. Finally, the existing characteristics of the text style transfer model are summarized, and the future development trend of the text style transfer model based on deep learning is analyzed and forecasted.
Generalization of CNOT-based Discrete Circular Quantum Walk: Simulation and Effect of Gate Errors

We investigate the counterparts of random walk in universal quantum computing and their implementation using standard quantum circuits. Quantum walk have been recently well investigated for traversing graphs with certain oracles. We focus our study on traversing a 1-D graph, namely a circle, and show how to implement discrete circular quantum walk in quantum circuits built with universal CNOT and single quit gates. We review elementary quantum gates and circuit decomposition and propose a a generalized version of the all CNOT based quantum discrete circular walk. We simulated these circuits on an IBM quantum supercomputer London IBM-Q with 5 qubits. This quantum computer has non perfect gates based on superconducting qubits, therefore we analyze the impact of errors on the fidelity of the Walker circuit.
How eigenmode self-interaction affects zonal flows and convergence of tokamak core turbulence with toroidal system size

Self-interaction is the process by which a microinstability eigenmode that is extended along the direction parallel to the magnetic field interacts non-linearly with itself. This effect is particularly significant in gyrokinetic simulations accounting for kinetic passing electron dynamics and is known to generate stationary $E\times B$ zonal flow shear layers at radial locations near low-order mode rational surfaces (Weikl et al. Phys. Plasmas, vol. 25, 2018, 072305). We find that self-interaction, in fact, plays a very significant role in also generating fluctuating zonal flows, which is critical to regulating turbulent transport throughout the radial extent. Unlike the usual picture of zonal flow drive in which microinstability eigenmodes coherently amplify the flow via modulational instabilities, the self-interaction drive of zonal flows from these eigenmodes are uncorrelated with each other. It is shown that the associated shearing rate of the fluctuating zonal flows therefore reduces as more toroidal modes are resolved in the simulation. In simulations accounting for the full toroidal domain, such an increase in the density of toroidal modes corresponds to an increase in the toroidal system size, leading to a finite system size effect that is distinct from the well-known profile shearing effect.
Geoopt: Riemannian Optimization in PyTorch

Geoopt is a research-oriented modular open-source package for Riemannian Optimization in PyTorch. The core of Geoopt is a standard Manifold interface that allows for the generic implementation of optimization algorithms. Geoopt supports basic Riemannian SGD as well as adaptive optimization algorithms. Geoopt also provides several algorithms and arithmetic methods for supported manifolds, which allow composing geometry-aware neural network layers that can be integrated with existing models.
Spatial and interannual patterns in growth of an exploited coral‐reef fish

Patterns of growth in an exploited reef fish Lethrinus miniatus were examined over 5 years (1995–1999) at two spatial scales: (1) among regions of the Great Barrier Reef (GBR) separated by >100 km and (2) among reefs within each of these regions, separated by ≤10 km. Mean annual growth of L. miniatus varied significantly among years, but this variation was consistent among ages and regions, indicating that factors that influence temporal patterns in growth were not age-specific and operated at relatively large spatial scales. Significant variation in growth was also observed among some reefs within regions, although the greatest variation was among regions. The average maximum fork length (LF∞) and average maximum mass (M∞) varied significantly among regions, suggesting that productivity of L. miniatus is likely to vary among regions of the GBR. There was also significantly greater mass of fish for a given LF in two regions, which magnified the regional differences in M∞. The observed temporal and spatial variation in growth highlighted the importance of a multi-scale approach to population studies and assessment of fish stocks.
Properties of triangulated and quotient
categories arising from n-Calabi–Yau triples

Let $k$ be a field, $n\geq 3$ an integer and $\mathcal{T}$ a $k$-linear triangulated category with a triangulated subcategory $\mathcal{T}^{fd}$ and a subcategory $\mathcal{M}=\text{add}(M)$ such that $(\mathcal{T}, \mathcal{T}^{fd}, \mathcal{M})$ is an $n$-Calabi-Yau triple. For every integer $m$ and every object $X$ in $\mathcal{T}$, there is a unique, up to isomorphism, truncation triangle of the form \begin{align*} X^{\leq m}\rightarrow X\rightarrow X^{\geq m+1}\rightarrow\Sigma X^{\leq m}, \end{align*} with respect to the $t$-structure $((\Sigma^{ -m}\mathcal{M})^{\perp_\mathcal{T}})$. In this paper, we prove some properties of the triangulated categories $\mathcal{T}$ and $\mathcal{T}/\mathcal{T}^{fd}$. Our first result gives a relation between the Hom-spaces in these categories, using limits and colimits. Our second result is a Gap Theorem in $\mathcal{T}$, showing when the truncation triangles split. Moreover, we apply our two theorems to present an alternative proof to a result by Guo, originally stated in a more specific setup of dg $k$-algebras $A$ and subcategories of the derived category of dg $A$-modules. This proves that $\mathcal{T}/\mathcal{T}^{fd}$ is Hom-finite and $(n-1)$-Calabi-Yau, its object $M$ is $(n-1)$-cluster tilting and the endomorphism algebras of $M$ over $\mathcal{T}$ and over $\mathcal{T}/\mathcal{T}^{fd}$ are isomorphic. Note that these properties make $\mathcal{T}/\mathcal{T}^{fd}$ a generalisation of the cluster category.
Photometry and performance of SPECULOOS-South

SPECULOOS-South, an observatory composed of four independent 1-m robotic telescopes, located at ESO Paranal, Chile, started scientific operation in 2019 January. This Southern hemisphere facility operates as part of the Search for Habitable Planets EClipsing ULtra-cOOl Stars (SPECULOOS), an international network of 1-m-class telescopes surveying for transiting terrestrial planets around the nearest and brightest ultracool dwarfs (UCDs). To automatically and efficiently process the observations of SPECULOOS-South, and to deal with the specialized photometric requirements of UCD targets, we present our automatic pipeline. This pipeline includes an algorithm for automated differential photometry and an extensive correction technique for the effects of telluric water vapour, using ground measurements of the precipitable water vapour. Observing very red targets in the near-infrared can result in photometric systematics in the differential light curves, related to the temporally-varying, wavelength-dependent opacity of the Earth’s atmosphere. These systematics are sufficient to affect the daily quality of the light curves, the longer time-scale variability study of our targets and even mimic transit-like signals. Here we present the implementation and impact of our water vapour correction method. Using the 179 nights and 98 targets observed in the I + z′ filter by SPECULOOS-South since 2019 January, we show the impressive photometric performance of the facility (with a median precision of ∼1.5 mmag for 30-min binning of the raw, non-detrended light curves) and assess its detection potential. We compare simultaneous observations with SPECULOOS-South and TESS, to show that we readily achieve high-precision, space-level photometry for bright, UCDs, highlighting SPECULOOS-South as the first facility of its kind.
TAG : Type Auxiliary Guiding for Code Comment Generation

Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e.g., operator, string, etc. However, introducing the type information into the existing framework is non-trivial due to the hierarchical dependence among the type information. In order to address the issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for the code comment generation task which considers the source code as an N-ary tree with type information associated with each node. Specifically, our framework is featured with a Type-associated Encoder and a Type-restricted Decoder which enables adaptive summarization of the source code. We further propose a hierarchical reinforcement learning method to resolve the training difficulties of our proposed framework. Extensive evaluations demonstrate the state-of-the-art performance of our framework with both the auto-evaluated metrics and case studies.
A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure

Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years. However, all the previous studies on text-level discourse parsing adopt bottom-up approaches, which much limit the DRS determination on local information and fail to well benefit from global information of the overall discourse. In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for text-level DRS parsing. On the basis, we propose a top-down neural architecture toward text-level DRS parsing. In particular, we cast discourse parsing as a recursive split point ranking task, where a split point is classified to different levels according to its rank and the elementary discourse units (EDUs) associated with it are arranged accordingly. In this way, we can determine the complete DRS as a hierarchical tree structure via an encoder-decoder with an internal stack. Experimentation on both the English RST-DT corpus and the Chinese CDTB corpus shows the great effectiveness of our proposed top-down approach towards text-level DRS parsing.
A Framework for Designing and Evaluating Solar Flare Forecasting Systems

Disturbances in space weather can negatively affect several fields, including aviation and aerospace, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are the most significant events that can affect the Earth's atmosphere, thus leading researchers to drive efforts on their forecasting. The related literature is comprehensive and holds several systems proposed for flare forecasting. However, most techniques are tailor-made and designed for specific purposes, not allowing researchers to customize them in case of changes in data input or in the prediction algorithm. This paper proposes a framework to design, train, and evaluate flare prediction systems which present promising results. Our proposed framework involves model and feature selection, randomized hyper-parameters optimization, data resampling, and evaluation under operational settings. Compared to baseline predictions, our framework generated some proof-of-concept models with positive recalls between 0.70 and 0.75 for forecasting $\geq M$ class flares up to 96 hours ahead while keeping the area under the ROC curve score at high levels.
Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization

We study the problem of learning exploration-exploitation strategies that effectively adapt to dynamic environments, where the task may change over time. While RNN-based policies could in principle represent such strategies, in practice their training time is prohibitive and the learning process often converges to poor solutions. In this paper, we consider the case where the agent has access to a description of the task (e.g., a task id or task parameters) at training time, but not at test time. We propose a novel algorithm that regularizes the training of an RNN-based policy using informed policies trained to maximize the reward in each task. This dramatically reduces the sample complexity of training RNN-based policies, without losing their representational power. As a result, our method learns exploration strategies that efficiently balance between gathering information about the unknown and changing task and maximizing the reward over time. We test the performance of our algorithm in a variety of environments where tasks may vary within each episode.
Predation by Atractosteus tropicus on Bufo marinus and its possible affect on population in a lowland tropical wet rainforest

Fish predation can affect amphibian populations. Most examples report invasive fish species and their negative effect on specific amphibians. Here we provide an occurrence of natural fish predation by the tropical gar Atractosteus tropicus on the cane toad Bufo marinus. We observed physical damage caused by A. tropicus attacking B. marinus. When adult B. marinus were removed from the population subadult toads refilled the adults niche, resupplementing the population. Local observations of predation by fish on amphibians are important to differentiate natural population fluctuations from amphibian population declines.
Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC

In this paper, a data-driven approach is proposed to jointly design the common sensing (measurement) matrix and jointly support recovery method for complex signals, using a standard deep auto-encoder for real numbers. The auto-encoder in the proposed approach includes an encoder that mimics the noisy linear measurement process for jointly sparse signals with a common sensing matrix, and a decoder that approximately performs jointly sparse support recovery based on the empirical covariance matrix of noisy linear measurements. The proposed approach can effectively utilize the feature of common support and properties of sparsity patterns to achieve high recovery accuracy, and has significantly shorter computation time than existing methods. We also study an application example, i.e., device activity detection in Multiple-Input Multiple-Output (MIMO)-based grant-free random access for massive machine type communications (mMTC). The numerical results show that the proposed approach can provide pilot sequences and device activity detection with better detection accuracy and substantially shorter computation time than well-known recovery methods.
On Deep Learning for Radio Resource Management in A Non-stationary Radio Environment

This paper studies practical limitations of learning methods for resource management in non-stationary radio environment. We propose two learning models carefully designed to support rate maximization objective under user mobility. We study the effects of practical systems such as latency and reliability on the rate maximization with deep learning models. For common testing in the non-stationary environment we present a generic dataset generation method to benchmark across different learning models versus traditional optimal resource management solutions. Our results indicate that learning models have practical challenges related to training limiting their applications. The models need environment-specific design to reach the accuracy of an optimal algorithm.
Pay as You Go: A Generic Crypto Tolling Architecture

The imminent pervasive adoption of vehicular communication, either localized (ETSI ITS G5, IEEE WAVE or cellular D2D), long range (5G) or hybrid, will foster a richer service ecosystem for vehicular applications. The appearance of new cryptography-based solutions envisaging digital identity and currency exchange are set to stem new approaches for existing and future challenges. This article presents a novel tolling architecture that harnesses the availability of 5G C-V2X connectivity for open road tolling using smartphones, IOTA as the digital currency, and Hyperledger Indy for identity validation. An experimental feasibility analysis is used to validate the proposed architecture for secure, private, and convenient electronic toll payment.
Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization

The Surface Realization Shared Tasks of 2018 and 2019 were Natural Language Generation shared tasks with the goal of exploring approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages. In the 2018 shared task there was very little difference in the absolute performance of systems trained with and without additional, synthetically created data, and a new rule prohibiting the use of synthetic data was introduced for the 2019 shared task. Contrary to the findings of the 2018 shared task, we show, in experiments on the English 2018 dataset, that the use of synthetic data can have a substantial positive effect – an improvement of almost 8 BLEU points for a previously state-of-the-art system. We analyse the effects of synthetic data, and we argue that its use should be encouraged rather than prohibited so that future research efforts continue to explore systems that can take advantage of such data.
Temporal Event Segmentation using Attention-based Perceptual Prediction Model for Continual Learning

Temporal event segmentation of a long video into coherent events requires a high level understanding of activities' temporal features. The event segmentation problem has been tackled by researchers in an offline training scheme, either by providing full, or weak, supervision through manually annotated labels or by self-supervised epoch based training. In this work, we present a continual learning perceptual prediction framework (influenced by cognitive psychology) capable of temporal event segmentation through understanding of the underlying representation of objects within individual frames. Our framework also outputs attention maps which effectively localize and track events-causing objects in each frame. The model is tested on a wildlife monitoring dataset in a continual training manner resulting in $80\%$ recall rate at $20\%$ false positive rate for frame level segmentation. Activity level testing has yielded $80\%$ activity recall rate for one false activity detection every 50 minutes.
Direct numerical simulation of the combustion of a suspended droplet in normal gravity

DropletSMOKE++ is a multiphase CFD framework based on OpenFOAM, originally developed and validated for suspended droplets evaporation in a gravity field and convective conditions. In this work the solver is further extended to account for gas-phase combustion introducing: (i) an operator-splitting methodology to efficiently solve the gas-phase chemistry with large kinetic mechanisms, (ii) a model for the flame radiation and (iii) a double vaporization model to account for possible boiling. This allows to simulate the combustion of suspended fuel droplets in normal gravity with a detailed description of the gas-phase chemistry, representing the novelty and the main objective of this work. The numerical model is applied to simulate the vaporization, ignition and combustion of a methanol droplet suspended on a quartz fiber at different oxygen concentrations. The numerical results are compared with recent experimental data, showing a satisfactory agreement in terms of diameter decay, radial temperature profiles and sensitivity to the oxygen concentration in the gas-phase. In particular, the burning rate is found to be significantly affected by thermal conduction from the fiber, due to its relatively large size and the high temperatures involved in the combustion process. On the other hand the fiber perturbs the flame itself, providing a partial quenching close to its surface. The droplet combustion behavior has been compared to the one predicted in microgravity conditions, evidencing a lower standoff ratio, a higher flame temperature and an intense internal circulation. The gas-phase chemistry has been analyzed in terms of distribution of the main species in the gas phase, showing a local accumulation of (i) intermediate oxidation products at the fiber (due to the quenching) and (ii) water at the surface, which partially condenses on the droplet surface affecting the vaporization rate.
Event Cartography: Latent Point Process Embeddings

Many important phenomena arise naturally as temporal point processes with different types of events inﬂuencing future events in complex ways. Estimation of multivariate point processes is a notorious proposition. We take inspiration from spatiotemporal point processes, where relationships depend only on relative distances in real Euclidean space, to suggest embedding arbitrary event types in a latent space. We demonstrate that we can simultaneously learn this embedding and a point process model to recover relationships among events
AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance

Deep learning inference is increasingly run at the edge. As the programming and system stack support becomes mature, it enables acceleration opportunities within a mobile system, where the system performance envelope is scaled up with a plethora of programmable co-processors. Thus, intelligent services designed for mobile users can choose between running inference on the CPU or any of the co-processors on the mobile system, or exploiting connected systems, such as the cloud or a nearby, locally connected system. By doing so, the services can scale out the performance and increase the energy efficiency of edge mobile systems. This gives rise to a new challenge - deciding when inference should run where. Such execution scaling decision becomes more complicated with the stochastic nature of mobile-cloud execution, where signal strength variations of the wireless networks and resource interference can significantly affect real-time inference performance and system energy efficiency. To enable accurate, energy-efficient deep learning inference at the edge, this paper proposes AutoScale. AutoScale is an adaptive and light-weight execution scaling engine built upon the custom-designed reinforcement learning algorithm. It continuously learns and selects the most energy-efficient inference execution target by taking into account characteristics of neural networks and available systems in the collaborative cloud-edge execution environment while adapting to the stochastic runtime variance. Real system implementation and evaluation, considering realistic execution scenarios, demonstrate an average of 9.8 and 1.6 times energy efficiency improvement for DNN edge inference over the baseline mobile CPU and cloud offloading, while meeting the real-time performance and accuracy requirement.