Miracle-dz/UniFAR-FTUs huggingface dataset:
Cropland data fusion and correction using spatial analysis techniques and the Google Earth Engine

ABSTRACT Accurate regional identification of cropland quantities and spatial distributions is important for cropland monitoring, food security, and sustainable regional development. Various countries and organizations have produced series of land-cover products. However, variability among remote sensors, land-cover classification schemes, and classification methods has resulted in discrepancies. In this study, we develop a novel method to improve cropland data accuracy for the Belt and Road (B&R) region, by fusing and correcting four cropland products: CCI-LC, GFSAD30, MCD12Q1, and FROM-GLC. Spatial analysis techniques are implemented, including climate stratification, consistency assessment, and statistical filtering, to develop training samples for model correction. The Google Earth Engine (GEE) platform and random forest (RF) algorithm are executed with these training samples to correct fused multi-data product and generate a corrected 2015 cropland product. The corrected product indicates that cropland accounts for 14.94% of the B&R region, which is closer to the results found via FAO statistics than the results from any of the four individual land-cover products. On the national scale, the root mean square error between the corrected cropland product quantities and FAO statistics is 11.39% and the correlation coefficient value is 0.77. This indicates that the method exhibits better fitting characteristics. The accuracies of the areas of inconsistency among the four cropland products and our corrected product are assessed using 3112 visually interpreted samples and Google Earth. The overall accuracy of the corrected cropland product is 77.54% in inconsistent areas. The highest accuracy produced by the corrected cropland product indicates the effectiveness of our method, which can rapidly improve cropland data accuracy in heterogeneous regions. Combining the training samples produced by fusing existing cropland products and updating techniques with multi-source remote sensing data from the GEE platform, we foresee potential applications to update global cropland product.
Monte Carlo simulation of liquid water in a magnetic field

A Monte Carlo simulation of liquid water was carried out in order to study the influence of an external magnetic field on the internal energy and heat capacity of pure water. Analysis of the structure of water was made by calculating the radial distribution function of water molecules. A multipeak function of the magnetic effect versus magnetic flux density was found. A significant change in the internal energy and heat capacity occurred at a magnetic flux density of about 0.2 T. In particular, when the magnetic flux density increases, the second peak of the water–water radial distribution function increases markedly. This has been demonstrated by calculations for both 64- and 125-molecule systems.
Radicalization and violent extremism: Perspectives from research on group processes and intergroup relations

Radicalization and violent extremism leading to violent protests, repression, and terrorist attacks constitute important issues in our world. Social psychological group processes and intergroup dynamics play a key role in creating resilience against or facilitating the path towards violent extremism. The present body of work brings together and furthers our understanding of these factors by integrating insights from other fields, most centrally political science. In this introductory article, we summarize the state of the literature and suggest a social psychological research agenda for studying radicalization and violent extremism. This agenda (a) focuses on how the radicalization process commences and festers in ordinary people; (b) is ambitious in bringing research into the real world with protesters, the repressed, and groups engaged in conflict; (c) is pragmatic in measuring outcomes of interest; and (d) applies field experimental methodology, among other methodologies. With this perspective, we argue, social psychology is ideally positioned for a new decade of impactful research into radicalization and violent extremism.
Review of PM2.5 and PM10 Apportionment for Fossil Fuel Combustion and Other Sources by the Chemical Mass Balance Receptor Model

This review examines how the Chemical Mass Balance (CMB) receptor model has been used to quantify source contributions from fossil fuel combustion and other sources to ambient concentrations of PM2.5 and PM10 for urban and regional scales. Nonfossil fuel sources, such as fugitive dust, cooking, vegetative burning, and natural or human-caused biogenics must be considered together with fossil-fuel sources in a CMB analysis to obtain closure for PM2.5 and PM10 mass. CMB analyses in 22 different studies have found fossil fuel combustion to be a large contributor to PM2.5 and PM10 concentrations, with most of the primary contributions originating form diesel- and gasoline-powered vehicle exhaust. Primary contributions from ducted sources, such as coal- and oil-fired power stations, are negligible when these facilities have been modernized with effective pollution controls, but they have been shown to be large contributors without these controls. Secondary sulfates and nitrates from fossil fuel combustion are r.
Partially View-aligned Clustering

In this paper, we study one challenging issue in multi-view data clustering. To be speciﬁc, for two data matrices X (1) and X (2) corresponding to two views, we do not assume that X (1) and X (2) are fully aligned in row-wise. Instead, we assume that only a small portion of the matrices has established the correspondence in advance. Such a partially view-aligned problem (PVP) could lead to the intensive labor of capturing or establishing the aligned multi-view data, which has less been touched so far to the best of our knowledge. To solve this practical and challenging problem, we propose a novel multi-view clustering method termed partially view-aligned clustering (PVC). To be speciﬁc, PVC proposes to use a differentiable surrogate of the non-differentiable Hungarian algorithm and recasts it as a pluggable module. As a result, the category-level correspondence of the unaligned data could be established in a latent space learned by a neural network, while learning a common space across different views using the “aligned” data. Extensive experimental results show promising results of our method in clustering partially view-aligned data.
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data

A common goal in the analysis of neural data is to compress large population recordings into sets of interpretable, low-dimensional latent trajectories. This problem can be approached using Gaussian process (GP)-based methods which provide uncertainty quantiﬁcation and principled model selection. However, standard GP priors do not distinguish between underlying dynamical processes and other forms of temporal autocorrelation. Here, we propose a new family of “dynamical” priors over trajectories, in the form of GP covariance functions that express a property shared by most dynamical systems: temporal non-reversibility. Non-reversibility is a universal signature of autonomous dynamical systems whose state trajectories follow consistent ﬂow ﬁelds, such that any observed trajectory could not occur in reverse. Our new multi-output GP kernels can be used as drop-in replacements for standard kernels in multivariate regression, but also in latent variable models such as Gaussian process factor analysis (GPFA). We therefore introduce GPFADS (Gaussian Process Factor Analysis with Dynamical Structure), which models single-trial neural population activity using low-dimensional, non-reversible latent processes. Unlike previously proposed non-reversible multi-output kernels, ours admits a Kronecker factorization enabling fast and memory-efﬁcient learning and inference. We apply GPFADS to synthetic data and show that it correctly recovers ground truth phase portraits. GPFADS
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting

This work addresses efﬁcient inference and learning in switching Gaussian linear dynamical systems using a Rao-Blackwellised particle ﬁlter and a corresponding Monte Carlo objective. To improve the forecasting capabilities, we extend this classical model by conditionally linear state-to-switch dynamics, while leaving the partial tractability of the conditional Gaussian linear part intact. Furthermore, we use an auxiliary variable approach with a decoder-type neural network that allows for more complex non-linear emission models and multivariate observations. We propose a Monte Carlo objective that leverages the conditional linearity by computing the corresponding conditional expectations in closed-form and a suitable proposal distribution that is factorised similarly to the optimal proposal distribution. We evaluate our approach on several popular time series forecasting datasets as well as image streams of simulated physical systems. Our results show improved forecasting performance compared to other deep state-space model approaches.
Appendix to: BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization

One of the earliest commonly-used packages is Spearmint [94], which implements a variety of modeling techniques such as MCMC hyperparameter sampling and input warping [95]. Spearmint also supports parallel optimization via fantasies, and constrained optimization with the expected improvement and predictive entropy search acquisition functions [31, 38]. Spearmint was among the first libraries to make BO easily accessible to the end user.
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network

Real-world timeseries have complex underlying temporal dynamics and the detection of anomalies is challenging. In this paper, we propose the Temporal Hierarchical One-Class (THOC) network, a temporal one-class classiﬁcation model for timeseries anomaly detection. It captures temporal dynamics in multiple scales by using a dilated recurrent neural network with skip connections. Using multiple hyperspheres obtained with a hierarchical clustering process, a one-class objective called Multiscale Vector Data Description is deﬁned. This allows the temporal dynamics to be well captured by a set of multi-resolution temporal clusters. To further facilitate representation learning, the hypersphere centers are encouraged to be orthogonal to each other, and a self-supervision task in the temporal domain is added. The whole model can be trained end-to-end. Extensive empirical studies on various real-world timeseries demonstrate that the proposed THOC network outperforms recent strong deep learning baselines on timeseries anomaly detection.
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method

Many recent reinforcement learning (RL) methods learn stochastic policies with entropy regularization for exploration and robustness. However, in continuous action spaces, integrating entropy regularization with expressive policies is challenging and usually requires complex inference procedures. To tackle this problem, we propose a novel regularization method that is compatible with a broad range of expressive policy architectures. An appealing feature is that, the estimation of our regularization terms is simple and efﬁcient even when the policy distributions are unknown. We show that our approach can effectively promote the exploration in continuous action spaces. Based on our regularization, we propose an off-policy actor-critic algorithm. Experiments demonstrate that the proposed algorithm outperforms state-of-the-art regularized RL methods in continuous control tasks.
A Catalyst Framework for Minimax Optimization

We introduce a generic two-loop scheme for smooth minimax optimization with strongly-convex-concave objectives. Our approach applies the accelerated proximal point framework (or Catalyst) to the associated dual problem and takes full advantage of existing gradient-based algorithms to solve a sequence of well-balanced strongly-convex-strongly-concave minimax problems. Despite its simplicity, this leads to a family of near-optimal algorithms with improved complexity over all existing methods designed for strongly-convex-concave minimax problems. Additionally, we obtain the ﬁrst variance-reduced algorithms for this class of minimax problems with ﬁnite-sum structure and establish faster convergence rate than batch algorithms. Furthermore, when extended to the nonconvex-concave minimax optimization, our algorithm again achieves the state-of-the-art complexity for ﬁnding a stationary point. We carry out several numerical experiments showcasing the superiority of the Catalyst framework in practice.
The PACE 2020 Parameterized Algorithms and Computational Experiments Challenge: Treedepth

This year’s Parameterized Algorithms and Computational Experiments challenge (PACE 2020) was devoted to the problem of computing the treedepth of a given graph. Altogether 51 participants from 20 teams, 12 countries and 3 continents submitted their implementations to the competition. In this report, we describe the setup of the challenge, the selection of benchmark instances and the ranking of the participating teams. We also brieﬂy discuss the approaches used in the submitted solvers and the diﬀerences in their performance on our benchmark dataset. 2012 ACM Subject Classiﬁcation Theory of computation → Graph algorithms analysis; Theory of computation → Parameterized complexity and exact algorithms
Supplementary Material for Characterizing emergent representations in a space of candidate learning rules for deep networks

We apply singular value decomposition (SVD) to the dataset’s input-output correlation matrix to extract the component of the input-output mapping for different hierarchical levels. Suppose the desired (target) output matrix is given by Y as shown in the main paper Fig. 1b, and input matrix is X where examples are placed in columns. In X, each object’s perceptual representation x (a column vector, where μ = 1.P indexing objects) is encoded by a one-hot input vector (Kronecker delta δμi). Thus, the input-output correlation matrix is Σ = YX . We use SVD on Σ, i.e., Σ = USV , which results in three key elements fully characterizing the input-output mapping to be learned (visualized in Supp. Fig. 1). For the case of hierarchically structured data from a binary tree, the SVD structure conforms to hierarchical distinctions in the dataset [4]. The first element is U, a feature-synthesizer matrix in which each column (a particular semantic dimension or ‘mode’) contains positive (negative) values for semantic features that objects categorized along this mode do (do not) possess. The second element is S, a singular value matrix that has nonzero values only on the diagonal, and these values are arranged in a descending order. And the last element is V , whose rows are object-analyzer vectors, whereby a binary code is assigned to classify objects according to each semantic mode (e.g., the 2nd row of V indicates the first 4 objects are plants, while the last 4 objects are animals, see Fig. 1b in the main paper).
Post-training Iterative Hierarchical Data Augmentation for Deep Networks

In this paper, we propose a new iterative hierarchical data augmentation (IHDA) method to ﬁne-tune trained deep neural networks to improve their generalization performance. The IHDA is motivated by three key insights: (1) Deep networks (DNs) are good at learning multi-level representations from data. (2) Performing data augmentation (DA) in the learned feature spaces of DNs can signiﬁcantly improve their performance. (3) Implementing DA in hard-to-learn regions of a feature space can effectively augment the dataset to improve generalization. Accordingly, the IHDA performs DA in a deep feature space, at level l , by transforming it into a distribution space and synthesizing new samples using the learned distributions for data points that lie in hard-to-classify regions, which is estimated by analyzing the neighborhood characteristics of each data point. The synthesized samples are used to ﬁne-tune the parameters of the subsequent layers. The same procedure is then repeated for the feature space at level l + 1 . To avoid overﬁtting, the concept of dropout probability is employed, which is gradually relaxed as the IHDA works towards high-level feature spaces. IHDA provided a state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet for several DNs, and beat the performance of existing state-of-the-art DA approaches for the same networks on these datasets. Finally, to demonstrate its domain-agnostic properties, we show the signiﬁcant improvements that IHDA provided for a deep neural network on a non-image wearable sensor-based activity recognition benchmark.
Analytical Solution of Nonlinear Fractional Volterra Population Growth Model Using the Modified Residual Power Series Method

In this paper, we introduce an analytical approximate solution of nonlinear fractional Volterra population growth model based on the Caputo fractional derivative and the Riemann fractional integral of the symmetry order. The residual power series method and Adomain decomposition method are implemented to find an approximate solution of this problem. The convergence analysis of the proposed technique has been proved. A numerical example is given to illustrate the method.
The Potts-Ising model for discrete multivariate data

Modeling dependencies in multivariate discrete data is a challenging problem, especially in high dimensions. The Potts model is a versatile such model, suitable when each coordinate is a categorical variable. However, the full Potts model has too many parameters to be accurately ﬁt when the number of categories is large. We introduce a variation on the Potts model that allows for general categorical marginals and Ising-type multivariate dependence. This reduces the number of parameters from Ω( d 2 K 2 ) in the full Potts model to O ( d 2 + Kd ) , where K is the number of categories and d is the dimension of the data. We show that the complexity of ﬁtting this new Potts-Ising model is the same as that of an Ising model. In particular, adopting the neighborhood regression framework, the model can be ﬁt by solving d separate logistic regressions. We demonstrate the ability of the model to capture multivariate dependencies in real data by comparing with existing approaches.
RANet: Region Attention Network for Semantic Segmentation

Recent semantic segmentation methods model the relationship between pixels to construct the contextual representations. In this paper, we introduce the Region Attention Network (RANet), a novel attention network for modeling the relationship between object regions. RANet divides the image into object regions, where we select the representative information. In contrast to the previous methods, RANet configures the information pathways between the pixels in different regions, enabling the region interaction to exchange the regional context for enhancing all of the pixels in the image. We train the construction of object regions, the selection of the representative regional contents, the configuration of information pathways and the context exchange between pixels, jointly, to improve the segmentation accuracy. We extensively evaluate our method on the challenging segmentation benchmarks, demonstrating that RANet effectively helps to achieve the state-of-the-art results. Code will be available at: https://github.com/dingguo1996/RANet.
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering

Compositional generalization is a key challenge in grounding natural language to visual perception. While deep learning models have achieved great success in multimodal tasks like visual question answering, recent studies have shown that they fail to generalize to new inputs that are simply an unseen combination of those seen in the training distribution [6]. In this paper, we propose to tackle this challenge by employing neural factor graphs to induce a tighter coupling between concepts in different modalities (e.g. images and text). Graph representations are inherently compositional in nature and allow us to capture entities, attributes and relations in a scalable manner. Our model ﬁrst creates a multimodal graph, processes it with a graph neural network to induce a factor correspondence matrix, and then outputs a symbolic program to predict answers to questions. Empirically, our model achieves close to perfect scores on a caption truth prediction problem and state-of-the-art results on the recently introduced CLOSURE dataset, improving on the mean overall accuracy across seven compositional templates by 4.77% over previous approaches. 2
Debiasing Averaged Stochastic Gradient Descent to handle missing values

Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning. However, a major caveat of large data is their incompleteness. We propose an averaged stochastic gradient algorithm handling missing values in linear models. This approach has the merit to be free from the need of any data distribution modeling and to account for heterogeneous missing proportion. In both streaming and finite-sample settings, we prove that this algorithm achieves convergence rate of O( 1 n ) at the iteration n, the same as without missing values. We show the convergence behavior and the relevance of the algorithm not only on synthetic data but also on real data sets, including those collected from medical register.
Signal Passing Self-Assembly Simulates Tile Automata

The natural process of self-assembly has been studied through various abstract models due to the abundant applications that beneﬁt from self-assembly. Many of these diﬀerent models emerged in an eﬀort to capture and understand the fundamental properties of diﬀerent physical systems and the mechanisms by which assembly may occur. A newly proposed model, known as Tile Automata, oﬀers an abstract toolkit to analyze and compare the algorithmic properties of diﬀerent self-assembly systems. In this paper, we show that for every Tile Automata system, there exists a Signal-passing Tile Assembly system that can simulate it. Finally, we connect our result with a recent discovery showing that Tile Automata can simulate Amoebot programmable matter systems, thus showing that the Signal-passing Tile Assembly can simulate any Amoebot system.
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

Contextual policies are used in many settings to customize system parameters and actions to the speciﬁcs of a particular setting. In some real-world settings, such as randomized controlled trials or A/B tests, it may not be possible to measure policy outcomes at the level of context—we observe only aggregate rewards across a distribution of contexts. This makes policy optimization much more difﬁcult because we must solve a high-dimensional optimization problem over the entire space of contextual policies, for which existing optimization methods are not suitable. We develop effective models that leverage the structure of the search space to enable contextual policy optimization directly from the aggregate rewards using Bayesian optimization. We use a collection of simulation studies to characterize the performance and robustness of the models, and show that our approach of inferring a low-dimensional context embedding performs best. Finally, we show successful contextual policy optimization in a real-world video bitrate policy problem.