Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Land Use on the Soil Microbial Community in the Songnen Grassland of Northeast China

Land use change obviously changes the plant community composition and soil properties of grasslands and thus affects multiple functions and services of grassland ecosystems. However, the response mechanisms of soil microorganisms, key drivers of the nutrient cycle and other soil functions during changes in grassland use type and associated vegetation are not well understood. In this study, Illumina high-throughput sequencing was used to analyze the changes in the soil microbial community structure of four grassland use types: exclosure (EL), mowed land (ML), grazed land (GL), and farmland (FL) in the Songnen Plain of Northeast China. The results showed that the FL and EL had significantly higher soil total nitrogen (TN) and lower soil electrical conductivity (EC) and pH than GL and ML. In contrast, the GL and ML had higher soil bulk density (BD) and organic matter, respectively, than the other land use types. In addition, the values of the Shannon diversity and Pielou’s evenness indexes were highest in the EL of all the land use types. Based on the high-throughput sequencing results, we observed high levels of α diversity in the FL for both bacteria and fungi. A structural equation model (SEM) revealed that pH and EC had a direct and positive effect on the bacterial community structure and composition. In addition, plant taxonomic diversity (according to the Shannon diversity and Pielou’s evenness indexes) indirectly affected the bacterial community composition via soil pH and EC. Notably, fungal composition was directly and positively correlated with soil nutrients and the value of Pielou’s evenness index changed with land use type. In conclusion, soil properties and/or plant diversity might drive the changes in the soil microbial community structure and composition in different grassland use types.
Impact of Foreign Language Classroom Anxiety on Higher Education Students Academic Success: Mediating Role of Emotional Intelligence and Moderating Influence of Classroom Environment

The current study aimed to investigate the impact of foreign language classroom anxiety (FLCA) on academic success through mediating role of emotional intelligence communication (EIC) and moderating role of class room environment. Due to the disruptive impacts of the COVID-19 pandemic, teaching and learning were moved online nation-wide. The convenient sampling technique was used, for data collection from Chinese university students. There was a total of 615 students that participated in the survey and data gathered in 5 months from November 2021 till March 2022. Covariance-based structural equation modeling (CB-SEM) in SPSS V.25 and AMOS V.22 was used to assess model fitness and hypotheses, as well as construct reliability and validity of the measurement model. The results revealed that FLCA is negatively and significantly influence students’ academic success. Furthermore, EIC as a mediator significantly and positively mediates the relationship between FLCA and academic success. The current study shows that emotional intelligence has the ability to reduce students’ foreign language anxiety and so improve their language skills. Lastly, classroom environment positively and significantly moderates the relationship between FLCA and emotional intelligence communication.
Analysis of the Effect of SNAI Family in Breast Cancer and Immune Cell

SNAI family members are transcriptional repressors that induce epithelial-mesenchymal transition during biological development. SNAIs both have tumor-promoting and tumor-inhibiting effect. There are key regulatory effects on tumor onset and development, and patient prognosis in infiltrations of immune cell and tumor microenvironmental changes. However, the relationships between SNAIs and immune cell infiltration remain unclear. We comprehensively analyzed the roles of SNAIs in cancer. We used Oncomine and TCGA data to analyze pan-cancer SNAI transcript levels. By analyzing UALCAN data, we found correlations between SNAI transcript levels and breast cancer patient characteristics. Kaplan–Meier plotter analysis revealed that SNAI1 and SNAI2 have a bad prognosis, whereas SNAI3 is the opposite. Analysis using the cBio Cancer Genomics Portal revealed alterations in SNAIs in breast cancer subtypes. Gene Ontology analysis and gene set enrichment analysis were used to analyze differentially expressed genes related to SNAI proteins in breast cancer. We used TIMER to analyze the effects of SNAI transcript levels, mutations, methylation levels, and gene copy number in the infiltration of immune cell. Further, we found the relationships between immune cell infiltration, SNAI expression levels, and patient outcomes. To explore how SNAI proteins affect immune cell, we further studied the correlations between immunomodulator expression, chemokine expression, and SNAI expression. The results showed that SNAI protein levels were correlated with the expression of several immunomodulators and chemokines. Through analysis of PharmacoDB data, we identified antitumor drugs related to SNAI family members and analyzed their IC50 effects on various breast cancer cell lines. In summary, our study revealed that SNAI family members regulate different immune cells infiltrations by gene copy number, mutation, methylation, and expression level. SNAI3 and SNIA1/2 have opposite regulatory effects. They all play a key role in tumor development and immune cell infiltration, and can provide a potential target for drug therapy.
Multi-objective maintenance strategy for in-service corroding pipelines using genetic algorithms

Abstract The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the optimisation are the maximum annual conditional probabilities of small leak and burst, respectively, of all the pipe joints included in the pipeline segment over a predefined time horizon, and the total present-value cost of corrosion repair. The allowable annual probabilities of small leak and burst, and the annual repair budget are treated as constraints in the optimisation. The proposed optimal maintenance strategy is illustrated using a natural gas pipeline segment consisting of 90 corroding pipe joints. The analysis results indicate that a diverse set of solutions are included in the obtained Pareto front, which allow the decision-maker to select the maintenance plan achieving the desired tradeoff between the reliability and cost. The approach presented in this study can be incorporated in the practical optimal maintenance planning of corroding pipelines subjected to safety and resource constraints.
Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach

The REINFORCE algorithm from Williams is popular in policy gradient (PG) for solving reinforcement learning (RL) problems. Meanwhile, the theoretical form of PG is from Sutton et al. Although both formulae prescribe PG, their precise connections are not yet illustrated. Recently, Nota and Thomas (2020) have found that the ambiguity causes implementation errors. Motivated by the ambiguity and implementation incorrectness, we study PG from a perturbation perspective. In particular, we derive PG in a unified framework, precisely clarify the relation between PG implementation and theory, and echo back the findings by Nota and Thomas. Diving into factors contributing to empirical successes of the existing erroneous implementations, we find that small approximation error and the experience replay mechanism play critical roles.
Be Like Water: Adaptive Floating Point for Machine Learning

In the pursuit of optimizing memory and compute density to accelerate machine learning applications, reduced precision training and inference has been an active area of research. While some approaches selectively apply low precision computations, this may require costly off-chip data transfers or mixed precision support. In this paper, we propose a novel numerical representation, Adaptive Floating Point (AFP), that dynamically adjusts to the characteristics of deep learning data. AFP requires no changes to the model topology, requires no additional training, and applies to all layers of DNN models. We evaluate AFP on a spectrum of representative models in computer vision and NLP, and show that our technique enables ultra-low precision inference of deep learning models while providing accuracy comparable to full precision inference. By dynamically adjusting to ML data, AFP increases memory density by 1.6x, 1.6x, and 3.2x and compute density by 4x, 1.3x, and 12x when compared to BFP, BFloat16, and FP32.
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning

Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem. Recent works based on this paradigm have achieved state-of-the-art results in several of the mostly deterministic offline Atari and D4RL benchmarks. However, because these methods jointly model the states and actions as a single sequencing problem, they struggle to disentangle the effects of the policy and world dynamics on the return. Thus, in adversarial or stochastic environments, these methods lead to overly optimistic behavior that can be dangerous in safety-critical systems like autonomous driving. In this work, we propose a method that addresses this optimism bias by explicitly disentangling the policy and world models, which allows us at test time to search for policies that are robust to multiple possible futures in the environment. We demonstrate our method's superior performance on a variety of autonomous driving tasks in simulation.
Actor-Critic based Improper Reinforcement Learning

We consider an improper reinforcement learning setting where a learner is givenM base controllers for an unknown Markov decision process, and wishes to combine them optimally to produce a potentially new controller that can outperform each of the base ones. This can be useful in tuning across controllers, learnt possibly in mismatched or simulated environments, to obtain a good controller for a given target environment with relatively few trials. Towards this, we propose two algorithms: (1) a Policy Gradient-based approach; and (2) an algorithm that can switch between a simple Actor-Critic (AC) based scheme and a Natural Actor-Critic (NAC) scheme depending on the available information. Both algorithms operate over a class of improper mixtures of the given controllers. For the first case, we derive convergence rate guarantees assuming access to a gradient oracle. For the AC-based approach we provide convergence rate guarantees to a stationary point in the basic AC case and to a global optimum in the NAC case. Numerical results on (i) the standard control theoretic benchmark of stabilizing an cartpole; and (ii) a constrained queueing task show that our improper policy optimization algorithm can stabilize the system even when the base policies at its disposal are unstable.
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem. In this paper, we (1) present a novel, flexible and efficient method, namely advanced max pressure (Advanced-MP), taking both running and queuing vehicles into consideration to decide whether to change current signal phase; (2) inventively design the traffic movement representation with the efficient pressure and effective running vehicles from Advanced-MP, namely advanced traffic state (ATS); and (3) develop a reinforcement learning (RL) based algorithm template, called Advanced-XLight, by combining ATS with the latest RL approaches, and generate two RL algorithms, namely"Advanced-MPLight"and"Advanced-CoLight"from Advanced-XLight. Comprehensive experiments on multiple real-world datasets show that: (1) the Advanced-MP outperforms baseline methods, and it is also efficient and reliable for deployment; and (2) Advanced-MPLight and Advanced-CoLight can achieve the state-of-the-art.
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution

Task-free continual learning (CL) aims to learn a non-stationary data stream without explicit task definitions and not forget previous knowledge. The widely adopted memory replay approach could gradually become less effective for long data streams, as the model may memorize the stored examples and overfit the memory buffer. Second, existing methods overlook the high uncertainty in the memory data distribution since there is a big gap between the memory data distribution and the distribution of all the previous data examples. To address these problems, for the first time, we propose a principled memory evolution framework to dynamically evolve the memory data distribution by making the memory buffer gradually harder to be memorized with distributionally robust optimization (DRO). We then derive a family of methods to evolve the memory buffer data in the continuous probability measure space with Wasserstein gradient flow (WGF). The proposed DRO is w.r. t the worst-case evolved memory data distribution, thus guarantees the model performance and learns significantly more robust features than existing memory-replay-based methods. Extensive experiments on existing benchmarks demonstrate the effectiveness of the proposed methods for alleviating forgetting. As a by-product of the proposed framework, our method is more robust to adversarial examples than existing task-free CL methods. Code is available on GitHub \url{https://github.com/joey-wang123/DRO-Task-free}
Injecting Logical Constraints into Neural Networks via Straight-Through Estimators

Injecting discrete logical constraints into neural network learning is one of the main challenges in neuro-symbolic AI. We find that a straight-through-estimator, a method introduced to train binary neural networks, could effectively be applied to incorporate logical constraints into neural network learning. More specifically, we design a systematic way to represent discrete logical constraints as a loss function; minimizing this loss using gradient descent via a straight-through-estimator updates the neural network's weights in the direction that the binarized outputs satisfy the logical constraints. The experimental results show that by leveraging GPUs and batch training, this method scales significantly better than existing neuro-symbolic methods that require heavy symbolic computation for computing gradients. Also, we demonstrate that our method applies to different types of neural networks, such as MLP, CNN, and GNN, making them learn with no or fewer labeled data by learning directly from known constraints.
Residual-Based Sampling for Online Outlier-Robust PCA

Outlier-robust principal component analysis (OR-PCA) has been broadly applied in scientific discovery in the last decades. In this paper, we study online ORPCA, an important variant that addresses the practical challenge that the data points arrive in a sequential manner and the goal is to recover the underlying subspace of the clean data with one pass of the data. Our main contribution is the first provable algorithm that enjoys comparable recovery guarantee to the best known batch algorithm, while significantly improving upon the state-of-the-art online ORPCA algorithms. The core technique is a robust version of the residual norm which, informally speaking, leverages not only the importance of a data point, but also how likely it behaves as an outlier.
Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer

Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The task is to predict the reactants given a core product. With the advancement of machine learning, computer-aided synthesis planning has gained increasing interest. Numerous methods were proposed to solve this problem with different levels of dependency on additional chemical knowledge. In this paper, we propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction without relying on any cheminformatics tools for molecule editing. Via the proposed local attention head, the model can jointly encode the molecular sequence and graph, and efficiently exchange information between the local reactive region and the global reaction context. Retroformer reaches the new state-of-the-art accuracy for the end-to-end template-free retrosynthesis, and improves over many strong baselines on better molecule and reaction validity. In addition, its generative procedure is highly interpretable and controllable. Overall, Retroformer pushes the limits of the reaction reasoning ability of deep generative models.
Disentangling Disease-related Representation from Obscure for Disease Prediction

Disease-related representations play a crucial role in image-based disease prediction such as cancer diagnosis, due to its considerable generalization capacity. However, it is still a challenge to identify lesion characteristics in obscured images, as many lesions are obscured by other tissues. In this paper, to learn the representations for identifying obscured lesions, we propose a disentanglement learning strategy under the guidance of alpha blending generation in an encoder-decoder framework (DAB-Net). Specifically, we take mammo-gram mass benign/malignant classification as an example. In our framework, composite obscured mass images are generated by alpha blending and then explicitly disentangled into disease-related mass features and interference glands features. To achieve disentanglement learning, features of these two parts are decoded to reconstruct the mass and the glands with corresponding reconstruction losses, and only disease-related mass features are fed into the classifier for disease prediction. Experimental results on one public dataset DDSM and three in-house datasets demonstrate that the proposed strategy can achieve state-of-the-art performance. DAB-Net achieves substantial improvements of 3.9% ∼ 4.4% AUC in obscured cases. Besides, the visualization analysis shows the model can better disentangle the mass and glands in the obscured image, suggesting the effectiveness of our solution in exploring the hidden characteristics in this challenging problem.
Role-based Multiplex Network Embedding

In recent years, multiplex network embedding has received great attention from researchers. However, existing multiplex network embedding meth-ods neglect structural role information, which can be used to determine the structural similarity be-tween nodes. To overcome this shortcoming, this work proposes a simple, effective, role-based embedding method for multiplex networks, called RMNE. The RMNE uses the structural role information of nodes to preserve the structural similarity between nodes in the entire multiplex network. Speciﬁcally, a role-modiﬁed random walk is designed to generate node sequences of each node, which can capture both the within-layer neighbors, structural role members, and cross-layer structural role members of a node. Additionally, the variant of RMNE extends the existing collaborative embedding method by unifying the structural role information into our method to obtain the role-based node representations. Finally, the proposed meth-ods are evaluated on the network reconstruction, node classiﬁcation, link prediction, and multi-class edge classiﬁcation tasks. The experimental results on eight public, real-world multiplex networks demonstrate that the proposed methods outperform state-of-the-art baseline methods.
SpaceMAP: Visualizing High-Dimensional Data by Space Expansion

Dimensionality reduction (DR) of high-dimensional data is of theoretical and practical interest in machine learning. However, there exist intriguing, non-intuitive discrepancies between the geometry of high-and low-dimensional space. We look into such discrepancies and propose a novel visualization method called Space-based Manifold Approximation and Projection (SpaceMAP). Our method establishes an analytical transformation on distance metrics between spaces to address the “crowding problem” in DR. With the proposed equivalent extended distance (EED), we are able to match the capacity of high-and low-dimensional space in a principled manner. To handle complex data with different manifold properties, we propose hierarchical manifold approximation to model the similarity function in a data-specific manner. We evaluated SpaceMAP on a range of synthetic and real datasets with varying manifold properties, and demonstrated its excellent performance in comparison with classical and state-of-the-art DR methods. In particular, the concept of space expansion provides a generic framework for understanding nonlinear DR methods including the popular t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP).
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity

In distributionally robust Bayesian optimization (DRBO), an exact computation of the worst-case expected value requires solving an expensive convex optimization problem. We develop a fast approximation of the worst-case expected value based on the notion of worst-case sensitivity that caters to arbitrary convex distribution distances. We provide a regret bound for our novel DRBO algorithm with the fast approximation, and empirically show it is competitive with that using the exact worst-case expected value while incur-ring signiﬁcantly less computation time. In order to guide the choice of distribution distance to be used with DRBO, we show that our approximation implicitly optimizes an objective close to an interpretable risk-sensitive value.
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning

In cross-device Federated Learning (FL), the communication cost of transmitting full-precision models between edge devices and a central server is a significant bottleneck, due to expensive, unreliable, and low-bandwidth wireless connections. As a solution, we propose a novel FL framework named QSFL , towards optimizing FL uplink (client-to-server) communication at both client and model levels . At the client level, we design a Qualification Judgment (QJ) algorithm to sample high-qualification clients to upload models. At the model level, we explore a Sparse Cyclic Sliding Segment (SCSS) algorithm to further compress transmitted models. We prove that QSFL can converge over wall-to-wall time, and develop an optimal hyperparameter searching algorithm based on theoretical analysis to enable QSFL to make the best trade-off between model accuracy and communication cost. Experimental results show that QSFL achieves state-of-the-art compression ratios with marginal model accuracy degradation.
A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms

In modern machine learning systems, distributed algorithms are deployed across applications to ensure data privacy and optimal utilization of computational resources. This work offers a fresh perspective to model, analyze, and design distributed optimization algorithms through the lens of stochastic multi-rate feedback control. We show that a substantial class of distributed algorithms—including popular Gradient Tracking for decentralized learning, and FedPD and Scaffold for federated learning—can be modeled as a certain discrete-time stochastic feedback-control system, possibly with multiple sampling rates. This key observation allows us to develop a generic framework to analyze the convergence of the entire algorithm class. It also enables one to easily add desirable features such as differential privacy guarantees, or to deal with practical settings such as partial agent participation, communication compression, and imperfect communication in algorithm design and analysis.