Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissecting phylogenetic fuzzy weighting: theory and application in metacommunity phylogenetics

Metacommunity phylogenetics aims at evaluating environmental and/or historical factors driving clade distribution. Phylogenetic fuzzy weighting (PFW) describes clade distribution across metacommunities based on fuzzy sets defined by phylogenetic relatedness among species. The method enables analysing environmental and/or biogeographic determinants of clade distribution. PFW also offers an exploratory tool for visualizing clade distribution via Principal Coordinates of Phylogenetic Structure (PCPS). In this article, we describe the theoretical properties and biological backgrounds of PFW and evaluate its statistical performance (type I error and statistical power) in assessing environmental and phylogenetic determinants of species distribution in comparison with other phylobetadiversity methods (COMDIST, COMDISTNT, Rao's H and UniFrac). The statistical performance of PFW and the other phylobetadiversity metrics was tested by (i) simulating metacommunities under different species assembly scenarios (species distribution influenced or not by environment and/or phylogeny), niche breadth tolerance and species pool sizes; (ii) submitting community matrices to PFW and deriving pairwise phylogenetic dissimilarities between communities (DP) and PCPS; (iii) submitting these metrics and the other phylobetadiversity methods to different analytical approaches (Mantel test, regression on dissimilarity matrices – ADONIS, and GLM) to evaluate the influence of environment and phylogeny on metacommunity phylogenetic structure; and (iv) estimating type I error and power estimates via alternative permutation procedures. Results demonstrated that PFW provides robust assessment of environmental and phylogenetic drivers of species distribution across metacommunities. Although all methods had acceptable type I error for both Mantel test and ADONIS, only PFW showed acceptable power for both tests. Rao's H had acceptable power only for Mantel test, while COMDIST had acceptable power only for ADONIS. COMDISTNT and UniFrac showed poor statistical performance for both tests. Conversely, GLM had acceptable power only for the first PCPS. Performing ADONIS on DP provides a robust overall assessment of environmental and phylogenetic drivers of species distribution. On the other hand, performing PCPS analysis after rejecting the null hypotheses via ADONIS allows identifying the phylogenetic nodes mostly associated with environmental gradients. PFW enables synthesizing and analysing phylogenetic patterns in metacommunities, allowing attaining a more complete portrait of ecological and evolutionary drivers of species distribution.
A Distance-Preserving Matrix Sketch

Abstract Visualizing very large matrices involves many formidable problems. Various popular solutions to these problems involve sampling, clustering, projection, or feature selection to reduce the size and complexity of the original task. An important aspect of these methods is how to preserve relative distances between points in the higher-dimensional space after reducing rows and columns to fit in a lower dimensional space. This aspect is important because conclusions based on faulty visual reasoning can be harmful. Judging dissimilar points as similar or similar points as dissimilar on the basis of a visualization can lead to false conclusions. To ameliorate this bias and to make visualizations of very large datasets feasible, we introduce two new algorithms that, respectively, select a subset of rows and columns of a rectangular matrix. This selection is designed to preserve relative distances as closely as possible. We compare our matrix sketch to more traditional alternatives on a variety of artificial and real datasets. Supplementary materials for this article are available online.
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget

BigGAN is the state-of-the-art in high-resolution image generation, successfully leveraging advancements in scalable computing and theoretical understanding of generative adversarial methods to set new records in conditional image generation. A major part of BigGAN's success is due to its use of large mini-batch sizes during training in high dimensions. While effective, this technique requires an incredible amount of compute resources and/or time (256 TPU-v3 Cores), putting the model out of reach for the larger research community. In this paper, we present not-so-BigGAN, a simple and scalable framework for training deep generative models on high-dimensional natural images. Instead of modelling the image in pixel space like in BigGAN, not-so-BigGAN uses wavelet transformations to bypass the curse of dimensionality, reducing the overall compute requirement significantly. Through extensive empirical evaluation, we demonstrate that for a fixed compute budget, not-so-BigGAN converges several times faster than BigGAN, reaching competitive image quality with an order of magnitude lower compute budget (4 Telsa-V100 GPUs).
Five New Philippine Fishes

IN working over the very extensive collections made by me in the Philippine Islands in 1931, I have found many fishes new to the Philippines, and also a number of new species. Five of the latter are here presented. The types are deposited in the zoological museum of Leland Stanford Junior University. , In a later paper will be presented a number of species known only from Bleeker's types or otherwise notable for their rarity.
Impersonation-as-a-Service: Characterizing the Emerging Criminal Infrastructure for User Impersonation at Scale

In this paper we provide evidence of an emerging criminal infrastructure enabling impersonation attacks at scale. Impersonation-as-a-Service (IMPaaS) allows attackers to systematically collect and enforce user profiles (consisting of user credentials, cookies, device and behavioural fingerprints, and other metadata) to circumvent risk-based authentication system and effectively bypass multi-factor authentication mechanisms. We present the IMPaaS model and evaluate its implementation by analysing the operation of a large, invite-only, Russian IMPaaS platform providing user profiles for more than 260,000 Internet users worldwide. Our findings suggest that the IMPaaS model is growing, and provides the mechanisms needed to systematically evade authentication controls across multiple platforms, while providing attackers with a reliable, up-to-date, and semi-automated environment enabling target selection and user impersonation against Internet users as scale.
SoK: Certified Robustness for Deep Neural Networks

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to adversarial attacks, which have brought great concerns when deploying these models to safety-critical applications such as autonomous driving. Different defense approaches have been proposed against adversarial attacks, including: a) empirical defenses, which can usually be adaptively attacked again without providing robustness certification; and b) certifiably robust approaches, which consist of robustness verification providing the lower bound of robust accuracy against any attacks under certain conditions and corresponding robust training approaches. In this paper, we systematize certifiably robust approaches and related practical and theoretical implications and findings. We also provide the first comprehensive benchmark on existing robustness verification and training approaches on different datasets. In particular, we 1) provide a taxonomy for the robustness verification and training approaches, as well as summarize the methodologies for representative algorithms, 2) reveal the characteristics, strengths, limitations, and fundamental connections among these approaches, 3) discuss current research progresses, theoretical barriers, main challenges, and future directions for certifiably robust approaches for DNNs, and 4) provide an open-sourced unified platform to evaluate 20+ representative certifiably robust approaches.
Privacy-Preserving Machine Learning in Untrusted Clouds Made Simple

We present a practical framework to deploy privacy-preserving machine learning (PPML) applications in untrusted clouds based on a trusted execution environment (TEE). Specifically, we shield unmodified PyTorch ML applications by running them in Intel SGX enclaves with encrypted model parameters and encrypted input data to protect the confidentiality and integrity of these secrets at rest and during runtime. We use the open-source Graphene library OS with transparent file encryption and SGX-based remote attestation to minimize porting effort and seamlessly provide file protection and attestation. Our approach is completely transparent to the machine learning application: the developer and the end-user do not need to modify the ML application in any way.
Chrysanthemum Breeding for Resistance to White Rust

A series of experiments were conducted to promote chrysanthemum breeding for resistance to white rust (Puccinia horiana P. Hennings), For testing the resistance to white rust, three plants of each species and cultivar were incubated in a box kept at 17'C under 100% relative humidity and the inoculation was effected by hanging leaves with telia actively producing sporidia from the ceiling of the box. The inoculum concentration was restricted to ca. 70 sporidia per 1mm2 leaf area. Reliable criteria for grading and rating inLection were established based principally on the extent of telium formation on the fully unfolded top leaves. White rust resistance of chrysanthernum cultivars could be evaluated by this testing method at an early stage of the plants, for their reactions to white rust at the young plant stage and the flowering stage were highly correlated. Variation in resistance depending on the fungal isolates was found in the tests of 40 cultivars inoculated with 6 typical isolates in Japan, suggesting physiological specialization in the pathogen. Among 6 isolates, some showed a wide range of pathogenicity and others medium and narrow ranges. Besids the existence of unstable cultivars showing various reactions to the fungal isolates, cultivars with stable resistance to all the isolates were also found.
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective

To put a state-of-the-art neural network to practical use, it is necessary to design a model that has a good trade-off between the resource consumption and performance on the test set. Many researchers and engineers are developing methods that enable training or designing a model more efficiently. Developing an efficient model includes several strategies such as network architecture search, pruning, quantization, knowledge distillation, utilizing cheap convolution, regularization, and also includes any craft that leads to a better performance-resource trade-off. When combining these technologies together, it would be ideal if one source of performance improvement does not conflict with others. We call this property as the orthogonality in model efficiency. In this paper, we focus on knowledge distillation and demonstrate that knowledge distillation methods are orthogonal to other efficiency-enhancing methods both analytically and empirically. Analytically, we claim that knowledge distillation functions analogous to a ensemble method, bootstrap aggregating. This analytical explanation is provided from the perspective of implicit data augmentation property of knowledge distillation. Empirically, we verify knowledge distillation as a powerful apparatus for practical deployment of efficient neural network, and also introduce ways to integrate it with other methods effectively.
Social Analytics of Team Interaction using Dynamic Complexity Heat Maps and Network Visualizations

Given the increasing complexity of many sociotechnical work domains, effective teamwork is becoming more and more crucial. While face-to-face communication contributes to effective teamwork, understanding the time-varying nature and structure of team communication is limited. In this work, we combine sensor-based social analytics of Socio-metric badges (Rhythm Badge) with two visualization techniques (Dynamic Complexity Heat Maps and Network Visualizations) in order to advance an intuitive way of understanding the dynamics of team interaction. To demonstrate the utility of our approach, we provide a case study that examines one team's interaction for a Lost at Sea simulation. Our results show that we are able to recover transitions in the task and in the team interaction. Not only this, but we uncover structural changes in the energy and engagement of team members and visualize these in networks. Taken together, this work represents an important first step at identifying critical transitions/events in team communication and interaction patterns that form the basis for many promising future studies focused on optimizing team performance and well-being.
Half-Magnetic Topological Insulator with Magnetization-Induced Dirac Gap at a Selected Surface

Topological magnets are a new family of quantum materials providing great potential to realize emergent phenomena, such as quantum anomalous Hall effect and axion-insulator state. Here we present our discovery that stoichiometric ferromagnet MnBi8Te13 with natural heterostructure MnBi2Te4-(Bi2Te3)3 is an unprecedented half-magnetic topological insulator, with the magnetization existing at the MnBi2Te4 surface but not at the opposite surface terminated by triple Bi2Te3 layers. Our angle-resolved photoemission spectroscopy measurements unveil a massive Dirac gap at the MnBi2Te4 surface, and gapless Dirac cone on the other side. Remarkably, the Dirac gap (~28 meV) at MnBi2Te4 surface decreases monotonically with increasing temperature and closes right at the Curie temperature, thereby representing the first smoking-gun spectroscopic evidence of magnetization-induced topological surface gap among all known magnetic topological materials. We further demonstrate theoretically that the half-magnetic topological insulator is desirable to realize the half-quantized surface anomalous Hall effect, which serves as a direct proof of the general concept of axion electrodynamics in condensed matter systems.
The Effects of Organizational Service-orientation on Business Performance

This empirical research examines the effects of organizational service-orientation on business performance such as employee satisfaction, customer satisfaction and financial performance comprehensively. The service-orientation has been considered very importantly for delivering excellent service quality and customer value in many existing studies. Also service type's arbitrating effects on the relationship between service-orientation factors and business performance are investigated in this research. The data were collected from employees in hospitals and fast-foods with the questionnaire. And the several statistical methods were used for analysing. Consequently we could find that the comprehensive service-orientation factors gave the significant effects on the business performance. And service type's arbitrating effects sere apparent. It was revealed that the customization of service package and the customers' influence on service process must be considered as the important variables which have the significant effects on business performance.
Bayesian Inverse Reinforcement Learning for Collective Animal Movement

Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the self propelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.
Integrated Environmental Management Systems in the implementation of projects

Environmental management during project implementation is an essential area for improving the effectiveness of Environmental Impact Assessment (EIA) and its South African counterpart, Integrated Environmental Management (IEM). The objectives and components of an Environmental Management Plan (EMP) in the implementation phase are outlined. An EMP should contain a summary of impacts, mitigation measures for specific impacts, inspection procedures, a monitoring programme, and audit requirements. Factors determining the need for environmental management are listed.
HSFM-Σnn: Combining a Feedforward Motion Prediction Network and Covariance Prediction

In this paper, we propose a new method for motion prediction: HSFM-$\Sigma$nn. Our proposed method combines two different approaches: a feedforward network whose layers are model-based transition functions using the HSFM and a Neural Network (NN), on each of these layers, for covariance prediction. We will compare our method with classical methods for covariance estimation showing their limitations. We will also compare with a learning-based approach, social-LSTM, showing that our method is more precise and efficient.
Strategizing COVID-19 lockdowns using mobility patterns

During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance.
Thick Strip Model for Vortex-Induced Vibration of Two Flexible Cylinders in Tandem Arrangements

The interference phenomenon among multiple flexible cylinders is an important factor in designing offshore structures, which has been extensively studied by researchers in the past decades. In this paper, Vortex-induced Vibration (VIV) responses of two flexible cylinders experiencing the stepped flow with tandem arrangement are mainly studied using the viv-Thick-SJTU solver, which is developed based on the open source toolbox OpenFOAM and the Bernoulli-Euler bending beam model combining with the thick strip model. Hydrodynamic forces are calculated in each three-dimensional (3D) fluid strip, while vibrations of the cylinder are computed using the Finite Element Method (FEM) through the Newmark-β algrithm. The dynamic mesh technique is adopted to update the computational mesh through the cubic spline interpolation algorithm at every time step. Effects of the gap ratio between two tandem cylinders on vibration characteristic are mainly studied, while gap ratios are set as 4 and 10. Firstly, the validation of the code is conducted. Then, the modal decomposition method and Fast Fourier Transform (FFT) method are used to get the dominant vibration mode and vibration features of both cylinders.
Real-time Trajectory Tracking of a Quadrotor using Adaptive Backstepping Controller and RNN based Uncertainty Observer

This paper presents an approach for position and attitude control of a quadrotor using adaptive backstepping technique along with an uncertainty observer via Recurrent Neural Network (RNN). The quadrotor dynamics are expressed as two subsystems, namely translational and rotational, on which the backstepping control law has been developed. In comparison with feedforward neural networks, RNN has better dynamic characteristics and approximation capabilities. Therefore, an RNN based uncertainty observer has been employed to accommodate the system uncertainties as well as the unknown external disturbances. The proposed controller consists of two parts - an adaptive backstepping based controller that contains an RNN observer and a robust controller to deal with the approximation error induced by the RNN. The RNN parameters have been updated via an update law based on Lyapunov stability theory in an online manner where the overall system stability is also guaranteed. The proposed approach has been implemented in simulations for trajectory tracking of the quadrotor in the presence of parametric uncertainties and external disturbances. Also, the hardware results are presented to show the effectiveness of the proposed approach on DJI Matrice 100.
Online 3D Frontier-Based UGV and UAV Exploration Using Direct Point Cloud Visibility

While robots have long been proposed as a tool to reduce human personnel’s exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.