Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Kernel Density Estimation with Median-of-Means principle

In this paper, we introduce a robust nonparametric density estimator combining the popular Kernel Density Estimation method and the Median-of-Means principle (MoM-KDE). This estimator is shown to achieve robustness to any kind of anomalous data, even in the case of adversarial contamination. In particular, while previous works only prove consistency results under known contamination model, this work provides finite-sample high-probability error-bounds without a priori knowledge on the outliers. Finally, when compared with other robust kernel estimators, we show that MoM-KDE achieves competitive results while having significant lower computational complexity.
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

The theory of evolutionary computation for discrete search spaces has made significant progress since the early 2010s. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime analysis of evolutionary algorithms, highlights recent theoretical insights on parameter tuning and parameter control, and summarizes the latest advances for stochastic and dynamic problems. We regard how evolutionary algorithms optimize submodular functions, and we give an overview over the large body of recent results on estimation of distribution algorithms. Finally, we present the state of the art of drift analysis, one of the most powerful analysis technique developed in this field.
Tracking the distribution and impacts of diseases with biological records and distribution modelling

Species distribution modelling is widely used in epidemiology for mapping spatial patterns and the risk of introduction of diseases and vectors and also for predicting how exposure may alter given future environmental change, motivated by the high societal impact and the multiple environmental drivers of disease outbreaks. Although pathogens and vectors have historically been sparsely recorded, monitoring systems and media sources are generating novel, online data sources on occurrence. Moreover, increasing ecological realism is being incorporated into distribution modelling techniques, focussing on dispersal, biotic interactions and evolutionary constraints that shape species distributions alongside abiotic factors and biases in recording effort, common to pathogens and vectors and wildlife species. Considering pathogens and arthropod vector systems with high impact on plant, animal and human health, the present review describes how biological records for vectors and pathogens arise, introduces the concepts behind distribution models and illustrates the potential for ecologically realistic distribution models to yield insight into the establishment and spread of pathogens. Because distribution modellers aim to provide policy makers with evidence and maps for planning and evaluation of disease mitigation measures, we highlight factors that currently constrain direct translation of models to policy. Disease distributions will be better understood and mapped in the future given improved occurrence data access and integration and combined (correlative and mechanistic) modelling approaches that are developed iteratively in concert with stakeholders.
In-Silico Analysis of Structural Properties of Pathogen-Rela ted Protein (PR1) in Potato Somatic Hybrid

Pathogen-related protein (PR) are induced in the ho st plants as a defense response during infection wi th oomycetes, fungi, bacteria, viruses, insect attack, wounding, salinity, exposure to harsh chemicals an d UV light. PR proteins are used as marker gene for syst emic acquired resistance. The three-dimensional structure of pathogen related protein from somatic hybrid of Solanum tuberosum (+) S. pinnatisectum was constructed from using the crystal structure of P14 A from Solanum lycopersicum (PDB ID: 1CFE) as template using Modeller9.11. The best model was further assessed by PROCHECK, ProSA, and ERRAT plot in order to analyze the quality and reliabilit y of generated model. The overall quality of comput ed model from Ramachandran plot revealed that 99.1% amino acid sequence in favored and allowed regions. The StPR1 forms α-β-α sandwich fold with four α helices ( α1-α4) and four stranded β sheets ( β1-β4) which forms a tight topology stabilized by hydrogen bondi ng. The three-dimensional structure of PR from the somatic hybrid has a conserved cleft with two histi dine and two glutamate residues which forms a putat ive active site in the model protein. This model might be helpful in developing new strategies to develop transgenic plants with increased fungal resistance in the field in response to disease and stress.
Tusas: A fully implicit parallel approach for coupled nonlinear equations

We develop a fully-coupled, fully-implicit approach for phase-field modeling of solidification in metals and alloys. Predictive simulation of solidification in pure metals and metal alloys remains a significant challenge in the field of materials science, as microstructure formation during the solidification process plays a critical role in the properties and performance of the solid material. Our simulation approach consists of a finite element spatial discretization of the fully-coupled nonlinear system of partial differential equations at the microscale, which is treated implicitly in time with a preconditioned Jacobian-free Newton-Krylov method. The approach allows time steps larger than those restricted by the traditional explicit CFL limit and is algorithmically scalable as well as efficient due to an effective preconditioning strategy based on algebraic multigrid and block factorization. We implement this approach in the open-source Tusas framework, which is a general, flexible tool developed in C++ for solving coupled systems of nonlinear partial differential equations. The performance of our approach is analyzed in terms of algorithmic scalability and efficiency, while the computational performance of Tusas is presented in terms of parallel scalability and efficiency on emerging heterogeneous architectures. We demonstrate that modern algorithms, discretizations, and computational science, and heterogeneous hardware provide a robust route for predictive phase-field simulation of microstructure evolution during additive manufacturing.
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model

We develop a model for representing visual texture in a low-dimensional feature space, along with a novel self-supervised learning objective that is used to train it on an unlabeled database of texture images. Inspired by the architecture of primate visual cortex, the model uses a first stage of oriented linear filters (corresponding to cortical area V1), consisting of both rectified units (simple cells) and pooled phase-invariant units (complex cells). These responses are processed by a second stage (analogous to cortical area V2) consisting of convolutional filters followed by half-wave rectification and pooling to generate V2 'complex cell' responses. The second stage filters are trained on a set of unlabeled homogeneous texture images, using a novel contrastive objective that maximizes the distance between the distribution of V2 responses to individual images and the distribution of responses across all images. When evaluated on texture classification, the trained model achieves substantially greater data-efficiency than a variety of deep hierarchical model architectures. Moreover, we show that the learned model exhibits stronger representational similarity to texture responses of neural populations recorded in primate V2 than pre-trained deep CNNs.
Simplicity Out of Complexity: Band Structure for W20O58 Superconductor

The band structure, density of states, and the Fermi surface of a recently discovered superconductor, oxygen-deficient tungsten oxide WO2.9 that is equivalent to W20O58, is studied within the density functional theory (DFT) in the generalized gradient approximation (GGA). Here we show that despite the extremely complicated structure containing 78 atoms in the unit cell, the low-energy band structure is quite feasible. Fermi level is crossed by no more than 10 bands per one spin projection (and even 9 bands per pseudospin projection when the spin-orbit coupling is considered) originating from the t2g 5d-orbitals of tungsten atoms forming zigzag chains. These bands become occupied because of the specific zigzag octahedra distortions. To demonstrate the role of distortions, we compare band structures of W20O58 with the real crystal structure and with the idealized one. We also propose a basis for a minimal low-energy tight-binding model for W20O58.
Genetic Divergence in Pea

An analysis on genetic variability of 100 pea germ plasm, collected from different geographical sources, using Mahanolobis's statistical distance, suggested that the varieties could be grouped into 14 clusters. Grouping of varieties in different clusters was not related to their geographical origin. The maximum intercluster distance was observed between cluster I and XIV, followed by VII and XIV. Cluster XI was more diverse than others.
Sampling from a k-DPP without looking at all items

Determinantal point processes (DPPs) are a useful probabilistic model for selecting a small diverse subset out of a large collection of items, with applications in summarization, stochastic optimization, active learning and more. Given a kernel function and a subset size $k$, our goal is to sample $k$ out of $n$ items with probability proportional to the determinant of the kernel matrix induced by the subset (a.k.a. $k$-DPP). Existing $k$-DPP sampling algorithms require an expensive preprocessing step which involves multiple passes over all $n$ items, making it infeasible for large datasets. A naive heuristic addressing this problem is to uniformly subsample a fraction of the data and perform $k$-DPP sampling only on those items, however this method offers no guarantee that the produced sample will even approximately resemble the target distribution over the original dataset. In this paper, we develop an algorithm which adaptively builds a sufficiently large uniform sample of data that is then used to efficiently generate a smaller set of $k$ items, while ensuring that this set is drawn exactly from the target distribution defined on all $n$ items. We show empirically that our algorithm produces a $k$-DPP sample after observing only a small fraction of all elements, leading to several orders of magnitude faster performance compared to the state-of-the-art.
Training highly effective connectivities within neural networks with randomly initialized, fixed weights

We present some novel, straightforward methods for training the connection graph of a randomly initialized neural network without training the weights. These methods do not use hyperparameters defining cutoff thresholds and therefore remove the need for iteratively searching optimal values of such hyperparameters. We can achieve similar or higher performances than in the case of training all weights, with a similar computational cost as for standard training techniques. Besides switching connections on and off, we introduce a novel way of training a network by flipping the signs of the weights. If we try to minimize the number of changed connections, by changing less than 10\% of the total it is already possible to reach more than 90\% of the accuracy achieved by standard training. We obtain good results even with weights of constant magnitude or even when weights are drawn from highly asymmetric distributions. These results shed light on the over-parameterization of neural networks and on how they may be reduced to their effective size.
Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique, (b) caching strategy, and edge network. We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks

We present an explicit deep neural network construction that transforms uniformly distributed one-dimensional noise into an arbitrarily close approximation of any two-dimensional Lipschitz-continuous target distribution. The key ingredient of our design is a generalization of the "space-filling" property of sawtooth functions discovered in (Bailey & Telgarsky, 2018). We elicit the importance of depth - in our neural network construction - in driving the Wasserstein distance between the target distribution and the approximation realized by the network to zero. An extension to output distributions of arbitrary dimension is outlined. Finally, we show that the proposed construction does not incur a cost - in terms of error measured in Wasserstein-distance - relative to generating $d$-dimensional target distributions from $d$ independent random variables.
Characterisation of RNaseH-LTR sections of Ty1-copia retrotransposons in apple and fingerprinting of four apple clones by S-SAP analysis

Summary Retrotransposons are the most widespread class of eukaryotic transposable elements and play an important role in genome evolution. Several of these elements have been sequenced and were found to contain different cis-regulatory elements involved in the response to different stress conditions in their long terminal repeat sequences (LTRs). Retrotransposon-based marker systems have proved useful because of their distribution, ubiquity, and prevalence in plant genomes. Here we report on the isolation and characterisation of new RNaseH-LTR sections of the Ty1-copia-like retro-elements in apple DNA. Twenty clones were identified with a variable degree of homology to the Ty1-copia RNaseH gene. Nineteen had the characteristic RNaseH gene, stop codon, polypurine tract (PPT), and putative 3’-LTR sequence elements. The RNaseH sections were aligned using ClustalV to seek potential relationships between sequences. This analysis revealed six distinct Clusters. Conserved motifs, corresponding to cis-regulatory elements, were discovered in the LTRs using plantCARE software and four were characterised. From one putative LTR fragment, we then designed a primer, which, through sequence-specific amplification polymorphism (S-SAP), could distinguish between ‘Wellspur Delicious’, ‘Hardispur Delicious’ and ‘Starkrimson’, and ‘Starking’ apple, by fingerprinting. The first three varieties originated from ‘Starking’ through bud mutations. Our results suggest that these bud mutations appeared to have been derived from retrotransposon insertions.
Deriving Neural Network Design and Learning from the Probabilistic Framework of Chain Graphs

The last decade has witnessed a boom of neural network (NN) research and applications achieving state-of-the-art results in various domains. Yet, most advances on architecture and learning have been discovered empirically in a trial-and-error manner such that a more systematic exploration is difficult. Their theoretical analyses are limited and a unifying framework is absent. In this paper, we tackle this issue by identifying NNs as chain graphs (CGs) with chain components modeled as bipartite pairwise conditional random fields, and feed-forward as a form of approximate probabilistic inference. We show that from this CG interpretation we can systematically formulate an extensive range of the empirically discovered results, including various network designs (e.g., CNN, RNN, ResNet), activation functions (e.g., sigmoid, tanh, softmax, (leaky) ReLU) and regularizations (e.g., weight decay, dropout, BatchNorm). Furthermore, guided by this interpretation, we are able to derive "the preferred form" of residual block, recover the simple yet powerful IndRNN model and discover a new stochastic inference procedure: the partially collapsed feed-forward inference. We believe that our work can provide a well-founded formulation to analyze the nature and design of NNs, and can serve as a unifying theoretical framework for deep learning research.
Study of subpopulations of the chestnut blight (Cryphonectria parasitica) fungus in the Carpathian basin

During the last few decades destruction of European chestnut (Castanea sativa) populations caused by the chestnut blight fungus (Cryphonectria parasitica) has increased in Central Europe. A total of 650 isolates (from 21 chestnut stands in southern and western parts of Hungary, three stands in Romania and six different sites in the Sub-Carpathion region of the Ukraine) were examined for virulence, classification of vegetative compatibility types (vc types) and for the presence of dsRNA. Up to now 18 vc types have been found in Hungarian chestnut stands, but only one in Romania and one in the Sub-Carpathion region of the Ukraine. Long dsRNA fragments with similar electrophoretic characteristics were found in 36 Hungarian hypovirulent strains. These hypovirulent strains showed reduced virulence in vivo, as well as the in vitro (RADOCZ 1998a).
Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression

Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high risks can be recovered or not depends, to a large extent, on how early they will be treated appropriately before irreversible consequences are caused to the patients by the virus. In this research, we reported an explainable, intuitive, and accurate machine learning model based on logistic regression to predict the fatality rate of COVID-19 patients using only three important blood biomarkers, including lactic dehydrogenase, lymphocyte (%) and high-sensitivity C-reactive protein, and their interactions. We found that when the fatality probability produced by the logistic regression model was over 0.8, the model had the optimal performance in that it was able to predict patient fatalities more than 11.30 days on average with maximally 34.91 days in advance, an accumulative f1-score of 93.76% and and an accumulative accuracy score of 93.92%. Such a model can be used to identify COVID-19 patients with high risks with three blood biomarkers and help the medical systems around the world plan critical medical resources amid this pandemic.
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

A deep reinforcement learning (DRL) agent observes its states through observations, whichmay contain natural measurement errors or adversarial noises. Since the observations deviatefrom the true states, they can mislead the agent into making suboptimal actions. Several workshave shown this vulnerability via adversarial attacks, but existing approaches on improving therobustness of DRL under this setting have limited success and lack for theoretical principles. Weshow that naively applying existing techniques on improving robustness for classification tasks,like adversarial training, are ineffective for many RL tasks. We propose the state-adversarialMarkov decision process (SA-MDP) to study the fundamental properties of this problem, anddevelop a theoretically principled policy regularization which can be applied to a large familyof DRL algorithms, including proximal policy optimization (PPO), deep deterministic policygradient (DDPG) and deep Q networks (DQN), for both discrete and continuous action controlproblems. We significantly improve the robustness of PPO, DDPG and DQN agents under asuite of strong white box adversarial attacks, including new attacks of our own. Additionally, wefind that a robust policy noticeably improves DRL performance even without an adversary in anumber of environments. Our code is available at this https URL
Generalizing Correspondence Analysis for Applications in Machine Learning

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from epidemiology to social sciences. However, current methods for CA do not scale to large, high-dimensional datasets. In this paper, we provide a novel interpretation of CA in terms of an information-theoretic quantity called the principal inertia components. We show that estimating the principal inertia components, which consists in solving a functional optimization problem over the space of finite variance functions of two random variable, is equivalent to performing CA. We then leverage this insight to design algorithms to perform CA at scale. Specifically, we demonstrate how the principal inertia components can be reliably approximated from data using deep neural networks. Finally, we show how the maximally correlated embeddings of pairs of random variables in CA further play a central role in several learning problems including multi-view and multi-modal learning methods and visualization of classification boundaries.