Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of two types of lime on some soil physical attributes of an oxisol from Brazil

Abstract Liming is a very important practice, especially in acid soils such as some Oxisols from Brazil, in which the chemical effects of liming are reasonably known. However, the effects of liming on soil physical properties are either not well known or, at least, controversial. In order to verify, under field conditions, the effect of liming on some soil physical attributes such us the aggregate stability, the water dispersible clay, the water retention were evaluated in a citrus liming experiment on a Dark Red Latosol (Typic Haplorthox) clay texture, from Cordeiropolis, Sao Paulo State, Brazil. The soil samples were collected in 0–10 cm and 10–20 cm depths, three years after the application of lime in the experiment, which had two replications per treatment. Calcitic and dolomitic limes were used with application rates of 0, 3, and 9 Mg ha‐1, with a total of five treatments, i.e., one control and two rates of application of each kind of lime. The results showed an increase in the aggregate stability. A.
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning

Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially compelling due to the strong representational power induced by the network. However, inference in GPs, whether with or without DKL, can be computationally challenging on large datasets. Here, we propose GP-Tree, a novel method for multi-class classification with Gaussian processes and DKL. We develop a tree-based hierarchical model in which each internal node of the tree fits a GP to the data using the P\'olya Gamma augmentation scheme. As a result, our method scales well with both the number of classes and data size. We demonstrate the effectiveness of our method against other Gaussian process training baselines, and we show how our general GP approach achieves improved accuracy on standard incremental few-shot learning benchmarks.
Revisiting Language Encoding in Learning Multilingual Representations

Transformer has demonstrated its great power to learn contextual word representations for multiple languages in a single model. To process multilingual sentences in the model, a learnable vector is usually assigned to each language, which is called"language embedding". The language embedding can be either added to the word embedding or attached at the beginning of the sentence. It serves as a language-specific signal for the Transformer to capture contextual representations across languages. In this paper, we revisit the use of language embedding and identify several problems in the existing formulations. By investigating the interaction between language embedding and word embedding in the self-attention module, we find that the current methods cannot reflect the language-specific word correlation well. Given these findings, we propose a new approach called Cross-lingual Language Projection (XLP) to replace language embedding. For a sentence, XLP projects the word embeddings into language-specific semantic space, and then the projected embeddings will be fed into the Transformer model to process with their language-specific meanings. In such a way, XLP achieves the purpose of appropriately encoding"language"in a multilingual Transformer model. Experimental results show that XLP can freely and significantly boost the model performance on extensive multilingual benchmark datasets. Codes and models will be released at https://github.com/lsj2408/XLP.
Phase-Modulated Radar Waveform Classification Using Deep Networks

—We consider the problem of classifying noisy, phase-modulated radar waveforms. While traditionally this has been accomplished by applying classical machine-learning algorithms on hand-crafted features, it has recently been shown that better performance can be attained by training deep neural networks (DNNs) to classify raw I/Q waveforms. However, existing DNNs assume time-synchronized waveforms and do not exploit complex-valued signal structure, and many aspects of the their DNN design and training are suboptimal. We demonstrate that, with an improved DNN architecture and training procedure, it is possible to reduce classiﬁcation error from 18% to 0.14% on asynchronous waveforms from the SIDLE dataset. Unlike past work, we furthermore demonstrate that accurate classiﬁcation of multiple overlapping waveforms is also possible, by achieving 4.0% error with 4 asynchronous SIDLE waveforms.
Quantifying environment and population diversity in multi-agent reinforcement learning

Generalization is a major challenge for multi-agent reinforcement learning. How well does an agent perform when placed in novel environments and in interactions with new co-players? In this paper, we investigate and quantify the relationship between generalization and diversity in the multi-agent domain. Across the range of multi-agent environments considered here, procedurally generating training levels signiﬁcantly improves agent performance on held-out levels. However, agent performance on the speciﬁc levels used in training sometimes declines as a result. To better understand the effects of co-player variation, our experiments introduce a new environment-agnostic measure of behavioral diversity. Results demonstrate that population size and intrinsic motivation are both effective methods of generating greater population diversity. In turn, training with a diverse set of co-players strengthens agent performance in some (but not all) cases.
Analysis of feature learning in weight-tied autoencoders via the mean field lens

Autoencoders are among the earliest introduced nonlinear models for unsupervised learning. Although they are widely adopted beyond research, it has been a longstanding open problem to understand mathematically the feature extraction mechanism that trained nonlinear autoencoders provide. In this work, we make progress in this problem by analyzing a class of two-layer weight-tied nonlinear autoencoders in the mean field framework. Upon a suitable scaling, in the regime of a large number of neurons, the models trained with stochastic gradient descent are shown to admit a mean field limiting dynamics. This limiting description reveals an asymptotically precise picture of feature learning by these models: their training dynamics exhibit different phases that correspond to the learning of different principal subspaces of the data, with varying degrees of nonlinear shrinkage dependent on the $\ell_{2}$-regularization and stopping time. While we prove these results under an idealized assumption of (correlated) Gaussian data, experiments on real-life data demonstrate an interesting match with the theory. The autoencoder setup of interests poses a nontrivial mathematical challenge to proving these results. In this setup, the"Lipschitz"constants of the models grow with the data dimension $d$. Consequently an adaptation of previous analyses requires a number of neurons $N$ that is at least exponential in $d$. Our main technical contribution is a new argument which proves that the required $N$ is only polynomial in $d$. We conjecture that $N\gg d$ is sufficient and that $N$ is necessarily larger than a data-dependent intrinsic dimension, a behavior that is fundamentally different from previously studied setups.
Geometry in classical statistical thermodynamics

A Euclidean geometry for classical thermodynamics is discussed. The central physical idea is that it is useful to characterize the system in terms of a number of mean values of ‘‘relevant’’ observables. These mean values are written, as usual, as an expectation ∑iAi pi over a (classical) probability distribution. The expectation value is then interpreted as a scalar product between vectors belonging to dual spaces. A metric is introduced via the transformation from one space to another. In terms of the metric, the scalar product of two vectors belonging to the same space (e.g., two probability distributions or two observables) can be defined. In the space of all states the metric does not depend on the state of the system and the curvature tensor vanishes, i.e., the space is Euclidean.
Modeling of thermosensitive stereoregular polymers within the coarse-grained force field: Poly(N-isopropylacrylamide) as a benchmark case

In this article we introduce MARTINI model of a widely used thermoresponsive polymer, Poly(Nisopropylacrylamid)e. Our model takes into account polymer’s stereoregularity (i.e. tacticity). We highlight the effect of changing the polarity of coarse-grained particles comprising the polymer on its ability to exhibit a temperature-dependent coil-to-globule transition. Our approach allows to study the behavior of thermoresponsive polymers at large time and length scales.
Significant Otter: Understanding the Role of Biosignals in Communication

With the growing ubiquity of wearable devices, sensed physiological responses provide new means to connect with others. While recent research demonstrates the expressive potential for biosignals, the value of sharing these personal data remains unclear. To understand their role in communication, we created Significant Otter, an Apple Watch/iPhone app that enables romantic partners to share and respond to each other’s biosignals in the form of animated otter avatars. In a one-month study with 20 couples, participants used Significant Otter with biosignals sensing OFF and ON. We found that while sensing OFF enabled couples to keep in touch, sensing ON enabled easier and more authentic communication that fostered social connection. However, the addition of biosignals introduced concerns about autonomy and agency over the messages they sent. We discuss design implications and future directions for communication systems that recommend messages based on biosignals.
Persistent currents in Bose–Bose mixtures after an interspecies interaction quench

We study the persistent currents and interspecies entanglement generation in a Bose–Bose mixture formed by two atomic gases (hereafter labeled by the letters A and B) trapped in a one-dimensional ring lattice potential with an artificial gauge field after a sudden quench from zero to strong interactions between the two gases. Assuming that the strength of these interactions is much larger than the single species energies and that the gas A is initially in the Mott-insulator regime, we show that the current of the gas B is reduced with respect to its value prior the interaction quench. Averaging fast oscillations out, the relative decrease of this current is independent of the initial visibility and Peierls phase of the gas B and behaves quadratically with the visibility of the gas A. The second Rényi entropy of the reduced state measuring the amount of entanglement between the two gases is found to scale linearly with the number of sites and to be proportional to the relative decrease of the current.
Statistical parameter estimation of multimode multiphoton-subtracted thermal states of light

Thermal states of light are widely used in quantum optics for various quantum phenomena testing. Particularly, they can be utilized for characterization of photon creation and photon annihilation operations. During the last decade the problem of photon subtraction from multimode quantum states become of much significance. Therefore, in this work we present a technique for statistical parameter estimation of multimode multiphoton subtracted thermal states of light, which can be used for multimode photon annihilation test.
ResNet-LDDMM: Advancing the LDDMM Framework Using Deep Residual Networks

In deformable registration, the Riemannian framework – Large Deformation Diffeomorphic Metric Mapping, or LDDMM for short – has inspired numerous techniques for comparing, deforming, averaging and analyzing shapes or images. Grounded in flows of vector fields, akin to the equations of motion used in fluid dynamics, LDDMM algorithms solve the flow equation in the space of plausible deformations, i.e., diffeomorphisms. In this work, we make use of deep residual neural networks to solve the non-stationary ODE (flow equation) based on an Euler's discretization scheme. The central idea is to represent time-dependent velocity fields as fully connected ReLU neural networks (building blocks) and derive optimal weights by minimizing a regularized loss function. Computing minimizing paths between deformations, thus between shapes, turns to find optimal network parameters by back-propagating over the intermediate building blocks. Geometrically, at each time step, our algorithm searches for an optimal partition of the space into multiple polytopes, and then computes optimal velocity vectors as affine transformations on each of these polytopes. As a result, different parts of the shape, even if they are close (such as two fingers of a hand), can be made to belong to different polytopes, and therefore be moved in different directions without costing too much energy. Importantly, we show how diffeomorphic transformations, or more precisely bilipshitz transformations, are predicted by our registration algorithm. We illustrate these ideas on diverse registration problems of 3D shapes under complex topology-preserving transformations. We thus provide essential foundations for more advanced shape variability analysis under a novel joint geometric-neural networks Riemannian-like framework, i.e., ResNet-LDDMM.
Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits

Assessing the exploitability of software vulnerabilities at the time of disclosure is difficult and error-prone, as features extracted via technical analysis by existing metrics are poor predictors for exploit development. Moreover, exploitability assessments suffer from a class bias because"not exploitable"labels could be inaccurate. To overcome these challenges, we propose a new metric, called Expected Exploitability (EE), which reflects, over time, the likelihood that functional exploits will be developed. Key to our solution is a time-varying view of exploitability, a departure from existing metrics. This allows us to learn EE using data-driven techniques from artifacts published after disclosure, such as technical write-ups and proof-of-concept exploits, for which we design novel feature sets. This view also allows us to investigate the effect of the label biases on the classifiers. We characterize the noise-generating process for exploit prediction, showing that our problem is subject to the most challenging type of label noise, and propose techniques to learn EE in the presence of noise. On a dataset of 103,137 vulnerabilities, we show that EE increases precision from 49% to 86% over existing metrics, including two state-of-the-art exploit classifiers, while its precision substantially improves over time. We also highlight the practical utility of EE for predicting imminent exploits and prioritizing critical vulnerabilities. We develop EE into an online platform which is publicly available at https://exploitability.app/.
User Characteristics of Olympic Gold Medallists on Instagram: A Quantitative Analysis of Rio2016

The purpose of this study is to examine Olympic champions’ characteristics on Instagram to first understand whether differences exist between male and female athletes and then to find possible correlations between these characteristics. We utilized a content analytic method to analyse Olympic gold medallists’ photographs on Instagram. By this way we fetched data from Instagram pages of all those Rio2016 Olympic gold medallists who had their account publically available. The analysis of data revealed the existence of a positive monotonic relationship between the ratio of following/follower and the ratio of engagement/follower for men gold medallists, and a strong negative monotonic relationship between age and ratio of self-presenting post of both men and women gold medallists which even take a linear form for men. These findings aligned with the relative theories and literature may come together to help the athletes to manage and expand their personal brand in social media.
Surrogate Monte Carlo

This article proposes an artificial data generating algorithm that is simple and easy to customize. <br>The fundamental concept is to perform random permutation of Monte Carlo generated random<br>numbers which conform to the unconditional probability distribution of the original real time series. <br>Similar to constraint surrogate methods, random permutations are only accepted if a given objective<br>function is minimized. The objective function is selected in order to describe the most important<br>features of the stochastic process. The algorithm is demonstrated by producing simulated log-returns of the S&amp;P 500 stock index.
All You Need is DAG

We present DAG-Rider, the first asynchronous Byzantine Atomic Broadcast protocol that achieves optimal resilience, optimal amortized communication complexity, and optimal time complexity. DAG-Rider is post-quantum safe and ensures that all values proposed by correct processes eventually get delivered. We construct DAG-Rider in two layers: In the first layer, processes reliably broadcast their proposals and build a structured Directed Acyclic Graph (DAG) of the communication among them. In the second layer, processes locally observe their DAGs and totally order all proposals with no extra communication.
Constraining red supergiant mass-loss prescriptions through supernova radio properties

Supernova properties in radio strongly depend on their circumstellar environment and they are an important probe to investigate the mass loss of supernova progenitors. Recently, core-collapse supernova observations in radio have been assembled and the rise time and peak luminosity distribution of core-collapse supernovae at 8.4 GHz has been estimated. In this paper, we constrain the mass-loss prescriptions for red supergiants by using the rise time and peak luminosity distribution of Type II supernovae in radio. We take the de Jager and van Loon mass-loss rates for red supergiants, calculate the rise time and peak luminosity distribution based on them, and compare the results with the observed distribution. We found that the de Jager mass-loss rate explains the widely spread radio rise time and peak luminosity distribution of Type II supernovae well, while the van Loon mass-loss rate predicts a relatively narrow range for the rise time and peak luminosity. We conclude that the mass-loss prescriptions of red supergiants should have strong dependence on the luminosity as in the de Jager mass-loss rate to reproduce the widely spread distribution of the rise time and peak luminosity in radio observed in Type II supernovae.
Enhancing Hierarchical Information by Using Metric Cones for Graph Embedding

Graph embedding is becoming an important method with applications in various areas, including social networks and knowledge graph completion. In particular, Poincar´e embedding has been proposed to capture the hierarchical structure of graphs, and its effectiveness has been reported. However, most of the existing methods have isometric mappings in the embedding space, and the choice of the origin point can be arbitrary. This fact is not desirable when the distance from the origin is used as an indicator of hierarchy, as in the case of Poincar´e embedding. In this paper, we propose graph embedding in a metric cone to solve such a problem, and we gain further beneﬁts: 1) we provide an indicator of hierarchical information that is both geometrically and intuitively natural to interpret, 2) we can extract the hierarchical structure from a graph embedding output of other methods by learning additional one-dimensional parameters, and 3) we can change the curvature of the embedding space via a hyperparameter.
Topological Deep Learning: Classification Neural Networks

Topological deep learning is a formalism that is aimed at introducing topological language to deep learning for the purpose of utilizing the minimal mathematical structures to formalize problems that arise in a generic deep learning problem. This is the first of a sequence of articles with the purpose of introducing and studying this formalism. In this article, we define and study the classification problem in machine learning in a topological setting. Using this topological framework, we show when the classification problem is possible or not possible in the context of neural networks. Finally, we demonstrate how our topological setting immediately illuminates aspects of this problem that are not as readily apparent using traditional tools.
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks

Unstructured pruning reduces the memory footprint in deep neural networks (DNNs). Recently, researchers proposed different types of structural pruning intending to reduce also the computation complexity. In this work, we first suggest a new measure called mask-diversity which correlates with the expected accuracy of the different types of structural pruning. We focus on the recently suggested N:M fine-grained block sparsity mask, in which for each block of M weights, we have at least N zeros. While N:M fine-grained block sparsity allows acceleration in actual modern hardware, it can be used only to accelerate the inference phase. In order to allow for similar accelerations in the training phase, we suggest a novel transposable fine-grained sparsity mask, where the same mask can be used for both forward and backward passes. Our transposable mask guarantees that both the weight matrix and its transpose follow the same sparsity pattern; thus, the matrix multiplication required for passing the error backward can also be accelerated. We formulate the problem of finding the optimal transposable-mask as a minimum-cost flow problem. Additionally, to speed up the minimum-cost flow computation, we also introduce a fast linear-time approximation that can be used when the masks dynamically change during training. Our experiments suggest a 2x speed-up in the matrix multiplications with no accuracy degradation over vision and language models. Finally, to solve the problem of switching between different structure constraints, we suggest a method to convert a pre-trained model with unstructured sparsity to an N:M fine-grained block sparsity model with little to no training. A reference implementation can be found at https://github.com/papers-submission/structured_transposable_masks.
Fluid-structure interaction simulations with a LES filtering approach in solids4Foam

Abstract The goal of this paper is to test solids4Foam, the fluid-structure interaction (FSI) toolbox developed for foam-extend (a branch of OpenFOAM), and assess its flexibility in handling more complex flows. For this purpose, we consider the interaction of an incompressible fluid described by a Leray model with a hyperelastic structure modeled as a Saint Venant-Kirchho material. We focus on a strongly coupled, partitioned fluid-structure interaction (FSI) solver in a finite volume environment, combined with an arbitrary Lagrangian-Eulerian approach to deal with the motion of the fluid domain. For the implementation of the Leray model, which features a nonlinear differential low-pass filter, we adopt a three-step algorithm called Evolve-Filter-Relax. We validate our approach against numerical data available in the literature for the 3D cross flow past a cantilever beam at Reynolds number 100 and 400.