Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPACT OF FLUE GAS DESULFURIZATION-CALCIUM SULFITE AND GYPSUM ON SOIL MICROBIAL ACTIVITY AND WHEAT GROWTH

Calcium sulfite (CaSO3·0.5H2O), a common by-product of coal combustion and flue gas desulfurization (FGD), spontaneously converts to gypsum (CaSO4·2H2O) with exposure to air and water. Although gypsum is a well-known soil amendment, the oxidation of SO32− to SO42− consumes oxygen and may have detrimental effects on plant root growth and soil microbial activity if FGD-CaSO3 is land applied. Because the oxidation rate is pH dependent, we conducted greenhouse tests to evaluate the effects of FGD-CaSO3 applied at rates of 0, 2.2, 4.4, and 8.8 Mg ha−1 on wheat growth, soil enzyme activities, and the chemical properties of two soils with differing pH (4.0 vs. 6.2). A gypsum treatment applied at the rate of 2.2 Mg ha−1 was used as a positive control. Exchangeable Ca2+ and water-extractable Ca2+ and SO42− increased significantly with increasing FGD-CaSO3 application rates in both soils, indicating rapid oxidation of SO32− to SO42− when neither water nor oxygen was limiting. No changes in soil pH were measured. Applications of 2.2, 4.4, or 8.8 Mg CaSO3 ha−1 to the pH 6.2 soil produced no effect on wheat growth or the uptake of N, P, Ca2+, and Mg2+. The uptake of SO42−-S increased, whereas K uptake decreased. No significant differences in the activities of urease, &bgr;-glucosidase, alkaline phosphatase, or arylsulfatase were observed relative to a control. In the acid soil, an application of 2.2 Mg ha−1 FGD-CaSO3 increased wheat root growth and dry matter yield compared with an untreated control. The uptake of N, P, Ca2+, and K+ also increased presumably because of enhanced root development resulting from decreases in exchangeable Al3+ and increases in soluble Ca2+. Wheat growth and alkaline phosphatase and arylsulfatase activities were significantly inhibited by addition of 8.8 Mg ha−1 of FGD-CaSO3 compared with the untreated control or the same soil receiving 2.2 Mg ha−1 gypsum. We conclude that surface applications of FGD-CaSO3 may be as effective as gypsum for inhibiting soil crusting, improving water infiltration, and promoting the movement of Ca2+ into acid subsoils. Moreover, application rates of equal to or less than 4.4 Mg ha−1 should have no negative impact on soil microbial activities or plant growth.
Role of solutal free convection on interdiffusion in a horizontal microfluidic channel

We studied the role of solutal free convection on the transport of a buoyant solute at the microfluidic scales, 5--500 \textmu{}m, both for a two-dimensional slit and a three-dimensional microchannel. Our theoretical predictions using both asymptotic analytical solutions and accurate numerical resolutions describe the different solute transport regimes.
Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence

Stochastic Gradient Descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous shared-memory parallel SGD (AsyncSGD), including synchronization-free algorithms, e.g. HOGWILD! , have received interest in certain contexts, due to reduced overhead compared to synchronous parallelization. Despite that they induce staleness and inconsistency, they have shown speedup for problems satisfying smooth, strongly convex targets, and gradient sparsity. Recent works take important steps towards understanding the potential of parallel SGD for problems not conforming to these strong assumptions, in particular for deep learning (DL). There is however a gap in current literature in understanding when AsyncSGD algorithms are useful in practice, and in particular how mechanisms for synchronization and consistency play a role. We contribute with answering questions in this gap by studying a spectrum of parallel algorithmic implementations ofAsyncSGD, aiming to understand how shared-data synchronization influences the convergence properties in fundamental DL applications. We focus on the impact of consistency-preserving non-blocking synchronization in SGD convergence, and in sensitivity to hyper-parameter tuning. We propose Leashed-SGD, an extensible algorithmic framework of consistency-preserving implementations of AsyncSGD, employing lock-free synchronization, effectively balancing throughput and latency. Leashed-SGD features a natural contention-regulating mechanism, as well as dynamic memory management, allocating space only when needed. We argue analytically about the dynamics of the algorithms, memory consumption, the threads’ progress over time, and the expected contention. We provide a comprehensive empirical evaluation, validating the analytical claims, benchmarking the proposed Leashed-SGD framework, and comparing to baselines for two prominent deep learning (DL) applications: multilayer perceptrons (MLP) and convolutional neural networks (CNN). We observe the crucial impact of contention, staleness and consistency and show how, thanks to the aforementioned properties, Leashed-SGD provides significant improvements in stability as well as wall-clock time to convergence (from 20–80% up to $ 4 \times$improvements) compared to the standard lock-based AsyncSGD algorithm and HOGWILD! , while reducing the overall memory footprint.
Distributed Algorithms for Linearly-Solvable Optimal Control in Networked Multi-Agent Systems

Distributed algorithms for both discrete-time and continuous-time linearly solvable optimal control (LSOC) problems of networked multi-agent systems (MASs) are investigated in this paper. A distributed framework is proposed to partition the optimal control problem of a networked MAS into several local optimal control problems in factorial subsystems, such that each (central) agent behaves optimally to minimize the joint cost function of a subsystem that comprises a central agent and its neighboring agents, and the local control actions (policies) only rely on the knowledge of local observations. Under this framework, we not only preserve the correlations between neighboring agents, but moderate the communication and computational complexities by decentralizing the sampling and computational processes over the network. For discrete-time systems modeled by Markov decision processes, the joint Bellman equation of each subsystem is transformed into a system of linear equations and solved using parallel programming. For continuous-time systems modeled by It\^o diffusion processes, the joint optimality equation of each subsystem is converted into a linear partial differential equation, whose solution is approximated by a path integral formulation and a sample-efficient relative entropy policy search algorithm, respectively. The learned control policies are generalized to solve the unlearned tasks by resorting to the compositionality principle, and illustrative examples of cooperative UAV teams are provided to verify the effectiveness and advantages of these algorithms.
No-Substitution k -means Clustering with Low Center Complexity and Memory

Clustering is a fundamental task in machine learning. Given a dataset X = { x 1 , . x n } , the goal of k -means clustering is to pick k “centers” from X in a way that minimizes the sum of squared distances from each point to its nearest center. We consider k -means clustering in the online, no substitution setting, where one must decide whether to take x t as a center immediately upon streaming it and cannot remove centers once taken. The online, no substitution setting is challenging for clustering–one can show that there exist datasets X for which any O (1) -approximation k -means algorithm must have center complexity Ω( n ) , meaning that it takes Ω( n ) centers in expectation. Bhattacharjee and Moshkovitz (2020) reﬁned this bound by deﬁning a complexity measure called Lower α,k ( X ) , and proving that any α -approximation algorithm must have center complexity Ω( Lower α,k ( X )) . They then complemented their lower bound by giving a O ( k 3 ) -approximation algorithm with center complexity ˜ O ( k 2 Lower k 3 ,k ( X )) , thus showing that their parameter is a tight measure of required center complexity. However, a major drawback of their algorithm is its memory requirement, which is O ( n ) . This makes the algorithm impractical for very large datasets. In this work, we strictly improve upon their algorithm on all three fronts; we develop a 36 - approximation algorithm with center complexity ˜ O ( kLower 36 ,k ( X )) that uses only O ( k ) additional memory. In addition to having nearly optimal memory, this algorithm is the ﬁrst known algorithm with center complexity bounded by Lower 36 ,k ( X ) that is a true O (1) -approximation with its approximation factor being independent of k or n .
From Ramanujan to renormalization: the art of doing away with divergences and arriving at physical results

A century ago Srinivasa Ramanujan --- the great self-taught Indian genius of mathematics --- died, shortly after returning from Cambridge, UK, where he had collaborated with Godfrey Hardy. Ramanujan contributed numerous outstanding results to different branches of mathematics, like analysis and number theory, with a focus on special functions and series. Here we refer to apparently weird values which he assigned to two simple divergent series, $\sum_{n \geq 1} n$ and $\sum_{n \geq 1} n^{3}$. These values are sensible, however, as analytic continuations, which correspond to Riemann's $\zeta$-function. Moreover, they have applications in physics: we discuss the vacuum energy of the photon field, from which one can derive the Casimir force, which has been experimentally measured. We discuss its interpretation, which remains controversial. This is a simple way to illustrate the concept of renormalization, which is vital in quantum field theory.
Counting rational points close to $p$-adic integers and applications in Diophantine approximation

We find upper and lower bounds on the number of rational points that are $\psi$-approximations of some $p$-adic integer. Lattice point counting techniques are used to find the upper bound result, and a Pigeon-hole principle style argument is used to find the lower bound result. We use these results to find the Hausdorff dimension for the set of $p$-adic weighted simultaneously approximable points intersected with $p$-adic coordinate hyperplanes. For the lower bound result we show that the set of rational points that $\tau$-approximate a $p$-adic integer form a set of resonant points that can be used to construct a local ubiquitous system of rectangles.
Biometrics in the Era of COVID-19: Challenges and Opportunities

Since early 2020, the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range of different measures have been implemented worldwide to reduce the rate of new infections and to manage the pressure on national health services. A primary strategy has been to reduce gatherings and the potential for transmission through the prioritisation of remote working and education. Enhanced hand hygiene and the use of facial masks have decreased the spread of pathogens when gatherings are unavoidable. These particular measures present challenges for reliable biometric recognition, e.g., for facial-, voice- and hand-based biometrics. At the same time, new challenges create new opportunities and research directions, e.g., renewed interest in non-constrained iris or periocular recognition, touch-less fingerprint- and vein-based authentication and the use of biometric characteristics for disease detection. This article presents an overview of the research carried out to address those challenges and emerging opportunities.
Recurrent Rational Networks

Latest insights from biology show that intelligence does not only emerge from the connections between the neurons, but that individual neurons shoulder more computational responsibility. Current Neural Network architecture design and search are biased on ﬁxed activation functions. Using more advanced learnable activation functions provide Neural Networks with higher learning capacity. However, general guidance for building such networks is still missing. In this work, we ﬁrst explain why rationals offer an optimal choice for activation functions. We then show that they are closed under residual connections, and inspired by recurrence for residual networks we derive a self-regularized version of Rationals: Recurrent Rationals. We demonstrate that (Recurrent) Rational Networks lead to high performance improvements on Image Classiﬁcation and Deep Reinforcement Learning.
Bulk geometry in gauge/gravity duality and color degrees of freedom

U(N) supersymmetric Yang-Mills theory naturally appears as the low-energy effective theory of a system of N D-branes and open strings between them. Transverse spatial directions emerge from scalar fields, which are N ×N matrices with color indices; roughly speaking, the eigenvalues are the locations of D-branes. In the past, it was argued that this simple ‘emergent space’ picture cannot be used in the context of gauge/gravity duality, because the ground-state wave function delocalizes at large N , leading to a conflict with the locality in the bulk geometry. In this paper we show that this conventional wisdom is not correct: the ground-state wave function does not delocalize, and there is no conflict with the locality of the bulk geometry. This conclusion is obtained by clarifying the meaning of the ‘diagonalization of a matrix’ in Yang-Mills theory, which is not as obvious as one might think. This observation opens up the prospect of characterizing the bulk geometry via the color degrees of freedom in Yang-Mills theory, all the way down to the center of the bulk. 1 ar X iv :2 10 2. 08 98 2v 1 [ he pth ] 1 7 Fe b 20 21
A Late-time Galaxy-targeted Search for the Radio Counterpart of GW190814

GW190814 was a compact object binary coalescence detected in gravitational waves by Advanced LIGO and Advanced Virgo that garnered exceptional community interest due to its excellent localization and the uncertain nature of the binary’s lighter-mass component (either the heaviest known neutron star, or the lightest known black hole). Despite extensive follow-up observations, no electromagnetic counterpart has been identified. Here, we present new radio observations of 75 galaxies within the localization volume at Δt ≈ 35–266 days post-merger. Our observations cover ∼32% of the total stellar luminosity in the final localization volume and extend to later timescales than previously reported searches, allowing us to place the deepest constraints to date on the existence of a radio afterglow from a highly off-axis relativistic jet launched during the merger (assuming that the merger occurred within the observed area). For a viewing angle of ∼46° (the best-fit binary inclination derived from the gravitational wave signal) and assumed electron and magnetic field energy fractions of ϵ e = 0.1 and ϵ B = 0.01, we can rule out a typical short gamma-ray burst-like Gaussian jet with an opening angle of 15° and isotropic-equivalent kinetic energy 2 × 1051 erg propagating into a constant-density medium n ≳ 0.1 cm−3. These are the first limits resulting from a galaxy-targeted search for a radio counterpart to a gravitational wave event, and we discuss the challenges—and possible advantages—of applying similar search strategies to future events using current and upcoming radio facilities.
Theory meets Practice: worst case behavior of quantile algorithms

Estimating the distribution and quantiles of data is a foundational task in data mining and data science. We study algorithms which provide accurate results for extreme quantile queries using a small amount of space, thus helping to understand the tails of the input distribution. Namely, we focus on two recent state-of-the-art solutions: t -digest and ReqSketch . While t -digest is a popular compact summary which works well in a variety of settings, ReqSketch comes with formal accuracy guarantees at the cost of its size growing as new observations are inserted. In this work, we provide insight into which conditions make one preferable to the other. Namely, we show how to construct inputs for t -digest that induce an almost arbitrarily large error and demonstrate that it fails to provide accurate results even on i.i. d. samples from a highly non-uniform distribution. We propose practical improvements to ReqSketch , making it faster than t -digest, while its error stays bounded on any instance. Still, our results conﬁrm that t -digest remains more accurate on the “non-adversarial” data encountered in practice.
The Galactic H ii Region Luminosity Function at Radio and Infrared Wavelengths

The Galactic H ii region luminosity function (LF) is an important metric for understanding global star formation properties of the Milky Way, but only a few studies have been done, and all use relatively small numbers of H ii regions. We use a sample of 797 first Galactic quadrant H ii regions compiled from the Wide-field Infrared Survey Explorer Catalog of Galactic H ii Regions to examine the form of the LF at multiple infrared and radio wavelengths. Our sample is statistically complete for all regions powered by single stars of type O9.5V and earlier. We fit the LF at each wavelength with single and double power laws. Averaging the results from all wavelengths, the mean of the best-fit single power-law index is 〈α〉 = −1.75 ± 0.01. The mean best-fit double power-law indices are 〈α 1〉 = −1.40 ± 0.03 and 〈α 2〉 = −2.33 ± 0.04. We conclude that neither a single nor a double power law is strongly favored over the other. The LFs show some variation when we separate the H ii region sample into subsets by heliocentric distance, physical size, Galactocentric radius, and location relative to the spiral arms, but blending individual H ii regions into larger complexes does not change the value of the power-law indices of the best-fit LF models. The consistency of the power-law indices across multiple wavelengths suggests that the LF is independent of wavelength. This implies that infrared and radio tracers can be employed in place of Hα.
A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space

Recent studies revealed the mathematical connection of deep neural network (DNN) and dynamic system. However, the fundamental principle of DNN has not been fully characterized with dynamic system in terms of optimization and generalization. To this end, we build the connection of DNN and continuity equation where the measure is conserved to model the forward propagation process of DNN which has not been addressed before. DNN learns the transformation of the input distribution to the output one. However, in the measure space, there are infinite curves connecting two distributions. Which one can lead to good optimization and generaliztion for DNN? By diving the optimal transport theory, we find DNN with weight decay attempts to learn the geodesic curve in the Wasserstein space, which is induced by the optimal transport map. Compared with plain network, ResNet is a better approximation to the geodesic curve, which explains why ResNet can be optimized and generalize better. Numerical experiments show that the data tracks of both plain network and ResNet tend to be line-shape in term of line-shape score (LSS), and the map learned by ResNet is closer to the optimal transport map in term of optimal transport score (OTS). In a word, we conclude a mathematical principle of deep learning is to learn the geodesic curve in the Wasserstein space; and deep learning is a great engineering realization of continuous transformation in high-dimensional space.
Sliced $\mathcal{L}_{2}$ Distance for Colour Grading

We propose a new method with L2 distance that maps one N-dimensional distribution to another, taking into account available information about correspondences. We solve the high-dimensional problem in 1D space using an iterative projection approach. To show the potentials of this mapping, we apply it to colour transfer between two images that exhibit overlapped scenes. Experiments show quantitative and qualitative competitive results as compared with the state of the art colour transfer methods.
On the Impact of Attachment Strategies for Payment Channel Networks

Payment channel networks, such as Bitcoin’s Lightning Network, promise to improve the scalability of blockchain systems by processing the majority of transactions off-chain. Due to the design, the positioning of nodes in the network topology is a highly influential factor regarding the experienced performance, costs, and fee revenue of network participants. As a consequence, today’s Lightning Network is built around a small number of highly-connected hubs. Recent literature shows the centralizing tendencies to be incentive-compatible and at the same time detrimental to security and privacy. The choice of attachment strategies therefore becomes a crucial factor for the future of such systems. In this paper, we provide an empirical study on the (local and global) impact of various attachment strategies for payment channel networks. To this end, we introduce candidate strategies from the field of graph theory and analyze them with respect to their computational complexity as well as their repercussions for end users and service providers. Moreover, we evaluate their long-term impact on the network topology.
Planets Across Space and Time (PAST). I. Characterizing the Memberships of Galactic Components and Stellar Ages: Revisiting the Kinematic Methods and Applying to Planet Host Stars

Over 4000 exoplanets have been identified and thousands of candidates are to be confirmed. The relations between the characteristics of these planetary systems and the kinematics, Galactic components, and ages of their host stars have yet to be well explored. To address these questions, we conduct a research project, dubbed Planets Across Space and Time (PAST). To do this, one of the key steps is to accurately characterize the planet host stars. In this paper, Paper I of the PAST series, we revisit the kinematic method for classification of Galactic components and extend the applicable range of velocity ellipsoid from ∼100 pc to ∼1500 pc from the Sun in order to cover most known planet hosts. Furthermore, we revisit the age–velocity dispersion relation (AVR), which allows us to derive kinematic ages with a typical uncertainty of 10–20% for an ensemble of stars. Applying the above revised methods, we present a catalog of kinematic properties (i.e., Galactic positions, velocities, and the relative membership probabilities among the thin disk, thick disk, Hercules stream, and the halo) as well as other basic stellar parameters for 2174 host stars of 2872 planets by combining data from Gaia, LAMOST, APOGEE, RAVE, and the NASA exoplanet archive. The revised kinematic method and AVR, as well as the stellar catalog of kinematic properties and ages, lay the foundation for future studies of exoplanets in space and time in the Galactic context.
ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models

System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called Elixir, where user feedback on explanations is leveraged for pairwise learning of user preferences. Elixir leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.