Miracle-dz/UniFAR-FTUs huggingface dataset:
From energy profiles to structure–reactivity correlations

Models for the energy profile along the reaction coordinate are utilized to determine the barrier's height and location as a function of ΔG. These form the basis for structure–reactivity correlations and afford a unified formulation for various postulates in the field of physical organic chemistry. Current experimental evidence is examined for the resulting correlations, and some of their applications as an aid to the chemical kineticist are presented.
Integrated 3C in NOMA-Enabled Remote-E-Health Systems

A novel framework is proposed to integrate communication, control, and computing (3C) into fifth generation and beyond (5GB) wireless networks for satisfying the ultra-reliable low-latency connectivity requirements of remote e-health systems. Non-orthogonal multiple access (NOMA)-enabled 5GB network architecture is envisioned, while the benefits of bringing it to remote e-health systems are demonstrated. First, the application of NOMA into e-health systems is presented. To elaborate further, a unified NOMA framework for e-health is proposed. As a further advance, NOMA-enabled autonomous robotics (NOMA-ARs) systems and NOMA-enabled edge intelligence (NOMA-EI) toward remote e-health are discussed. Furthermore, a pair of case studies are provided to show the great performance enhancement with the use of NOMA in typical application scenarios of 5GB in remote e-health systems. Finally, potential research challenges and opportunities are discussed.
Accelerating Radiation Therapy Dose Calculation with Nvidia GPUs

Radiation Treatment Planning (RTP) is the process of planning the appropriate external beam radiotherapy to combat cancer in human patients. RTP is a complex and compute-intensive task, which often takes a long time (several hours) to compute. Reducing this time allows for higher productivity at clinics and more sophisticated treatment planning, which can materialize in better treatments. The state-of-the-art in medical facilities uses general-purpose processors (CPUs) to perform many steps in the RTP process. In this paper, we explore the use of accelerators to reduce RTP calculating time. We focus on the step that calculates the dose using the Graphics Processing Unit (GPU), which we believe is an excellent candidate for this computation type. Next, we create a highly optimized implementation for a custom Sparse Matrix-Vector Multiplication (SpMV) that operates on numerical formats unavailable in state-of-the-art SpMV libraries (e.g., Ginkgo and cuSPARSE). We show that our implementation is several times faster than the baseline (up-to 4x) and has a higher operational intensity than similar (but different) versions such as Ginkgo and cuSPARSE.
Quantization of Calogero-Painlevé System and Multi-Particle Quantum Painlevé Equations II-VI

We consider the isomonodromic formulation of the Calogero-Painlevé multi-particle systems and proceed to their canonical quantization. We then proceed to the quantum Hamiltonian reduction on a special representation to radial variables, in analogy with the classical case and also with the theory of quantum Calogero equations. This quantized version is compared to the generalization of a result of Nagoya on integral representations of certain solutions of the quantum Painlevé equations. We also provide multi-particle generalizations of these integral representations.
Nonequilibrium Green-Kubo relations for hydrodynamic transport from an equilibrium-like fluctuation-response equality

Near equilibrium, Green-Kubo relations provide microscopic expressions for macroscopic transport coefficients in terms of equilibrium correlation functions. At their core, they are based on the intimate relationship between response and fluctuations as embodied by the equilibrium fluctuation-dissipation theorem, a connection generically broken far from equilibrium. In this work, we identify a class of perturbations whose response around far-from-equilibrium steady states is linked to steady-state correlation functions via an equilibrium-like fluctuation-response equality. We then utilize this prediction to substantiate linearized hydrodynamic transport equations that describe how spatial inhomogeneities in macroscopic nonequilibrium systems relax. As a consequence, we derive nonequilibrium Green-Kubo relations for the transport coefficients of two types of hydrodynamic variables: local conserved densities and broken-symmetry modes. A byproduct of this work is to provide a theoretical foundation for the validity of Onsager's regression hypothesis around nonequilibrium steady states. Our predictions are analytically and numerically corroborated for two model systems: density diffusion in a fluid of soft, spherical active Brownian particles and phase diffusion in the noisy Kuramoto model on a square lattice.
Training GANs with Stronger Augmentations via Contrastive Discriminator

Recent works in Generative Adversarial Networks (GANs) are actively revisiting various data augmentation techniques as an effective way to prevent discriminator overfitting. It is still unclear, however, that which augmentations could actually improve GANs, and in particular, how to apply a wider range of augmentations in training. In this paper, we propose a novel way to address these questions by incorporating a recent contrastive representation learning scheme into the GAN discriminator, coined ContraD. This"fusion"enables the discriminators to work with much stronger augmentations without increasing their training instability, thereby preventing the discriminator overfitting issue in GANs more effectively. Even better, we observe that the contrastive learning itself also benefits from our GAN training, i.e., by maintaining discriminative features between real and fake samples, suggesting a strong coherence between the two worlds: good contrastive representations are also good for GAN discriminators, and vice versa. Our experimental results show that GANs with ContraD consistently improve FID and IS compared to other recent techniques incorporating data augmentations, still maintaining highly discriminative features in the discriminator in terms of the linear evaluation. Finally, as a byproduct, we also show that our GANs trained in an unsupervised manner (without labels) can induce many conditional generative models via a simple latent sampling, leveraging the learned features of ContraD. Code is available at https://github.com/jh-jeong/ContraD.
An experience-based recommendation system to support migrations of Android applications from Java to Kotlin

—In 2017, Google announced Kotlin as an ofﬁcial Android programming language, and more recently, as the preferred programming language to build applications. These facts motivated developers to migrate their applications, which is challenging because each migrated piece of code must be tested after the migration to ensure it preserves the expected behavior. Due to the interoperability between Java and Kotlin, most developers decided to migrate their applications gradually. Thus, developers have to decide which ﬁle(s) to migrate ﬁrst on each migration step. However, there are no tools available to help developers make these choices. This paper presents an approach to support a gradual migration of Android applications that given a version of an application written in Java and eventually, in Kotlin, it suggests the most convenient ﬁles to migrate. To this end, we built a large-scale corpus of open-source projects that migrated Java ﬁles to Kotlin. Then, we trained a learning to rank model using the information extracted from these projects. To validate our model, we verify whether these recommendations made by them correspond to real migrations. The results showed our approach modestly outperforms random approaches. Since most Android applications are written in Java, we conclude that our approach may signiﬁcantly impact Android applications’ development. Therefore, we consider this result is the ﬁrst step into long-term research towards a model capable of predicting precisely ﬁle-level migration, establishing the initial baseline on ﬁle migrations.
Weak lensing scattering transform: dark energy and neutrino mass sensitivity

As weak lensing surveys become deeper, they reveal more non-Gaussian aspects of the convergence field which can only be extracted using statistics beyond the power spectrum. In a companion paper, we showed that the scattering transform, a novel statistic borrowing mathematical concepts from convolutional neural networks, is a powerful tool for cosmological parameter estimation in the non-Gaussian regime. Here, we extend that analysis to explore its sensitivity to dark energy and neutrino mass parameters with weak lensing surveys. We first use image synthesis to show visually that, compared to the power spectrum and bispectrum, the scattering transform provides a better statistical vocabulary to characterize the perceptual properties of lensing mass maps. We then show that it is also better suited for parameter inference: (i) it provides higher sensitivity in the noiseless regime, and (ii) at the noise level of Rubin-like surveys, though the constraints are not significantly tighter than those of the bispectrum, the scattering coefficients have a more Gaussian sampling distribution, which is an important property for likelihood parametrization and accurate cosmological inference. We argue that the scattering coefficients are preferred statistics considering both constraining power and likelihood properties.
Fault Current Limiter Dynamic Voltage Restorer (FCL-DVR) With Reduced Number of Components

In this article, a new dual-function fault-current limiter-dynamic voltage restorer (FCL-DVR) topology is proposed. The proposed structure, in addition to performing routine FCL tasks, can be used to improve the voltage quality of point of common coupling. A salient feature of this FCL-DVR is its reduced number of semiconductor switches and gate drive and control circuit components. Perhaps, variety structures of FCL-DVR have been proposed but most distinctive feature of proposed structure is lower power loss. The operation modes and the control strategy of the FCL have been presented and studied. In addition, the proposed structure has been compared with other structures to prove the efficiency of the proposed structure. The simulation results as well as experimental outcomes from a laboratory scaled-down prototype are provided, which prove the efficiency and feasibility of the proposed structure.
A New Modified Newton-Type Iteration Methods for Solving Generalized Absolute Value Equations

For the large sparse generalized absolute value equation (GAVE), based on the shift splitting for the coefficient matrix of linear part, we establish a new modified Newton-type (NMN) iteration method. The convergence conditions of the NMN iteration method are discussed in depth. Furthermore, some specific sufficient convergence conditions are derived when the coefficient matrix is a symmetric positive definite matrix and an H+-matrix. Both two numerical examples indicate that the NMN iteration method is an effective approach to solve the GAVE, especially when the coefficient matrix is indefinite.
A Comprehensive Analysis of Allostery in 14-3-3 ζ Docking Proteins using the Spatial Convolution Model (SCM)

The Spatial Convolution Model (SCM) analyzes allostery based on the spatial evolution of the docking protein elastic media, whereby convolution of the media in response to wave propagation is solved as a function of Z fluctuations and backbone vibration modes. We show that although the elastic media is a complex three-dimensional structure allostery behaves as if it occurs along a stretched oscillating string, where inhomogeneities along the string effect local entropies responsible for ligand binding and transduction of allosteric waves. To identify inhomogeneities along the string, we ignored local density and tension changes during wave propagation and resolved helix wave and physical properties by applying molecular string and beam bending theories. Importantly, we show that allostery occurs at three major scales and that propagation of standing waves create a rolling entropy which drives entropy transfers between fields. Conversion of resonance energy to quantum harmonic oscillators allowed us to consider effects of damping and interactions with the surrounding media as well as to model effects of residue interaction strength on entropy transfer.
The Muskat problem with $C^1$ data

The Muskat equation is an important model in the analysis of free surface flows, which describes the dynamics of two incompressible and immiscible fluids with different densities and viscosities separated by a porous media whose velocities obey Darcy’s law (see [23,30]). Its main feature is that it is a fractional degenerate parabolic equation. This feature is shared by several equations which have attracted a lot of attention in recent years, like the surface quasi-geostrophic equation, the Hele-Shaw equation and the fractional porous media equation. Let us introduce the Muskat problem. We consider the dynamics of a time-dependent curve Σ(t) separating two domains Ω1(t) and Ω2(t). Under the supposition that Σ(t) is the graph of some function, we introduce the following notations
Pose-GNN : Camera Pose Estimation System Using Graph Neural Networks

We propose a novel image based localization system using graph neural networks (GNN). The pretrained ResNet50 convolutional neural network (CNN) architecture is used to extract the important features for each image. Following, the extracted features are input to GNN to find the pose of each image by either using the image features as a node in a graph and formulate the pose estimation problem as node pose regression or modelling the image features themselves as a graph and the problem becomes graph pose regression. We do an extensive comparison between the proposed two approaches and the state of the art single image localization methods and show that using GNN leads to enhanced performance for both indoor and outdoor environments.
Beyond perturbation theory in inflation

Inflationary perturbations are approximately Gaussian and deviations from Gaussianity are usually calculated using in-in perturbation theory. This method, however, fails for unlikely events on the tail of the probability distribution: in this regime non-Gaussianities are important and perturbation theory breaks down for |ζ| ≳ |f NL|-1. In this paper we show that this regime is amenable to a semiclassical treatment, ħ → 0. In this limit the wavefunction of the Universe can be calculated in saddle-point, corresponding to a resummation of all the tree-level Witten diagrams. The saddle can be found by solving numerically the classical (Euclidean) non-linear equations of motion, with prescribed boundary conditions. We apply these ideas to a model with an inflaton self-interaction ∝λζ̇4. Numerical and analytical methods show that the tail of the probability distribution of ζ goes as exp(-λ-1/4ζ3/2), with a clear non-perturbative dependence on the coupling. Our results are relevant for the calculation of the abundance of primordial black holes.
Cembrane Diterpenes Chemistry and Biological Properties

Cembrane-type diterpenoids are a large and structurally varied group of natural products isolated from both terrestrial and marine organisms. The present paper reviews all the metabolites of cembrane diterpenes, reported up to 2010. The natural products discussed in this review can be divided into several different structural families featuring a variety of ring sizes and oxidation patterns. The currently known biological activities will be presented as well.
Low overhead universality and quantum supremacy using only 
Z
 control

We consider a model of quantum computation we call"Varying-$Z$"(V$Z$), defined by applying controllable $Z$-diagonal Hamiltonians in the presence of a uniform and constant external $X$-field, and prove that it is universal, even in 1D. Universality is demonstrated by construction of a universal gate set with $O(1)$ depth overhead. We then use this construction to describe a circuit whose output distribution cannot be classically simulated unless the polynomial hierarchy collapses, with the goal of providing a low-resource method of demonstrating quantum supremacy. The V$Z$ model can achieve quantum supremacy in $O(n)$ depth, equivalent to the random circuit sampling models despite a higher degree of homogeneity: it requires no individually addressed $X$-control.
On the Role of Images for Analyzing Claims in Social Media

Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
The least favorable noise

Suppose that a random variable X of interest is observed perturbed by independent additive noise Y . This paper concerns the “the least favorable perturbation” ˆ Y ε , which maximizes the prediction error E ( X − E ( X | X + Y )) 2 in the class of Y with var ( Y ) ≤ ε . We ﬁnd a characterization of the answer to this question, and show by example that it can be surprisingly complicated. However, in the special case where X is inﬁnitely divisible, the solution is complete and simple. We also explore the conjecture that noisier Y makes prediction worse.
New Experimental 23Na(α, p)26Mg Reaction Rate for Massive Star and Type Ia Supernova Models

The 23Na(α, p)26Mg reaction has been identified as having a significant impact on the nucleosynthesis of several nuclei between Ne and Ti in Type Ia supernovae, and of 23Na and 26Al in massive stars. The reaction has been subjected to renewed experimental interest recently, motivated by high uncertainties in early experimental data and in the statistical Hauser-Feshbach models used in reaction rate compilations. Early experiments were affected by target deterioration issues and unquantifiable uncertainties. Three new independent measurements instead are utilizing inverse kinematics and Rutherford scattering monitoring to resolve this. In this work we present directly measured angular distributions of the emitted protons to eliminate a discrepancy in the assumptions made in the recent reaction rate measurements, which results in cross sections differing by a factor of 3. We derive a new combined experimental reaction rate for the 23Na(α, p)26Mg reaction with a total uncertainty of 30% at relevant temperatures. Using our new 23Na(α, p)26Mg rate, the 26Al and 23Na production uncertainty is reduced to within 8%. In comparison, using the factor of 10 uncertainty previously recommended by the rate compilation STARLIB, 26Al and 23Na production was changing by more than a factor of 2. In Type Ia supernova conditions, the impact on production of 23Na is constrained to within 15%.
Optimizing edge sets in networks to produce ground truth communities based on modularity

We consider two new problems regarding the impact of edge addition or removal on the modularity of partitions (or community structures) in a network. The first problem seeks to add edges to enforce that a desired partition is one partition that maximizes modularity. The second problem seeks to find the sparsest representation of a network that has the same partition with maximum modularity as the original network. We present integer programming formulations, a row generation algorithm, and heuristic algorithms to solve these problems. Further, we demonstrate a counter‐intuitive behavior of modularity that makes the development of heuristics for general networks difficult. We then present results on a selection of social and illicit networks from the literature.
A Note on Over- and Under-Representation Among Populations with Normally-Distributed Traits

Among several subpopulations of a given species with a normally-distributed trait, such as height, blood pressure, or various test scores in humans, one and only one of the subpopulations will always strongly dominate all the others in the right tail, that is, in the high-end values of that trait. Examples are given to show that this is not true in general for other common classes of continuous centrallysymmetric unimodal distributions such as Laplace, nor even for other bell-shaped distributions such as Cauchy.
M ar 2 02 1 IV Regression with Possibly Uncorrelated Instruments

This paper proposes a closed-form linear IV estimator which allows endogenous covariates to be weakly correlated or un-correlated but mean-dependent on instruments. Identification rests on (1) a weak uncorrelatedness exclusion restriction and (2) a weak relevance condition where covariates are mean-dependent on instruments. The significant weakening of the relevance condition does not come at the cost of a stronger exclusion restriction. The estimator is root-n-consistent and asymptotically normal. Monte Carlo simulations show the estimator exploits unknown forms of both monotone and non-monotone identifying variation equally well, and it incurs less bias and size distortion relative to conventional IV methods when instruments are weak. An empirical example illustrates the practical usefulness of the estimator.
Virtual Probes of Mineral–Water Interfaces: The More Flops, the Better!

New approaches are allowing computer simulations to be compared quantitatively with experimental results, and they are also raising new questions about reactivity at mineral–water interfaces. Molecular simulations not only help us to understand experimental observations, they can also be used to test hypotheses about the properties of geochemical systems. These new approaches include rigorous calibration of simulation models against thermodynamic properties and atomic structure. They also encompass rare event theory methods that allow simulation of slow, complex mineral surface reactions. Here, we give an overview of how these techniques have been applied to simulate mineral–water interface structure, growth/dissolution mechanisms, and cluster formation.
In Situ Synthesis of Polymer Nanocomposites from PANI/PAA/MWCNTs: Analysis and Characterization

We are proposing a polyaniline (PANI)/polyacrylic acid (PAA)/multiwalled carbon nanotube (MWCNT) composite by in situ chemical polymerization method. For making polyaniline more user friendly and with desired properties, bulky polymeric materials have been incorporated, and the material prepared shows improved catalytic, electrochemical, and electrical behavior. We reinforced the MWCNTs in the PANI-PAA system for introducing the benefits of MWCNTs to the composite. It is well reported that carbon nanotubes act as conducting fillers (increase the bulk conductivity of the material), improve electro-catalytic properties, and increase the surface area of the material. The composite was characterized by FT-IR, UV-visible, TEM, SEM, and conductivity measurement techniques. The composites were prepared in two different acids, hydrochloric acid and sulfuric acid. It was observed that the composite prepared in the presence of sulfuric acid is better than that prepared in hydrochloric acid in terms of more quinoid units than benzenoid units and less possibility of dedoping of anions along with MWCNTs.