Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-covalent Interaction Between Oligodeoxynucleotide and Alkaloid Compounds via Negative Ion Electrospray Mass Spectrometry(I)

Oligodeoxynucleotide from SARS virus was selected and synthesized. Its complexes with alkaloid compounds were investigated by electrospray mass spectrometry. We found that three alkaloid compounds could interact with the DNA target molecule in five alkaloids analyzed. With increasing molar ratio of the three alkaloid compounds, the complexes between alkaloids and DNA with different stoichiometric ratios were found in their MS spectra. The order of the alkalinity of their gas phase corresponds to that of liquid phase, that is: PalmatineJatrorrhizineBerberin. The relative abundance of these complexes in mass spectra can assess the relative affinities of above three alkaloid compounds with the DNA target molecule. In this paper, the binding sites between jatrorrhizine and DNA was deduced from the interaction experiments of jatrorrhizine with three nucleosides.
Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization

Dysarthric speech detection (DSD) systems aim to detect characteristics of the neuromotor disorder from speech. Such systems are particularly susceptible to domain mismatch where the training and testing data come from the source and target domains respectively, but the two domains may differ in terms of speech stimuli, disease etiology, etc. It is hard to acquire labelled data in the target domain, due to high costs of annotating sizeable datasets. This paper makes a first attempt to formulate cross-domain DSD as an unsupervised domain adaptation (UDA) problem. We use labelled source-domain data and unlabelled target-domain data, and propose a multi-task learning strategy, including dysarthria presence classification (DPC), domain adversarial training (DAT) and mutual information minimization (MIM), which aim to learn dysarthria-discriminative and domain-invariant biomarker embeddings. Specifically, DPC helps biomarker embeddings capture critical indicators of dysarthria; DAT forces biomarker embeddings to be indistinguishable in source and target domains; and MIM further reduces the correlation between biomarker embeddings and domain-related cues. By treating the UASPEECH and TORGO corpora respectively as the source and target domains, experiments show that the incorporation of UDA attains absolute increases of 22.2% and 20.0% respectively in utterance-level weighted average recall and speaker-level accuracy.
Detox Browser - Towards Filtering Sensitive Content On the Web

The annual consumption of web-based resources is increasing at a very fast rate, mainly due to an increase in affordability and accessibility of the internet. Many are relying on the web to get diverse perspectives, but at the same time, it can expose them to content that is harmful to their mental well-being. Catchy headlines and emotionally charged articles increase the number of readers which in turn increases ad revenue for websites. When a user consumes a large quantity of negative content, it adversely impacts the user's happiness and has a significant impact on his/her mood and state of mind. Many studies carried out during the COVID-19 pandemic has shown that people across the globe irrespective of their country of origin have experienced higher levels of anxiety and depression. Web filters can help in constructing a digital environment that is more suitable for people prone to depression, anxiety and stress. A significant amount of work has been done in the field of web filtering, but there has been limited focus on helping Highly Sensitive Persons (HSP's) or those with stress disorders induced by trauma. Through this paper, we propose detox Browser, a simple tool that enables end-users to tune out of or control their exposure to topics that can affect their mental well being. The extension makes use of sentiment analysis and keywords to filter out flagged content from google search results and warns users if any blacklisted topics are detected when navigating across websites
Strong Correlation between Fe ii/Mg ii Ratio and Eddington Ratio of Type 1 Active Galactic Nuclei

The Fe ii/Mg ii line flux ratio has been used as an indicator of the Fe/Mg abundance ratio in the broad-line region (BLR) of active galactic nuclei (AGNs). On the basis of archival rest-frame UV spectra obtained via the Hubble Space Telescope and the Sloan Digital Sky Survey, we investigate the Fe ii/Mg ii ratios of type 1 AGNs at z < 2. Over wide dynamic ranges of AGN properties (i.e., black hole mass, AGN luminosity, and Eddington ratio), we confirm that the Fe ii/Mg ii ratio strongly correlates with Eddington ratio but not with black hole mass, AGN luminosity, or redshift. Our results suggest that the metallicity in the BLR are physically related to the accretion activity of AGNs, but not to the global properties of galaxies (i.e., galaxy mass and luminosity). With regard to the relation between the BLR metallicity and the accretion rate of AGNs, we discuss that metal cooling may play an important role in enhancing the gas inflow into the central region of host galaxies, resulting in the high accretion rate of AGNs.
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network

Training a Convolutional Neural Network (CNN) to be robust against rotation has mostly been done with data augmentation. In this paper, another progressive vision of research direction is highlighted to encourage less dependence on data augmentation by achieving structural rotational invariance of a network. The deep equivariance-bridged SO(2) invariant network is proposed to echo such vision. First, Self-Weighted Nearest Neighbors Graph Convolutional Network (SWN-GCN) is proposed to implement Graph Convolutional Network (GCN) on the graph representation of an image to acquire rotationally equivariant representation, as GCN is more suitable for constructing deeper network than spectral graph convolution-based approaches. Then, invariant representation is eventually obtained with Global Average Pooling (GAP), a permutation-invariant operation suitable for aggregating high-dimensional representations, over the equivariant set of vertices retrieved from SWN-GCN. Our method achieves the state-of-the-art image classification performance on rotated MNIST and CIFAR-10 images, where the models are trained with a non-augmented dataset only. Quantitative validations over invariance of the representations also demonstrate strong invariance of deep representations of SWN-GCN over rotations.
Local asymptotics of cross-validation in least-squares density estimation

In model selection, several types of cross-validation are commonly used and many variants have been introduced. While consistency of some of these methods has been proven, their rate of convergence to the oracle is generally still unknown. Until now, an asymptotic analysis of crossvalidation able to answer this question has been lacking. Existing results focus on the "pointwise" estimation of the risk of a single estimator, whereas analysing model selection requires understanding how the CV risk varies with the model. In this article, we investigate the asymptotics of the CV risk in the neighbourhood of the optimal model, for trigonometric series estimators in density estimation. Asymptotically, simple validation and "incomplete" V−fold CV behave like the sum of a convex function fn and a symmetrized Brownian changed in time Wgn/V . We argue that this is the right asymptotic framework for studying model selection.
Introducing PathQuery, Google's Graph Query Language

We introduce PathQuery, a graph query language developed to scale with Google’s query and data volumes as well as its internal developer community. PathQuery supports flexible and declarative semantics. We have found that this enables query developers to think in a naturally “graphy” design space and to avoid the additional cognitive e ort of coordinating numerous joins and subqueries often required to express an equivalent query in a relational space. Despite its traversal-oriented syntactic style, PathQuery has a foundation on a custom variant of relational algebra – the exposition of which we presently defer – allowing for the application of both common and novel optimizations. We believe that PathQuery has withstood a “test of time” at Google, under both large scale and low latency requirements. We thus share herein a language design that admits a rigorous declarative semantics, has scaled well in practice, and provides a natural syntax for graph traversals while also admitting complex graph patterns.
Backreaction from inhomogeneous matter fields during large-scale structure formation

We study how inhomogeneities of the cosmological fluid fields backreact on the homogeneous part of energy density and how they modify the Friedmann equations. In general, backreaction requires to go beyond the pressureless ideal fluid approximation, and this can lead to a reduced growth of cosmological large scale structure. Since observational evidence favours evolution close to the standard growing mode in the linear regime, we focus on two-component fluids in which the non-ideal fluid is gravitationally coupled to cold dark matter and in which a standard growing mode persists. This is realized, e.g. for a baryonic fluid coupled to cold dark matter. We calculate the backreaction for this case and for a wide range of other two-fluid models. Here the effect is either suppressed because the non-ideal matter properties are numerically too small, or because they lead to a too stringent UV cut-off of the integral over the power spectrum that determines backreaction. We discuss then matter field backreaction from a broader perspective and generalize the formalism such that also far-from-equilibrium scenarios relevant to late cosmological times and non-linear scales can be addressed in the future.
A learned conditional prior for the VAE acoustic space of a TTS system

Many factors influence speech yielding different renditions of a given sentence. Generative models, such as variational autoencoders (VAEs), capture this variability and allow multiple renditions of the same sentence via sampling. The degree of prosodic variability depends heavily on the prior that is used when sampling. In this paper, we propose a novel method to compute an informative prior for the VAE latent space of a neural text-to-speech (TTS) system. By doing so, we aim to sample with more prosodic variability, while gaining controllability over the latent space's structure. By using as prior the posterior distribution of a secondary VAE, which we condition on a speaker vector, we can sample from the primary VAE taking explicitly the conditioning into account and resulting in samples from a specific region of the latent space for each condition (i.e. speaker). A formal preference test demonstrates significant preference of the proposed approach over standard Conditional VAE. We also provide visualisations of the latent space where well-separated condition-specific clusters appear, as well as ablation studies to better understand the behaviour of the system.
Searching for Point Locations Using Lines

Summary Suppose there are n points that we wish to locate on a plane. Instead of the locations of the points, we are given all the lines of k distinct slopes that contain the points. We show that the minimum number of slopes needed, in general, to find all the point locations is n + 1 and we provide an algorithm to do so.
Software-Defined Networking for Data Centre Network Management: A Survey

Data centres are growing in numbers and size, and their networks expanding to carry larger amounts of traffic. The traffic profile is constantly varying, particularly in cloud data centres where tenants arrive, leave, and may change their resource requirements in between, and so the network configuration must change at a commensurate rate. Software-Defined Networking - programmatic control of network configuration - has been critical to meeting the demands of modern data centre network management, and has been the subject of intense focus by the research community, working in conjunction with industry. In this survey, we review Software-Defined Networking research targeting the management and operation of data centre networks.
LNIRT: An R Package for Joint Modeling of Response Accuracy and Times

In \textit{computer-based testing} it has become standard to collect response accuracy (RA) and response times (RTs) for each test item. IRT models are used to measure a latent variable (e.g., ability, intelligence) using the RA observations. The information in the RTs can help to improve routine operations in (educational) testing, and provide information about speed of working. In modern applications, the joint models are needed to integrate RT information in a test analysis. The R-package LNIRT supports fitting joint models through a user-friendly setup which only requires specifying RA, RT data, and the total number of Gibbs sampling iterations. More detailed specifications of the analysis are optional. The main results can be reported through the summary functions, but output can also be analysed with Markov chain Monte Carlo (MCMC) output tools (i.e., coda, mcmcse). The main functionality of the LNIRT package is illustrated with two real data applications.
Associated Molecular and Atomic Clouds with X-Ray Shell of Superbubble 30 Doradus C in the LMC

30 Doradus C is a superbubble that emits the brightest non-thermal X- and TeV gamma-rays in the Local Group. To explore the detailed connection between the high-energy radiation and the interstellar medium, we have carried out new CO and Hi observations using the Atacama Large Millimeter/Submillimeter Array (ALMA), Atacama Submillimeter Telescope Experiment, and the Australia Telescope Compact Array with resolutions of up to 3 pc. The ALMA data of 12CO(J = 1–0) emission revealed 23 molecular clouds, with typical diameters of ∼6–12 pc and masses of ∼600–10,000 M ⊙. A comparison with the X-rays of XMM–Newton at ∼3 pc resolution shows that X-rays are enhanced toward these clouds. The CO data were combined with the Hi to estimate the total interstellar protons. A comparison of the interstellar proton column density and the X-rays revealed that the X-rays are enhanced with the total proton column density. These are most likely to be caused by the shock-cloud interaction, which is modeled by magnetohydrodynamical simulations (Inoue et al. 2012). We also note a trend for the X-ray photon index to vary with distance from the center of the high-mass star cluster. This suggests that the cosmic-ray electrons are accelerated by one or multiple supernovae in the cluster. Based on these results, we discuss the role of the interstellar medium in cosmic-ray particle acceleration.
How COVID-19 Has Changed Crowdfunding: Evidence From GoFundMe

While the long-term effects of the COVID-19 pandemic have yet to be determined, its immediate impact on crowdfunding is nonetheless significant. This study adopts a computational approach to better understanding this consequence. We aim to gain insight into whether and how the COVID-19 pandemic has changed crowdfunding. Using a unique dataset of all GoFundMe campaigns published over the past 2 years, we explore the factors that have led to successfully funded crowdfunding projects. In particular, we study a corpus of 36,370 projects from November 2018 to December 2020 by analyzing cover images and other attributes commonly found on crowdfunding sites. We first construct a classifier and a regression model to assess the importance of features based on XGBoost. Next, we employ counterfactual analysis to investigate the causality between features and the success of crowdfunding. Furthermore, sentiment analysis and paired sample t-tests are performed to examine differences in crowdfunding campaigns before and after the COVID-19 outbreak in March 2020. Findings suggest a significant racial disparity in crowdfunding success. In addition, sad emotions expressed in a campaign's description became significant after the COVID-19 outbreak. This study enriches our understanding of the impact of the COVID-19 pandemic on crowdfunding as well as the prevalence of discrimination in crowdfunding.
Anomaly Detection in Dynamic Graphs via Transformer

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. Recent deep learning-based approaches have shown promising results over shallow methods. However, they fail to address two core challenges of anomaly detection in dynamic graphs: the lack of informative encoding for unattributed nodes and the difficulty of learning discriminate knowledge from coupled spatial-temporal dynamic graphs. To overcome these challenges, in this paper, we present a novel <underline><bold>T</bold></underline>ransformer-based <underline><bold>A</bold></underline>nomaly <underline><bold>D</bold></underline>etection framework for <underline><bold>DY</bold></underline>namic graphs (<bold>TADDY</bold>). Our framework constructs a comprehensive node encoding strategy to better represent each node’s structural and temporal roles in an evolving graphs stream. Meanwhile, TADDY captures informative representation from dynamic graphs with coupled spatial-temporal patterns via a dynamic graph transformer model. The extensive experimental results demonstrate that our proposed TADDY framework outperforms the state-of-the-art methods by a large margin on six real-world datasets.
Active Offline Policy Selection

This paper addresses the problem of policy selection in domains with abundant logged data, but with a restricted interaction budget. Solving this problem would enable safe evaluation and deployment of ofﬂine reinforcement learning policies in industry, robotics, and recommendation domains among others. Several off-policy evaluation (OPE) techniques have been proposed to assess the value of policies using only logged data. However, there is still a big gap between the evaluation by OPE and the full online evaluation. Yet, large amounts of online interactions are often not possible in practice. To overcome this problem, we introduce active ofﬂine policy selection — a novel sequential decision approach that combines logged data with online interaction to identify the best policy. We use OPE estimates to warm start the online evaluation. Then, in order to utilize the limited environment interactions wisely we decide which policy to evaluate next based on a Bayesian optimization method with a kernel that represents policy similarity. We use multiple benchmarks, including real-world robotics, with a large number of candidate policies to show that the proposed approach improves upon state-of-the-art OPE estimates and pure online policy evaluation 2 .
Divergences in gravitational-wave emission and absorption from extreme mass ratio binaries

Enrico Barausse,1, 2 Emanuele Berti,3 Vitor Cardoso,4 Scott A. Hughes,5 and Gaurav Khanna6, 7 1SISSA, Via Bonomea 265, 34136 Trieste, Italy and INFN Sezione di Trieste 2IFPU Institute for Fundamental Physics of the Universe, Via Beirut 2, 34014 Trieste, Italy 3Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, US 4CENTRA, Departamento de Física, Instituto Superior Técnico – IST, Universidade de Lisboa – UL, Avenida Rovisco Pais 1, 1049 Lisboa, Portugal 5Department of Physics and MIT Kavli Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 6Department of Physics, University of Rhode Island, Kingston, RI 02881 7Center for Scientific Computing & Visualization Research and Physics Department, University of Massachusetts, Dartmouth, MA 02747
A Probabilistic Representation of DNNs: Bridging Mutual Information and Generalization

Recently, Mutual Information (MI) has attracted attention in bounding the generalization error of Deep Neural Networks (DNNs). However, it is intractable to accurately estimate the MI in DNNs, thus most previous works have to relax the MI bound, which in turn weakens the information theoretic explanation for generalization. To address the limitation, this paper introduces a probabilistic representation of DNNs for accurately estimating the MI. Leveraging the proposed MI estimator, we validate the information theoretic explanation for generalization, and derive a tighter generalization bound than the state-of-the-art relaxations.
Cyclic and non-cyclic division algebras of finite dp-rank

Milliet asks the following question: given two prime numbers $p\neq q$, is there a division algebra of characteristic $p$ which is of dp-rank $q^2$ and of dimension $q^2$ over its center? We answer in the affirmative. We also give an example of a finite burden central division algebra over some ultraproduct of $p$-adic numbers. As a conclusion we revisit an example of Albert to prove that there exists non-cyclic division algebras of finite dp-rank.
Epitaxial growth and magnetic characterization of EuSe thin films with various crystalline orientations

We report different growth modes and corresponding magnetic properties of thin EuSe films grown by molecular beam epitaxy on BaF2, Pb1-xEuxSe, GaAs and Bi2Se3 substrates. We show that EuSe growth predominantly in (001) orientation on GaAs(111) and Bi2Se3, but along (111) crystallographic direction on BaF2 (111) and Pb1-xEuxSe (111). High resolution transmission electron microscopy measurements reveal an abrupt and highly crystalline interface for both (001) and (111) EuSe films. In agreement with previous studies, ordered magnetic phases include antiferromagnetic, ferrimagnetic and ferromagnetic phases. In contrast to previous studies, we found strong hysteresis for the antiferromagnetic-ferrimagnetic transition. An ability to grow epitaxial films of EuSe on Bi2Se3 and of Bi2Se3 on EuSe enables further investigation of interfacial exchange interactions between various phases of an insulating metamagnetic material and a topological insulator.
A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction

In this paper, we propose a novel edge-editing approach to extract relation information from a document. We treat the relations in a document as a relation graph among entities in this approach. The relation graph is iteratively constructed by editing edges of an initial graph, which might be a graph extracted by another system or an empty graph. The way to edit edges is to classify them in a close-first manner using the document and temporally-constructed graph information; each edge is represented with a document context information by a pretrained transformer model and a graph context information by a graph convolutional neural network model. We evaluate our approach on the task to extract material synthesis procedures from materials science texts. The experimental results show the effectiveness of our approach in editing the graphs initialized by our in-house rule-based system and empty graphs.
Centrality Measures in Interval-Weighted Networks

Centrality measures are used in network science to assess the centrality of vertices or the position they occupy in a network. There are a large number of centrality measures according to some criterion. However, the generalizations of the most well-known centrality measures for weighted networks, degree centrality, closeness centrality and betweenness centrality have solely assumed the edge weights to be constants. This article proposes a methodology to generalize degree, closeness and betweenness centralities taking into account the variability of edge weights in the form of closed intervals (interval-weighted networks, IWN). We apply our centrality measures approach to two real-world IWN. The first is a commuter network in mainland Portugal, between the 23 NUTS 3 Regions. The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015.