nils-herrmann/clean_scirepeval huggingface dataset:
Tale of tails using rule augmented sequence labeling for event extraction

The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data. Moreover, the task becomes complex for tail (rarely occurring) labels wherein extremely less data is available. In this paper, we present a new dataset (InDEE-2019) in the disaster domain for multiple Indic languages, collected from news websites. Using this dataset, we evaluate several rule-based mechanisms to augment deep learning based models. We formulate our problem of event extraction as a sequence labeling task and perform extensive experiments to study and understand the effectiveness of different approaches. We further show that tail labels can be easily incorporated by creating new rules without the requirement of large annotated data.
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization

Recent years have seen increased interest in performance guarantees of gradient descent algorithms for non-convex optimization. A number of works have uncovered that gradient noise plays a critical role in the ability of gradient descent recursions to efficiently escape saddle-points and reach second-order stationary points. Most available works limit the gradient noise component to be bounded with probability one or sub-Gaussian and leverage concentration inequalities to arrive at high-probability results. We present an alternate approach, relying primarily on mean-square arguments and show that a more relaxed relative bound on the gradient noise variance is sufficient to ensure efficient escape from saddle-points without the need to inject additional noise, employ alternating step-sizes or rely on a global dispersive noise assumption, as long as a gradient noise component is present in a descent direction for every saddle-point.
Digraphs are different: why directionality matters in complex systems

Many networks describing complex systems are directed: the interactions between elements are not symmetric. Recent work has shown that these networks can display properties such as trophic coherence or non-normality, which in turn affect stability, percolation and other dynamical features. I show here that these topological properties have a common origin, in that the edges of directed networks can be aligned—or not—with a global direction. And I illustrate how this can lead to rich and unexpected dynamical behaviour even in the simplest of models.
Topic Augmented Generator for Abstractive Summarization

Steady progress has been made in abstractive summarization with attention-based sequence-to-sequence learning models. In this paper, we propose a new decoder where the output summary is generated by conditioning on both the input text and the latent topics of the document. The latent topics, identified by a topic model such as LDA, reveals more global semantic information that can be used to bias the decoder to generate words. In particular, they enable the decoder to have access to additional word co-occurrence statistics captured at document corpus level. We empirically validate the advantage of the proposed approach on both the CNN/Daily Mail and the WikiHow datasets. Concretely, we attain strongly improved ROUGE scores when compared to state-of-the-art models.
Measurement of the high-energy all-flavor neutrino-nucleon cross section with IceCube

The flux of high-energy neutrinos passing through the Earth is attenuated due to their interactions with matter. Their transmission probability is modulated by the neutrino interaction cross section and affects the arrival flux at the IceCube Neutrino Observatory, a cubic-kilometer neutrino detector embedded in the South Pole ice sheet. We present a measurement of the neutrino-nucleon cross section between 60 TeV-10 PeV using the high-energy starting events (HESE) sample from IceCube with 7.5 years of data.
Evaluating Hierarchies through A Partially Observable Markov Decision Processes Methodology

Hierarchical clustering has been shown to be valuable in many scenarios, e.g. catalogues, biology research, image processing, and so on. Despite its usefulness to many situations, there is no agreed methodology on how to properly evaluate the hierarchies produced from different techniques, particularly in the case where ground-truth labels are unavailable. This motivates us to propose a framework for assessing the quality of hierarchical clustering allocations which covers the case of no ground-truth information. Such a quality measurement is useful, for example, to assess the hierarchical structures used by online retailer websites to display their product catalogues. Differently to all the previous measures and metrics, our framework tackles the evaluation from a decision theoretic perspective. We model the process as a bot searching stochastically for items in the hierarchy and establish a measure representing the degree to which the hierarchy supports this search. We employ the concept of Partially Observable Markov Decision Processes (POMDP) to model the uncertainty, the decision making, and the cognitive return for searchers in such a scenario. In this paper, we fully discuss the modeling details and demonstrate its application on some datasets.
Mobile community sensing with smallholder farmers in a developing nation; A scaled pilot for crop health monitoring

Previously, crowdsourcing experiments in surveillance of crop diseases and pest have been trialed as small scale community sensing campaigns with select cohort of smallholder farmers, extension and experts. While those pilots have demonstrated the viability of community sensing with mobile phones to collect massive amounts of real-time data all year round, to compliment low-resourced agricultural expert surveys, they are limited in generalising ideas for scaled implementations of a community sensing system with farmer communities. This work presents a case of scaled deployment of the mobile ad hoc surveillance for crowdsourcing real-time surveillance data on cassava from over 175 smallholder farmers across Uganda. This paper describes a modified mobile ad hoc surveillance ecosystem to suite smallholder farmer agents, a communication model and data collection model designed to cover the spatial interests for the scale of surveillance, a deployment plan, the training methodology and incentives structure. The paper also presents very early results of contributions from farmer agents, that could be usable in monitoring the movement of planting materials between districts, mapping cassava varieties, multiplication sites, and communities with little or no access to agricultural extension services, and possibly guide precision expert surveys in areas of high disease incidence.
Upgraded antennas for pulsar observations in the Argentine Institute of Radio astronomy

The Argentine Institute of Radio astronomy (IAR) is equipped with two single-dish 30 m radio antennas capable of performing daily observations of pulsars and radio transients in the southern hemisphere at 1.4 GHz.
Aims. We aim to introduce to the international community the upgrades performed and to show that the IAR observatory has become suitable for investigations in numerous areas of pulsar radio astronomy, such as pulsar timing arrays, targeted searches of continuous gravitational waves sources, monitoring of magnetars and glitching pulsars, and studies of a short time scale interstellar scintillation.
Methods. We refurbished the two antennas at IAR to achieve high-quality timing observations. We gathered more than 1000 h of observations with both antennas in order to study the timing precision and sensitivity they can achieve.
Results. We introduce the new developments for both radio telescopes at IAR. We present daily observations of the millisecond pulsar J0437−4715 with timing precision better than 1 μs. We also present a follow-up of the reactivation of the magnetar XTE J1810–197 and the measurement and monitoring of the latest  2019) glitch of the Vela pulsar (J0835–4510).
 We show that IAR is capable of performing pulsar monitoring in the 1.4 GHz radio band for long periods of time with a daily cadence. This opens up the possibility of pursuing several goals in pulsar science, including coordinated multi-wavelength observations with other observatories. In particular, daily observations of the millisecond pulsar J0437−4715 would increase the sensitivity of pulsar timing arrays. We also show IAR’s great potential for studying targets of opportunity and transient phenomena, such as magnetars, glitches, and fast-radio-burst sources.
Inflation in Supergravity from Field Redefinitions

Supergravity (SUGRA) theories are specified by a few functions, most notably the real Kähler function denoted by G ( T i , T ¯ i ) = K + log | W | 2 , where K is a real Kähler potential, and W is a holomorphic superpotential. A field redefinition T i → f 1 ( T i ) changes neither the theory nor the Kähler geometry. Similarly, the Kähler transformation, K → K + f 2 + f ¯ 2 , W → e − f 2 W where f 2 is holomorphic and leaves G and hence the theory and the geometry invariant. However, if we perform a field redefinition only in K ( T i , T ¯ i ) → K ( f ( T i ) , f ( T ¯ i ) ) , while keeping the same superpotential W ( T i ) , we get a different theory, as G is not invariant under such a transformation while maintaining the same Kähler geometry. This freedom of choosing f ( T i ) allows construction of an infinite number of new theories given a fixed Kähler geometry and a predetermined superpotential W. Our construction generalizes previous ones that were limited by the holomorphic property of W. In particular, it allows for novel inflationary SUGRA models and particle phenomenology model building, where the different models correspond to different choices of field redefinitions. We demonstrate this possibility by constructing several prototypes of inflationary models (hilltop, Starobinsky-like, plateau, log-squared and bell-curve) all in flat Kähler geometry and an originally renormalizable superpotential W. The models are in accord with current observations and predict r ∈ [ 10 − 6 , 0.06 ] spanning several decades that can be easily obtained. In the bell-curve model, there also exists a built-in gravitational reheating mechanism with T R ∼ O ( 10 7 G e V ) .
Twitter Sentiment on Affordable Care Act using Score Embedding

In this paper we introduce score embedding, a neural network based model to learn interpretable vector representations for words. Score embedding is a supervised method that takes advantage of the labeled training data and the neural network architecture to learn interpretable representations for words. Health care has been a controversial issue between political parties in the United States. In this paper we use the discussions on Twitter regarding different issues of affordable care act to identify the public opinion about the existing health care plans using the proposed score embedding. Our results indicate our approach effectively incorporates the sentiment information and outperforms or is at least comparable to the state-of-the-art methods and the negative sentiment towards "TrumpCare" was consistently greater than neutral and positive sentiment over time.
Analysis of Memory Capacity for Deep Echo State Networks

In this paper, the echo state network (ESN) memory capacity, which represents the amount of input data an ESN can store, is analyzed for a new type of deep ESNs. In particular, two deep ESN architectures are studied. First, a parallel deep ESN is proposed in which multiple reservoirs are connected in parallel allowing them to average outputs of multiple ESNs, thus decreasing the prediction error. Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series. This series ESN architecture can capture more features between the input sequence and the output sequence thus improving the overall prediction accuracy. Fundamental analysis shows that the memory capacity of parallel ESNs is equivalent to that of a traditional shallow ESN, while the memory capacity of series ESNs is smaller than that of a traditional shallow ESN. In terms of normalized root mean square error, simulation results show that the parallel deep ESN achieves 38.5% reduction compared to the traditional shallow ESN while the series deep ESN achieves 16.8% reduction.
Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low inter-rater reliability, limiting generalizability to conversations spanning multiple domains. To address these gaps, we created a new Response Quality annotation scheme, based on which we developed turn-level User Satisfaction metric. We introduced five new domain-independent feature sets and experimented with six machine learning models to estimate the new satisfaction metric. 
Using Response Quality annotation scheme, across randomly sampled single and multi-turn conversations from 26 domains, we achieved high inter-annotator agreement (Spearman's rho 0.94). The Response Quality labels were highly correlated (0.76) with explicit turn-level user ratings. Gradient boosting regression achieved best correlation of ~0.79 between predicted and annotated user satisfaction labels. Multi Layer Perceptron and Gradient Boosting regression models generalized to an unseen domain better (linear correlation 0.67) than other models. Finally, our ablation study verified that our novel features significantly improved model performance.
Asymptotic degree distributions in random threshold graphs

We discuss several limiting degree distributions for a class of random threshold graphs in the many node regime. This analysis is carried out under a weak assumption on the distribution of the underlying fitness variable. This assumption, which is satisfied by the exponential distribution, determines a natural scaling under which the following limiting results are shown: The nodal degree distribution, i.e., the distribution of any node, converges in distribution to a limiting pmf. However, for each $d=0,1, \ldots $, the fraction of nodes with given degree $d$ converges only in distribution to a non-degenerate random variable $\Pi(d)$ (whose distribution depends on $d$),and not in probability to the aforementioned limiting nodal pmf as is customarily expected. The distribution of $\Pi(d)$ is identified only through its characteristic function. Implications of this result include: (i) The empirical node distribution may not be used as a proxy for or as an estimate to the limiting nodal pmf; (ii) Even in homogeneous graphs, the network-wide degree distribution and the nodal degree distribution may capture vastly different information; and (iii) Random threshold graphs with exponential distributed fitness do not provide an alternative scale-free model to the Barabasi-Albert model as was argued by some authors; the two models cannot be meaningfully compared in terms of their degree distributions!
CommentsRadar: Dive into Unique Data on All Comments on the Web

We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites. The engine aggregatesarticles and comments for these articles, extracts named entities,links them together and with knowledge base entries, performssentiment analysis, and aggregates the results, aiming to mine fortemporal trends and other insights. In this work, we present thegeneral engine, discuss the models used for all steps of this pipeline,and introduce several case studies that discover important insightsfrom online commenting data.
Semi-Implicit Graph Variational Auto-Encoders

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a hierarchical variational framework to enable neighboring node sharing for better generative modeling of graph dependency structure, together with a Bernoulli-Poisson link decoder. Not only does this hierarchical construction provide a more flexible generative graph model to better capture real-world graph properties, but also does SIG-VAE naturally lead to semi-implicit hierarchical variational inference that allows faithful modeling of implicit posteriors of given graph data, which may exhibit heavy tails, multiple modes, skewness, and rich dependency structures. Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction. Extensive experiments with a variety of graph data show that SIG-VAE significantly outperforms state-of-the-art methods on several different graph analytic tasks.
A Gaia-Enceladus Analog in the EAGLE Simulation: Insights into the Early Evolution of the Milky Way

We identify a simulated Milky Way analog in the eagle suite of cosmological hydrodynamical simulations. This galaxy not only shares similar global properties as the Milky Way, but was specifically selected because its merger history resembles that currently known for the Milky Way. In particular we find that this Milky Way analog has experienced its last significant merger (with a stellar mass ratio ∼0.2) at z ∼ 1.2. We show that this merger affected both the dynamical properties of the stars present at the time, contributing to the formation of a thick disk, and also leading to a significant increase in the star formation rate of the host. This object is thus particularly suitable for understanding the early evolutionary history of the Milky Way. It is also an ideal candidate for re-simulation with much higher resolution, as this would allow addressing a plethora of interesting questions such as, for example, the specific distribution of dark matter near the Sun.
BoxNet: A Deep Learning Method for 2D Bounding Box Estimation from Bird's-Eye View Point Cloud

We present a learning-based method to estimate the object bounding box from its 2D bird's-eye view (BEV) LiDAR points. Our method, entitled BoxNet, exploits a simple deep neural network that can efficiently handle unordered points. The method takes as input the 2D coordinates of all the points and the output is a vector consisting of both the box pose (position and orientation in LiDAR coordinate system) and its size (width and length). In order to deal with the angle discontinuity problem, we propose to estimate the double-angle sinusoidal values rather than the angle itself. We also predict the center relative to the point cloud mean to boost the performance of estimating the location of the box. The proposed method does not rely on the ordering of points as in many existing approaches, and can accurately predict the actual size of the bounding box based on the prior information that is obtained from the training data. BoxNet is validated using the KITTI 3D object dataset, with significant improvement compared with the state-of-the-art non-learning based methods.
Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items

A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items. It must adapt to changing user food preferences under uncertain visual and physical environments. Different food items in different environmental conditions require different manipulation strategies for successful bite acquisition. Therefore, a key challenge is how to handle previously unseen food items with very different success rate distributions over strategy. Combining low-level controllers and planners into discrete action trajectories, we show that the problem can be represented using a linear contextual bandit setting. We construct a simulated environment using a doubly robust loss estimate from previously seen food items, which we use to tune the parameters of off-the-shelf contextual bandit algorithms. Finally, we demonstrate empirically on a robot- assisted feeding system that, even starting with a model trained on thousands of skewering attempts on dissimilar previously seen food items, e-greedy and LinUCB algorithms can quickly converge to the most successful manipulation strategy.
Spectral theory for the stability of dynamical systems on large oriented locally tree-like graphs

We develop a mathematical theory for the linear stability of stationary states in large dynamical systems modelled by a set of randomly coupled differential equations on a locally tree-like network. Our approach provides analytical expressions for the leading eigenvalue of random matrices that describe the interactions between the degrees of freedom; the sign of the leading eigenvalue characterizes the system stability. We illustrate this approach on oriented random graphs with a prescribed degree distribution and find that the leading eigenvalue is universal in the sense that it only depends on a few ensemble parameters, including the mean degree and a degree correlation coefficient. In addition, we also characterize the unstable mode of the system of interest by deriving analytical expressions for the statistics of the components of the right and left eigenvectors associated with the leading eigenvalue. Finally, we briefly discuss how this approach can be extended to models with diagonal disorder and non-oriented couplings.
Fuzzy C-Means Clustering and Sonification of HRV Features

Linear and non-linear measures of heart rate variability (HRV) are widely investigated as non-invasive indicators of health. Stress has a profound impact on heart rate, and different meditation techniques have been found to modulate heartbeat rhythm. This paper aims to explore the process of identifying appropriate metrices from HRV analysis for sonification. Sonification is a type of auditory display involving the process of mapping data to acoustic parameters. This work explores the use of auditory display in aiding the analysis of HRV leveraged by unsupervised machine learning techniques. Unsupervised clustering helps select the appropriate features to improve the sonification interpretability. Vocal synthesis sonification techniques are employed to increase comprehension and learnability of the processed data displayed through sound. These analyses are early steps in building a real-time sound-based biofeedback training system.
Feature Interaction-aware Graph Neural Networks

Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks. However, most real-world graphs often come with high-dimensional and sparse node features, rendering the learned node representations from existing GNN architectures less expressive. In this paper, we propose \textit{Feature Interaction-aware Graph Neural Networks (FI-GNNs)}, a plug-and-play GNN framework for learning node representations encoded with informative feature interactions. Specifically, the proposed framework is able to highlight informative feature interactions in a personalized manner and further learn highly expressive node representations on feature-sparse graphs. Extensive experiments on various datasets demonstrate the superior capability of FI-GNNs for graph learning tasks.
Colloquium
: Neutrino detectors as tools for nuclear security

In 1930, Wolfgang Pauli proposed that conservation of energy and momentum in beta decay required the existence of a new particle: the neutrino. Since then experimental methods have matured to the point that neutrino detection now has an important practical application, namely, enhancing nuclear security. In this Colloquium the challenges and advances in the field of neutrino detection and their use in national security are discussed.