nils-herrmann/clean_scirepeval huggingface dataset:
The H i velocity function: a test of cosmology or baryon physics?

Accurately predicting the shape of the H i velocity function (VF) of galaxies is regarded widely as a fundamental test of any viable dark matter model. Straightforward analyses of cosmological N-body simulations imply that the Λ cold dark matter (ΛCDM) model predicts an overabundance of low circular velocity galaxies when compared to observed H i VFs. More nuanced analyses that account for the relationship between galaxies and their host haloes suggest that how we model the influence of baryonic processes has a significant impact on H i VF predictions. We explore this in detail by modelling H i emission lines of galaxies in the shark semi-analytic galaxy formation model, built on the surfs suite of ΛCDM N-body simulations. We create a simulated ALFALFA survey, in which we apply the survey selection function and account for effects such as beam confusion, and compare simulated and observed H i velocity width distributions, finding differences of ≲ 50 per cent, orders of magnitude smaller than the discrepancies reported in the past. This is a direct consequence of our careful treatment of survey selection effects and, importantly, how we model the relationship between galaxy and halo circular velocity – the H i mass–maximum circular velocity relation of galaxies is characterized by a large scatter. These biases are complex enough that building a VF from the observed H i linewidths cannot be done reliably.
Grounding Object Detections With Transcriptions

A vast amount of audio-visual data is available on the Internet thanks to video streaming services, to which users upload their content. However, there are difficulties in exploiting available data for supervised statistical models due to the lack of labels. Unfortunately, generating labels for such amount of data through human annotation can be expensive, time-consuming and prone to annotation errors. In this paper, we propose a method to automatically extract entity-video frame pairs from a collection of instruction videos by using speech transcriptions and videos. We conduct experiments on image recognition and visual grounding tasks on the automatically constructed entity-video frame dataset of How2. The models will be evaluated on new manually annotated portion of How2 dev5 and val set and on the Flickr30k dataset. This work constitutes a first step towards meta-algorithms capable of automatically construct task-specific training sets.
Landslide Geohazard Assessment with Convolutional Neural Networks Using Sentinel-2 Imagery Data

In this paper, the authors aim to combine the latest state of the art models in image recognition with the best publicly available satellite images to create a system for landslide risk mitigation. We focus first on landslide detection and further propose a similar system to be used for prediction. Such models are valuable as they could easily be scaled up to provide data for hazard evaluation, as satellite imagery becomes increasingly available. The goal is to use satellite images and correlated data to enrich the public repository of data and guide disaster relief efforts for locating precise areas where landslides have occurred. Different image augmentation methods are used to increase diversity in the chosen dataset and create more robust classification. The resulting outputs are then fed into variants of 3-D convolutional neural networks. A review of the current literature indicates there is no research using CNNs (Convolutional Neural Networks) and freely available satellite imagery for classifying landslide risk. The model has shown to be ultimately able to achieve a significantly better than baseline accuracy.
Model risk in credit risk

We provide sharp analytical upper and lower bounds for value‐at‐risk (VaR) and sharp bounds for expected shortfall (ES) of portfolios of any dimension subject to default risk. To do so, the main methodological contribution of the paper consists in analytically finding the convex hull generators for the class of exchangeable Bernoulli variables with given mean and for the class of exchangeable Bernoulli variables with given mean and correlation in any dimension. Using these analytical results, we first describe all possible dependence structures for default, in the class of finite sequences of exchangeable Bernoulli random variables. We then measure how model risk affects VaR and ES.
Deep Learning-Based Decoding for Constrained Sequence Codes

Constrained sequence codes have been widely used in modern communication and data storage systems. Sequences encoded with constrained sequence codes satisfy constraints imposed by the physical channel, hence enabling efficient and reliable transmission of coded symbols. Traditional encoding and decoding of constrained sequence codes rely on table look-up, which is prone to errors that occur during transmission. In this paper, we introduce constrained sequence decoding based on deep learning. With multiple layer perception (MLP) networks and convolutional neural networks (CNNs), we are able to achieve low bit error rates that are close to maximum a posteriori probability (MAP) decoding as well as improve the system throughput. Moreover, implementation of capacity-achieving fixed-length codes, where the complexity is prohibitively high with table look-up decoding, becomes practical with deep learning-based decoding.
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language

Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge. However, such systems are difficult to apply to problems involving natural language, due to its large linguistic variability. In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret. In this paper, we describe a model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language. Specifically, we propose to use an Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders. We fine-tune the representations for the similarity function via backpropagation. This leads to a system that can apply rule-based reasoning to natural language, and induce domain-specific natural language rules from training data. We evaluate the proposed system on two different question answering tasks, showing that it outperforms two baselines – BiDAF (Seo et al., 2016a) and FastQA( Weissenborn et al., 2017) on a subset of the WikiHop corpus and achieves competitive results on the MedHop data set (Welbl et al., 2017).
Novel signatures of dark matter in laser-interferometric gravitational-wave detectors

Dark matter may induce apparent temporal variations in the physical "constants", including the electromagnetic fine-structure constant and fermion masses. In particular, a coherently oscillating classical dark-matter field may induce apparent oscillations of physical constants in time, while the passage of macroscopic dark-matter objects (such as topological defects) may induce apparent transient variations in the physical constants. In this paper, we point out several new signatures of the aforementioned types of dark matter that can arise due to the geometric asymmetry created by the beam-splitter in a two-arm laser interferometer. These new signatures include dark-matter-induced time-varying size changes of a freely-suspended beam-splitter and associated time-varying shifts of the main reflecting surface of the beam-splitter that splits and recombines the laser beam, as well as time-varying refractive-index changes in the freely-suspended beam-splitter and time-varying size changes of freely-suspended arm mirrors. We demonstrate that existing ground-based experiments already have sufficient sensitivity to probe extensive regions of unconstrained parameter space in models involving oscillating scalar dark-matter fields and domain walls composed of scalar fields. In the case of oscillating dark-matter fields, Michelson interferometers $-$ in particular, the GEO600 detector $-$ are especially sensitive. The sensitivity of Fabry-Perot-Michelson interferometers, including LIGO, VIRGO and KAGRA, to oscillating dark-matter fields can be significantly increased by making the thicknesses of the freely-suspended Fabry-Perot arm mirrors different in the two arms. We also discuss how small-scale Michelson interferometers, such as the Fermilab holometer, could be used to perform resonant narrowband searches for oscillating dark-matter fields with enhanced sensitivity to dark matter.
Improving Visual Question Answering by Referring to Generated Paragraph Captions

Paragraph-style image captions describe diverse aspects of an image as opposed to the more common single-sentence captions that only provide an abstract description of the image. These paragraph captions can hence contain substantial information of the image for tasks such as visual question answering. Moreover, this textual information is complementary with visual information present in the image because it can discuss both more abstract concepts and more explicit, intermediate symbolic information about objects, events, and scenes that can directly be matched with the textual question and copied into the textual answer (i.e., via easier modality match). Hence, we propose a combined Visual and Textual Question Answering (VTQA) model which takes as input a paragraph caption as well as the corresponding image, and answers the given question based on both inputs. In our model, the inputs are fused to extract related information by cross-attention (early fusion), then fused again in the form of consensus (late fusion), and finally expected answers are given an extra score to enhance the chance of selection (later fusion). Empirical results show that paragraph captions, even when automatically generated (via an RL-based encoder-decoder model), help correctly answer more visual questions. Overall, our joint model, when trained on the Visual Genome dataset, significantly improves the VQA performance over a strong baseline model.
Machine Learning on EPEX Order Books: Insights and Forecasts

This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected demand. Appropriate feature extraction for the order book data is developed. Using cross-validation to optimise hyperparameters, neural networks and random forests are proposed and compared to statistical reference models. The machine learning models outperform traditional approaches.
Extensions of Generic DOL for Generic Ontology Design Patterns

Generic ontologies were introduced as an extension (Generic DOL) of the Distributed Ontology, Modeling and Specification Language, DOL, with the aim to provide a language for Generic Ontology Design Patterns. In this paper we present a number of new language constructs that increase the expressivity and the generality of Generic DOL, among them sequential and optional parameters, list parameters with recursion, and local sub-patterns. These are illustrated with non-trivial patterns: generic value sets and (nested) qualitatively graded relations, demonstrated as definitional building blocks in an application domain.
Cumulative Merging Percolation and the Epidemic Transition of the Susceptible-Infected-Susceptible Model in Networks

We consider cumulative merging percolation (CMP), a long-range percolation process describing the iterative merging of clusters in networks, depending on their mass and mutual distance. For a specific class of CMP processes, which represents a generalization of degree-ordered percolation, we derive a scaling solution on uncorrelated complex networks, unveiling the existence of diverse mechanisms leading to the formation of a percolating cluster. The scaling solution accurately reproduces universal properties of the transition. This finding is used to infer the critical properties of the susceptible-infected-susceptible model for epidemics in infinite and finite power-law distributed networks. Here, discrepancies between analytical approaches and numerical results regarding the finite-size scaling of the epidemic threshold are a crucial open issue in the literature. We find that the scaling exponent assumes a nontrivial value during a long preasymptotic regime. We calculate this value, finding good agreement with numerical evidence. We also show that the crossover to the true asymptotic regime occurs for sizes much beyond currently feasible simulations. Our findings allow us to rationalize and reconcile all previously published results (both analytical and numerical), thus ending a long-standing debate.
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle

$Q$-learning with function approximation is one of the most popular methods in reinforcement learning. Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i.e, approximating $Q$-functions with linear functions, it is still an open problem on how to design a provably efficient algorithm that learns a near-optimal policy. The key challenges are how to efficiently explore the state space and how to decide when to stop exploring in conjunction with the function approximation scheme. 
The current paper presents a provably efficient algorithm for $Q$-learning with linear function approximation. Under certain regularity assumptions, our algorithm, Difference Maximization $Q$-learning (DMQ), combined with linear function approximation, returns a near-optimal policy using a polynomial number of trajectories. Our algorithm introduces a new notion, the Distribution Shift Error Checking (DSEC) oracle. This oracle tests whether there exists a function in the function class that predicts well on a distribution $\mathcal{D}_1$, but predicts poorly on another distribution $\mathcal{D}_2$, where $\mathcal{D}_1$ and $\mathcal{D}_2$ are distributions over states induced by two different exploration policies. For the linear function class, this oracle is equivalent to solving a top eigenvalue problem. We believe our algorithmic insights, especially the DSEC oracle, are also useful in designing and analyzing reinforcement learning algorithms with general function approximation.
An IR-based approach towards automated integration of geo-spatial datasets in map-based software systems

Data is arguably the most valuable asset of the modern world. In this era, the success of any data-intensive solution relies on the quality of data that drives it. Among vast amount of data that are captured, managed, and analyzed everyday, geospatial data are one of the most interesting class of data that hold geographical information of real-world phenomena and can be visualized as digital maps. Geo-spatial data is the source of many enterprise solutions that provide local information and insights. Companies often aggregate geospacial datasets from various sources in order to increase the quality of such solutions. However, a lack of a global standard model for geospatial datasets makes the task of merging and integrating datasets difficult and error prone. Traditionally, this aggregation was accomplished by domain experts manually validating the data integration process by merging new data sources and/or new versions of previous data against conflicts and other requirement violations. However, this manual approach is not scalable is a hinder toward rapid release when dealing with big datasets which change frequently. Thus more automated approaches with limited interaction with domain experts is required. As a first step to tackle this problem, we have leveraged Information Retrieval (IR) and geospatial search techniques to propose a systematic and automated conflict identification approach. To evaluate our approach, we conduct a case study in which we measure the accuracy of our approach in several real-world scenarios and followed by interviews with Localintel Inc. software developers to get their feedbacks.
IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering

This paper presents the experiments accomplished as a part of our participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task. We participated in all the three tasks defined in this particular shared task. The tasks are viz. i. Natural Language Inference (NLI) ii. Recognizing Question Entailment(RQE) and their application in medical Question Answering (QA). We submitted runs using multiple deep learning based systems (runs) for each of these three tasks. We submitted five system results in each of the NLI and RQE tasks, and four system results for the QA task. The systems yield encouraging results in all the three tasks. The highest performance obtained in NLI, RQE and QA tasks are 81.8%, 53.2%, and 71.7%, respectively.
An interpretable machine-learning framework for dark matter halo formation

We present a generalization of our recently proposed machine-learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the formation of haloes over the mass range 11.4 ≤ log (M/M⊙) ≤ 13.4. The algorithm is trained on an N-body simulation to infer the final mass of the halo to which each dark matter particle will later belong. We then quantify the difference in the predictive accuracy between machine-learning models using a metric based on the Kullback–Leibler divergence. We first train the algorithm with information about the density contrast in the particles’ local environment. The addition of tidal shear information does not yield an improved halo collapse model over one based on density information alone; the difference in their predictive performance is consistent with the statistical uncertainty of the density-only based model. This result is confirmed as we verify the ability of the initial conditions-to-halo mass mapping learnt from one simulation to generalize to independent simulations. Our work illustrates the broader potential of developing interpretable machine-learning frameworks to gain physical understanding of non-linear large-scale structure formation.
Direct detection of primordial black hole relics as dark matter

If dark matter is composed of primordial black holes, such black holes can span an enormous range of masses. A variety of observational constraints exist on massive black holes, and black holes with masses below 1015 g are often assumed to have completely evaporated by the present day. But if the evaporation process halts at the Planck scale, it would leave behind a stable relic, and such objects could constitute the entirety of dark matter. Neutral Planck-scale relics are effectively invisible to both astrophysical and direct detection searches. However, we argue that such relics may typically carry electric charge, making them visible to terrestrial detectors. We evaluate constraints and detection prospects in detail, and show that if not already ruled out by monopole searches, this scenario can be largely explored within the next decade using existing or planned experimental equipment. A single detection would have enormous implications for cosmology, black hole physics, and quantum gravity.
Oscillons and dark matter

Oscillons are bound states sustained by self-interactions that appear in rather generic scalar models. They can be extremely long-lived and have a built-in formation mechanism—parametric resonance instability. These features suggest that oscillons can affect the standard picture of scalar ultra-light dark matter (ULDM) models. We explore this idea along two directions. First, we investigate numerically oscillon lifetimes and their dependence on the shape of the potential. We find that scalar potentials that occur in well motivated axion-like models can lead to oscillons that live up to 108 cycles or more. Second, we discuss the observational constraints on the ULDM models once the presence of oscillons is taken into account. For a wide range of axion masses, oscillons decay around or after matter-radiation equality and can thus act as early seeds for structure formation. We also discuss the possibility that oscillons survive up to today. In this case they can most easily play the role of dark matter.
Variants of the Smith-Wilson method with a view towards applications

We propose two variants of the Smith-Wilson method for practical application in the insurance industry. Our first variant relaxes the Smith-Wilson energy and can be used to incorporate less reliable market data with a certain weight rather than disregarding it completely. This is particularly useful for deriving yield curves in the IFRS 17 accounting regime, where there is a mandate to incorporate all available market data. 
A second variant incorporates the requirement to reach the ultimate forward rate at a prescribed term into the problem formulation. This provides a natural way to fulfil the Solvency II convergence requirement and is more elegant than the current methodology adapting the term-scale parameter to control convergence.
Model independent analysis of MeV scale dark matter. II. Implications from 
e−e+
 colliders and direct detection

Dark matter particles with masses in the sub-GeV range have escaped severe constraints from direct detection experiments such as LUX, PANDAX-II and XENON100 as the corresponding recoil energies are, largely, lower than the detector thresholds. In a companion paper, we demonstrated, in a model independent approach, that a significantly large fraction of the parameter space escapes the cosmological and astrophysical constraints. We show here, though, that the remaining parameter space lends itself to the possibility of discovery at both direct detection experiments (such as CRESST-II) as well as in a low-energy collider such as Belle-II.
Automatic Relevance Determination Bayesian Neural Networks for Credit Card Default Modelling

Credit risk modelling is an integral part of the global financial system. While there has been great attention paid to neural network models for credit default prediction, such models often lack the required interpretation mechanisms and measures of the uncertainty around their predictions. This work develops and compares Bayesian Neural Networks(BNNs) for credit card default modelling. This includes a BNNs trained by Gaussian approximation and the first implementation of BNNs trained by Hybrid Monte Carlo(HMC) in credit risk modelling. The results on the Taiwan Credit Dataset show that BNNs with Automatic Relevance Determination(ARD) outperform normal BNNs without ARD. The results also show that BNNs trained by Gaussian approximation display similar predictive performance to those trained by the HMC. The results further show that BNN with ARD can be used to draw inferences about the relative importance of different features thus critically aiding decision makers in explaining model output to consumers. The robustness of this result is reinforced by high levels of congruence between the features identified as important using the two different approaches for training BNNs.