nils-herrmann/clean_scirepeval huggingface dataset:
Assembly of randomly placed parts realized by using only one robot arm with a general parallel-jaw gripper

In industry assembly lines, parts feeding machines are widely employed as the prologue of the whole procedure. They play the role of sorting the parts randomly placed in bins to the state with specified pose. With the help of the parts feeding machines, the subsequent assembly processes by robot arm can always start from the same condition. Thus it is expected that function of parting feeding machine and the robotic assembly can be integrated with one robot arm. This scheme can provide great flexibility and can also contribute to reduce the cost. The difficulties involved in this scheme lie in the fact that in the part feeding phase, the pose of the part after grasping may be not proper for the subsequent assembly. Sometimes it can not even guarantee a stable grasp. In this paper, we proposed a method to integrate parts feeding and assembly within one robot arm. This proposal utilizes a specially designed gripper tip mounted on the jaws of a two-fingered gripper. With the modified gripper, in-hand manipulation of the grasped object is realized, which can ensure the control of the orientation and offset position of the grasped object. The proposal in this paper is verified by a simulated assembly in which a robot arm completed the assembly process including parts picking from bin and a subsequent peg-in-hole assembly.
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking

In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-the-art language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set.
Adversarial Vulnerability Bounds for Gaussian Process Classification

Machine learning (ML) classification is increasingly used in safety-critical systems. Protecting ML classifiers from adversarial examples is crucial. We propose that the main threat is that of an attacker perturbing a confidently classified input to produce a confident misclassification. To protect against this we devise an adversarial bound (AB) for a Gaussian process classifier, that holds for the entire input domain, bounding the potential for any future adversarial method to cause such misclassification. This is a formal guarantee of robustness, not just an empirically derived result. We investigate how to configure the classifier to maximise the bound, including the use of a sparse approximation, leading to the method producing a practical, useful and provably robust classifier, which we test using a variety of datasets.
Challenging Deep Image Descriptors for Retrieval in Heterogeneous Iconographic Collections

This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view contents. For this purpose, we introduce a novel dataset, namely Alegoria dataset, consisting of 12,952 iconographic contents representing landscapes of the French territory, and encapsultating a large range of intra-class variations of appearance which were finely labelled. Six deep features (DELF, NetVLAD, GeM, MAC, RMAC, SPoC) and a hand-crafted local descriptor (ORB) are evaluated against these variations. Their performance are discussed, with the objective of providing the reader with research directions for improving image description techniques dedicated to complex heterogeneous datasets that are now increasingly present in topical applications targeting heritage valorization.
PgNN: Physics-guided Neural Network for Fourier Ptychographic Microscopy

Fourier ptychography (FP) is a newly developed computational imaging approach that achieves both high resolution and wide field of view by stitching a series of low-resolution images captured under angle-varied illumination. So far, many supervised data-driven models have been applied to solve inverse imaging problems. These models need massive amounts of data to train, and are limited by the dataset characteristics. In FP problems, generic datasets are always scarce, and the optical aberration varies greatly under different acquisition conditions. To address these dilemmas, we model the forward physical imaging process as an interpretable physics-guided neural network (PgNN), where the reconstructed image in the complex domain is considered as the learnable parameters of the neural network. Since the optimal parameters of the PgNN can be derived by minimizing the difference between the model-generated images and real captured angle-varied images corresponding to the same scene, the proposed PgNN can get rid of the problem of massive training data as in traditional supervised methods. Applying the alternate updating mechanism and the total variation regularization, PgNN can flexibly reconstruct images with improved performance. In addition, the Zernike mode is incorporated to compensate for optical aberrations to enhance the robustness of FP reconstructions. As a demonstration, we show our method can reconstruct images with smooth performance and detailed information in both simulated and experimental datasets. In particular, when validated in an extension of a high-defocus, high-exposure tissue section dataset, PgNN outperforms traditional FP methods with fewer artifacts and distinguishable structures.
Timelike geodesics in naked singularity and black hole spacetimes

We derive here the orbit equations of particles in naked singularity spacetimes, namely the Bertrand (BST) and Janis-Newman-Winicour (JNW) geometries, and for the Schwarzschild black hole. We plot the orbit equations and find the Perihelion precession of the orbits of particles in the BST and JNW spacetimes and compare these with the Schwarzschild black hole spacetime. We find and discuss different distinguishing properties in the effective potentials and orbits of particle in BST, JNW and Schwarzschild spacetimes, and the particle trajectories are shown for the matching of BST with an external Schwarzschild spacetime. We show that the nature of perihelion precession of orbits in BST and Schwarzschild spacetimes are similar, while in the JNW case the nature of perihelion precession of orbits is opposite to that of the Schwarzschild and BST spacetimes. Other interesting and important features of these orbits are pointed out.
An Edit-centric Approach for Wikipedia Article Quality Assessment

We propose an edit-centric approach to assess Wikipedia article quality as a complementary alternative to current full document-based techniques. Our model consists of a main classifier equipped with an auxiliary generative module which, for a given edit, jointly provides an estimation of its quality and generates a description in natural language. We performed an empirical study to assess the feasibility of the proposed model and its cost-effectiveness in terms of data and quality requirements.
Efficient Prealignment of CT Scans for Registration through a Bodypart Regressor

Convolutional neural networks have not only been applied for classification of voxels, objects, or images, for instance, but have also been proposed as a bodypart regressor. We pick up this underexplored idea and evaluate its value for registration: A CNN is trained to output the relative height within the human body in axial CT scans, and the resulting scores are used for quick alignment between different timepoints. Preliminary results confirm that this allows both fast and robust prealignment compared with iterative approaches.
A Split-and-Recombine Approach for Follow-up Query Analysis

Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising.
Multiple retrograde substructures in the Galactic halo: A shattered view of Galactic history

Several kinematic and chemical substructures have been recently found amongst Milky Way halo stars with retrograde motions. It is currently unclear how these various structures are related to each other. This Letter aims to shed light on this issue.
Methods. We explore the retrograde halo with an augmented version of the Gaia DR2 RVS sample, extended with data from three large spectroscopic surveys, namely RAVE, APOGEE, and LAMOST. In this dataset, we identify several structures using the HDBSCAN clustering algorithm. We discuss their properties and possible links using all the available chemical and dynamical information.
Results. In concordance with previous work, we find that stars with [Fe/H] < −1 have more retrograde motions than those with [Fe/H] > −1. The retrograde halo contains a mixture of debris from objects like Gaia-Enceladus, Sequoia, and even the chemically defined thick disc. We find that the Sequoia has a smaller range in orbital energies than previously suggested and is confined to high energy. Sequoia could be a small galaxy in itself, but since it overlaps both in integrals-of-motion space and chemical abundance space with the less bound debris of Gaia-Enceladus, its nature cannot yet be fully settled. In the low-energy part of the halo, we find evidence for at least one more distinct structure: Thamnos. Stars in Thamnos are on low-inclination, mildly eccentric retrograde orbits, moving at vϕ ≈ −150 km s−1, and are chemically distinct from the other structures.
 Even with the excellent Gaia DR2 data, piecing together all the fragments found in the retrograde halo remains challenging. At this point, we are very much in need of large datasets with high-quality high-resolution spectra and tailored high-resolution hydrodynamical simulations of galaxy mergers.
Collapse of Social Engagement and its Prevention by Local Recruitments

Social networks sometimes collapse. The threshold model of collective actions has been widely adopted as a theoretical paradigm to understand this phenomenon, but previous investigations focused on the irreversible K-core pruning process starting from random initial activity patterns. Here we study the vulnerability and resilience of social engagement as a network alliance problem, and find that equilibrium alliance configurations (but not the out-of-equilibrium ones obtained from irreversible pruning) may experience two distinct dynamical transitions as the number of active nodes gradually shrinks. In the intermediate phase bounded by the weak and strong tipping points, an equilibrium alliance is highly vulnerable to single-node-triggered cascading failures, yet all these global collapse events can be successfully suppressed by a simple least-effort local recruitment mechanism which flips a few flippable inactive nodes.
Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

Domain-specific knowledge graphs constructed from natural language text are ubiquitous in today's world. In many such scenarios the base text, from which the knowledge graph is constructed, concerns itself with practical, on-hand, actual or ground-reality information about the domain. Product documentation in software engineering domain are one example of such base texts. Other examples include blogs and texts related to digital artifacts, reports on emerging markets and business models, patient medical records, etc. Though the above sources contain a wealth of knowledge about their respective domains, the conceptual knowledge on which they are based is often missing or unclear. Access to this conceptual knowledge can enormously increase the utility of available data and assist in several tasks such as knowledge graph completion, grounding, querying, etc. 
Our contributions in this paper are twofold. First, we propose a novel Markovian stochastic model for document generation from conceptual knowledge. The uniqueness of our approach lies in the fact that the conceptual knowledge in the writer's mind forms a component of the parameter set of our stochastic model. Secondly, we solve the inverse problem of learning the best conceptual knowledge from a given document, by finding model parameters which maximize the likelihood of generating the specific document over all possible parameter values. This likelihood maximization is done using an application of Baum-Welch algorithm, which is a known special case of Expectation-Maximization (EM) algorithm. We run our conceptualization algorithm on several well-known natural language sources and obtain very encouraging results. The results of our extensive experiments concur with the hypothesis that the information contained in these sources has a well-defined and rigorous underlying conceptual structure, which can be discovered using our method.
SCDP: Systematic Rateless Coding for Efficient Data Transport in Data Centres

In this paper we propose SCDP, a novel, general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, natively supports one-to-many and many-to-one data communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, in-network packet trimming and Multi-Level Feedback Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many-to-one data transport; (2) on top of RaptorQ codes, receiver-driven flow control, in combination with in-network packet trimming, enable efficient usage of network resources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of  codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluate SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better compared to NDP. For short and long unicast flows, SCDP performs equally well or better compared to NDP.
Automobile Theft Detection by Clustering Owner Driver Data

As automobiles become intelligent, automobile theft methods are evolving intelligently. Therefore automobile theft detection has become a major research challenge. Data-mining, biometrics, and additional authentication methods have been proposed to address automobile theft, in previous studies. Among these methods, data-mining can be used to analyze driving characteristics and identify a driver comprehensively. However, it requires a labeled driving dataset to achieve high accuracy. It is impractical to use the actual automobile theft detection system because real theft driving data cannot be collected in advance. Hence, we propose a method to detect an automobile theft attempt using only owner driving data. We cluster the key features of the owner driving data using the k-means algorithm. After reconstructing the driving data into one of these clusters, theft is detected using an error from the original driving data. To validate the proposed models, we tested our actual driving data and obtained 99% accuracy from the best model. This result demonstrates that our proposed method can detect vehicle theft by using only the car owner's driving data.
Inclusive and diffractive dijet photoproduction in UPCs at the LHC in NLO QCD

We present a next-to-leading order QCD calculation of inclusive dijet photoproduction in ultraperipheral Pb-Pb collisions at the LHC and show that the results agree very well with various kinematic distributions measured by the ATLAS collaboration. The effect of including these data in nCTEQ or EPPS16 nuclear parton density functions (nPDFs) is then studied using the Bayesian reweighting technique. For an assumed total error of 5\% on the final data, its inclusion would lead to a significant reduction of the nPDF uncertainties of up to a factor of two at small values of the parton momentum fraction. As an outlook, we discuss future analyes of diffractive nPDFs, which are so far completely unknown.
On the design, implementation, and use of laziness in R

The R programming language has been lazy for over twenty-five years. This paper presents a review of the design and implementation of call-by-need in R, and a data-driven study of how generations of programmers have put laziness to use in their code. We analyze 16,707 packages and observe the creation of 270.9 B promises. Our data suggests that there is little supporting evidence to assert that programmers use laziness to avoid unnecessary computation or to operate over infinite data structures. For the most part R code appears to have been written without reliance on, and in many cases even knowledge of, delayed argument evaluation. The only significant exception is a small number of packages which leverage call-by-need for meta-programming.
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans

Over the last few years, deep learmng has proven to be a great solution to many problems, such as image or text classification. Recently, deep learning-based solutions have outperformed humans on selected benchmark datasets, yielding a promising future for scientific and real-world applications. Training of deep learning models requires vast amounts of high quality data to achieve such supreme performance. In real-world scenarios, obtaining a large, coherent, and properly labeled dataset is a challenging task. This is especially true in medical applications, where high-quality data and annotations are scarce and the number of expert annotators is limited. In this paper, we investigate the impact of corrupted ground-truth masks on the performance of a neural network for a brain tumor segmentation task. Our findings suggest that a) the performance degrades about 8% less than it could be expected from simulations, b) a neural network learns the simulated biases of annotators, c) biases can be partially mitigated by using an inversely-biased dice loss function.
Acoustic scene analysis with multi-head attention networks

Acoustic Scene Classification (ASC) is a challenging task, as a single scene may involve multiple events that contain complex sound patterns. For example, a cooking scene may contain several sound sources including silverware clinking, chopping, frying, etc. What complicates ASC more is that classes of different activities could have overlapping sounds patterns (e.g. both cooking and dishwashing could have silverware clinking sound). In this paper, we propose a multi-head attention network to model the complex temporal input structures for ASC. The proposed network takes the audio's time-frequency representation as input, and it leverages standard VGG plus LSTM layers to extract high-level feature representation. Further more, it applies multiple attention heads to summarize various patterns of sound events into fixed dimensional representation, for the purpose of final scene classification. The whole network is trained in an end-to-end fashion with back-propagation. Experimental results confirm that our model discovers meaningful sound patterns through the attention mechanism, without using explicit supervision in the alignment. We evaluated our proposed model using DCASE 2018 Task 5 dataset, and achieved competitive performance on par with previous winner's results.
To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods

: To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we employ the three graph Laplacian based semi-supervised ranking methods to solve the irregular trade behavior detection problem. Experimental results show that that the un-normalized and symmetric normalized graph Laplacian based semisupervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.
Solving Financial Regulatory Compliance Using Software Contracts

Ensuring compliance with various laws and regulations is of utmost priority for financial institutions. Traditional methods in this area have been shown to be inefficient. Manual processing does not scale well. Automated efforts are hindered due to the lack of formalization of domain knowledge and problems of integrating such knowledge into software systems. In this work we propose an approach to tackle these issues by encoding them into software contracts using a Controlled Natural Language. In particular, we encode a portion of the Money Market Statistical Reporting (MMSR) regulations into contracts specified by the clojure.spec framework. We show how various features of a contract framework, in particular clojure.spec, can help to tackle issues that occur when dealing with compliance: validation with explanations and test data generation. We benchmark our proposed solution and show that this approach can effectively solve compliance issues in this particular use case.
RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation

Following navigation instructions in natural language (NL) requires a composition of language, action, and knowledge of the environment. Knowledge of the environment may be provided via visual sensors or as a symbolic world representation referred to as a map. Previous work on map-based NL navigation relied on small artificial worlds with a fixed set of entities known in advance. Here we introduce the Realistic Urban Navigation (RUN) task, aimed at interpreting NL navigation instructions based on a real, dense, urban map. Using Amazon Mechanical Turk, we collected a dataset of 2515 instructions aligned with actual routes over three regions of Manhattan. We then empirically study which aspects of a neural architecture are important for the RUN success, and empirically show that entity abstraction, attention over words and worlds, and a constantly updating world-state, significantly contribute to task accuracy.
Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment

Extensive research has recently shown that recurrent neural language models are able to process a wide range of grammatical phenomena. How these models are able to perform these remarkable feats so well, however, is still an open question. To gain more insight into what information LSTMs base their decisions on, we propose a generalisation of Contextual Decomposition (GCD). In particular, this setup enables us to accurately distil which part of a prediction stems from semantic heuristics, which part truly emanates from syntactic cues and which part arise from the model biases themselves instead. We investigate this technique on tasks pertaining to syntactic agreement and co-reference resolution and discover that the model strongly relies on a default reasoning effect to perform these tasks.