nils-herrmann/clean_scirepeval huggingface dataset:
Five decades of US, UK, German and Dutch music charts show that cultural processes are accelerating

Analysing the timeline of US, UK, German and Dutch music charts, we find that the evolution of album lifetimes and of the size of weekly rank changes provide evidence for an acceleration of cultural processes. For most of the past five decades, number one albums needed more than a month to climb to the top, nowadays an album is in contrast top ranked either from the start, or not at all. Over the last three decades, the number of top-listed albums increased as a consequence from roughly a dozen per year, to about 40. The distribution of album lifetimes evolved during the last decades from a log-normal distribution to a power law, a profound change. Presenting an information–theoretical approach to human activities, we suggest that the fading relevance of personal time horizons may be causing this phenomenon. Furthermore, we find that sales and airplay- based charts differ statistically and that the inclusion of streaming affects chart diversity adversely. We point out in addition that opinion dynamics may accelerate not only in cultural domains, as found here, but also in other settings, in particular in politics, where it could have far reaching consequences.
Cross-Sensor Pore Detection in High-Resolution Fingerprint Images

With the emergence of high-resolution fingerprint sensors, there has been a lot of focus on level-3 fingerprint features, especially the pores, for the next generation automated fingerprint recognition systems (AFRS). Following the success of deep learning in various computer vision tasks, researchers have developed learning-based approaches for detection of pores in high-resolution fingerprint images. Generally, learning-based approaches provide better performance than hand-crafted feature-based approaches. However, domain adaptability of the existing learning-based pore detection methods has never been studied. In this paper, we study this aspect and propose an approach for pore detection in cross-sensor scenarios. For this purpose, we have generated an in-house 1000 dpi fingerprint dataset with ground truth pore coordinates (referred to as IITI-HRFP-GT), and evaluated the performance of the existing learning-based pore detection approaches. The core of the proposed approach for detection of pores in cross-sensor scenarios is DeepDomainPore, which is a residual learning-based convolutional neural network (CNN) trained for pore detection. The domain adaptability in DeepDomainPore is achieved by embedding a gradient reversal layer between the CNN and a domain classifier network. The proposed approach achieves state-of-the-art performance in a cross-sensor scenario involving public high-resolution fingerprint datasets with 88.12% true detection rate and 83.82% F-score.
Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining

Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high-quality solutions can lead to an efficient exploration of the search space, along with a significant reduction of computational time. In this work, a GRASP-based state-of-the-art heuristic for the Minimum Latency Problem (MLP) is improved by means of data mining techniques for two MLP variants. Computational experiments showed that the approaches with data mining were able to match or improve the solution quality for a large number of instances, together with a substantial reduction of running time. In addition, 88 new cost values of solutions are introduced into the literature. To support our results, tests of statistical significance, impact of using mined patterns, equal time comparisons and time-to-target plots are provided.
Moments of nucleon generalized parton distributions from lattice QCD simulations at physical pion mass

We present results for the moments of nucleon isovector vector and axial generalised parton distribution functions computed within lattice QCD. Three ensembles of maximally twisted mass clover-improved fermions simulated with a physical value of the pion mass are analyzed. Two of these ensembles are generated using two degenerate light quarks. A third ensemble is used having, in addition to the light quarks, strange and charm quarks in the sea. A careful analysis of the convergence to the ground state is carried out that is shown to be essential for extracting the correct nucleon matrix elements. This allows a controlled determination of the unpolarised, helicity and tensor second Mellin moments. The vector and axial-vector generalised form factors are also computed as a function of the momentum transfer square up to about 1 GeV$^2$. The three ensembles allow us to check for unquenching effects and to assess lattice finite volume effects.
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog

Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals. With the growing needs to handle complex goals across multiple domains, such manually designed reward functions are not affordable to deal with the complexity of real-world tasks. To this end, we propose Guided Dialog Policy Learning, a novel algorithm based on Adversarial Inverse Reinforcement Learning for joint reward estimation and policy optimization in multi-domain task-oriented dialog. The proposed approach estimates the reward signal and infers the user goal in the dialog sessions. The reward estimator evaluates the state-action pairs so that it can guide the dialog policy at each dialog turn. Extensive experiments on a multi-domain dialog dataset show that the dialog policy guided by the learned reward function achieves remarkably higher task success than state-of-the-art baselines.
Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension

In this work, we propose to use linguistic annotations as a basis for a Discourse-Aware Semantic Self-Attention encoder that we employ for reading comprehension on narrative texts. We extract relations between discourse units, events, and their arguments as well as coreferring mentions, using available annotation tools. Our empirical evaluation shows that the investigated structures improve the overall performance (up to +3.4 Rouge-L), especially intra-sentential and cross-sentential discourse relations, sentence-internal semantic role relations, and long-distance coreference relations. We show that dedicating self-attention heads to intra-sentential relations and relations connecting neighboring sentences is beneficial for finding answers to questions in longer contexts. Our findings encourage the use of discourse-semantic annotations to enhance the generalization capacity of self-attention models for reading comprehension.
DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks

Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation. However, these models are known to have several problems, especially in the context of chit-chat based dialogue systems: they tend to generate short and dull responses that are often too generic. Furthermore, these models do not ground conversational responses on knowledge and facts, resulting in turns that are not accurate, informative and engaging for the users. In this paper, we propose and experiment with a series of response generation models that aim to serve in the general scenario where in addition to the dialogue context, relevant unstructured external knowledge in the form of text is also assumed to be available for models to harness. Our proposed approach extends pointer-generator networks (See et al., 2017) by allowing the decoder to hierarchically attend and copy from external knowledge in addition to the dialogue context. We empirically show the effectiveness of the proposed model compared to several baselines including (Ghazvininejadet al., 2018; Zhang et al., 2018) through both automatic evaluation metrics and human evaluation on ConvAI2 dataset.
Facial age estimation by deep residual decision making

Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks. However, it has not been applied to deep neural decision forest (NDF). In this paper we incorporate residual learning into NDF and the resulting model achieves state-of-the-art level accuracy on three public age estimation benchmarks while requiring less memory and computation. We further employ gradient-based technique to visualize the decision-making process of NDF and understand how it is influenced by facial image inputs. The code and pre-trained models will be available at this https URL.
Information-Theoretic Lower Bounds for Compressive Sensing With Generative Models

It has recently been shown that for compressive sensing, significantly fewer measurements may be required if the sparsity assumption is replaced by the assumption the unknown vector lies near the range of a suitably-chosen generative model. In particular, in (Bora <italic>et al.</italic>, 2017) it was shown roughly <inline-formula> <tex-math notation="LaTeX">$O(k\log L)$ </tex-math></inline-formula> random Gaussian measurements suffice for accurate recovery when the generative model is an <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-Lipschitz function with bounded <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dimensional inputs, and <inline-formula> <tex-math notation="LaTeX">$O(kd \log w)$ </tex-math></inline-formula> measurements suffice when the generative model is a <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-input ReLU network with depth <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and width <inline-formula> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula>. In this paper, we establish corresponding algorithm-independent lower bounds on the sample complexity using tools from minimax statistical analysis. In accordance with the above upper bounds, our results are summarized as follows: (i) We construct an <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-Lipschitz generative model capable of generating group-sparse signals, and show that the resulting necessary number of measurements is <inline-formula> <tex-math notation="LaTeX">$\Omega (k \log L)$ </tex-math></inline-formula>; (ii) Using similar ideas, we construct ReLU networks with high depth and/or high depth for which the necessary number of measurements scales as <inline-formula> <tex-math notation="LaTeX">$\Omega \left({kd \frac {\log w}{\log n}}\right)$ </tex-math></inline-formula> (with output dimension <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>), and in some cases <inline-formula> <tex-math notation="LaTeX">$\Omega (kd \log w)$ </tex-math></inline-formula>. As a result, we establish that the scaling laws derived in (Bora <italic>et al.</italic>, 2017) are optimal or near-optimal in the absence of further assumptions.
Language Tasks and Language Games: On Methodology in Current Natural Language Processing Research

"This paper introduces a new task and a new dataset", "we improve the state of the art in X by Y" -- it is rare to find a current natural language processing paper (or AI paper more generally) that does not contain such statements. What is mostly left implicit, however, is the assumption that this necessarily constitutes progress, and what it constitutes progress towards. Here, we make more precise the normally impressionistically used notions of language task and language game and ask how a research programme built on these might make progress towards the goal of modelling general language competence.
Data Augmentation with Atomic Templates for Spoken Language Understanding

Spoken Language Understanding (SLU) converts user utterances into structured semantic representations. Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, especially when domain changes or a new domain comes. In this work, we propose a data augmentation method with atomic templates for SLU, which involves minimum human efforts. The atomic templates produce exemplars for fine-grained constituents of semantic representations. We propose an encoder-decoder model to generate the whole utterance from atomic exemplars. Moreover, the generator could be transferred from source domains to help a new domain which has little data. Experimental results show that our method achieves significant improvements on DSTC 2&3 dataset which is a domain adaptation setting of SLU.
Reinforcement Learning: Prediction, Control and Value Function Approximation

With the increasing power of computers and the rapid development of self-learning methodologies such as machine learning and artificial intelligence, the problem of constructing an automatic Financial Trading Systems (FTFs) becomes an increasingly attractive research topic. An intuitive way of developing such a trading algorithm is to use Reinforcement Learning (RL) algorithms, which does not require model-building. In this paper, we dive into the RL algorithms and illustrate the definitions of the reward function, actions and policy functions in details, as well as introducing algorithms that could be applied to FTFs.
Semantic Hypergraphs

Existing computational methods for the analysis of corpora of text in natural language are still far from approaching a human level of understanding. We attempt to advance the state of the art by introducing a model and algorithmic framework to transform text into recursively structured data. We apply this to the analysis of news titles extracted from a social news aggregation website. We show that a recursive ordered hypergraph is a sufficiently generic structure to represent significant number of fundamental natural language constructs, with advantages over conventional approaches such as semantic graphs. We present a pipeline of transformations from the output of conventional NLP algorithms to such hypergraphs, which we denote as semantic hypergraphs. The features of these transformations include the creation of new concepts from existing ones, the organisation of statements into regular structures of predicates followed by an arbitrary number of entities and the ability to represent statements about other statements. We demonstrate knowledge inference from the hypergraph, identifying claims and expressions of conflicts, along with their participating actors and topics. We show how this enables the actor-centric summarization of conflicts, comparison of topics of claims between actors and networks of conflicts between actors in the context of a given topic. On the whole, we propose a hypergraphic knowledge representation model that can be used to provide effective overviews of a large corpus of text in natural language.
Image Captioning with Sparse Recurrent Neural Network

Recurrent Neural Network (RNN) has been widely used to tackle a wide variety of language generation problems and are capable of attaining state-of-the-art (SOTA) performance. However despite its impressive results, the large number of parameters in the RNN model makes deployment to mobile and embedded devices infeasible. Driven by this problem, many works have proposed a number of pruning methods to reduce the sizes of the RNN model. In this work, we propose an end-to-end pruning method for image captioning models equipped with visual attention. Our proposed method is able to achieve sparsity levels up to 97.5% without significant performance loss relative to the baseline (~ 2% loss at 40x compression after fine-tuning). Our method is also simple to use and tune, facilitating faster development times for neural network practitioners. We perform extensive experiments on the popular MS-COCO dataset in order to empirically validate the efficacy of our proposed method.
Stochastic AUC Maximization with Deep Neural Networks

Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model. Building on the saddle point reformulation of a surrogated loss of AUC, the problem can be cast into a {\it non-convex concave} min-max problem. The main contribution made in this paper is to make stochastic AUC maximization more practical for deep neural networks and big data with theoretical insights as well. In particular, we propose to explore Polyak-Łojasiewicz (PL) condition that has been proved and observed in deep learning, which enables us to develop new stochastic algorithms with even faster convergence rate and more practical step size scheme. An AdaGrad-style algorithm is also analyzed under the PL condition with adaptive convergence rate. Our experimental results demonstrate the effectiveness of the proposed algorithms.
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation

Generating paraphrases from given sentences involves decoding words step by step from a large vocabulary. To learn a decoder, supervised learning which maximizes the likelihood of tokens always suffers from the exposure bias. Although both reinforcement learning (RL) and imitation learning (IL) have been widely used to alleviate the bias, the lack of direct comparison leads to only a partial image on their benefits. In this work, we present an empirical study on how RL and IL can help boost the performance of generating paraphrases, with the pointer-generator as a base model. Experiments on the benchmark datasets show that (1) imitation learning is constantly better than reinforcement learning; and (2) the pointer-generator models with imitation learning outperform the state-of-the-art methods with a large margin.
Dark matter and no dark matter: on the halo mass of NGC 1052

The NGC 1052 group, and in particular the discovery of two ultra-diffuse galaxies with very low internal velocity dispersions, has been the subject of much attention recently. Here we present radial velocities for a sample of 77 globular clusters associated with NGC 1052 obtained on the Keck telescope. Their mean velocity and velocity dispersion are consistent with that of the host galaxy. Using a simple tracer mass estimator, we infer the enclosed dynamical mass and dark matter fraction of NGC 1052. Extrapolating our measurements with a Navarro–Frenk–White (NFW) mass profile we infer a total halo mass of 6.2(±0.2) × 1012 M⊙. This mass is fully consistent with that expected from the stellar mass–halo mass relation, suggesting that NGC 1052 has a normal dark matter halo mass (i.e. it is not deficient in dark matter in contrast to two ultra-diffuse galaxies in the group). We present a phase-space diagram showing the galaxies that lie within the projected virial radius (390 kpc) of NGC 1052. Finally, we briefly discuss the two dark matter-deficient galaxies (NGC 1052-DF2 and NGC 1052-DF4) and consider whether modified Newtonian dynamics (MOND) can account for their low observed internal velocity dispersions.
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

We propose a Markov chain Monte Carlo (MCMC) algorithm based on third-order Langevin dynamics for sampling from distributions with log-concave and smooth densities. The higher-order dynamics allow for more flexible discretization schemes, and we develop a specific method that combines splitting with more accurate integration. For a broad class of $d$-dimensional distributions arising from generalized linear models, we prove that the resulting third-order algorithm produces samples from a distribution that is at most $\varepsilon > 0$ in Wasserstein distance from the target distribution in $O\left(\frac{d^{1/3}}{ \varepsilon^{2/3}} \right)$ steps. This result requires only Lipschitz conditions on the gradient. For general strongly convex potentials with $\alpha$-th order smoothness, we prove that the mixing time scales as $O \left(\frac{d^{1/3}}{\varepsilon^{2/3}} + \frac{d^{1/2}}{\varepsilon^{1/(\alpha - 1)}} \right)$.
NuSTAR Discovery of Dead Quasar Engine in Arp 187

Recent active galactic nucleus (AGN) and quasar surveys have revealed a population showing rapid AGN luminosity variability by a factor of ∼10. Here we present the most drastic AGN luminosity decline by a factor of ≳103 constrained by a NuSTAR X-ray observation of the nearby galaxy Arp 187, which is a promising “dead” quasar whose current activity seems quiet but whose past activity of erg s−1 is still observable at a large scale by its light echo. The obtained upper bound of the X-ray luminosity is , corresponding to , indicating an inactive central engine. Even if a putative torus model with cm−2 is assumed, the strong upper bound still holds with or . Given the expected size of the narrow-line region, this luminosity decrease by a factor of ≳103 must have occurred within ≲104 yr. This extremely rapid luminosity/accretion shutdown is puzzling, and it requires one burst-like accretion mechanism producing a clear outer boundary for an accretion disk. We raise two possible scenarios realizing such an accretion mechanism: a mass accretion (1) by the tidal disruption of a molecular cloud and/or (2) by the gas depletion as a result of vigorous nuclear star formation after rapid mass inflow to the central engine.
A measure for quantum paths, gravity and spacetime microstructure

The number of classical paths of a given length, connecting any two events in a (pseudo) Riemannian spacetime is, of course, infinite. It is, however, possible to define a useful, finite, measure  see text] for the effective number of quantum paths [of length  see text] connecting two events  in an arbitrary spacetime. When  see text], this reduces to  see text] giving the measure for closed quantum loops of length  see text] containing an event  see text]. Both  see text] and  see text] are well-defined and depend only on the geometry of the spacetime. Various other physical quantities like, for e.g. the effective Lagrangian, can be expressed in terms of  see text]. The corresponding measure for the total path length contributed by the closed loops, in a spacetime region  see text], is given by the integral of  see text] over  see text]. Remarkably enough  see text], the Ricci scalar; i.e. the measure for the total length contributed by infinitesimal closed loops in a region of spacetime gives us the Einstein–Hilbert action. Its variation, when we vary the metric, can provide a new route towards induced/emergent gravity descriptions. In the presence of a background electromagnetic field, the corresponding expressions for  see text] and  see text] can be related to the holonomies of the field. The measure  see text] can also be used to evaluate a wide class of path integrals for which the action and the measure are arbitrary functions of the path length. As an example, I compute a modified path integral which incorporates the zero-point-length in the spacetime. I also describe several other properties of  see text] and outline a few simple applications.