nils-herrmann/clean_scirepeval huggingface dataset:
Improving Neural Story Generation by Targeted Common Sense Grounding

Stories generated with neural language models have shown promise in grammatical and stylistic consistency. However, the generated stories are still lacking in common sense reasoning, e.g., they often contain sentences deprived of world knowledge. We propose a simple multi-task learning scheme to achieve quantitatively better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. When combined with our two-stage fine-tuning pipeline, our method achieves improved common sense reasoning and state-of-the-art perplexity on the WritingPrompts (Fan et al., 2018) story generation dataset.
OpenSpiel: A Framework for Reinforcement Learning in Games

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. This document serves both as an overview of the code base and an introduction to the terminology, core concepts, and algorithms across the fields of reinforcement learning, computational game theory, and search.
An Evaluation of Feature Matchers for Fundamental Matrix Estimation

Matching two images while estimating their relative geometry is a key step in many computer vision applications. For decades, a well-established pipeline, consisting of SIFT, RANSAC, and 8-point algorithm, has been used for this task. Recently, many new approaches were proposed and shown to outperform previous alternatives on standard benchmarks, including the learned features, correspondence pruning algorithms, and robust estimators. However, whether it is beneficial to incorporate them into the classic pipeline is less-investigated. To this end, we are interested in i) evaluating the performance of these recent algorithms in the context of image matching and epipolar geometry estimation, and ii) leveraging them to design more practical registration systems. The experiments are conducted in four large-scale datasets using strictly defined evaluation metrics, and the promising results provide insight into which algorithms suit which scenarios. According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator. They show remarkable performances and have potentials to a large part of computer vision tasks. To facilitate future research, the full evaluation pipeline and the proposed methods are made publicly available.
Improving Automatic Jazz Melody Generation by Transfer Learning Techniques

In this paper, we tackle the problem of transfer learning for Jazz automatic generation. Jazz is one of representative types of music, but the lack of Jazz data in the MIDI format hinders the construction of a generative model for Jazz. Transfer learning is an approach aiming to solve the problem of data insufficiency, so as to transfer the common feature from one domain to another. In view of its success in other machine learning problems, we investigate whether, and how much, it can help improve automatic music generation for under-resourced musical genres. Specifically, we use a recurrent variational autoencoder as the generative model, and use a genre-unspecified dataset as the source dataset and a Jazz-only dataset as the target dataset. Two transfer learning methods are evaluated using six levels of source-to-target data ratios. The first method is to train the model on the source dataset, and then fine-tune the resulting model parameters on the target dataset. The second method is to train the model on both the source and target datasets at the same time, but add genre labels to the latent vectors and use a genre classifier to improve Jazz generation. Our subjective evaluation shows that both methods outperform the baseline method that uses Jazz data only for training by a large margin. Among the two methods, the first method seems to perform better. Our evaluation also shows the limits of existing objective metrics in evaluating the performance of music generation models.
Supporting stylists by recommending fashion style

Outfittery is an online personalized styling service targeted at men. We have hundreds of stylists who create thousands of bespoke outfits for our customers every day. A critical challenge faced by our stylists when creating these outfits is selecting an appropriate item of clothing that makes sense in the context of the outfit being created, otherwise known as style fit. Another significant challenge is knowing if the item is relevant to the customer based on their tastes, physical attributes and price sensitivity. At Outfittery we leverage machine learning extensively and combine it with human domain expertise to tackle these challenges. We do this by surfacing relevant items of clothing during the outfit building process based on what our stylist is doing and what the preferences of our customer are. In this paper we describe one way in which we help our stylists to tackle style fit for a particular item of clothing and its relevance to an outfit. A thorough qualitative and quantitative evaluation highlights the method's ability to recommend fashion items by style fit.
Partially-supervised Mention Detection

Learning to detect entity mentions without using syntactic information can be useful for integration and joint optimization with other tasks. However, it is common to have partially annotated data for this problem. Here, we investigate two approaches to deal with partial annotation of mentions: weighted loss and soft-target classification. We also propose two neural mention detection approaches: a sequence tagging, and an exhaustive search. We evaluate our methods with coreference resolution as a downstream task, using multitask learning. The results show that the recall and F1 score improve for all methods.
A Convolutional Neural Network With Mapping Layers for Hyperspectral Image Classification

In this article, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low-dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancies and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on the convolutional layer to extract the spectral–spatial features for HSI. We tested our MCNN on three data sets of Indian Pines, University of Pavia, and Salinas, and we achieved the classification accuracy of 98.3%, 99.5%, and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve classification accuracy and save much time consumption.
Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation

Based Conversation (BBCs) have been introduced to help conversational systems avoid generating overly generic responses. In a BBC, the conversation is grounded in a knowledge source. A key challenge in BBCs is Knowledge Selection (KS): given a conversational context, try to find the appropriate background knowledge (a text fragment containing related facts or comments, etc.) based on which to generate the next response. Previous work addresses KS by employing attention and/or pointer mechanisms. These mechanisms use a local perspective, i.e., they select a token at a time based solely on the current decoding state. We argue for the adoption of a global perspective, i.e., pre-selecting some text fragments from the background knowledge that could help determine the topic of the next response. We enhance KS in BBCs by introducing a Global-to-Local Knowledge Selection (GLKS) mechanism. Given a conversational context and background knowledge, we first learn a topic transition vector to encode the most likely text fragments to be used in the next response, which is then used to guide the local KS at each decoding timestamp. In order to effectively learn the topic transition vector, we propose a distantly supervised learning schema. Experimental results show that the GLKS model significantly outperforms state-of-the-art methods in terms of both automatic and human evaluation. More importantly, GLKS achieves this without requiring any extra annotations, which demonstrates its high degree of scalability.
Embarrassingly Simple Binary Representation Learning

Recent binary representation learning models usually require sophisticated binary optimization, similarity measure or even generative models as auxiliaries. However, one may wonder whether these non-trivial components are needed to formulate practical and effective hashing models. In this paper, we answer the above question by proposing an embarrassingly simple approach to binary representation learning. With a simple classification objective, our model only incorporates two additional fully-connected layers onto the top of an arbitrary backbone network, for binary latents and semantic labels respectively, whilst complying with the binary constraints during training. The proposed model lower-bounds the Information Bottleneck (IB) between data samples and their semantics, and can be related to many recent 'learning to hash' paradigms. We show that, when properly designed, even such a simple network can generate effective binary codes, by fully exploring data semantics without any held-out alternating updating steps or auxiliary models. Experiments are conducted on conventional large-scale benchmarks, i.e., CIFAR-10, NUS-WIDE, and ImageNet, where the proposed simple model outperforms the state-of-the-art methods.
Spiral-arm instability – III. Fragmentation of primordial protostellar discs

We study the gravitational instability and fragmentation of primordial protostellar discs by using high-resolution cosmological hydrodynamics simulations. We follow the formation and evolution of spiral arms in protostellar discs, examine the dynamical stability, and identify a physical mechanism of secondary protostar formation. We use linear perturbation theory based on the spiral-arm instability (SAI) analysis in our previous studies. We improve the analysis by incorporating the effects of finite thickness and shearing motion of arms, and derive the physical conditions for SAI in protostellar discs. Our analysis predicts accurately the stability and the onset of arm fragmentation that is determined by the balance between self-gravity and gas pressure plus the Coriolis force. Formation of secondary and multiple protostars in the discs is explained by the SAI, which is driven by self-gravity and thus can operate without rapid gas cooling. We can also predict the typical mass of the fragments, which is found to be in good agreement with the actual masses of secondary protostars formed in the simulation.
Revenue Sharing in the Internet: A Moral Hazard Approach and a Net-neutrality Perspective

Revenue sharing contracts between Content Providers (CPs) and Internet Service Providers (ISPs) can act as leverage for enhancing the infrastructure of the Internet. ISPs can be incentivized to make investments in network infrastructure that improve Quality of Service (QoS) for users if attractive contracts are negotiated between them and CPs. The idea here is that part of the net profit gained by CPs are given to ISPs to invest in the network. The Moral Hazard economic framework is used to model such an interaction, in which a principal determines a contract, and an agent reacts by adapting her effort. In our setting, several competitive CPs interact through one common ISP. Two cases are studied: (i) the ISP differentiates between the CPs and makes a (potentially) different investment to improve the QoS of each CP, and (ii) the ISP does not differentiate between CPs and makes a common investment for both. The last scenario can be viewed as \emph{network neutral behavior} on the part of the ISP. We analyse the optimal contracts and show that the CP that can better monetize its demand always prefers the non-neutral regime. Interestingly, ISP revenue, as well as social utility, are also found to be higher under the non-neutral regime.
Rethinking Attribute Representation and Injection for Sentiment Classification

Text attributes, such as user and product information in product reviews, have been used to improve the performance of sentiment classification models. The de facto standard method is to incorporate them as additional biases in the attention mechanism, and more performance gains are achieved by extending the model architecture. In this paper, we show that the above method is the least effective way to represent and inject attributes. To demonstrate this hypothesis, unlike previous models with complicated architectures, we limit our base model to a simple BiLSTM with attention classifier, and instead focus on how and where the attributes should be incorporated in the model. We propose to represent attributes as chunk-wise importance weight matrices and consider four locations in the model (i.e., embedding, encoding, attention, classifier) to inject attributes. Experiments show that our proposed method achieves significant improvements over the standard approach and that attention mechanism is the worst location to inject attributes, contradicting prior work. We also outperform the state-of-the-art despite our use of a simple base model. Finally, we show that these representations transfer well to other tasks. Model implementation and datasets are released here: https://github.com/rktamplayo/CHIM.
Dynamics of charged and rotating NUT black holes in Rastall gravity

In this work, the Kerr–Newman-NUT black hole solution in Rastall gravity is proposed and it turns out that the horizon is  see text] dependence. Black hole dynamics such as the event horizons, ergosurface, zero angular momentum observer (ZAMO), thermodynamic properties, and the equatorial circular orbit around the black hole such as static radius limit, null equatorial circular orbit, and innermost stable circular orbit are investigated in this work. How the NUT and Rastall parameter affect the dynamic of the black hole is also shown.
A Survey on Bias and Fairness in Machine Learning

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.
Systematics in the ALMA Proposal Review Rankings

The results from the ALMA proposal peer review process in Cycles 0–6 are analyzed to identify any systematics in the scientific rankings that may signify bias. Proposal rankings are analyzed with respect to the experience level of a Principal Investigator (PI) in submitting ALMA proposals, regional affiliation (Chile, East Asia, Europe, North America, or Other), and gender. The analysis was conducted for both the Stage 1 rankings, which are based on the preliminary scores from the reviewers, and the Stage 2 rankings, which are based on the final scores from the reviewers after participating in a face-to-face panel discussion. Analysis of the Stage 1 results shows that PIs who submit an ALMA proposal in multiple cycles have systematically better proposal ranks than PIs who have submitted proposals for the first time. In terms of regional affiliation, PIs from Europe and North America have better Stage 1 rankings than PIs from Chile and East Asia. Consistent with Lonsdale et al. 2016, proposals led by men have better Stage 1 rankings than women when averaged over all cycles. This trend was most noticeably present in Cycle 3, but no discernible differences in the Stage 1 rankings are present in recent cycles. Nonetheless, in each cycle to date, women have had a lower proposal acceptance rate than men even after differences in demographics are considered. Comparison of the Stage 1 and Stage 2 rankings reveal no significant changes in the distribution of proposal ranks by experience level, regional affiliation, or gender as a result of the panel discussions, although the proposal ranks for East Asian PIs show a marginally significant improvement from Stage 1 to Stage 2 when averaged over all cycles. Thus any systematics in the proposal rankings are introduced primarily in the Stage 1 process and not from the face-to-face discussions. These results are discussed in the context of potential language and cultural biases, but any conclusions on the origin of the observed systematics remain speculative.
Expansion Method for Pricing Foreign Exchange Options Under Stochastic Volatility and Interest Rates

Some expansion methods have been proposed for approximately pricing options which has no exact closed formula. Benhamou et al. (2010) presents the smart expansion method that directly expands the expectation value of payoff function with respect to the volatility of volatility, then uses it to price options in the stochastic volatility model. In this paper, we apply their method to the stochastic volatility model with stochastic interest rates, and present the expansion formula for pricing options up to the second order. Then the numerical studies are performed to compare our approximation formula with the Monte-Carlo simulation. It is found that our formula shows the numerically comparable results with the method proposed by Grzelak et al. (2012) which uses the approximation of characteristic function.
Semi-supervised Learning for Word Sense Disambiguation

This work is a study of the impact of multiple aspects in a classic unsupervised word sense disambiguation algorithm. We identify relevant factors in a decision rule algorithm, including the initial labeling of examples, the formalization of the rule confidence, and the criteria for accepting a decision rule. Some of these factors are only implicitly considered in the original literature. We then propose a lightly supervised version of the algorithm, and employ a pseudo-word-based strategy to evaluate the impact of these factors. The obtained performances are comparable with those of highly optimized formulations of the word sense disambiguation method.
Singular Lagrangians and the dynamics of the "Kill the Winner" model and its descendants

We consider certain analytical features of the "Kill the Winner" model which is a stochastic model that can explain among other things competition among species and simultaneous predation on the competing species. The model equations are shown to admit a Jacobi Last Multiplier which in turns allows for the construction of a Lagrangian. The Lagrangian is of singular nature so that construction of the Hamiltonian via a Legendre transformation is not possible. A Hamiltonian description of the model therefore requires the introduction of Dirac brackets. Explicit results are presented for the model and its reductions.
Preventing the Generation of Inconsistent Sets of Classification Rules

In recent years, the interest in interpretable classification models has grown. One of the proposed ways to improve the interpretability of a rule-based classification model is to use sets (unordered collections) of rules, instead of lists (ordered collections) of rules. One of the problems associated with sets is that multiple rules may cover a single instance, but predict different classes for it, thus requiring a conflict resolution strategy. In this work, we propose two algorithms capable of finding feature-space regions inside which any created rule would be consistent with the already existing rules, preventing inconsistencies from arising. Our algorithms do not generate classification models, but are instead meant to enhance algorithms that do so, such as Learning Classifier Systems. Both algorithms are described and analyzed exclusively from a theoretical perspective, since we have not modified a model-generating algorithm to incorporate our proposed solutions yet. This work presents the novelty of using conflict avoidance strategies instead of conflict resolution strategies.
Fusing heterogeneous data sets

In systems biology, it is common to measure biochemical entities at different levels of the same biological system. One of the central problems for the data fusion of such data sets is the heterogeneity of the data. This thesis discusses two types of heterogeneity. The first one is the type of data, such as metabolomics, proteomics and RNAseq data in genomics. These different omics data reflect the properties of the studied biological system from different perspectives. The second one is the type of scale, which indicates the measurements obtained at different scales, such as binary, ordinal, interval and ratio-scaled variables. In this thesis, we developed several statistical methods capable to fuse data sets of these two types of heterogeneity. The advantages of the proposed methods in comparison with other approaches are assessed using comprehensive simulations as well as the analysis of real biological data sets.
Supersymmetry and the collider dark matter picture

One of the key questions in particle physics and astrophysics is the nature of dark matter, the existence of which has been confirmed in many astrophysical and cosmological observations. Besides direct and indirect detection experiments, collider searches for dark matter offer the unique possibility to not only detect dark matter particles but in the case of discovery to also study their properties by making statements about the potential underlying theory. The search program for dark matter at the ATLAS and CMS experiments at the Large Hadron Collider is comprehensive, and includes both supersymmetric dark matter candidates and other alternatives. This review presents the latest status in these searches, with special focus on supersymmetric dark matter particles.