nils-herrmann/clean_scirepeval huggingface dataset:
Inhomogeneous spin excitations in weakly coupled spin-
12
 chains

L. Shen, E. Campillo, O. Zaharko, P. Steffens, M. Boehm, K. Beauvois, B. Ouladdiaf, Z. He, D. Prabhakaran, A. T. Boothroyd, and E. Blackburn Division of Synchrotron Radiation Research, Lund University, SE-22100 Lund, Sweden Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institut, CH-5232 Villigen PSI, Switzerland Institut Laue-Langevin, 71 Avenue des Martyrs, 38042 Grenoble Cedex 9, France State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China Department of Physics, University of Oxford, Clarendon Laboratory, Oxford, OX1 3PU, United Kingdom
Enhancing Machine Translation with Dependency-Aware Self-Attention

Most neural machine translation models only rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. In this work, we investigate different approaches to incorporate syntactic knowledge in the Transformer model and also propose a novel, parameter-free, dependency-aware self-attention mechanism that improves its translation quality, especially for long sentences and in low-resource scenarios. We show the efficacy of each approach on WMT English-German and English-Turkish, and WAT English-Japanese translation tasks.
Triangulation pointing to core-collapse supernovae with next-generation neutrino detectors

A core-collapse supernova releases the vast majority of the gravitational binding energy of its compact remnant in the form of neutrinos over an interval of a few tens of seconds. In the event of a core-collapse supernova within our galaxy, multiple current and future neutrino detectors would see a large burst in activity. Neutrinos escape a supernova hours before light does, so any prompt information about the supernova's direction that can be inferred via the neutrino signal will help to enable early electromagnetic observations of the supernova. While there are methods to determine the direction via intrinsic directionality of some neutrino-matter interaction channels, a complementary method which will reach maturity with the next generation of large neutrino detectors is the use of relative neutrino arrival times at different detectors around the globe. To evaluate this triangulation method for realistic detector configurations of the next few decades, we generate random supernova neutrino signals with realistic detector assumptions, and quantify the error in expected time delay between detections. We investigate a practical and robust method of estimating the time differences between burst detections, also correcting for detection efficiency bias. With this method, we determine the pointing precision of supernova neutrino triangulation as a function of supernova distance and location, detectors used, detector background level and neutrino mass ordering assumption. Under favorable conditions, the 1$\sigma$ supernova search area from triangulation could be reduced to a few percent of the sky. It should be possible to implement this method with low latency under realistic conditions.
Stable Manipulation in Voting

We introduce the problem of {\em stable manipulation} where the manipulators need to compute if there exist votes for the manipulators which make their preferred alternative win the election even if the manipulators' knowledge about others' votes are little inaccurate, that is, manipulation remains successful even under small perturbation of the non-manipulators' votes. We show that every scoring rule, maximin, Bucklin, and simplified Bucklin voting rules are stably manipulable in polynomial time for single manipulator. In contrast, stable manipulation becomes intractable for the Copeland$^\alpha$ voting rule for every $\alpha\in[0,1]$ even for single manipulator. However for a constant number of alternatives, we show that the stable manipulation problem is polynomial time solvable for every anonymous and efficient voting rules. Finally we empirically show that the probability that a uniformly random profile is stably manipulable decreases drastically even if manipulator possess little uncertainty about others' votes.
Equalizing Recourse across Groups

The rise in machine learning-assisted decision-making has led to concerns about the fairness of the decisions and techniques to mitigate problems of discrimination. If a negative decision is made about an individual (denying a loan, rejecting an application for housing, and so on) justice dictates that we be able to ask how we might change circumstances to get a favorable decision the next time. Moreover, the ability to change circumstances (a better education, improved credentials) should not be limited to only those with access to expensive resources. In other words, \emph{recourse} for negative decisions should be considered a desirable value that can be equalized across (demographically defined) groups. This paper describes how to build models that make accurate predictions while still ensuring that the penalties for a negative outcome do not disadvantage different groups disproportionately. We measure recourse as the distance of an individual from the decision boundary of a classifier. We then introduce a regularized objective to minimize the difference in recourse across groups. We explore linear settings and further extend recourse to non-linear settings as well as model-agnostic settings where the exact distance from boundary cannot be calculated. Our results show that we can successfully decrease the unfairness in recourse while maintaining classifier performance.
Look and Modify: Modification Networks for Image Captioning

Attention-based neural encoder-decoder frameworks have been widely used for image captioning. Many of these frameworks deploy their full focus on generating the caption from scratch by relying solely on the image features or the object detection regional features. In this paper, we introduce a novel framework that learns to modify existing captions from a given framework by modeling the residual information, where at each timestep the model learns what to keep, remove or add to the existing caption allowing the model to fully focus on "what to modify" rather than on "what to predict". We evaluate our method on the COCO dataset, trained on top of several image captioning frameworks and show that our model successfully modifies captions yielding better ones with better evaluation scores.
Auto-GNN: Neural Architecture Search of Graph Neural Networks

Graph neural networks (GNN) has been successfully applied to operate on the graph-structured data. Given a specific scenario, rich human expertise and tremendous laborious trials are usually required to identify a suitable GNN architecture. It is because the performance of a GNN architecture is significantly affected by the choice of graph convolution components, such as aggregate function and hidden dimension. Neural architecture search (NAS) has shown its potential in discovering effective deep architectures for learning tasks in image and language modeling. However, existing NAS algorithms cannot be directly applied to the GNN search problem. First, the search space of GNN is different from the ones in existing NAS work. Second, the representation learning capacity of GNN architecture changes obviously with slight architecture modifications. It affects the search efficiency of traditional search methods. Third, widely used techniques in NAS such as parameter sharing might become unstable in GNN. 
To bridge the gap, we propose the automated graph neural networks (AGNN) framework, which aims to find an optimal GNN architecture within a predefined search space. A reinforcement learning based controller is designed to greedily validate architectures via small steps. AGNN has a novel parameter sharing strategy that enables homogeneous architectures to share parameters, based on a carefully-designed homogeneity definition. Experiments on real-world benchmark datasets demonstrate that the GNN architecture identified by AGNN achieves the best performance, comparing with existing handcrafted models and tradistional search methods.
Heterogeneous wealth distribution, round-trip trading and the emergence of volatility clustering in Speculation Game

This study is a detailed analysis of Speculation Game, a minimal agent-based model of financial markets, in which the round-trip trading and the dynamic wealth evolution with variable trading volumes are implemented. Instead of herding behavior, we find that the emergence of volatility clustering can be induced by the heterogeneous wealth distribution among traders. In particular, the spontaneous redistribution of market wealth through repetitions of round-trip trades can widen the wealth disparity and establish the Pareto distribution of the capital size. In the meantime, large fluctuations in price return are brought on by the intermittent placements of the relatively big orders from rich traders. Empirical data are used to support the scenario derived from the model.
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models

We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher ROUGE scores. We provide extensive comparisons with strong baseline methods, prior state of the art work as well as multiple variants of our approach including those using only transformers, only extractive techniques and combinations of the two. We examine these models using four different summarization tasks and datasets: arXiv papers, PubMed papers, the Newsroom and BigPatent datasets. We find that transformer based methods produce summaries with fewer n-gram copies, leading to n-gram copying statistics that are more similar to human generated abstracts. We include a human evaluation, finding that transformers are ranked highly for coherence and fluency, but purely extractive methods score higher for informativeness and relevance. We hope that these architectures and experiments may serve as strong points of comparison for future work.  The abstract above was collaboratively written by the authors and one of the models presented in this paper based on an earlier draft of this paper.
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons

This paper studies the problem of finding the exact ranking from noisy comparisons. A noisy comparison over a set of $m$ items produces a noisy outcome about the most preferred item, and reveals some information about the ranking. By repeatedly and adaptively choosing items to compare, we want to fully rank the items with a certain confidence, and use as few comparisons as possible. Different from most previous works, in this paper, we have three main novelties: (i) compared to prior works, our upper bounds (algorithms) and lower bounds on the sample complexity (aka number of comparisons) require the minimal assumptions on the instances, and are not restricted to specific models; (ii) we give lower bounds and upper bounds on instances with \textit{unequal} noise levels; and (iii) this paper aims at the \textit{exact} ranking without knowledge on the instances, while most of the previous works either focus on approximate rankings or study exact ranking but require prior knowledge. We first derive lower bounds for pairwise ranking (i.e., compare two items each time), and then propose (nearly) \textit{optimal} pairwise ranking algorithms. We further make extensions to listwise ranking (i.e., comparing multiple items each time). Numerical results also show our improvements against the state of the art.
Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively. Specifically, for the critics, the expected absolute Bellman error-based updating rule is used to choose the worst critic to be updated in each time step. Subsequently, to calculate the loss function with more accurate target value for the chosen critic, Pseudo Q-learning, which uses subgreedy policy to replace the greedy policy in Q-learning, is developed for continuous action spaces, and Multi Pseudo Q-learning (MPQ) is proposed to reduce the overestimation of action-value function and to stabilize the learning. As for the actors, deterministic policy gradient is applied to update the weights, and the final learned policy is defined as the average of all actors to avoid large but bad updates. Moreover, the stability analysis of the learning is given qualitatively. The effectiveness and generality of the proposed MPQ-based deterministic policy gradient (MPQ-DPG) algorithm are verified by the application on AUV with two different reference trajectories. In addition, the results demonstrate high-level tracking control accuracy and stable learning of MPQ-DPG. Besides, the results also validate that increasing the number of the actors and critics will further improve the performance.
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View

Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing issue (indistinguishable representations of nodes in different classes). In this work, we present a systematic and quantitative study on the over-smoothing issue of GNNs. First, we introduce two quantitative metrics, MAD and MADGap, to measure the smoothness and over-smoothness of the graph nodes representations, respectively. Then, we verify that smoothing is the nature of GNNs and the critical factor leading to over-smoothness is the low information-to-noise ratio of the message received by the nodes, which is partially determined by the graph topology. Finally, we propose two methods to alleviate the over-smoothing issue from the topological view: (1) MADReg which adds a MADGap-based regularizer to the training objective; (2) AdaEdge which optimizes the graph topology based on the model predictions. Extensive experiments on 7 widely-used graph datasets with 10 typical GNN models show that the two proposed methods are effective for relieving the over-smoothing issue, thus improving the performance of various GNN models.
AutoML for Contextual Bandits

Contextual Bandits is one of the widely popular techniques used in applications such as personalization, recommendation systems, mobile health, causal marketing etc . As a dynamic approach, it can be more efficient than standard A/B testing in minimizing regret. We propose an end to end automated meta-learning pipeline to approximate the optimal Q function for contextual bandits problems. We see that our model is able to perform much better than random exploration, being more regret efficient and able to converge with a limited number of samples, while remaining very general and easy to use due to the meta-learning approach. We used a linearly annealed e-greedy exploration policy to define the exploration vs exploitation schedule. We tested the system on a synthetic environment to characterize it fully and we evaluated it on some open source datasets to benchmark against prior work. We see that our model outperforms or performs comparatively to other models while requiring no tuning nor feature engineering.
Impact of Uncertainties in Astrophysical Reaction Rates on Nucleosynthesis in the νp Process

The $\nu p$ process appears in proton-rich, hot matter which is expanding in a neutrino wind and may be realised in explosive environments such as core-collapse supernovae or in outflows from accretion disks. The impact of uncertainties in nuclear reaction cross sections on the finally produced abundances has been studied by applying Monte Carlo variation of all astrophysical reaction rates in a large reaction network. As the detailed astrophysical conditions of the $\nu p$ process still are unknown, a parameter study was performed, with 23 trajectories covering a large range of entropies and $Y_\mathrm{e}$. The resulting abundance uncertainties are given for each trajectory.  It is found that it is possible to reproduce the solar $^{92}$Mo/$^{94}$Mo abundance ratio within nuclear uncertainties, even within a single trajectory. The solar values of the abundances in the Kr-Sr region relative to the Mo region, however, cannot be achieved within a single trajectory. They may still be obtained from a weighted superposition of different trajectories, though, depending on the actual conditions in the production site. For a stronger constraint of the required conditions, it would be necessary to reduce the uncertainties in the 3$\alpha$ and $^{56}$Ni(n,p)$^{56}$Co rates at temperatures $T>3$ GK.
Recognition Of Surface Defects On Steel Sheet Using Transfer Learning

Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods mainly extract features manually and use machine learning classifiers to recognize defects, which cannot tackle the situation, where there are few data available to train and confine to a certain scene. Therefore, in this paper, a new approach is proposed which consists of part of pretrained VGG16 as a feature extractor and a new CNN neural network as a classifier to recognize the defect of steel strip surface based on the feature maps created by the feature extractor. Our method achieves an accuracy of 99.1% and 96.0% while the dataset contains 150 images each class and 10 images each class respectively, which is much better than previous methods.
Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning

The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account of maximizing returns. Therefore, this paper proposes a deep reinforcement learning based trading agent that can manage the portfolio considering not only profit maximization but also risk restraint. We also propose a new target policy to allow the trading agent to learn to prefer low-risk actions. The new target policy can be reflected in the update by adjusting the greediness for the optimal action through the hyper parameter. The proposed trading agent verifies the performance through the data of the cryptocurrency market. The Cryptocurrency market is the best test-ground for testing our trading agents because of the huge amount of data accumulated every minute and the market volatility is extremely large. As a experimental result, during the test period, our agents achieved a return of 1800% and provided the least risky investment strategy among the existing methods. And, another experiment shows that the agent can maintain robust generalized performance even if market volatility is large or training period is short.
Compiling PL/SQL Away

"PL/SQL functions are slow," is common developer wisdom that derives from the tension between set-oriented SQL evaluation and statement-by-statement PL/SQL interpretation. We pursue the radical approach of compiling PL/SQL away, turning interpreted functions into regular subqueries that can then be efficiently evaluated together with their embracing SQL query, avoiding any PL/SQL to SQL context switches. Input PL/SQL functions may exhibit arbitrary control flow. Iteration, in particular, is compiled into SQL-level recursion. RDBMSs across the board reward this compilation effort with significant run time savings that render established developer lore questionable.
A greedy constructive algorithm for the optimization of neural network architectures

In this work we propose a new method to optimize the architecture of an artificial neural network. The algorithm proposed, called Greedy Search for Neural Network Architecture, aims to minimize the complexity of the architecture search and the complexity of the final model selected without compromising the predictive performance. The reduction of the computational cost makes this approach appealing for two reasons. Firstly, there is a need from domain scientists to easily interpret predictions returned by a deep learning model and this tends to be cumbersome when neural networks have complex structures. Secondly, the use of neural networks is challenging in situations with compute/memory limitations. Promising numerical results show that our method is competitive against other hyperparameter optimization algorithms for attainable performance and computational cost. We also generalize the definition of adjusted score from linear regression models to neural networks. Numerical experiments are presented to show that the adjusted score can boost the greedy search to favor smaller architectures over larger ones without compromising the predictive performance.
Fast mmwave Beam Alignment via Correlated Bandit Learning

Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.
Relationships from Entity Stream

Relational reasoning is a central component of intelligent behavior, but has proven difficult for neural networks to learn. The Relation Network (RN) module was recently proposed by DeepMind to solve such problems, and demonstrated state-of-the-art results on a number of datasets. However, the RN module scales quadratically in the size of the input, since it calculates relationship factors between every patch in the visual field, including those that do not correspond to entities. In this paper, we describe an architecture that enables relationships to be determined from a stream of entities obtained by an attention mechanism over the input field. The model is trained end-to-end, and demonstrates equivalent performance with greater interpretability while requiring only a fraction of the model parameters of the original RN module.