nils-herrmann/clean_scirepeval huggingface dataset:
Dependent Pearl: Normalization by realizability

For those of us who generally live in the world of syntax, semantic proof techniques such as reducibility, realizability or logical relations seem somewhat magical despite -- or perhaps due to -- their seemingly unreasonable effectiveness. Why do they work? At which point in the proof is "the real work" done? 
Hoping to build a programming intuition of these proofs, we implement a normalization argument for the simply-typed lambda-calculus with sums: instead of a proof, it is described as a program in a dependently-typed meta-language. 
The semantic technique we set out to study is Krivine's classical realizability, which amounts to a proof-relevant presentation of reducibility arguments -- unary logical relations. Reducibility assigns a predicate to each type, realizability assigns a set of realizers, which are abstract machines that extend lambda-terms with a first-class notion of contexts. Normalization is a direct consequence of an adequacy theorem or "fundamental lemma", which states that any well-typed term translates to a realizer of its type. 
We show that the adequacy theorem, when written as a dependent program, corresponds to an evaluation procedure. In particular, a weak normalization proof precisely computes a series of reduction from the input term to a normal form. Interestingly, the choices that we make when we define the reducibility predicates -- truth and falsity witnesses for each connective -- determine the evaluation order of the proof, with each datatype constructor behaving in a lazy or strict fashion. 
While most of the ideas in this presentation are folklore among specialists, our dependently-typed functional program provides an accessible presentation to a wider audience. In particular, our work provides a gentle introduction to abstract machine calculi which have recently been used as an effective research vehicle.
Query-Based Named Entity Recognition

In this paper, we propose a new strategy for the task of named entity recognition (NER). We cast the task as a query-based machine reading comprehension task: e.g., the task of extracting entities with PER is formalized as answering the question of "which person is mentioned in the text ?". Such a strategy comes with the advantage that it solves the long-standing issue of handling overlapping or nested entities (the same token that participates in more than one entity categories) with sequence-labeling techniques for NER. Additionally, since the query encodes informative prior knowledge, this strategy facilitates the process of entity extraction, leading to better performances. We experiment the proposed model on five widely used NER datasets on English and Chinese, including MSRA, Resume, OntoNotes, ACE04 and ACE05. The proposed model sets new SOTA results on all of these datasets.
Model for heterogeneous reaction-diffusion systems with application to one epidemic

The dynamics of ecological as well as chemical systems may depend on heterogeneous configurations. Heterogeneity in reaction-diffusion systems often increase modelling and simulating difficulties when non-linear effects are present. One synthetic epidemic system with short range heterogeneous composition is modelled and its space-time evolution studied using maximum heterogeneity details. Two other modelling alternatives are applied, one of them using elementary mean-field variables, one other using non-localized geometrical parameters, so avoiding the limitations of the used mean-field model, while keeping significant features of more detailed models. Both the detailed and the mean-field models are solved by means of the standard finite volume method. The model with less defined geometry is solved by means of one modified version of the finite volume method. Simulation results of the three models are compared. At the high diffusion range all models behave similarly. At moderate diffusion fluxes, the numerical results of the model with reduced geometric details are in excellent agreement with the results of the detailed model. The simple mean-field model presents limited accuracy at low and moderate values of the diffusion coefficient.
Hyper-Path-Based Representation Learning for Hyper-Networks

Network representation learning has aroused widespread interests in recent years. While most of the existing methods deal with edges as pairwise relationships, only a few studies have been proposed for hyper-networks to capture more complicated tuplewise relationships among multiple nodes. A hyper-network is a network where each edge, called hyperedge, connects an arbitrary number of nodes. Different from conventional networks, hyper-networks have certain degrees of indecomposability such that the nodes in a subset of a hyperedge may not possess a strong relationship. That is the main reason why traditional algorithms fail in learning representations in hyper-networks by simply decomposing hyperedges into pairwise relationships. In this paper, we firstly define a metric to depict the degrees of indecomposability for hyper-networks. Then we propose a new concept called hyper-path and design hyper-path-based random walks to preserve the structural information of hyper-networks according to the analysis of the indecomposability. Then a carefully designed algorithm, Hyper-gram, utilizes these random walks to capture both pairwise relationships and tuplewise relationships in the whole hyper-networks. Finally, we conduct extensive experiments on several real-world datasets covering the tasks of link prediction and hyper-network reconstruction, and results demonstrate the rationality, validity, and effectiveness of our methods compared with those existing state-of-the-art models designed for conventional networks or hyper-networks.
A framework for anomaly detection using language modeling, and its applications to finance

In the finance sector, studies focused on anomaly detection are often associated with time-series and transactional data analytics. In this paper, we lay out the opportunities for applying anomaly and deviation detection methods to text corpora and challenges associated with them. We argue that language models that use distributional semantics can play a significant role in advancing these studies in novel directions, with new applications in risk identification, predictive modeling, and trend analysis.
Targeted Mismatch Adversarial Attack: Query With a Flower to Retrieve the Tower

Access to online visual search engines implies sharing of private user content -- the query images. We introduce the concept of targeted mismatch attack for deep learning based retrieval systems to generate an adversarial image to conceal the query image. The generated image looks nothing like the user intended query, but leads to identical or very similar retrieval results. Transferring attacks to fully unseen networks is challenging. We show successful attacks to partially unknown systems, by designing various loss functions for the adversarial image construction. These include loss functions, for example, for unknown global pooling operation or unknown input resolution by the retrieval system. We evaluate the attacks on standard retrieval benchmarks and compare the results retrieved with the original and adversarial image.
Planning Beyond The Sensing Horizon Using a Learned Context

Last-mile delivery systems commonly propose the use of autonomous robotic vehicles to increase scalability and efficiency. The economic inefficiency of collecting accurate prior maps for navigation motivates the use of planning algorithms that operate in unmapped environments. However, these algorithms typically waste time exploring regions that are unlikely to contain the delivery destination. Context is key information about structured environments that could guide exploration toward the unknown goal location, but the abstract idea is difficult to quantify for use in a planning algorithm. Some approaches specifically consider contextual relationships between objects, but would perform poorly in object-sparse environments like outdoors. Recent deep learningbased approaches consider context too generally, making training/transferability difficult. Therefore, this work proposes a novel formulation of utilizing context for planning as an image-to-image translation problem, which is shown to extract terrain context from semantic gridmaps, into a metric that an exploration-based planner can use. The proposed framework has the benefit of training on a static dataset instead of requiring a time-consuming simulator. Across 42 test houses with layouts from satellite images, the trained algorithm enables a robot to reach its goal 189% faster than with a context-unaware planner, and within 63% of the optimal path computed with a prior map. The proposed algorithm is also implemented on a vehicle with a forward-facing camera in a high-fidelity, Unreal simulation of neighborhood houses.
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study

Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this paper, we propose a predictive modeling approach based on deep learning based feature representations and word embedding techniques. Our method uses different deep architectures (stacked sparse autoencoders, deep belief network, adversarial autoencoders and variational autoencoders) for feature representation in higher-level abstraction to obtain effective and robust features from EHRs, and then build prediction models on top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled data is scarce. We investigate the performance of representation learning through a supervised learning approach. Our focus is to present a comparative study to evaluate the performance of different deep architectures through supervised learning and provide insights in the choice of deep feature representation techniques. Our experiments demonstrate that for small data sets, stacked sparse autoencoder demonstrates a superior generality performance in prediction due to sparsity regularization whereas variational autoencoders outperform the competing approaches for large data sets due to its capability of learning the representation distribution.
Scalar dark matter, Neutrino mass, Leptogenesis and rare B decays in a $\rm U(1)_{B-L}$ model

We investigate the phenomenology of singlet scalar dark matter in a simple $\rm U(1)_{B-L}$ gauge extension of standard model, made anomaly free with four exotic fermions. The enriched scalar sector and the new gauge boson $Z^\prime$, associated with $\rm U(1)$ gauge extension, connect the dark sector to the visible sector. We compute relic density, consistent with Planck limit and $Z^\prime$ mediated dark matter-nucleon cross section, compatible with PandaX bound. The mass of $Z^\prime$ and the corresponding gauge coupling are constrained from LEP-II and LHC dilepton searches. We also briefly scrutinize the neutrino mass generation through radiative mechanism. Additionally, we restrict the new gauge parameters by using the existing data on branching ratios of rare $B(\tau)$ decay modes. With a slight modification of the same model, we discuss resonant leptogenesis phenomena with TeV scale exotic fermions to produce the observed baryon asymmetry of the Universe.
A tale of two test setups: The effect of randomizing a popular conceptual survey in electricity and magnetism

We describe a retrospective study of the responses to the Brief Electricity and Magnetism Assessment (BEMA) collected from a large population of 3480 students at a large public university. Two different online testing setups were employed for administering the BEMA. Our tale focuses on the interpretation of the data collected from these two testing setups. Our study brings new insight and awareness to the effect of the randomization on the BEMA test results. Starting from an a priori common sense model, we show simple methods to detect and separate guessing from the genuinely thought responses. In addition, we show that the group of responses with low scores (7 or less out of 30) had different answer choice strategies than the groups with average or high scores, and that the time-in-testing is an essential parameter to be reported. Our results suggest that the data should be cleaned to insure that only valid times-in-testing are included before reporting and comparing statistics. We analyze in detail the effect of the answer-options randomization and the effect of the shuffling of the questions order. Our study did not detect any significant effect of the option randomization alone on the BEMA scores, for valid times-in-testing. We found clear evidence that shuffling the order of the independent questions does not affect the scores of the BEMA test, while shuffling the order of the dependent questions does.
Open Event Extraction from Online Text using a Generative Adversarial Network

To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a document are generated from a single event. While this may be true for short text such as tweets, such an assumption does not generally hold for long text such as news articles. Moreover, Bayesian graphical models often rely on Gibbs sampling for parameter inference which may take long time to converge. To address these limitations, we propose an event extraction model based on Generative Adversarial Nets, called Adversarial-neural Event Model (AEM). AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events. A discriminator is used to distinguish documents reconstructed from the latent events and the original documents. A byproduct of the discriminator is that the features generated by the learned discriminator network allow the visualization of the extracted events. Our model has been evaluated on two Twitter datasets and a news article dataset. Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15% is observed in F-measure.
Holographic Code Rate

Holographic codes grown with perfect tensors on regular hyperbolic tessellations using an inflation rule protect quantum information stored in the bulk from errors on the boundary provided the code rate is less than one. Hyperbolic geometry bounds the holographic code rate and guarantees quantum error correction for codes grown with any inflation rule on all regular hyperbolic tessellations in a class whose size grows exponentially with the rank of the perfect tensors for rank five and higher. For the tile completion inflation rule, holographic triangle codes have code rate more than one but all others perform quantum error correction.
Exploring the Performance of Deep Residual Networks in Crazyhouse Chess

Crazyhouse is a chess variant that incorporates all of the classical chess rules, but allows users to drop pieces captured from the opponent as a normal move. Until 2018, all competitive computer engines for this board game made use of an alpha-beta pruning algorithm with a hand-crafted evaluation function for each position. Previous machine learning-based algorithms for just regular chess, such as NeuroChess and Giraffe, took hand-crafted evaluation features as input rather than a raw board representation. More recent projects, such as AlphaZero, reached massive success but required massive computational resources in order to reach its final strength. 
This paper describes the development of SixtyFour, an engine designed to compete in the chess variant of Crazyhouse with limited hardware. This specific variant poses a multitude of significant challenges due to its large branching factor, state-space complexity, and the multiple move types a player can make. We propose the novel creation of a neural network-based evaluation function for Crazyhouse. More importantly, we evaluate the effectiveness of an ensemble model, which allows the training time and datasets to be easily distributed on regular CPU hardware commodity. Early versions of the network have attained a playing level comparable to a strong amateur on online servers.
HyperService: Interoperability and Programmability Across Heterogeneous Blockchains

Blockchain interoperability, which allows state transitions across different blockchain networks, is critical functionality to facilitate major blockchain adoption. Existing interoperability protocols mostly focus on atomic token exchanges between blockchains. However, as blockchains have been upgraded from passive distributed ledgers into programmable state machines (thanks to smart contracts), the scope of blockchain interoperability goes beyond just token exchanges. In this paper, we present HyperService, the first platform that delivers interoperability and programmability across heterogeneous blockchains. HyperService is powered by two innovative designs: (i) a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and (ii) a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in approximately 35,000 lines of code to demonstrate its practicality. Our experiments show that (i) HyperService imposes reasonable latency, in order of seconds, on the end-to-end execution of cross-chain applications; (ii) the HyperService platform is scalable to continuously incorporate new large-scale production blockchains.
Color blending in outdoor optical see-through AR: The effect of real-world backgrounds on user interface color

When viewing augmented reality (AR) through an optical see-through head-mounted display (oHMD), the colors of AR elements become washed out as the luminance of the background increases. In addition, the colors of AR elements can shift and change depending on the color of the background that the AR elements are seen against. Both of these phenomena result from the way the color of an AR element blends with the color of the real-world background that the AR element is seen against. Although the fact that this color blending occurs is generally known, to date it has not been systematically studied or quantified. In this work, we sought to quantify this color blending by building an apparatus that allows precise and repeatable color measurements, and then conducting an empirical engineering study.
Dynamics-aware Embeddings

In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simultaneously learning embeddings of states and action sequences. These embeddings capture the structure of the environment's dynamics, enabling efficient policy learning. We demonstrate that our action embeddings alone improve the sample efficiency and peak performance of model-free RL on control from low-dimensional states. By combining state and action embeddings, we achieve efficient learning of high-quality policies on goal-conditioned continuous control from pixel observations in only 1-2 million environment steps.
LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm

Multiclass decomposition splits a multiclass classification problem into a series of independent binary learners and recomposes them by combining their outputs to reconstruct the multiclass classification results. Three widely-used realizations of such decomposition methods are One-Versus-All (OVA), One-Versus-One (OVO), and Error-Correcting-Output-Code (ECOC). While OVA and OVO are quite simple, both of them assume all classes are orthogonal which neglect the latent correlation between classes in real-world. Error-Correcting-Output-Code (ECOC) based decomposition methods, on the other hand, are more preferable due to its integration of the correlation among classes. However, the performance of existing ECOC-based methods highly depends on the design of coding matrix and decoding strategy. Unfortunately, it is quite uncertain and time-consuming to discover an effective coding matrix with appropriate decoding strategy. To address this problem, we propose LightMC, an efficient dynamic multiclass decomposition algorithm. Instead of using fixed coding matrix and decoding strategy, LightMC uses a differentiable decoding strategy, which enables it to dynamically optimize the coding matrix and decoding strategy, toward increasing the overall accuracy of multiclass classification, via back propagation jointly with the training of base learners in an iterative way. Empirical experimental results on several public large-scale multiclass classification datasets have demonstrated the effectiveness of LightMC in terms of both good accuracy and high efficiency.
Percolation on branching simplicial and cell complexes and its relation to interdependent percolation.

Network geometry has strong effects on network dynamics. In particular, the underlying hyperbolic geometry of discrete manifolds has recently been shown to affect their critical percolation properties. Here we investigate the properties of link percolation in nonamenable two-dimensional branching simplicial and cell complexes, i.e., simplicial and cell complexes in which the boundary scales like the volume. We establish the relation between the equations determining the percolation probability in random branching cell complexes and the equation for interdependent percolation in multiplex networks with interlayer degree correlation equal to one. By using this relation we show that branching cell complexes can display more than two percolation phase transitions: the upper percolation transition, the lower percolation transition, and one or more intermediate phase transitions. At these additional transitions the percolation probability and the fractal exponent both feature a discontinuity. Furthermore, by using the renormalization group theory we show that the upper percolation transition can belong to various universality classes including the Berezinskii-Kosterlitz-Thouless (BKT) transition, the discontinuous percolation transition, and continuous transitions with anomalous singular behavior that generalize the BKT transition.
Role detection in bicycle-sharing networks using multilayer stochastic block models

In urban systems, there is an interdependency between neighborhood roles and transportation patterns between neighborhoods. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the human mobility patterns of three major cities in the United States. We propose novel time-dependent stochastic block models, with degree-heterogeneous blocks and either mixed or discrete block membership, which classify nodes based on their time-dependent activity patterns. We apply these models to (1) detect the roles of bicycle-sharing stations and (2) describe the traffic within and between blocks of stations over the course of a day. Our models successfully uncover work blocks, home blocks, and other blocks; they also reveal activity patterns that are specific to each city. Our work gives insights for the design and maintenance of bicycle-sharing systems, and it contributes new methodology for community detection in temporal and multilayer networks with heterogeneous degrees.