nils-herrmann/clean_scirepeval huggingface dataset:
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing

Weighted model integration (WMI) is a very appealing framework for probabilistic inference: it allows to express the complex dependencies of real-world hybrid scenarios where variables are heterogeneous in nature (both continuous and discrete) via the language of Satisfiability Modulo Theories (SMT); as well as computing probabilistic queries with arbitrarily complex logical constraints. Recent work has shown WMI inference to be reducible to a model integration (MI) problem, under some assumptions, thus effectively allowing hybrid probabilistic reasoning by volume computations. In this paper, we introduce a novel formulation of MI via a message passing scheme that allows to efficiently compute the marginal densities and statistical moments of all the variables in linear time. As such, we are able to amortize inference for arbitrarily rich MI queries when they conform to the problem structure, here represented as the primal graph associated to the SMT formula. Furthermore, we theoretically trace the tractability boundaries of exact MI. Indeed, we prove that in terms of the structural requirements on the primal graph that make our MI algorithm tractable - bounding its diameter and treewidth - the bounds are not only sufficient, but necessary for tractable inference via MI.
EATEN: Entity-Aware Attention for Single Shot Visual Text Extraction

Extracting Text of Interest (ToI) from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ complicated engineering pipeline, which contains OCR and structure information extraction, to fulfill this task. This paper proposes an Entity-aware Attention Text Extraction Network called EATEN, which is an end-to-end trainable system to extract the ToIs without any post-processing. In the proposed framework, each entity is parsed by its corresponding entity-aware decoder, respectively. Moreover, we innovatively introduce a state transition mechanism which further improves the robustness of visual ToI extraction. In consideration of the absence of public benchmarks, we construct a dataset of almost 0.6 million images in three real-world scenarios (train ticket, passport and business card), which is publicly available at https://github.com/beacandler/EATEN. To the best of our knowledge, EATEN is the first single shot method to extract entities from images. Extensive experiments on these benchmarks demonstrate the state-of-the-art performance of EATEN.
Sampling Bias in Deep Active Classification: An Empirical Study

The exploding cost and time needed for data labeling and model training are bottlenecks for training DNN models on large datasets. Identifying smaller representative data samples with strategies like active learning can help mitigate such bottlenecks. Previous works on active learning in NLP identify the problem of sampling bias in the samples acquired by uncertainty-based querying and develop costly approaches to address it. Using a large empirical study, we demonstrate that active set selection using the posterior entropy of deep models like FastText.zip (FTZ) is robust to sampling biases and to various algorithmic choices (query size and strategies) unlike that suggested by traditional literature. We also show that FTZ based query strategy produces sample sets similar to those from more sophisticated approaches (e.g ensemble networks). Finally, we show the effectiveness of the selected samples by creating tiny high-quality datasets, and utilizing them for fast and cheap training of large models. Based on the above, we propose a simple baseline for deep active text classification that outperforms the state of the art. We expect the presented work to be useful and informative for dataset compression and for problems involving active, semi-supervised or online learning scenarios. Code and models are available at: https://github.com/drimpossible/Sampling-Bias-Active-Learning.
Doubly robust identification for causal panel data models

We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the observed and unobserved confounders. We focus on a different, complementary approach to identification where assumptions are made about the connection between the treatment assignment and the unobserved confounders. Such strategies are common in cross-section settings but rarely used with panel data. We introduce different sets of assumptions that follow the two paths to identification and develop a double robust approach. We propose estimation methods that build on these identification strategies.
Berry phase estimation in gate-based adiabatic quantum simulation

B.M. and J.F.R. acknowledge the FCT Functionalized Graphene for Quantum Technologies project (PTDC/FIS-NAN/4662/2014). G.C. acknowledges support from Fundacao para a Ciencia e Tecnologia (FCT) Ph.D. Scholarship No. SFRH/BD/138806/2018.
A Critical Analysis of Biased Parsers in Unsupervised Parsing

A series of recent papers has used a parsing algorithm due to Shen et al. (2018) to recover phrase-structure trees based on proxies for "syntactic depth." These proxy depths are obtained from the representations learned by recurrent language models augmented with mechanisms that encourage the (unsupervised) discovery of hierarchical structure latent in natural language sentences. Using the same parser, we show that proxies derived from a conventional LSTM language model produce trees comparably well to the specialized architectures used in previous work. However, we also provide a detailed analysis of the parsing algorithm, showing (1) that it is incomplete---that is, it can recover only a fraction of possible trees---and (2) that it has a marked bias for right-branching structures which results in inflated performance in right-branching languages like English. Our analysis shows that evaluating with biased parsing algorithms can inflate the apparent structural competence of language models.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Semantic code search is the task of retrieving relevant code given a natural language query. While related to other information retrieval tasks, it requires bridging the gap between the language used in code (often abbreviated and highly technical) and natural language more suitable to describe vague concepts and ideas. 
To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus. The corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation. In this article, we describe the methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task. 
We hope that CodeSearchNet Challenge encourages researchers and practitioners to study this interesting task further and will host a competition and leaderboard to track the progress on the challenge. We are also keen on extending CodeSearchNet Challenge to more queries and programming languages in the future.
Underwater Image Super-Resolution using Deep Residual Multipliers

We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In order to supervise the training, we formulate an objective function that evaluates the perceptual quality of an image based on its global content, color, and local style information. Additionally, we present USR-248, a large-scale dataset of three sets of underwater images of ‘high’ (640×480) and ‘low’ (80 × 60, 160 × 120, and 320×240) resolution. USR-248 contains paired instances for supervised training of 2×, 4×, or 8× SISR models. Furthermore, we validate the effectiveness of our proposed model through qualitative and quantitative experiments and compare the results with several state-of-the-art models’ performances. We also analyze its practical feasibility for applications such as scene understanding and attention modeling in noisy visual conditions.
A multi-level procedure for enhancing accuracy of machine learning algorithms

We propose a multi-level method to increase the accuracy of machine learning algorithms for approximating observables in scientific computing, particularly those that arise in systems modelled by differential equations. The algorithm relies on judiciously combining a large number of computationally cheap training data on coarse resolutions with a few expensive training samples on fine grid resolutions. Theoretical arguments for lowering the generalisation error, based on reducing the variance of the underlying maps, are provided and numerical evidence, indicating significant gains over underlying single-level machine learning algorithms, are presented. Moreover, we also apply the multi-level algorithm in the context of forward uncertainty quantification and observe a considerable speedup over competing algorithms.
Sustainable Research Software Hand-Over

Scientific software projects evolve rapidly in their initial development phase, yet at the end of a funding period, the completion of a research project, thesis, or publication, further engagement in the project may slow down or cease completely. To retain the invested effort for the sciences, this software needs to be preserved or handed over to a succeeding developer or team, such as the next generation of (PhD) students. 
Comparable guides provide top-down recommendations for project leads. This paper intends to be a bottom-up approach for sustainable hand-over processes from a developer's perspective. An important characteristic in this regard is the project's size, by which this guideline is structured. Furthermore, checklists are provided, which can serve as a practical guide for implementing the proposed measures.
Representing Split Graphs by Words

There is a long line of research in the literature dedicated to word-representable graphs, which generalize several important classes of graphs. However, not much is known about word-representability of split graphs, another important class of graphs. In this paper, we show that threshold graphs, a subclass of split graphs, are word-representable. Further, we prove a number of general theorems on word-representable split graphs, and use them to characterize computationally such graphs with cliques of size 5 in terms of nine forbidden subgraphs, thus extending the known characterization for word-representable split graphs with cliques of size 4. Moreover, we use split graphs, and also provide an alternative solution, to show that gluing two word-representable graphs in any clique of size at least 2 may, or may not, result in a word-representable graph. The two surprisingly simple solutions provided by us answer a question that was open for about ten years.
Modeling protein target search in human chromosomes

Several processes in the cell, such as gene regulation, start when key proteins recognise and bind to short DNA sequences. However, as these sequences can be hundreds of million times shorter than the genome, they are hard to find by simple diffusion: diffusion-limited association rates may underestimate $in~vitro$ measurements up to several orders of magnitude. Moreover, the rates increase if the DNA is coiled rather than straight. Here we model how this works $in~vivo$ in mammalian cells. We use chromatin-chromatin contact data from state-of-the-art Hi-C experiments to map the protein target-search onto a network problem. The nodes represent a DNA segment and the weight of the links is proportional to measured contact probabilities. We then put forward a master equation for the density of searching protein that allows us to calculate the association rates across the genome analytically. For segments where the rates are high, we find that they are enriched with active genes and have high RNA expression levels. This paper suggests that the DNA's 3D conformation is important for protein search times $in~vivo$ and offers a method to interpret protein-binding profiles in eukaryotes that cannot be explained by the DNA sequence itself.
Generating Philosophical Statements using Interpolated Markov Models and Dynamic Templates

Automatically imitating input text is a common task in natural language generation, often used to create humorous results. Classic algorithms for learning to imitate text, e.g. simple Markov chains, usually have a trade-off between originality and syntactic correctness. We present two ways of automatically parodying philosophical statements from examples overcoming this issue, and show how these can work in interactive systems as well. The first algorithm uses interpolated Markov models with extensions to improve the quality of the generated texts. For the second algorithm, we propose dynamically extracting templates and filling these with new content. To illustrate these algorithms, we implemented TorfsBot, a Twitterbot imitating the witty, semi-philosophical tweets of professor Rik Torfs, the previous KU Leuven rector. We found that users preferred generative models that focused on local coherent sentences, rather than those mimicking the global structure of a philosophical statement. The proposed algorithms are thus valuable new tools for automatic parody as well as template learning systems.
Generative Dialog Policy for Task-oriented Dialog Systems

There is an increasing demand for task-oriented dialogue systems which can assist users in various activities such as booking tickets and restaurant reservations. In order to complete dialogues effectively, dialogue policy plays a key role in task-oriented dialogue systems. As far as we know, the existing task-oriented dialogue systems obtain the dialogue policy through classification, which can assign either a dialogue act and its corresponding parameters or multiple dialogue acts without their corresponding parameters for a dialogue action. In fact, a good dialogue policy should construct multiple dialogue acts and their corresponding parameters at the same time. However, it's hard for existing classification-based methods to achieve this goal. Thus, to address the issue above, we propose a novel generative dialogue policy learning method. Specifically, the proposed method uses attention mechanism to find relevant segments of given dialogue context and input utterance and then constructs the dialogue policy by a seq2seq way for task-oriented dialogue systems. Extensive experiments on two benchmark datasets show that the proposed model significantly outperforms the state-of-the-art baselines. In addition, we have publicly released our codes.
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning

Controlling output length in neural language generation is valuable in many scenarios, especially for the tasks that have length constraints. A model with stronger length control capacity can produce sentences with more specific length, however, it usually sacrifices semantic accuracy of the generated sentences. Here, we denote a concept of Controllable Length Control (CLC) for the trade-off between length control capacity and semantic accuracy of the language generation model. More specifically, CLC is to alter length control capacity of the model so as to generate sentence with corresponding quality. This is meaningful in real applications when length control capacity and outputs quality are requested with different priorities, or to overcome unstability of length control during model training. In this paper, we propose two reinforcement learning (RL) methods to adjust the trade-off between length control capacity and semantic accuracy of length control models. Results show that our RL methods improve scores across a wide range of target lengths and achieve the goal of CLC. Additionally, two models LenMC and LenLInit modified on previous length-control models are proposed to obtain better performance in summarization task while still maintain the ability to control length.
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages

We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to a significant improvement in source-target translation. We propose three methods to increase the relation among source, pivot, and target languages in the pre-training: 1) step-wise training of a single model for different language pairs, 2) additional adapter component to smoothly connect pre-trained encoder and decoder, and 3) cross-lingual encoder training via autoencoding of the pivot language. Our methods greatly outperform multilingual models up to +2.6% BLEU in WMT 2019 French-German and German-Czech tasks. We show that our improvements are valid also in zero-shot/zero-resource scenarios.
Multi-user Augmented Reality Application for Video Communication in Virtual Space

Communication is the most useful tool to impart knowledge, understand ideas, clarify thoughts and expressions, organize plan and manage every single day-to-day activity. Although there are different modes of communication, physical barrier always affects the clarity of the message due to the absence of body language and facial expressions. These barriers are overcome by video calling, which is technically the most advance mode of communication at present. The proposed work concentrates around the concept of video calling in a more natural and seamless way using Augmented Reality (AR). AR can be helpful in giving the users an experience of physical presence in each other's environment. Our work provides an entirely new platform for video calling, wherein the users can enjoy the privilege of their own virtual space to interact with the individual's environment. Moreover, there is no limitation of sharing the same screen space. Any number of participants can be accommodated over a single conference without having to compromise the screen size.
Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the browser

In this work we explore a deep learning-based dialogue system that generates sarcastic and humorous responses from a conversation design perspective. We trained a seq2seq model on a carefully curated dataset of 3000 question-answering pairs, the core of our mean, grumpy, sarcastic chatbot. We show that end-to-end systems learn patterns very quickly from small datasets and thus, are able to transfer simple linguistic structures representing abstract concepts to unseen settings. We also deploy our LSTM-based encoder-decoder model in the browser, where users can directly interact with the chatbot. Human raters evaluated linguistic quality, creativity and human-like traits, revealing the system's strengths, limitations and potential for future research.
Creative GANs for generating poems, lyrics, and metaphors

Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for MLE lead to outputs that can be generic, repetitive and incoherent. In this work, we use a Generative Adversarial Network framework to alleviate this problem. We evaluate our framework on poetry, lyrics and metaphor datasets, each with widely different characteristics, and report better performance of our objective function over other generative models.
Defending Against Physically Realizable Attacks on Image Classification

We study the problem of defending deep neural network approaches for image classification from physically realizable attacks. First, we demonstrate that the two most scalable and effective methods for learning robust models, adversarial training with PGD attacks and randomized smoothing, exhibit very limited effectiveness against three of the highest profile physical attacks. Next, we propose a new abstract adversarial model, rectangular occlusion attacks, in which an adversary places a small adversarially crafted rectangle in an image, and develop two approaches for efficiently computing the resulting adversarial examples. Finally, we demonstrate that adversarial training using our new attack yields image classification models that exhibit high robustness against the physically realizable attacks we study, offering the first effective generic defense against such attacks.
Explaining the MiniBooNE excess by a decaying sterile neutrino with mass in the 250 MeV range

The MiniBooNE Collaboration has reported an excess of $460.5\ifmmode\pm\else\textpm\fi{}99.0$ electronlike events ($4.8\ensuremath{\sigma}$). We propose an explanation of these events in terms of a sterile neutrino decaying into a photon and a light neutrino. The sterile neutrino has a mass around 250 MeV, and it is produced from kaon decays in the proton beam target via mixing with the muon or the electron in the range ${10}^{\ensuremath{-}11}\ensuremath{\lesssim}|{U}_{\ensuremath{\ell}4}{|}^{2}\ensuremath{\lesssim}{10}^{\ensuremath{-}7}$ ($\ensuremath{\ell}=e$, $\ensuremath{\mu}$). The model can be tested by considering the time distribution of the events in MiniBooNE and by looking for single-photon events in running or upcoming neutrino experiments, in particular by the suite of liquid argon detectors in the short-baseline neutrino program at Fermilab.
Understanding Architectures Learnt by Cell-based Neural Architecture Search

Neural architecture search (NAS) generates architectures automatically for given tasks, e.g., image classification and language modeling. Recently, various NAS algorithms have been proposed to improve search efficiency and effectiveness. However, little attention is paid to understand the generated architectures, including whether they share any commonality. In this paper, we analyze the generated architectures and give our explanations of their superior performance. We firstly uncover that the architectures generated by NAS algorithms share a common connection pattern, which contributes to their fast convergence. Consequently, these architectures are selected during architecture search. We further empirically and theoretically show that the fast convergence is the consequence of smooth loss landscape and accurate gradient information conducted by the common connection pattern. Contracting to universal recognition, we finally observe that popular NAS architectures do not always generalize better than the candidate architectures, encouraging us to re-think about the state-of-the-art NAS algorithms.