nils-herrmann/clean_scirepeval huggingface dataset:
Notes on Latent Structure Models and SPIGOT

These notes aim to shed light on the recently proposed structured projected intermediate gradient optimization technique (SPIGOT, Peng et al., 2018). SPIGOT is a variant of the straight-through estimator (Bengio et al., 2013) which bypasses gradients of the argmax function by back-propagating a surrogate "gradient." We provide a new interpretation to the proposed gradient and put this technique into perspective, linking it to other methods for training neural networks with discrete latent variables. As a by-product, we suggest alternate variants of SPIGOT which will be further explored in future work.
Unbabel’s Participation in the WMT17 Translation Quality Estimation Shared Task

We present the contribution of the Unbabel team to the WMT 2019 Shared Task on Quality Estimation. We participated on the word, sentence, and document-level tracks, encompassing 3 language pairs: English-German, English-Russian, and English-French. Our submissions build upon the recent OpenKiwi framework: we combine linear, neural, and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models. We compare systems individually and propose new ensemble techniques for word and sentence-level predictions. We also propose a simple technique for converting word labels into document-level predictions. Overall, our submitted systems achieve the best results on all tracks and language pairs by a considerable margin.
Self-attention based BiLSTM-CNN classifier for the prediction of ischemic and non-ischemic cardiomyopathy

Heart Failure is a major component of healthcare expenditure and a leading cause of mortality worldwide. Despite higher inter-rater variability, endomyocardial biopsy (EMB) is still regarded as the standard technique, used to identify the cause (e.g. ischemic or non-ischemic cardiomyopathy, coronary artery disease, myocardial infarction etc.) of unexplained heart failure. In this paper, we focus on identifying cardiomyopathy as ischemic or non-ischemic. For this, we propose and implement a new unified architecture comprising CNN (inception-V3 model) and bidirectional LSTM (BiLSTM) with self-attention mechanism to predict the ischemic or non-ischemic to classify cardiomyopathy using histopathological images. The proposed model is based on self-attention that implicitly focuses on the information outputted from the hidden layers of BiLSTM. Through our results we demonstrate that this framework carries a high learning capacity and is able to improve the classification performance.
Automatic Generation of Personalized Comment Based on User Profile

Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation (NLG) tasks. Besides, since different user has different expression habits, it is necessary to take the user’s profile into consideration when generating comments. In this paper, we introduce the task of automatic generation of personalized comment (AGPC) for social media. Based on tens of thousands of users’ real comments and corresponding user profiles on weibo, we propose Personalized Comment Generation Network (PCGN) for AGPC. The model utilizes user feature embedding with a gated memory and attends to user description to model personality of users. In addition, external user representation is taken into consideration during the decoding to enhance the comments generation. Experimental results show that our model can generate natural, human-like and personalized comments.
NONOTO: A Model-agnostic Web Interface for Interactive Music Composition by Inpainting

Inpainting-based generative modeling allows for stimulating human-machine interactions by letting users perform stylistically coherent local editions to an object using a statistical model. We present NONOTO, a new interface for interactive music generation based on inpainting models. It is aimed both at researchers, by offering a simple and flexible API allowing them to connect their own models with the interface, and at musicians by providing industry-standard features such as audio playback, real-time MIDI output and straightforward synchronization with DAWs using Ableton Link.
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties

Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging. It is very often the case that some roll-outs result in failure behaviors, causing premature experiment detention. In such cases, the designer is forced to decide on heuristic cost penalties because the acquired data is often scarce, or not comparable with that of the stable policies. To overcome this, we propose a Bayesian model that captures exactly what we know about the cost of unstable controllers prior to data collection: Nothing, except that it should be a somewhat large number. The resulting Bayesian model, approximated with a Gaussian process, predicts high cost values in regions where failures are likely to occur. In this way, the model guides the BO exploration toward regions of stability. We demonstrate the benefits of the proposed model in several illustrative and statistical synthetic benchmarks, and also in experiments on a real robotic platform. In addition, we propose and experimentally validate a new BO method to account for unknown constraints. Such method is an extension of Max-Value Entropy Search, a recent information-theoretic method, to solve unconstrained global optimization problems.
Higher-Order Function Networks for Learning Composable 3D Object Representations

We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second 'mapping' network. This mapping network can be used to reconstruct an object by applying its encoded transformation to points randomly sampled from a simple geometric space, such as the unit sphere. We study the effectiveness of our method through various experiments on subsets of the ShapeNet dataset. We find that the proposed approach can reconstruct encoded objects with accuracy equal to or exceeding state-of-the-art methods with orders of magnitude fewer parameters. Our smallest mapping network has only about 7000 parameters and shows reconstruction quality on par with state-of-the-art object decoder architectures with millions of parameters. Further experiments on feature mixing through the composition of learned functions show that the encoding captures a meaningful subspace of objects.
Inconsistency Proofs for ASP: The ASP - DRUPE Format

solvers are highly-tuned and complex procedures that implicitly solve the consistency problem, i.e., deciding whether a logic program admits an answer set. Verifying whether a claimed answer set is formally a correct answer set of the program can be decided in polynomial time for (normal) programs. However, it is far from immediate to verify whether a program that is claimed to be inconsistent, indeed does not admit any answer sets. In this paper, we address this problem and develop the new proof format ASP-DRUPE for propositional, disjunctive logic programs, including weight and choice rules. ASP-DRUPE is based on the Reverse Unit Propagation (RUP) format designed for Boolean satisfiability. We establish correctness of ASP-DRUPE and discuss how to integrate it into modern ASP solvers. Later, we provide an implementation of ASP-DRUPE into the wasp solver for normal logic programs.
Evaluating the Effectiveness of Common Technical Trading Models

How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regression, K-Nearest Neighbor, and moving average crossovers, we can test how each model performs on the most popular stocks and largest indexes. With the results for each, we can compare the models, and understand which model reliably increases performance. The trials showed that while all three models reduced losses on stocks with strong overall downward trends, the two machine learning models did not work as well to increase profits. Moving averages crossovers outperformed a continuous investment every time, although did result in a more volatile investment as well. Furthermore, once finished creating the program that implements moving average crossover, what are the optimal periods to use? A massive test consisting of 169,880 trials, showed the best periods to use to increase investment performance (5,10) and to decrease volatility (33,44). In addition, the data showed numerous trends such as a smaller short SMA period is accompanied by higher performance. Plotting volatility against performance shows that the high risk, high reward saying holds true and shows that for investments, as the volatility increases so does its performance.
Semi-Supervised Tensor Factorization for Node Classification in Complex Social Networks

This paper proposes a method to guide tensor factorization, using class labels. Furthermore, it shows the advantages of using the proposed method in identifying nodes that play a special role in multi-relational networks, e.g. spammers. Most complex systems involve multiple types of relationships and interactions among entities. Combining information from different relationships may be crucial for various prediction tasks. Instead of creating distinct prediction models for each type of relationship, in this paper we present a tensor factorization approach based on RESCAL, which collectively exploits all existing relations. We extend RESCAL to produce a semi-supervised factorization method that combines a classification error term with the standard factor optimization process. The coupled optimization approach, models the tensorial data assimilating observed information from all the relations, while also taking into account classification performance. Our evaluation on real-world social network data shows that incorporating supervision, when available, leads to models that are more accurate.
Less (Data) Is More: Why Small Data Holds the Key to the Future of Artificial Intelligence

The claims that big data holds the key to enterprise successes and that Artificial Intelligence is going to replace humanity have become increasingly more popular over the past few years, both in academia and in the industry. However, while these claims may indeed capture some truth, they have also been massively oversold, or so we contend here. The goal of this paper is two-fold. First, we provide a qualified defence of the value of less data within the context of AI. This is done by carefully reviewing two distinct problems for big data driven AI, namely a) the limited track record of Deep Learning in key areas such as Natural Language Processing, b) the regulatory and business significance of being able to learn from few data points. Second, we briefly sketch what we refer to as a case of AI with humans and for humans, namely an AI paradigm whereby the systems we build are privacy-oriented and focused on human-machine collaboration, not competition. Combining our claims above, we conclude that when seen through the lens of cognitively inspired AI, the bright future of the discipline is about less data, not more, and more humans, not fewer.
Backreaction of electromagnetic fields and the Schwinger effect in pseudoscalar inflation magnetogenesis

We study magnetogenesis in axion-like inflation driven by a pseudoscalar field $\phi$ coupled axially to the electromagnetic (EM) field $(\beta/M_{p})\phi F_{\mu\nu}\tilde{F}^{\mu\nu}$ with dimensionless coupling constant $\beta$. A set of equations for the inflaton field, scale factor, and expectation values of quadratic functions of the EM field is derived. These equations take into account the Schwinger effect and the backreaction of generated EM fields on the Universe expansion. It is found that the backreaction becomes important when the EM energy density reaches the value $\rho_{EM}\sim (\sqrt{2\epsilon}/\beta)\rho_{\rm inf}$ ($\epsilon$ is the slow-roll parameter and $\rho_{\rm inf}$ is the energy density of the inflaton) slowing down the inflaton rolling and terminating magnetogenesis. The Schwinger effect becomes relevant when the electric energy density exceeds the value $\rho_{E}\sim \alpha_{EM}^{-3} (\rho_{\rm tot}^{2}/M_{p}^{4})$, where $\rho_{\rm tot}=3H^{2}M_{p}^{2}$ is the total energy density and $\alpha_{EM}$ is the EM coupling constant. For large $\beta$, produced charged particles could constitute a significant part of the Universe energy density even before the preheating stage. Numerically studying magnetogenesis in the $\alpha$-attractor model of inflation, we find that it is possible to generate helical magnetic fields with the maximal strength $10^{-15}\,{\rm G}$, however, only with the correlation length of order $1\,{\rm pc}$ at present.
A hybrid ion-atom trap with integrated high resolution mass spectrometer

In this article we describe the design, construction and implementation of our ion-atom hybrid system incorporating a high resolution time of flight mass spectrometer (TOFMS). Potassium atoms ($^{39}$K) in a Magneto Optical Trap (MOT) and laser cooled calcium ions ($^{40}$Ca$^+$) in a linear Paul trap are spatially overlapped and the combined trap is integrated with a TOFMS for radial extraction and detection of reaction products. We also present some experimental results showing interactions between $^{39}$K$^+$ and $^{39}$K, $^{40}$Ca$^+$ and $^{39}$K$^+$ as well as $^{40}$Ca$^+$ and $^{39}$K pairs. Finally, we discuss prospects for cooling CaH$^+$ molecular ions in the hybrid ion-atom system.
Partial Compilation of ASP Programs

is a well-known declarative formalism in logic programming. Efficient implementations made it possible to apply ASP in many scenarios, ranging from deductive databases applications to the solution of hard combinatorial problems. State-of-the-art ASP systems are based on the traditional ground&solve approach and are general-purpose implementations, i.e., they are essentially built once for any kind of input program. In this paper, we propose an extended architecture for ASP systems, in which parts of the input program are compiled into an ad-hoc evaluation algorithm (i.e., we obtain a specific binary for a given program), and might not be subject to the grounding step. To this end, we identify a condition that allows the compilation of a sub-program, and present the related partial compilation technique. Importantly, we have implemented the new approach on top of a well-known ASP solver and conducted an experimental analysis on publicly-available benchmarks. Results show that our compilation-based approach improves on the state of the art in various scenarios, including cases in which the input program is stratified or the grounding blow-up makes the evaluation unpractical with traditional ASP systems.
The MPGD-Based Photon Detectors for the upgrade of COMPASS RICH-1

After pioneering gaseous detectors of single photon for RICH applications using CsI solid state photocathodes in MWPCs within the RD26 collaboration and by the constructions for the RICH detector of the COMPASS experiment at CERN SPS, in 2016 we have upgraded COMPASS RICH by novel gaseous photon detectors based on MPGD technology. Four novel photon detectors, covering a total active area of 1.5 m2, have been installed in order to cope with the challenging effciency and stability requirements of the COMPASS physics programme. They are the first application in an experiment of MPGD-based single photon detectors. All aspects of the upgrade are presented, including engineering, mass production, quality assessment and performance. Perspectives for further developments in the field of gaseous single photon detectors are also indicated.
Inferring the 3D Shapes of Extremely Metal-poor Galaxies from Sets of Projected Shapes

The three-dimensional (3D) shape of a galaxy inevitably is tied to how it has formed and evolved and to its dark matter halo. Local extremely metal-poor galaxies (XMPs; defined as having an average gas-phase metallicity <0.1 solar) are important objects for understanding galaxy evolution largely because they appear to be caught in the act of accreting gas from the cosmic web, and their 3D shape may reflect this. Here, we report on the 3D shape of XMPs as inferred from their observed projected minor-to-major axial ratios using a hierarchical Bayesian inference model, which determines the likely shape and orientation of each galaxy, while simultaneously inferring the average shape and dispersion. We selected a sample of 149 XMPs and divided it into three subsamples according to physical size and found that (1) the stellar component of XMPs of all sizes tends to be triaxial, with an intermediate axis ≈0.7 times the longest axis and that (2) smaller XMPs tend to be relatively thicker, with the shortest axis going from ≈0.15 times the longest axis for the large galaxies to ≈0.4 for the small galaxies. We provide the inferred 3D shape and inclination of the individual XMPs in electronic format. We show that our results for the intermediate axis are not clouded by a selection effect against face-on XMPs. We discuss how an intermediate axis significantly smaller than the longest axis may be produced by several mechanisms, including lopsided gas accretion, non-axisymmetric star formation, or coupling with an elongated dark matter halo. Large relative thickness may reflect slow rotation, stellar feedback, or recent gas accretion.
A system of different layers of abstraction for artificial intelligence

The field of artificial intelligence (AI) represents an enormous endeavour of humankind that is currently transforming our societies down to their very foundations. Its task, building truly intelligent systems, is underpinned by a vast array of subfields ranging from the development of new electronic components to mathematical formulations of highly abstract and complex reasoning. This breadth of subfields renders it often difficult to understand how they all fit together into a bigger picture and hides the multi-faceted, multi-layered conceptual structure that in a sense can be said to be what AI truly is. In this perspective we propose a system of five levels/layers of abstraction that underpin many AI implementations. We further posit that each layer is subject to a complexity-performance trade-off whilst different layers are interlocked with one another in a control-complexity trade-off. This overview provides a conceptual map that can help to identify how and where innovation should be targeted in order to achieve different levels of functionality, assure them for safety, optimise performance under various operating constraints and map the opportunity space for social and economic exploitation.
The sameAs Problem: A Survey on Identity Management in the Web of Data

In a decentralised knowledge representation system such as the Web of Data, it is common and indeed desirable for different knowledge graphs to overlap. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Whilst the deductive value of such identity statements can be extremely useful in enhancing various knowledge-based systems, incorrect use of identity can have wide-ranging effects in a global knowledge space like the Web of Data. With several works already proven that identity in the Web is broken, this survey investigates the current state of this "sameAs problem". An open discussion highlights the main weaknesses suffered by solutions in the literature, and draws open challenges to be faced in the future.
SpanBERT: Improving Pre-training by Representing and Predicting Spans

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1
Chromatin state switching in a polymer model with mark-conformation coupling.

We investigate the phase transition properties of the polymer-Potts model, a chain composed of monomers with magnetic degrees of freedom, with the motivation to study the conformation and mark switching dynamics of chromatin. By the mean-field approximation, we find that the phase transition between the swollen-disordered state and the compact-ordered state is discrete; it is first order as in the long-range Potts model, but with a significantly larger jump in magnetization (i.e., mark coherence) upon the ordering transition. The results imply how small changes in epigenetic writer concentrations can lead to a macroscopic switching of the chromatin state, suggesting a simple mechanism of discrete switching observed, for instance, in cell differentiation.
Unsupervised Discovery of Decision States for Transfer in Reinforcement Learning

We present a hierarchical reinforcement learning (HRL) or options framework for identifying decision states. Informally speaking, these are states considered important by the agent's policy e.g. , for navigation, decision states would be crossroads or doors where an agent needs to make strategic decisions. While previous work (most notably Goyal et. al., 2019) discovers decision states in a task/goal specific (or 'supervised') manner, we do so in a goal-independent (or 'unsupervised') manner, i.e. entirely without any goal or extrinsic rewards. Our approach combines two hitherto disparate ideas - 1) \emph{intrinsic control} (Gregor et. al., 2016, Eysenbach et. al., 2018): learning a set of options that allow an agent to reliably reach a diverse set of states, and 2) \emph{information bottleneck} (Tishby et. al., 2000): penalizing mutual information between the option $\Omega$ and the states $s_t$ visited in the trajectory. The former encourages an agent to reliably explore the environment; the latter allows identification of decision states as the ones with high mutual information $I(\Omega; a_t | s_t)$ despite the bottleneck. Our results demonstrate that 1) our model learns interpretable decision states in an unsupervised manner, and 2) these learned decision states transfer to goal-driven tasks in new environments, effectively guide exploration, and improve performance.