nils-herrmann/clean_scirepeval huggingface dataset:
Building a Neural Semantic Parser from a Domain Ontology

Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be executed against a real-world environment such as a database. Scaling semantic parsing to arbitrary domains faces two interrelated challenges: obtaining broad coverage training data effectively and cheaply; and developing a model that generalizes to compositional utterances and complex intentions. We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms. In our framework meaning representations are described by sequences of natural language templates, where each template corresponds to a decomposed fragment of the underlying meaning representation. Although artificial, templates can be understood and paraphrased by humans to create natural utterances, resulting in parallel triples of utterances, meaning representations, and their decompositions. These allow us to train a neural semantic parser which learns to compose rules in deriving meaning representations. We crowdsource training data on six domains, covering both single-turn utterances which exhibit rich compositionality, and sequential utterances where a complex task is procedurally performed in steps. We then develop neural semantic parsers which perform such compositional tasks. In general, our approach allows to deploy neural semantic parsers quickly and cheaply from a given domain ontology.
Generalised uncertainty from geometric superpositions

Matthew J. Lake,1, 2, ∗ Marek Miller,3, 2 Ray F. Ganardi,2 Zheng Liu,2, 4 Shi-Dong Liang,1 and Tomasz Paterek2, 5 1School of Physics, Sun Yat-Sen University, Guangzhou 510275, China 2School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 3Instytut Fizyki Teoretycznej, Uniwersytet Wroclawski, pl. M. Borna 9, 50-204 Wroclaw, Poland 4Centre for Quantum Technologies, National University of Singapore, Singapore 5MajuLab, CNRS-UCA-SU-NUS-NTU International Joint Research Unit, UMI 3654 Singapore, Singapore
Optimal resource allocation for network functionality

The traditional approach to network robustness, is based on comparing network parameters before and after an event of nodes removal, such as the change in network diameter, the change in giant component size and the existence of giant component. Compared to the traditional approach, there is a later and innovative approach to network robustness, where the network functionality during its entire life span (during the node removal event) is considered. This approach considers nodes removal due to aging—non functionality of nodes when their survival time duration—their lifetime—is passed. Accordingly, a problem that has to be solved is: in the network design stage, how to allocate a budget of lifetime between the network’s nodes, such that the network functionality during all the stages of nodes removal due to aging, is maximized. To date, the problem has been solved only partially and numerically. In this paper we solve the problem analytically. Based on a local information only on the network’s degree distribution pk without knowing the network’s structure and topology, we derive a criterion for choosing the right set of nodes on which the total lifetime budget should be allocated, and find analytically the optimal way of allocating the lifetime budget between the chosen set’s nodes, such that the network robustness with consideration to its functionality in the entire life span, is maximized.
Nonlocal generalization of Galilean theories and gravity

In this paper we propose a wider class of symmetries including the Galilean shift symmetry as a subclass. We will show how to construct ghost-free nonlocal actions, consisting of infinite derivative operators, which are invariant under such symmetries, but whose functional form is not simply given by exponentials of entire functions. Motivated by this, we will consider the case of a scalar field and discuss the pole structure of the propagator which has infinitely many complex conjugate poles, but satisfies the tree-level unitarity. We will also consider the possibility to construct UV complete Galilean theories by showing how the ultraviolet behavior of loop integrals can be ameliorated. Moreover, we will consider kinetic operators respecting the same symmetries in the context of linearized gravity. In such a scenario, the graviton propagator turns out to be ghost-free and the spacetime metric generated by a point-like source is nonsingular. These new nonlocal models can be seen as an infinite derivative generalization of Lee-Wick theories and open a new branch of nonlocal theories.
Quantitative Analysis of Semiconductor Nanocrystal Ensemble Optical Extinction

The optical extinction coefficients of localized surface plasmon resonance (LSPR) in doped semiconductor nanocrystals (NCs) have intensities determined by the free charge carrier concentration and the mechanisms for damping the oscillation of those free carriers. We investigate the dependence of the extinction coefficient of tin-doped indium oxide (ITO) NCs on size and dopant concentration and find extinction coefficients as high as 56.5 um-1 in the near infrared for 7.5 atomic% Sn 20 nm diameter ITO NCs. We demonstrate a new fitting procedure for the optical extinction of an ensemble of well-dispersed NCs that accounts for NC size heterogeneity, electron concentration heterogeneity, surface scattering, and near-surface electron depletion due to surface states. The heterogeneous ensemble Drude approximation (HEDA) model utilizes the same number of variables as previous models and fits data as well or better while using inputs and fitting parameters that are described by physical phenomena. The model improves the understanding of free carrier motion in doped semiconductor NCs by more accurately extracting carrier concentration and carrier damping. The HEDA model captures individual NC optical properties and their contributions to the ensemble spectra. We find the peak extinction coefficient of an average NC varies linearly with the product of electron accessible volume fraction and electron concentration, normalized by damping.
Efficiently computable bounds for magic state distillation

Magic-state distillation (or nonstabilizer state manipulation) is a crucial component in the leading approaches to realizing scalable, fault-tolerant, and universal quantum computation. Related to nonstabilizer state manipulation is the resource theory of nonstabilizer states, for which one of the goals is to characterize and quantify nonstabilizerness of a quantum state. In this Letter, we introduce the family of thauma measures to quantify the amount of nonstabilizerness in a quantum state, and we exploit this family of measures to address several open questions in the resource theory of nonstabilizer states. As a first application, we establish the hypothesis testing thauma as an efficiently computable benchmark for the one-shot distillable nonstabilizerness, which in turn leads to a variety of bounds on the rate at which nonstabilizerness can be distilled, as well as on the overhead of magic-state distillation. We then prove that the max-thauma can be used as an efficiently computable tool in benchmarking the efficiency of magic-state distillation, and that it can outperform previous approaches based on mana. Finally, we use the min-thauma to bound a quantity known in the literature as the "regularized relative entropy of magic." As a consequence of this bound, we find that two classes of states with maximal mana, a previously established nonstabilizerness measure, cannot be interconverted in the asymptotic regime at a rate equal to one. This result resolves a basic question in the resource theory of nonstabilizer states and reveals a difference between the resource theory of nonstabilizer states and other resource theories such as entanglement and coherence.
Deep neural networks are biased towards simple functions

We prove that the binary classifiers of bit strings generated by random wide deep neural networks are biased towards simple functions. The simplicity is captured by the following two properties. For any given input bit string, the average Hamming distance of the closest input bit string with a different classification is at least $\sqrt{n\left/\left(2\pi\ln n\right)\right.}$, where $n$ is the length of the string. Moreover, if the bits of the initial string are flipped randomly, the average number of flips required to change the classification grows linearly with $n$. On the contrary, for a uniformly random binary classifier, the average Hamming distance of the closest input bit string with a different classification is one, and the average number of random flips required to change the classification is two. These results are confirmed by numerical experiments on deep neural networks with two hidden layers, and settle the conjecture stating that random deep neural networks are biased towards simple functions. The conjecture that random deep neural networks are biased towards simple functions was proposed and numerically explored in [Valle P\'erez et al., ] to explain the unreasonably good generalization properties of deep learning algorithms. By providing a precise characterization of the form of this bias towards simplicity, our results open the way to a rigorous proof of the generalization properties of deep learning algorithms in real-world scenarios.
Geometric construction of Heisenberg-Weil representations for finite unitary groups and Howe correspondences.

We give a geometric construction of the Heisenberg-Weil representation of a finite unitary group by the middle \'{e}tale cohomology of an algebraic variety over a finite field, whose rational points give a unitary Heisenberg group. Using also a Frobenius action, we give a geometric realization of the Howe correspondence for $(\mathit{Sp}_{2n},O_2^-)$ over any finite field including characteristic two. As an application, we show that unipotency is preserved under the Howe correspondence.
A New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System

In this paper, a new deep reinforcement learning based augmented general sequence tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence tagging model and a deep reinforcement learning (DRL) based augmented tagger. The augmented tagger helps improve system performance by modeling the data with minority tags. The new system is evaluated on SLU and NLU sequence tagging tasks using ATIS and CoNLL-2003 benchmark datasets, to demonstrate the new system's outstanding performance on general tagging tasks. Evaluated by F1 scores, it shows that the new system outperforms the current state-of-the-art model on ATIS dataset by 1.9% and that on CoNLL-2003 dataset by 1.4%.
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling

Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or “encoder-decoder” models), and generate the intents and semantic tags either using separate models. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. None of the approaches consider the cross-impact between the intent detection task and the slot filling task. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-art result on the benchmark ATIS data, with about 0.5% intent accuracy improvement and 0.9 % slot filling improvement.
The representation theory of the increasing monoid

We study the representation theory of the increasing monoid. Our results provide a fairly comprehensive picture of the representation category: for example, we describe the Grothendieck group (including the effective cone), classify injective objects, establish properties of injective and projective resolutions, construct a derived auto-duality, and so on. Our work is motivated by numerous connections of this theory to other areas, such as representation stability, commutative algebra, simplicial theory, and shuffle algebras.
Optimizing Market Making using Multi-Agent Reinforcement Learning

In this paper, reinforcement learning is applied to the problem of optimizing market making. A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents. The macro-agent optimizes on making the decision to buy, sell, or hold an asset. The micro-agent optimizes on placing limit orders within the limit order book. For the context of this paper, the proposed framework is applied and studied on the Bitcoin cryptocurrency market. The goal of this paper is to show that reinforcement learning is a viable strategy that can be applied to complex problems (with complex environments) such as market making.
Rasmussen invariant and the divisibility of Lee's class

We define a link invariant $s'_c(L)$ by a combination of some classical properties of its diagram $D$, and the $c$-divisibility of a specific class (generalized Lee's class) in the Khovanov-type link homology $H_c(D; R)$ determined by $D$ and an element $c$ in the base ring $R$. This invariant has many properties common to the Rasmussen's $s$-invariant: $s'_c$ is a link-concordance invariant, provides a lower bound for the slice genus, and the equality is sharp for positive knots. As a corollary an alternative proof for the Milnor conjecture follows. In particular if the base ring is the polynomial ring $\mathbb{Q}[h]$ and $c = h$, then $s'_h$ coincides with the $s$-invariant for knots.
Hierarchical Genetic Algorithms with evolving objective functions

We propose a framework of genetic algorithms which use multi-level hierarchies to solve an optimization problem by searching over the space of simpler objective functions. We solve a variant of Travelling Salesman Problem called \texttt{soft-TSP} and show that when the constraints on the overall objective function are changed the algorithm adapts to churn out solutions for the changed objective. We use this idea to speed up learning by systematically altering the constraints to find a more globally optimal solution. We also use this framework to solve polynomial regression where the actual objective function is unknown but searching over space of available objective functions yields a good approximate solution.
Causal faster-than-light travel from a localized second time coordinate

I present a {\em general relativistic} model with a compactified second time coordinate that {\em a priori} allows for causal, yet faster than light travel in the background of a FLRW geometry, by local modification of a higher dimensional background geometry, specifically with respect to the radius of the compactified time coordinate. The modification can be induced via the fields of the model. 
I show that one cannot convert (as possible in special relativistic models, or simple general relativistic models) the super-luminality into closed time-like loops violating causality, due to a novel combination of factors, at least for $v_{\rm max}\leq \sqrt{2}$. The physics of the second time is constrained by postulates derived from reasonable physical assumptions. 
I comment on the possibility of experimental implications of the model.
Learning Not to Learn: Training Deep Neural Networks With Biased Data

We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased. Since a neural network efficiently learns data distribution, a network is likely to learn the bias information to categorize input data. It leads to poor performance at test time, if the bias is, in fact, irrelevant to the categorization. In this paper, we formulate a regularization loss based on mutual information between feature embedding and bias. Based on the idea of minimizing this mutual information, we propose an iterative algorithm to unlearn the bias information. We employ an additional network to predict the bias distribution and train the network adversarially against the feature embedding network. At the end of learning, the bias prediction network is not able to predict the bias not because it is poorly trained, but because the feature embedding network successfully unlearns the bias information. We also demonstrate quantitative and qualitative experimental results which show that our algorithm effectively removes the bias information from feature embedding.
Informative Object Annotations: Tell Me Something I Don't Know

Capturing the interesting components of an image is a key aspect of image understanding. When a speaker annotates an image, selecting labels that are informative greatly depends on the prior knowledge of a prospective listener. Motivated by cognitive theories of categorization and communication, we present a new unsupervised approach to model this prior knowledge and quantify the informativeness of a description. Specifically, we compute how knowledge of a label reduces uncertainty over the space of labels and use this uncertainty reduction to rank candidate labels for describing an image. While the full estimation problem is intractable, we describe an efficient algorithm to approximate entropy reduction using a tree-structured graphical model. We evaluate our approach on the open-images dataset using a new evaluation set of 10K ground-truth ratings and find that it achieves over 65% agreement with human raters, close to the upper bound of inter-rater agreement and largely outperforming other unsupervised baseline approaches.
Same but different: distant supervision for predicting and understanding entity linking difficulty

Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-automated EL systems, while detecting latent factors that affect the EL performance, like corpus-specific features, can provide insights on how to improve a system based on the special characteristics of the underlying corpus. In this paper, we first introduce a consensus-based method to generate difficulty labels for entity mentions on arbitrary corpora. The difficulty labels are then exploited as training data for a supervised classification task able to predict the EL difficulty of entity mentions using a variety of features. Experiments over a corpus of news articles show that EL difficulty can be estimated with high accuracy, revealing also latent features that affect EL performance. Finally, evaluation results demonstrate the effectiveness of the proposed method to inform semi-automated EL pipelines.
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness

Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit. Whilst there is a lot of promising research currently being undertaken, the literature as a whole lacks: transparency; clear reporting to facilitate replicability; exploration for potential ethical concerns; and, clear demonstrations of effectiveness. There are many reasons for why these issues exist, but one of the most important that we provide a preliminary solution for here is the current lack of ML/AI- specific best practice guidance. Although there is no consensus on what best practice looks in this field, we believe that interdisciplinary groups pursuing research and impact projects in the ML/AI for health domain would benefit from answering a series of questions based on the important issues that exist when undertaking work of this nature. Here we present 20 questions that span the entire project life cycle, from inception, data analysis, and model evaluation, to implementation, as a means to facilitate project planning and post-hoc (structured) independent evaluation. By beginning to answer these questions in different settings, we can start to understand what constitutes a good answer, and we expect that the resulting discussion will be central to developing an international consensus framework for transparent, replicable, ethical and effective research in artificial intelligence (AI-TREE) for health.
Using Selmer Groups to compute Mordell-Weil Groups of Elliptic Curves

This master thesis describes how Selmer groups can be used to determine the Mordell-Weil group of elliptic curves over a number field K. The Mordell-Weil Theorem states that $E(K) = E(K)_{tors} \times Z^r$, where $r$ is the rank of $E$, and $E(K)_{tors}$ is the torsion subgroup, i.e. the group of points of finite order in $E(K)$. The group $E(K)_{tors}$ is finite and well understood. So, one tries to find a way to determine the rank $r$ of $E$, which is the major problem. The procedure described in this thesis shows how to transfer the computation of the weak Mordell-Weil group $E(K)/mE(K)$ to the existence or non-existence of a rational point on certain curves, called homogeneous spaces. If one can find some completion $K_v$ of $K$ such that the homogeneous space has no points in $K_v$, then it follows that it has no points in $K$. Under the assumption that the Shafarevich-Tate group is finite, the rank of Elliptic curves over $\mathbb{Q}$ with $j$-invariant $1728$ is fully determined in certain cases.
Cyclic Gerstenhaber–Schack cohomology

We show that the diagonal complex computing the Gerstenhaber-Schack cohomology of a bialgebra (that is, the cohomology theory governing bialgebra deformations) can be given the structure of an operad with multiplication if the bialgebra is a (not necessarily finite dimensional) Hopf algebra with invertible antipode; if the antipode is involutive, the operad is even cyclic. Therefore, the Gerstenhaber-Schack cohomology of any such Hopf algebra carries a Gerstenhaber resp. Batalin-Vilkovisky algebra structure; in particular, one obtains a cup product and a cyclic boundary B that generate the Gerstenhaber bracket, and that allows to define cyclic Gerstenhaber-Schack cohomology. In case the Hopf algebra in question is finite dimensional, the Gerstenhaber bracket turns out to be zero in cohomology and hence the interesting structure is not given by this e_2-algebra structure but rather by the resulting e_3-algebra structure, which is expressed in terms of the cup product and B.