nils-herrmann/clean_scirepeval huggingface dataset:
Crowdsourcing Universal Part-of-Speech Tags for Code-Switching

Code-switching is the phenomenon by which bilingual speakers switch between multiple languages during communication. The importance of developing language technologies for codeswitching data is immense, given the large populations that routinely code-switch. High-quality linguistic annotations are extremely valuable for any NLP task, and performance is often limited by the amount of high-quality labeled data. However, little such data exists for code-switching. In this paper, we describe crowd-sourcing universal part-of-speech tags for the Miami Bangor Corpus of Spanish-English code-switched speech. We split the annotation task into three subtasks: one in which a subset of tokens are labeled automatically, one in which questions are specifically designed to disambiguate a subset of high frequency words, and a more general cascaded approach for the remaining data in which questions are displayed to the worker following a decision tree structure. Each subtask is extended and adapted for a multilingual setting and the universal tagset. The quality of the annotation process is measured using hidden check questions annotated with gold labels. The overall agreement between gold standard labels and the majority vote is between 0.95 and 0.96 for just three labels and the average recall across part-of-speech tags is between 0.87 and 0.99, depending on the task.
Depth-Optimal Quantum Circuit Placement for Arbitrary Topologies

A significant hurdle towards realization of practical and scalable quantum computing is to protect the quantum states from inherent noises during the computation. In physical implementation of quantum circuits, a long-distance interaction between two qubits is undesirable since, it can be interpreted as a noise. Therefore, multiple quantum technologies and quantum error correcting codes strongly require the interacting qubits to be arranged in a nearest neighbor (NN) fashion. The current literature on converting a given quantum circuit to an NN-arranged one mainly considered chained qubit topologies or Linear Nearest Neighbor (LNN) topology. However, practical quantum circuit realizations, such as Nuclear Magnetic Resonance (NMR), may not have an LNN topology. To address this gap, we consider an arbitrary qubit topology. We present an Integer Linear Programming (ILP) formulation for achieving minimal logical depth while guaranteeing the nearest neighbor arrangement between the interacting qubits. We substantiate our claim with studies on diverse network topologies and prominent quantum circuit benchmarks.
Hydrogen bonding characterization in water and small molecules.

The prototypical hydrogen bond in water dimer and hydrogen bonds in the protonated water dimer, in other small molecules, in water cyclic clusters, and in ice, covering a wide range of bond strengths, are theoretically investigated by first-principles calculations based on density functional theory, considering not only a standard generalized gradient approximation functional but also, for the water dimer, hybrid and van der Waals corrected functionals. We compute structural, energetic, and electrostatic (induced molecular dipole moments) properties. In particular, hydrogen bonds are characterized in terms of differential electron density distributions and profiles, and of the shifts of the centres of maximally localized Wannier functions. The information from the latter quantities can be conveyed to a single geometric bonding parameter that appears to be correlated with the Mayer bond order parameter and can be taken as an estimate of the covalent contribution to the hydrogen bond. By considering the water trimer, the cyclic water hexamer, and the hexagonal phase of ice, we also elucidate the importance of cooperative/anticooperative effects in hydrogen-bonding formation.
Quantum mechanics from an epistemic state space

We derive the Hilbert space formalism of quantum mechanics from epistemic principles. A key assumption is that a physical theory that relies on entities or distinctions that are unknowable in principle gives rise to wrong predictions. An epistemic formalism is developed, where concepts like individual and collective knowledge are used, and knowledge may be actual or potential. The physical state S corresponds to the collective potential knowledge. The state S is a subset of a state space S = {Z}, such that S always contains several elements Z, which correspond to unattainable states of complete potential knowledge of the world. The evolution of S cannot be determined in terms of the individual evolution of the elements Z, unlike the evolution of an ensemble in classical phase space. The evolution of S is described in terms of sequential time n ∈ N, which is updated according to n→ n+ 1 each time potential knowledge changes. In certain experimental contexts C, there is knowledge at the start of the experiment at time n that a given series of properties P,P′, . . . will be observed within a given time frame, meaning that a series of values p, p′, . . . of these properties will become known. At time n, it is just known that these values belong to predefined, finite sets {p},{p′}, . . .. In such a context C, it is possible to define a complex Hilbert space HC on top of S , in which the elements are contextual state vectors S̄C. Born’s rule to calculate the probabilities to find the values p, p′, . . . is derived as the only generally applicable such rule. Also, we can associate a self-adjoint operator P̄ with eigenvalues {p} to each property P observed within C. These operators obey [P̄, P̄′] = 0 if and only if the precise values of P and P′ are simultaneoulsy knowable. The existence of properties whose precise values are not simulataneously knowable follows from the hypothesis that collective potential knowledge is always incomplete, corresponding to the abovementioned statement that S always contains several elements Z.
Data-Mining Textual Responses to Uncover Misconception Patterns

An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of instruction. In this paper, we propose a new natural language processing (NLP) framework to detect the common misconceptions among students' textual responses to open-response, short-answer questions. We introduce a probabilistic model for students' textual responses involving misconceptions and experimentally validate it on a real-world student-response dataset. Preliminary experimental results show that our proposed framework excels at classifying whether a response exhibits one or more misconceptions. More importantly, it can also automatically detect the common misconceptions exhibited across responses from multiple students to multiple questions; this is especially important at large scale, since instructors will no longer need to manually specify all possible misconceptions that students might exhibit.
Dirac and Nodal Line Magnons in Three-Dimensional Antiferromagnets.

We study the topological properties of magnon excitations in three-dimensional antiferromagnets, where the ground state configuration is invariant under time reversal followed by space inversion (PT symmetry). We prove that Dirac points and nodal lines, the former being the limiting case of the latter, are the generic forms of symmetry-protected band crossings between magnon branches. As a concrete example, we study a Heisenberg spin model for a "spin-web" compound, Cu_{3}TeO_{6}, and show the presence of the magnon Dirac points assuming a collinear magnetic structure. Upon turning on symmetry-allowed Dzyaloshinsky-Moriya interactions, which introduce a small noncollinearity in the ground state configuration, we find that the Dirac points expand into nodal lines with nontrivial Z_{2}-topological charge, a new type of nodal line not predicted in any materials so far.
An Earth-mass Planet in a 1 au Orbit around an Ultracool Dwarf

We combine Spitzer and ground-based Korea Microlensing Telescope Network microlensing observations to identify and precisely measure an Earth-mass ( ) planet OGLE-2016-BLG-1195Lb at orbiting a ultracool dwarf. This is the lowest-mass microlensing planet to date. At kpc, it is the third consecutive case among the Spitzer “Galactic distribution” planets toward the Galactic bulge that lies in the Galactic disk as opposed to the bulge itself, hinting at a skewed distribution of planets. Together with previous microlensing discoveries, the seven Earth-size planets orbiting the ultracool dwarf TRAPPIST-1, and the detection of disks around young brown dwarfs, OGLE-2016-BLG-1195Lb suggests that such planets might be common around ultracool dwarfs. It therefore sheds light on the formation of both ultracool dwarfs and planetary systems at the limit of low-mass protoplanetary disks.
Light-cone observables and gauge-invariance in the geodesic light-cone formalism

The remarkable properties of the geodesic light-cone (GLC) coordinates allow analytic expressions for the light-cone observables, providing a new non-perturbative way for calculating the effects of inhomogeneities in our Universe. However, the gauge-invariance of these expressions in the GLC formalism has not been shown explicitly. Here we provide this missing part of the GLC formalism by proving the gauge-invariance of the GLC expressions for the light-cone observables, such as the observed redshift, the luminosity distance, and the physical area and volume of the observed sources. Our study provides a new insight on the properties of the GLC coordinates and it complements the previous work by the GLC collaboration, leading to a comprehensive description of light propagation in the GLC representation.
The effect of early radiation in N-body simulations of cosmic structure formation

Newtonian N-body simulations have been employed successfully over the past decades for the simulation of the cosmological large-scale structure. Such simulations usually ignore radiation perturbations (photons and massless neutrinos) and the impact of general relativity (GR) beyond the background expansion. This approximation can be relaxed and we discuss three different approaches that are accurate to leading order in GR. For simulations that start at redshift less than about 100, we find that the presence of early radiation typically leads to per cent-level effects on the numerical power spectra at large scales. Our numerical results agree across the three methods, and we conclude that all of the three methods are suitable for simulations in a standard cosmology. Two of the methods modify the N-body evolution directly, while the third method can be applied as a post-processing prescription.
Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees

We develop polynomial-time heuristic methods to solve unimodular quadratic program (UQP) approximately, which is a known non-deterministic polynomial-time hard (NP-hard) problem. Several problems in active sensing and wireless communication applications boil down to UQPs. First, we derive a performance bound for a known UQP approximation method called dominant eigenvector matching heuristic. Next, we present two new polynomial-time heuristic methods inspired from the greedy strategy, and we provide performance guarantees for these methods with respect to the optimal objective.
Efficiently Clustering Very Large Attributed Graphs

Attributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.
Jointly optimizing placement and inference for beacon-based localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.
GraphZip: Dictionary-based Compression for Mining Graph Streams

A massive amount of data generated today on platforms such as social networks, telecommunication networks, and the internet in general can be represented as graph streams. Activity in a network's underlying graph generates a sequence of edges in the form of a stream; for example, a social network may generate a graph stream based on the interactions (edges) between different users (nodes) over time. While many graph mining algorithms have already been developed for analyzing relatively small graphs, graphs that begin to approach the size of real-world networks stress the limitations of such methods due to their dynamic nature and the substantial number of nodes and connections involved. 
In this paper we present GraphZip, a scalable method for mining interesting patterns in graph streams. GraphZip is inspired by the Lempel-Ziv (LZ) class of compression algorithms, and uses a novel dictionary-based compression approach in conjunction with the minimum description length principle to discover maximally-compressing patterns in a graph stream. We experimentally show that GraphZip is able to retrieve complex and insightful patterns from large real-world graphs and artificially-generated graphs with ground truth patterns. Additionally, our results demonstrate that GraphZip is both highly efficient and highly effective compared to existing state-of-the-art methods for mining graph streams.
Designing pairwise interactions that stabilize open crystals: Truncated square and truncated hexagonal lattices.

Using a recently introduced formulation of the ground-state inverse design problem for a targeted lattice [W. Piñeros et al.,  (2016)], we discover purely repulsive and isotropic pair interactions that stabilize low-density truncated square and truncated hexagonal crystals, as well as promote their assembly in Monte Carlo simulations upon isochoric cooling from a high-temperature fluid phase. The results illustrate that the primary challenge to stabilizing very open two-dimensional lattices is to design interactions that can favor the target structure over competing stripe microphases.
THE SET OF QUANTUM CORRELATIONS IS NOT CLOSED

We construct a linear system nonlocal game which can be played perfectly using a limit of finite-dimensional quantum strategies, but which cannot be played perfectly on any finite-dimensional Hilbert space, or even with any tensor-product strategy. In particular, this shows that the set of (tensor-product) quantum correlations is not closed. The constructed nonlocal game provides another counterexample to the ‘middle’ Tsirelson problem, with a shorter proof than our previous paper (though at the loss of the universal embedding theorem). We also show that it is undecidable to determine if a linear system game can be played perfectly with a finite-dimensional strategy, or a limit of finite-dimensional quantum strategies.
Binarsity: a penalization for one-hot encoded features in linear supervised learning

This paper deals with the problem of large-scale linear supervised learning in settings where a large number of continuous features are available. We propose to combine the well-known trick of one-hot encoding of continuous features with a new penalization called binarsity. In each group of binary features coming from the one-hot encoding of a single raw continuous feature, this penalization uses total-variation regularization together with an extra linear constraint to avoid collinearity within groups. Non-asymptotic oracle inequalities for generalized linear models are proposed, and numerical experiments illustrate the good performances of our approach on several datasets. It is also noteworthy that our method has a numerical complexity comparable to standard $\ell_1$ penalization.
AMAT: Medial Axis Transform for Natural Images

We introduce Appearance-MAT (AMAT), a generalization of the medial axis transform for natural images, that is framed as a weighted geometric set cover problem. We make the following contributions: i) we extend previous medial point detection methods for color images, by associating each medial point with a local scale; ii) inspired by the invertibility property of the binary MAT, we also associate each medial point with a local encoding that allows us to invert the AMAT, reconstructing the input image; iii) we describe a clustering scheme that takes advantage of the additional scale and appearance information to group individual points into medial branches, providing a shape decomposition of the underlying image regions. In our experiments, we show state-of-the-art performance in medial point detection on Berkeley Medial AXes (BMAX500), a new dataset of medial axes based on the BSDS500 database, and good generalization on the SK506 and WH-SYMMAX datasets. We also measure the quality of reconstructed images from BMAX500, obtained by inverting their computed AMAT. Our approach delivers significantly better reconstruction quality w.r.t. to three baselines, using just 10% of the image pixels. Our code and annotations are available at https://github.com/tsogkas/amat.
An explicit determination of the $K$-theoretic structure constants of the affine Grassmanian associated to $SL_2$

Let $G:=\widehat{SL_2}$ denote the affine Kac-Moody group associated to $SL_2$ and $\bar{\mathcal{X}}$ the associated affine Grassmanian. We determine an inductive formula for the Schubert basis structure constants in the torus-equivariant Grothendieck group of $\bar{\mathcal{X}}$. In the case of ordinary (non-equivariant) $K$-theory we find an explicit closed form for the structure constants. We also determine an inductive formula for the structure constants in the torus-equivariant cohomology ring, and use this formula to find closed forms for some of the structure constants.
Diffraction-Free Bloch Surface Waves.

Here, we demonstrate a diffraction-free Bloch surface wave sustained on all-dielectric multilayers that does not diffract after being passed through three obstacles or across a single mode fiber. It can propagate in a straight line for distances longer than 110 μm at a wavelength of 633 nm and could be applied as an in-plane optical virtual probe both in air and in an aqueous environment. Its ability to be used in water, its long diffraction-free distance, and its tolerance to multiple obstacles make this wave ideal for certain applications in areas such as the biological sciences, where many measurements are made on glass surfaces or for which an aqueous environment is required, and for high-speed interconnections between chips, where low loss is necessary.
Simplifying the Bible and Wikipedia Using Statistical Machine Translation

I started this work with the hope of generating a text synthesizer (like a musical synthesizer) that can imitate certain linguistic styles. Most of the report focuses on text simplification using statistical machine translation (SMT) techniques. I applied MOSES to a parallel corpus of the Bible (King James Version and Easy-to-Read Version) and that of Wikipedia articles (normal and simplified). I report the importance of the three main components of SMT---phrase translation, language model, and recording---by changing their weights and comparing the resulting quality of simplified text in terms of METEOR and BLEU. Toward the end of the report will be presented some examples of text "synthesized" into the King James style.
The nondegenerate generalized Kähler Calabi–Yau problem

We formulate a Calabi–Yau-type conjecture in generalized Kähler geometry, focusing on the case of nondegenerate Poisson structure. After defining natural Hamiltonian deformation spaces for generalized Kähler structures generalizing the notion of Kähler class, we conjecture unique solvability of Gualtieri’s Calabi–Yau equation within this class. We establish the uniqueness, and moreover show that all such solutions are actually hyper-Kähler metrics. We furthermore establish a GIT framework for this problem, interpreting solutions of this equation as zeroes of a moment map associated to a Hamiltonian action and finding a Kempf–Ness functional. Lastly we indicate the naturality of generalized Kähler–Ricci flow in this setting, showing that it evolves within the given Hamiltonian deformation class, and that the Kempf–Ness functional is monotone, so that the only possible fixed points for the flow are hyper-Kähler metrics. On a hyper-Kähler background, we establish global existence and weak convergence of the flow.
Gaussian Processes with Context-Supported Priors for Active Object Localization

We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide aprincipled and interpretable system amenable to high-level vision tasks. We address these issues with the current research. Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional neural network to approximate an offset distance from the target object. Next, we use a Gaussian Process to model this offset response signal over the search space of the target. We then employ a Bayesian active search for accurate localization of the target. In experiments, we compare our approach to a state-of-the-art bounding-box regression method for a challenging pedestrian localization task. Our method exhibits a substantial improvement over this baseline regression method.