nils-herrmann/clean_scirepeval huggingface dataset:
Distributed k-Means and k-Median Clustering on General Topologies

This paper provides new algorithms for distributed clustering for two popular center-based objectives, k-median and k-means. These algorithms have provable guarantees and improve communication complexity over existing approaches. Following a classic approach in clustering by \cite{har2004coresets}, we reduce the problem of finding a clustering with low cost to the problem of finding a coreset of small size. We provide a distributed method for constructing a global coreset which improves over the previous methods by reducing the communication complexity, and which works over general communication topologies. Experimental results on large scale data sets show that this approach outperforms other coreset-based distributed clustering algorithms.
Prediction with missing data via Bayesian Additive Regression Trees

We present a method for incorporating missing data into general prediction problems which use nonparametric statistical learning. We focus on a tree‐based method, Bayesian Additive Regression Trees (BART), enhanced with “Missingness Incorporated in Attributes,” a recently proposed approach for incorporating missingness into decision trees. This procedure extends the native partitioning mechanisms found in tree‐based models and does not require imputation. Simulations on generated models and real data indicate that our procedure offers promise for both selection model and pattern‐mixture frameworks as measured by out‐of‐sample predictive accuracy. We also illustrate BART's abilities to incorporate missingness into uncertainty intervals. Our implementation is readily available in the R package bartMachine. The Canadian Journal of Statistics 43: 224–239; 2015
HATS-3b: AN INFLATED HOT JUPITER TRANSITING AN F-TYPE STAR

We report the discovery by the HATSouth survey of HATS-3b, a transiting extrasolar planet orbiting a V = 12.4 F dwarf star. HATS-3b has a period of P = 3.5479 days, mass of Mp = 1.07 MJ, and radius of Rp = 1.38 RJ. Given the radius of the planet, the brightness of the host star, and the stellar rotational velocity (vsin i = 9.0 km s−1), this system will make an interesting target for future observations to measure the Rossiter–McLaughlin effect and determine its spin–orbit alignment. We detail the low-/medium-resolution reconnaissance spectroscopy that we are now using to deal with large numbers of transiting planet candidates produced by the HATSouth survey. We show that this important step in discovering planets produces log g and Teff parameters at a precision suitable for efficient candidate vetting, as well as efficiently identifying stellar mass eclipsing binaries with radial velocity semi-amplitudes as low as 1 km s−1.
Provable Inductive Matrix Completion

Consider a movie recommendation system where apart from the ratings information, side information such as user's age or movie's genre is also available. Unlike standard matrix completion, in this setting one should be able to predict inductively on new users/movies. In this paper, we study the problem of inductive matrix completion in the exact recovery setting. That is, we assume that the ratings matrix is generated by applying feature vectors to a low-rank matrix and the goal is to recover back the underlying matrix. Furthermore, we generalize the problem to that of low-rank matrix estimation using rank-1 measurements. We study this generic problem and provide conditions that the set of measurements should satisfy so that the alternating minimization method (which otherwise is a non-convex method with no convergence guarantees) is able to recover back the {\em exact} underlying low-rank matrix. 
In addition to inductive matrix completion, we show that two other low-rank estimation problems can be studied in our framework: a) general low-rank matrix sensing using rank-1 measurements, and b) multi-label regression with missing labels. For both the problems, we provide novel and interesting bounds on the number of measurements required by alternating minimization to provably converges to the {\em exact} low-rank matrix. In particular, our analysis for the general low rank matrix sensing problem significantly improves the required storage and computational cost than that required by the RIP-based matrix sensing methods \cite{RechtFP2007}. Finally, we provide empirical validation of our approach and demonstrate that alternating minimization is able to recover the true matrix for the above mentioned problems using a small number of measurements.
Information sharing and sorting in a community

We present the results of a detailed numerical study of a model for the sharing and sorting of information in a community consisting of a large number of agents. The information gathering takes place in a sequence of mutual bipartite interactions where randomly selected pairs of agents communicate with each other to enhance their knowledge and sort out the common information. Although our model is less restricted compared to the well-established naming game, the numerical results strongly indicate that the whole set of exponents characterizing this model are different from those of the naming game and they assume nontrivial values. Finally, it appears that in analogy to the emergence of clusters in the phenomenon of percolation, one can define clusters of agents here having the same information. We have studied in detail the growth of the largest cluster in this article and performed its finite-size scaling analysis.
Iterated Tabu Search Algorithm for Packing Unequal Circles in a Circle

This paper presents an Iterated Tabu Search algorithm (denoted by ITS-PUCC) for solving the problem of Packing Unequal Circles in a Circle. The algorithm exploits the continuous and combinatorial nature of the unequal circles packing problem. It uses a continuous local optimization method to generate locally optimal packings. Meanwhile, it builds a neighborhood structure on the set of local minimum via two appropriate perturbation moves and integrates two combinatorial optimization methods, Tabu Search and Iterated Local Search, to systematically search for good local minima. Computational experiments on two sets of widely-used test instances prove its effectiveness and efficiency. For the first set of 46 instances coming from the famous circle packing contest and the second set of 24 instances widely used in the literature, the algorithm is able to discover respectively 14 and 16 better solutions than the previous best-known records.
Robust Polylines Tracing for N-Symmetry Direction Field on Triangulated Surfaces

We are proposing an algorithm for tracing polylines that are oriented by a direction field defined on a triangle mesh. The challenge is to ensure that two such polylines cannot cross or merge. This property is fundamental for mesh segmentation and is impossible to enforce with existing algorithms. The core of our contribution is to determine how polylines cross each triangle. Our solution is inspired by EdgeMaps where each triangle boundary is decomposed into inflow and outflow intervals such that each inflow interval is mapped onto an outflow interval. To cross a triangle, we find the inflow interval that contains the entry point, and link it to the corresponding outflow interval, with the same barycentric coordinate. To ensure that polylines cannot merge or cross, we introduce a new direction field representation, we resolve the inflow/outflow interval pairing with a guaranteed combinatorial algorithm, and propagate the barycentric positions with arbitrary precision number representation. Using these techniques, two streamlines crossing the same triangle cannot merge or cross, but only locally overlap when all streamline extremities are located on the same edge. Cross-free and merge-free polylines can be traced on the mesh by iteratively crossing triangles. Vector field singularities and polyline/vertex crossing are characterized and consistently handled.
Una interpretaci\'on f\'isica de la constante de Planck

According to the commonly accepted interpretation of Quantum Mechanics, it is only possible to talk about the existence of elementary particles when they are detected by an experiment or by a classical measurement device. This has led to distortions with regard to the objective existence of reality, since it would be necessary an observer to materialize the elementary particle. This issue is solved when the elementary particle is placed in permanent interaction with the rest of the universe, so its existence would not depend on any intelligent observer and thus the objective range of elementary particles would be recovered. This article explores the interaction between elementary particles and a background action field of stochastic character and it is also determined that the standard deviation of the particle-background field interaction is essentially the Planck constant, since the expressions of the energy according to the Planck postulate and the momentum according to the Louis de Broglie thesis follow in a natural way. In addition, the expression for the wave function of a free particle is obtained from the solution of the integral equation that relates the momentum and the energy with the wave vector and the angular frequency of a Gaussian distribution. Also, the Schr\"odinger equation is deduced from the expression for the wave function of an elementary particle in a potential field. Finally, the uncertainty principle is obtained. The theory presented in this article rescues the objective nature of Quantum Mechanics which implies that elementary particles exist independently of observers since the classical measurement device in this theory, introduced by the authors, is the own universe.
Discriminating Higgs production mechanisms using jet energy profiles

We present a new tool for precision measurements of the Higgs boson production mechanisms at the LHC. We study events with a Higgs boson produced with two forward jets. Even with fairly stringent cuts, one expects a significant contamination of gluon fusion (GF) in addition to vector-boson fusion (VBF) in the event sample. By measuring the jet energy profile of the most central jet, we find that SM production can be distinguished from either pure VBF or pure GF at the 5 sigma level with 100 fb(-1) of luminosity at the 14 TeV LHC. Moreover, this discrimination technique can be used to validate or rule out new physics models that predict similar observable branching fractions as the 125 GeV Standard Model Higgs boson but have different production mechanisms.
Multiple accretion events as a trigger for Sagittarius A* activity

The Galactic center supermassive black hole is surrounded by orbiting clouds of gas. These clumps of gas may collide with each other, losing angular momentum and plunging towards the center. Observations of X-ray reflection from molecular clouds surrounding the Galactic center show evidence for enhanced activity of Sagittarius A* during the past few hundred years. These observations enable us to place constraints on the nature of past accretion events responsible for this enhanced activity. We model the source intrinsic luminosity of Sgr A* using multiple accretion events occurring at various moments in time, characterized by a range of angular momentum We also applied our scheme to the case of G2 cloud in the Galactic center.
Manipulation of molecules with electromagnetic fields

The goal of the present article is to review the major developments that have led to the current understanding of molecule–field interactions and experimental methods for manipulating molecules with electromagnetic fields. Molecule–field interactions are at the core of several, seemingly distinct areas of molecular physics. This is reflected in the organisation of this article, which includes sections on field control of molecular beams, external field traps for cold molecules, control of molecular orientation and molecular alignment, manipulation of molecules by non-conservative forces, ultracold molecules and ultracold chemistry, controlled many-body phenomena, entanglement of molecules and dipole arrays, and stability of molecular systems in high-frequency super-intense laser fields. The article contains 852 references.
(More) Efficient Reinforcement Learning via Posterior Sampling

Most provably-efficient reinforcement learning algorithms introduce optimism about poorly-understood states and actions to encourage exploration. We study an alternative approach for efficient exploration: posterior sampling for reinforcement learning (PSRL). This algorithm proceeds in repeated episodes of known duration. At the start of each episode, PSRL updates a prior distribution over Markov decision processes and takes one sample from this posterior. PSRL then follows the policy that is optimal for this sample during the episode. The algorithm is conceptually simple, computationally efficient and allows an agent to encode prior knowledge in a natural way. We establish an O(τS/√AT) bound on expected regret, where T is time, τ is the episode length and S and A are the cardinalities of the state and action spaces. This bound is one of the first for an algorithm not based on optimism, and close to the state of the art for any reinforcement learning algorithm. We show through simulation that PSRL significantly outperforms existing algorithms with similar regret bounds.
Secrecy Wireless Information and Power Transfer With MISO Beamforming

The dual use of radio signal for simultaneous wireless information and power transfer (SWIPT) has recently drawn significant attention. To meet the practical requirement that the energy receiver (ER) operates with significantly higher received power as compared to the conventional information receiver (IR), ERs need to be deployed in more proximity to the transmitter than IRs in the SWIPT system. However, due to the broadcast nature of wireless channels, one critical issue arises that the messages sent to IRs can be eavesdropped by ERs, which possess better channels from the transmitter. In this paper, we address this new physical-layer security problem in a multiuser multiple-input single-output (MISO) SWIPT system where one multi-antenna transmitter sends information and energy simultaneously to an IR and multiple ERs, each with one single antenna. Two problems are investigated with different practical aims: the first problem maximizes the secrecy rate for the IR subject to individual harvested energy constraints of ERs, while the second problem maximizes the weighted sum-energy transferred to ERs subject to a secrecy rate constraint for IR. We solve these two non-convex problems optimally by a general two-stage procedure. First, by fixing the signal-to-interference-plus-noise ratio (SINR) target for ERs or IR, we obtain the optimal transmit beamforming and power allocation solution by applying the technique of semidefinite relaxation (SDR). Then, each of the two problems is solved by a one-dimension search over the optimal SINR target for ERs or IR. Furthermore, for each problem, suboptimal solutions of lower complexity are proposed.
B-Spline Techniques for Volatility Modeling

This paper is devoted to the application of B-splines to volatility modeling, specifically the calibration of the leverage function in stochastic local volatility (SLV) models and the parameterization of an arbitrage-free implied volatility surface calibrated to sparse option data. We use an extension of classical B-splines obtained by including basis functions with infinite support. We first discuss the application of shape-constrained B-splines to the estimation of conditional expectations, not merely from a scatter plot but also from the given marginal distributions. One application is the Monte Carlo calibration of SLV models by Markov projection. We then present a new technique for the calibration of an implied volatility surface to sparse option data. We use a B-spline parameterization of the Radon–Nikodym derivative of the underlying’s risk-neutral probability density with respect to a roughly calibrated base model. We show that this method provides smooth arbitrage-free implied volatility surfaces. Finally, we sketch a Galerkin method with B-spline finite elements to the solution of the partial differential equation satisfied by the Radon–Nikodym derivative.
LLAMA: Leveraging Learning to Automatically Manage Algorithms

Algorithm portfolio and selection approaches have achieved remarkable improvements over single solvers. However, the implementation of such systems is often highly customised and specific to the problem domain. This makes it difficult for researchers to explore different techniques for their specific problems. We present LLAMA, a modular and extensible toolkit implemented as an R package that facilitates the exploration of a range of different portfolio techniques on any problem domain. It implements the algorithm selection approaches most commonly used in the literature and leverages the extensive library of machine learning algorithms and techniques in R. We describe the current capabilities and limitations of the toolkit and illustrate its usage on a set of example SAT problems.
An alternative proof of a result of Takaoka

In Karatzas and Kardaras's paper on semimartingale financial models, it is proved that the NUPBR condition is a property of the local characteristic of the asset process alone. In Takaoka's paper on NUPBR, it is proved that the NUPBR condition is equivalent to the existence of a simga-martingale deflator. However, Takaoka's paper founds its proof on Delbaen and Schachermayer's fundamental asset pricing theorem, i.e. the NFLVR condition, which is not a pure property of the local characteristic of the asset process. In this paper we give an alternative proof of the result of Takaoka, which makes use only the properties of the local characteristic of the asset process.
Web Synchronization Simulations using the ResourceSync Framework

Maintenance of multiple, distributed up-to-date copies of collections of changing Web resources is important in many application contexts and is often achieved using ad hoc or proprietary synchronization solutions. ResourceSync is a resource synchronization framework that integrates with the Web architecture and leverages XML sitemaps. We define a model for the ResourceSync framework as a basis for understanding its properties. We then describe experiments in which simulations of a variety of synchronization scenarios illustrate the effects of model configuration on consistency, latency, and data transfer efficiency. These results provide insight into which congurations are appropriate for various application scenarios.
Detectability of communities in heterogeneous networks.

Communities are fundamental entities for the characterization of the structure of real networks. The standard approach to the identification of communities in networks is based on the optimization of a quality function known as modularity. Although modularity has been at the center of an intense research activity and many methods for its maximization have been proposed, not much is yet known about the necessary conditions that communities need to satisfy in order to be detectable with modularity maximization methods. Here, we develop a simple theory to establish these conditions, and we successfully apply it to various classes of network models. Our main result is that heterogeneity in the degree distribution helps modularity to correctly recover the community structure of a network and that, in the realistic case of scale-free networks with degree exponent γ<2.5, modularity is always able to detect the presence of communities.
A DYNAMICAL SIGNATURE OF MULTIPLE STELLAR POPULATIONS IN 47 TUCANAE

Based on the width of its main sequence, and an actual observed split when viewed through particular filters, it is widely accepted that 47 Tucanae contains multiple stellar populations. In this contribution, we divide the main sequence of 47 Tuc into four color groups, which presumably represent stars of various chemical compositions. The kinematic properties of each of these groups are explored via proper motions, and a strong signal emerges of differing proper-motion anisotropies with differing main-sequence color; the bluest main-sequence stars exhibit the largest proper-motion anisotropy which becomes undetectable for the reddest stars. In addition, the bluest stars are also the most centrally concentrated. A similar analysis for Small Magellanic Cloud stars, which are located in the background of 47 Tuc on our frames, yields none of the anisotropy exhibited by the 47 Tuc stars. We discuss implications of these results for possible formation scenarios of the various populations.
Coherent ultrafast measurement of time-bin encoded photons.

Time-bin encoding is a robust form of optical quantum information, especially for transmission in optical fibers. To readout the information, the separation of the time bins must be larger than the detector time resolution, typically on the order of nanoseconds for photon counters. In the present work, we demonstrate a technique using a nonlinear interaction between chirped entangled time-bin photons and shaped laser pulses to perform projective measurements on arbitrary time-bin states with picosecond-scale separations. We demonstrate a tomographically complete set of time-bin qubit projective measurements and show the fidelity of operations is sufficiently high to violate the Clauser-Horne-Shimony-Holt-Bell inequality by more than 6 standard deviations.
Loop calculus and bootstrap-belief propagation for perfect matchings on arbitrary graphs

This manuscript discusses computation of the Partition Function (PF) and the Minimum Weight Perfect Matching (MWPM) on arbitrary, non-bipartite graphs. We present two novel problem formulations – one for computing the PF of a Perfect Matching (PM) and one for finding MWPMs – that build upon the inter-related Bethe Free Energy (BFE), Belief Propagation (BP), Loop Calculus (LC), Integer Linear Programming and Linear Programming frameworks. First, we describe an extension of the LC framework to the PM problem. The resulting formulas, coined (fractional) Bootstrap-BP, express the PF of the original model via the BFE of an alternative PM problem. We then study the zero-temperature version of this Bootstrap-BP formula for approximately solving the MWPM problem. We do so by leveraging the Bootstrap-BP formula to construct a sequence of MWPM problems, where each new problem in the sequence is formed by contracting odd-sized cycles (or blossoms) from the previous problem. This Bootstrap-and-Contract procedure converges reliably and generates an empirically tight upper bound for the MWPM. We conclude by discussing the relationship between our iterative procedure and the famous Blossom Algorithm of Edmonds '65 and demonstrate the performance of the Bootstrap-and-Contract approach on a variety of weighted PM problems.
Predictability of social interactions

The ability to predict social interactions between people has profound applications including targeted marketing and prediction of information diffusion and disease propagation. Previous work has shown that the location of an individual at any given time is highly predictable. This study examines the predictability of social interactions between people to determine whether interaction patterns are similarly predictable. I find that the locations and times of interactions for an individual are highly predictable; however, the other person the individual interacts with is less predictable. Furthermore, I show that knowledge of the locations and times of interactions has almost no effect on the predictability of the other person. Finally I demonstrate that a simple Markov chain model is able to achieve close to the upper bound in terms of predicting the next person with whom a given individual will interact.