nils-herrmann/clean_scirepeval huggingface dataset:
Proof of a Quantum Bousso Bound

We prove the generalized covariant entropy bound, Δ S ≤ ( A - A ′ ) / 4 G ℏ , for light-sheets with initial area A and final area A ′ . The entropy Δ S is defined as a difference of von Neumann entropies of an arbitrary state and the vacuum, with both states restricted to the light-sheet under consideration. The proof applies to free fields, in the limit where gravitational backreaction is small. We do not assume the null energy condition. In regions where it is violated, we find that the bound is protected by the defining property of light-sheets: that their null generators are nowhere expanding.
A Formal Analysis of Required Cooperation in Multi-Agent Planning

It is well understood that, through cooperation, multiple agents can achieve tasks that are unachievable by a single agent. However, there are no formal characterizations of situations where cooperation is required to achieve a goal, thus warranting the application of multiple agents. In this paper, we provide such a formal characterization for multi-agent planning problems with sequential action execution. We first show that determining whether there is required cooperation (RC) is in general intractable even in this limited setting. As a result, we start our analysis with a subset of more restrictive problems where agents are homogeneous. For such problems, we identify two conditions that can cause RC. We establish that when none of these conditions hold, the problem is single-agent solvable; otherwise, we provide upper bounds on the minimum number of agents required. For the remaining problems with heterogeneous agents, we further divide them into two subsets. For one of the subsets, we propose the concept of transformer agent to reduce the number of agents to be considered which is used to improve planning performance. We implemented a planner using our theoretical results and compared it with one of the best IPC CoDMAP planners in the centralized track. Results show that our planner provides significantly improved performance on IPC CoDMAP domains.
Compressive Sampling for Energy Spectrum Estimation of Turbulent Flows

Recent results from compressive sampling (CS) have demonstrated that accurate reconstruction of sparse signals often requires far fewer samples than suggested by the classical Nyquist--Shannon sampling theorem. Typically, signal reconstruction errors are measured in the $\ell^2$ norm and the signal is assumed to be sparse, compressible or having a prior distribution. Our spectrum estimation by sparse optimization (SpESO) method uses prior information about isotropic homogeneous turbulent flows with power law energy spectra and applies the methods of CS to 1-D and 2-D turbulence signals to estimate their energy spectra with small logarithmic errors. SpESO is distinct from existing energy spectrum estimation methods which are based on sparse support of the signal in Fourier space. SpESO approximates energy spectra with an order of magnitude fewer samples than needed with Shannon sampling. Our results demonstrate that SpESO performs much better than lumped orthogonal matching pursuit (LOMP), and as well or better than wavelet-based best M-term or M/2-term methods, even though these methods require complete sampling of the signal before compression.
An Importance Sampling Scheme on Dual Factor Graphs - II. Models with Strong Couplings

We consider the problem of estimating the partition function of the two-dimensional ferromagnetic Ising and Potts models in an external magnetic field. The estimation is done via importance sampling in the dual of the Forney factor graph representing the models. We present importance sampling schemes that can efficiently compute an estimate of the partition function in a wide range of model parameters. Emphasis is on models in which a subset of the coupling parameters is strong.
An Earth-Sized Planet in the Habitable Zone of a Cool Star

Starry Brightness The high photometric precision of NASA's Kepler observatory has enabled the detection of many planets because they cause slight dimming of their host stars as they orbit in front of them. From these data, Quintana et al. (p. 277) have spotted a five-planet system around a small star. Here, the outermost planet is only 10% larger than Earth and completes its 130-day orbit entirely within the habitable zone, where liquid water could exist on its surface. Similarly, Kepler can detect faint periodic brightenings, as Kruse and Agol (p. 275) have reported for the binary system KOI-3278. In this system, a white dwarf acts as a gravitational microlens when it passes in front of its Sun-like G-star companion every 88 days. The lensing effect allows the mass of the white dwarf to be estimated, which helps us to understand how similar binary systems may have evolved. NASA’s Kepler mission revealed that the fifth and outermost planet orbiting Kepler-186 is capable of hosting liquid water. The quest for Earth-like planets is a major focus of current exoplanet research. Although planets that are Earth-sized and smaller have been detected, these planets reside in orbits that are too close to their host star to allow liquid water on their surfaces. We present the detection of Kepler-186f, a 1.11 ± 0.14 Earth-radius planet that is the outermost of five planets, all roughly Earth-sized, that transit a 0.47 ± 0.05 solar-radius star. The intensity and spectrum of the star’s radiation place Kepler-186f in the stellar habitable zone, implying that if Kepler-186f has an Earth-like atmosphere and water at its surface, then some of this water is likely to be in liquid form.
An Adversarial Interpretation of Information-Theoretic Bounded Rationality

Recently, there has been a growing interest in modeling planning with information constraints. Accordingly, an agent maximizes a regularized expected utility known as the free energy, where the regularizer is given by the information divergence from a prior to a posterior policy. While this approach can be justified in various ways, including from statistical mechanics and information theory, it is still unclear how it relates to decision-making against adversarial environments. This connection has previously been suggested in work relating the free energy to risk-sensitive control and to extensive form games. Here, we show that a single-agent free energy optimization is equivalent to a game between the agent and an imaginary adversary. The adversary can, by paying an exponential penalty, generate costs that diminish the decision maker's payoffs. It turns out that the optimal strategy of the adversary consists in choosing costs so as to render the decision maker indifferent among its choices, which is a definining property of a Nash equilibrium, thus tightening the connection between free energy optimization and game theory.
DGFIndex for Smart Grid: Enhancing Hive with a Cost-Effective Multidimensional Range Index

In Smart Grid applications, as the number of deployed electric smart meters increases, massive amounts of valuable meter data is generated and collected every day. To enable reliable data collection and make business decisions fast, high throughput storage and high-performance analysis of massive meter data become crucial for grid companies. Considering the advantage of high efficiency, fault tolerance, and price-performance of Hadoop and Hive systems, they are frequently deployed as underlying platform for big data processing. However, in real business use cases, these data analysis applications typically involve multidimensional range queries (MDRQ) as well as batch reading and statistics on the meter data. While Hive is high-performance at complex data batch reading and analysis, it lacks efficient indexing techniques for MDRQ. 
 
In this paper, we propose DGFIndex, an index structure for Hive that efficiently supports MDRQ for massive meter data. DGFIndex divides the data space into cubes using the grid file technique. Unlike the existing indexes in Hive, which stores all combinations of multiple dimensions, DGFIndex only stores the information of cubes. This leads to smaller index size and faster query processing. Furthermore, with pre-computing user-defined aggregations of each cube, DGFIndex only needs to access the boundary region for aggregation query. Our comprehensive experiments show that DGFIndex can save significant disk space in comparison with the existing indexes in Hive and the query performance with DGFIndex is 2-50 times faster than existing indexes in Hive and HadoopDB for aggregation query, 2-5 times faster than both for non-aggregation query, 2-75 times faster than scanning the whole table in different query selectivity.
Measurement and Internalization of Systemic Risk in a Global Banking Network

The negative externalities from an individual bank failure to the whole system can be huge. One of the key purposes of bank regulation is to internalize the social costs of potential bank failures via capital charges. This study proposes a method to evaluate and allocate the systemic risk to different countries/regions using a Susceptible-Infected-Removable (SIR) type of epidemic spreading model and the Shapley value (SV) in game theory. The paper also explores features of a constructed bank network using real globe-wide banking data.
Forward–Backward Greedy Algorithms for Atomic Norm Regularization

In many signal processing applications, the aim is to reconstruct a signal that has a simple representation with respect to a certain basis or frame. Fundamental elements of the basis known as “atoms” allow us to define “atomic norms” that can be used to formulate convex regularizations for the reconstruction problem. Efficient algorithms are available to solve these formulations in certain special cases, but an approach that works well for general atomic norms, both in terms of speed and reconstruction accuracy, remains to be found. This paper describes an optimization algorithm called CoGEnT that produces solutions with succinct atomic representations for reconstruction problems, generally formulated with atomic-norm constraints. CoGEnT combines a greedy selection scheme based on the conditional gradient approach with a backward (or “truncation”) step that exploits the quadratic nature of the objective to reduce the basis size. We establish convergence properties and validate the algorithm via extensive numerical experiments on a suite of signal processing applications. Our algorithm and analysis also allow for inexact forward steps and for occasional enhancements of the current representation to be performed. CoGEnT can outperform the basic conditional gradient method, and indeed many methods that are tailored to specific applications, when the enhancement and truncation steps are defined appropriately. We also introduce several novel applications that are enabled by the atomic-norm framework, including tensor completion, moment problems in signal processing, and graph deconvolution.
Novel mechanism for J /ψ disintegration in relativistic heavy ion collisions

In this paper we discuss the possibility of $J/\ensuremath{\psi}$ disintegration due to $Z(3)$ domain walls that are expected to form in a QGP medium. These domain walls give rise to a localized color electric field, which disintegrates $J/\ensuremath{\psi}$, on interaction, by changing its color composition and simultaneously exciting it to higher states of $c\overline{c}$ system.
Quantum trajectories for a class of continuous matrix product input states

We introduce a new class of continuous matrix product (CMP) states and establish the stochastic master equations (quantum filters) for an arbitrary quantum system probed by a bosonic input field in this class of states. We show that this class of CMP states arise naturally as outputs of a Markovian model, and that input fields in these states lead to master and filtering (quantum trajectory) equations which are matrix-valued. Furthermore, it is shown that this class of CMP states include the (continuous-mode) single photon and time-ordered multi-photon states.
Converging Work-Talk Patterns in Online Task-Oriented Communities

Much of what we do is accomplished by working collaboratively with others, and a large portion of our lives are spent working and talking; the patterns embodied in the alternation of working and talking can provide much useful insight into task-oriented social behaviors. The available electronic traces of the different kinds of human activities in online communities are an empirical goldmine that can enable the holistic study and understanding of these social systems. Open Source Software (OSS) projects are prototypical examples of collaborative, task-oriented communities, depending on volunteers for high-quality work. Here, we use sequence analysis methods to identify the work-talk patterns of software developers in online communities of Open Source Software projects. We find that software developers prefer to persist in same kinds of activities, i.e., a string of work activities followed by a string of talk activities and so forth, rather than switch them frequently; this tendency strengthens with time, suggesting that developers become more efficient, and can work longer with fewer interruptions. This process is accompanied by the formation of community culture: developers’ patterns in the same communities get closer with time while different communities get relatively more different. The emergence of community culture is apparently driven by both “talk” and “work”. Finally, we also find that workers with good balance between “work” and “talk” tend to produce just as much work as those that focus strongly on “work”; however, the former appear to be more likely to continue to be active contributors in the communities.
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks

Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user's behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what queries she submitted, what ads she clicked or ignored, and how long she spent on the landing pages of clicked ads, etc. Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN). Compared to traditional methods, this framework directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that our approach can significantly improve the click prediction accuracy, compared to sequence-independent approaches.
Pion structure in the nuclear medium

The structure and electroweak properties of the pion in symmetric nuclear matter are presented in the framework of the Nambu--Jona-Lasinio model. The pion is described as a bound state of a dressed quark-antiquark pair governed by the Bethe-Salpeter equation. For the in-medium current-light-quark properties we use the quark-meson coupling model, which describes successfully the properties of hadron in a nuclear medium. We found that the light-quark condensates, the pion decay constant and pion-quark coupling constant decrease with increasing nuclear matter density. We then predict the modifications of the charge radius of the charged pion in nuclear matter.
Vacuum energy as dark matter

We consider the vacuum energy of massive quantum fields in an expanding universe. We define a conserved renormalized energy-momentum tensor by means of a comoving cutoff regularization. Using exact solutions for de Sitter space-time, we show that in a certain range of mass and renormalization scales there is a contribution to the vacuum energy density that scales as nonrelativistic matter and that such a contribution becomes dominant at late times. By means of the WKB approximation, we find that these results can be extended to arbitrary Robertson-Walker geometries. We study the range of parameters in which the vacuum energy density would be compatible with current limits on dark matter abundance. Finally, by calculating the vacuum energy in a perturbed Robertson-Walker background, we obtain the speed of sound of density perturbations and show that the vacuum energy density contrast can grow on sub-Hubble scales as in standard cold dark matter scenarios.
Robust and Computationally Feasible Community Detection in the Presence of Arbitrary Outlier Nodes

Community detection, which aims to cluster $N$ nodes in a given graph into $r$ distinct groups based on the observed undirected edges, is an important problem in network data analysis. In this paper, the popular stochastic block model (SBM) is extended to the generalized stochastic block model (GSBM) that allows for adversarial outlier nodes, which are connected with the other nodes in the graph in an arbitrary way. Under this model, we introduce a procedure using convex optimization followed by $k$-means algorithm with $k=r$. Both theoretical and numerical properties of the method are analyzed. A theoretical guarantee is given for the procedure to accurately detect the communities with small misclassification rate under the setting where the number of clusters can grow with $N$. This theoretical result admits to the best-known result in the literature of computationally feasible community detection in SBM without outliers. Numerical results show that our method is both computationally fast and robust to different kinds of outliers, while some popular computationally fast community detection algorithms, such as spectral clustering applied to adjacency matrices or graph Laplacians, may fail to retrieve the major clusters due to a small portion of outliers. We apply a slight modification of our method to a political blogs data set, showing that our method is competent in practice and comparable to existing computationally feasible methods in the literature. To the best of the authors' knowledge, our result is the first in the literature in terms of clustering communities with fast growing numbers under the GSBM where a portion of arbitrary outlier nodes exist.
Radiation trapping inside a hollow-core photonic crystal fiber

We report the radiation trapping effect inside a hollow-core photonic crystal fiber (PCF). An optical dipole trap was used to load and confine the atoms in the PCF without contacting the wall of the fiber. The transmission of a probe light propagating through the PCF was studied experimentally and theoretically. With the experimental results and theoretical predictions, we conclude that the radiation trapping can play a significant role and should be taken into account in the spectroscopic measurements inside the PCF.
Lasing and suppressed cavity-pulling effect of Cesium active optical clock

We experimentally demonstrate the collective emission behavior and suppressed cavity-pulling effect of four-level active optical clock with Cesium atoms. Thermal Cesium atoms in a glass cell velocity selective pumped with a 455.5 nm laser operating at 6S$_{1/2}$ to 7P$_{3/2}$ transition are used as lasing medium. Population inverted Cesium atoms between 7S$_{1/2}$ and 6P$_{3/2}$ levels are optical weakly coupled by a pair cavity mirrors working at deep bad-cavity regime with a finesse of 4.3, and the ratio between cavity bandwidth and gain bandwidth is approximately 45. With increased 455.5 nm pumping laser intensity, the output power of cesium active optical clock at 1469.9 nm from 7S$_{1/2}$ level to 6P$_{3/2}$ level shows a threshold and reach a power of 13 $\mu$W. Active optical clock would dramatically improve the optical clock stability since the lasing frequency does not follow the cavity length variation exactly, but in a form of suppressed cavity pulling effect. In this letter the cavity pulling effect is measured using a Fabry-Perot interferometer (FPI) to be reduced by a factor of 38.2 and 41.4 as the detuning between the 1469.9 nm cavity length of the Cs active optical clock and the Cs 1469.9 nm transition is set to be 140.8 MHz and 281.6 MHz respectively. The mechanism demonstrated here is of great significance for new generation optical clocks and can be applied to improve the stability of best optical clocks by at least two orders of magnitude.
Gradual Classical Logic for Attributed Objects

There is knowledge. There is belief. And there is tacit agreement.' 'We may talk about objects. We may talk about attributes of the objects. Or we may talk both about objects and their attributes.' This work inspects tacit agreements on assumptions about the relation between objects and their attributes, and studies a way of expressing them, presenting as the result what we term gradual logic in which the sense of truth gradually shifts. It extends classical logic instances with a new logical connective capturing the object-attribute relation. A formal semantics is presented. Decidability is proved. Para- consistent/epistemic/conditional/intensional/description/combined logics are compared.
Excited states in the neutron-rich nucleus $^{25}$F

The structure of the nucleus F-25(9) was investigated through in-beam. gamma-ray spectroscopy of the fragmentation of Ne-26 and Na-27,Na-28 ion beams. Based on the particle-gamma and particle-gamma gamma coincidence data, a level scheme was constructed and compared with shell model and coupled-cluster calculations. Some of the observed states were interpreted as quasi-single-particle states built on top of the closed-shell nucleus O-24, while the others were described as states arising from coupling of a single proton to the 2(broken vertical bar) core excitation of O-24.
Incorporating a Volatility Smile into the Markov-Functional Model

We study a Markov-Functional (MF) interest-rate model with Uncertain Volatility Displaced Diffusion (UVDD) digital mapping, which is consistent with the volatility-smile phenomenon observed in the option market. We first check the impact of pricing Bermudan swaptions by the model. Next, we also investigate the future smiles implied by the MF models and the smile dynamics implied by the UVDD model. Finally, we conduct hedging simulations against Bermudan swaptions to test extensively the hedge performance of this smile-consistenet MF model.
POLYNOMIAL TERM STRUCTURE MODELS

In this article, we explore a class of tractable interest rate models that have the property that the price of a zero-coupon bond can be expressed as a polynomial of a state diffusion process. Our results include a classification of all such time-homogeneous single-factor models in the spirit of Filipovic's maximal degree theorem for exponential polynomial models, as well as an explicit characterisation of the set of feasible parameters in the case when the factor process is bounded. Extensions to time-inhomogeneous and multi-factor polynomial models are also considered.