nils-herrmann/clean_scirepeval huggingface dataset:
Emergent Soft Monopole Modes in Weakly-Bound Deformed Nuclei

Based on the Hartree-Fock-Bogoliubov solutions in large deformed coordinate spaces, the finite amplitude method for quasiparticle random phase approximation (FAM-QRPA) has been implemented, providing a suitable approach to probe collective excitations of weakly-bound nuclei embedded in the continuum. The monopole excitation modes in Magnesium isotopes up to the neutron drip line have been studied with the FAM-QRPA framework on both the coordinate-space and harmonic oscillator basis methods. Enhanced soft monopole strengths and collectivity as a result of weak-binding effects have been unambiguously demonstrated.
Terrace Aware Phylogenomic Inference from Supermatrices

One approach in phylogenomics to infer the tree of life is based on concatenated multiple sequence alignments from many genes. Unfortunately, the resulting so-called supermatrix is usually sparse, that is, not every gene sequence is available for all species in the supermatrix. Due to the missing sequence information a phylogenetic inference, assuming that each gene evolves with its own substitution model, suffers from phylogenetic terraces on which many phylogenetic trees show the same likelihood. Here, we propose a phylogenetic terrace aware (PTA) data structure for efficient supermatrix based tree inference under partition models. PTA avoids likelihood computations for trees belonging to the same terrace. PTA is implemented in the IQ-TREE software, and leads to an 1.7 to 6-fold speedup for real data sets compared with a na\"ive implementation. Speedups are independent on terrace sizes but correlate with the amount of missing data. Thus, the PTA data structure is well suited for phylogenomic analyses. IQ-TREE source codes, binaries and documentation are freely available at this http URL .
Odor Landscapes in Turbulent Environments

The olfactory system of male moths is exquisitely sensitive to pheromones emitted by females and transported in the environment by atmospheric turbulence. Moths respond to minute amounts of pheromones and their behavior is sensitive to the fine-scale structure of turbulent plumes where pheromone concentration is detectible. The signal of pheromone whiffs is qualitatively known to be intermittent, yet quantitative characterization of its statistical properties is lacking. This challenging fluid dynamics problem is also relevant for entomology, neurobiology and the technological design of olfactory stimulators aimed at reproducing physiological odor signals in well-controlled laboratory conditions. Here, we develop a Lagrangian approach to the transport of pheromones by turbulent flows and exploit it to predict the statistics of odor detection during olfactory searches. The theory yields explicit probability distributions for the intensity and the duration of pheromone detections, as well as their spacing in time. Predictions are favorably tested by using numerical simulations, laboratory experiments and field data for the atmospheric surface layer. The resulting signal of odor detections lends to implementation with state-of-the-art technologies and quantifies the amount and the type of information that male moths can exploit during olfactory searches.
Projective Loop Quantum Gravity I. State Space

Instead of formulating the state space of a quantum field theory over one big Hilbert space, it has been proposed by Kijowski to describe quantum states as projective families of density matrices over a collection of smaller, simpler Hilbert spaces. Beside the physical motivations for this approach, it could help designing a quantum state space holding the states we need. In [Oko{\l}\'ow 2013, ] the description of a theory of Abelian connections within this framework was developed, an important insight being to use building blocks labeled by combinations of edges and surfaces. The present work generalizes this construction to an arbitrary gauge group G (in particular, G is neither assumed to be Abelian nor compact). This involves refining the definition of the label set, as well as deriving explicit formulas to relate the Hilbert spaces attached to different labels. 
If the gauge group happens to be compact, we also have at our disposal the well-established Ashtekar-Lewandowski Hilbert space, which is defined as an inductive limit using building blocks labeled by edges only. We then show that the quantum state space presented here can be thought as a natural extension of the space of density matrices over this Hilbert space. In addition, it is manifest from the classical counterparts of both formalisms that the projective approach allows for a more balanced treatment of the holonomy and flux variables, so it might pave the way for the development of more satisfactory coherent states.
Canonical derivation of effective potentials

A canonical formulation of effective equations describes quantum corrections by the back-reaction of moments on the dynamics of expectation values of a state. As a first step toward an extension to quantum-field theory, these methods are applied here to the derivation of effective potentials around a homogeneous vacuum expectation value of scalar fields. A comparison with the standard Coleman-Weinberg potential shows that the new methods correctly include all relevant quantum corrections. At the same time, the effective potential is shown to be correct also for non-Fock and mixed states. Several explicit results are derived in models of interacting scalars and fermions.
An Analytical Expression for the Hubble diagram of supernovae and gamma-ray bursts

A recent paper by Harmut Traunm\"uller shows that the most adequate equation to interpret the observations on magnitude and redshift from 892 type 1a supernovae would be mu = 5 log[(1+z) ln(1+z)] + const. We discuss this result which is exacly the one we have obtained few years ago when postulating a relation between the speed of light and the expansion of the universe. We also compare our analytical result to the conclusion of Marosi who studied 280 supernovae and gamma-ray bursts in the range 0.1014 < z < 8.1. The difference between his results and ours is at worst of 0.3 %.
DUM: Diversity-Weighted Utility Maximization for Recommendations

The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method for maximizing the utility of the recommended items subject to the diversity of user's tastes, and show that an optimal solution to this problem can be found greedily. We evaluate the proposed method in two online user studies as well as in an offline analysis incorporating a number of evaluation metrics. The results of evaluations show the superiority of our method over a number of baselines.
Why Quantum Coherence Is Not Important in the Fenna-Matthews-Olsen Complex.

We develop and present an improvement to the conventional technique for solving the Hierarchical Equations of Motion (HEOM), which can reduce the memory cost by up to 75% while retaining the same (or even better) convergence rate and accuracy. This allows for a full calculation of the population dynamics of the 24-site FMO trimer for long time scales with very little effort, and we present the first fully converged, exact results for the 7-site subsystem of the monomer, and for the full 24-site trimer. We then show where our exact 7-site results deviate from the approximation of Ishizaki and Fleming [A. Ishizaki and G. R. Fleming,  2009, 106, 17255]. Our exact results are then compared to calculations using the incoherent Förster theory, and it is found that the time scale of energy transfer is roughly the same, regardless of whether or not coherence is considered. This means that coherence is not likely to improve the efficiency of the transfer. In fact, the incoherent theory often tends to overpredict the rates of energy transfer, suggesting that, in some cases, quantum coherence may actually slow the photosynthetic process.
Spherically symmetric canonical quantum gravity

Canonical quantization of spherically symmetric space-times is carried out, using real-valued densitized triads and extrinsic curvature components, with specific factor ordering choices ensuring in an anomaly free quantum constraint algebra. Comparison with previous work  reveals that the resulting physical Hilbert space has the same form, although the basic canonical variables are different in the two approaches. As an extension, holonomy modifications from Loop Quantum Gravity are shown to deform the Dirac space-time algebra, while going beyond `effective' calculations.
Miniature Exoplanet Radial Velocity Array I: design, commissioning, and early photometric results

The Miniature Exoplanet Radial Velocity Array (MINERVA) is a U.S.-based observational facility dedicated to the discovery and characterization of exoplanets around a nearby sample of bright stars. MINERVA employs a robotic array of four 0.7-m telescopes outfitted for both high-resolution spectroscopy and photometry, and is designed for completely autonomous operation. The primary science program is a dedicated radial velocity survey and the secondary science objective is to obtain high-precision transit light curves. The modular design of the facility and the flexibility of our hardware allows for both science programs to be pursued simultaneously, while the robotic control software provides a robust and efficient means to carry out nightly observations. We describe the design of MINERVA, including major hardware components, software, and science goals. The telescopes and photometry cameras are characterized at our test facility on the Caltech campus in Pasadena, California, and their on-sky performance is validated. The design and simulated performance of the spectrograph is briefly discussed as we await its completion. New observations from our test facility demonstrate sub-mmag photometric precision of one of our radial velocity survey targets, and we present new transit observations and fits of WASP-52b—a known hot-Jupiter with an inflated radius and misaligned orbit. The process of relocating the MINERVA hardware to its final destination at the Fred Lawrence Whipple Observatory in southern Arizona has begun, and science operations are expected to commence in 2015.
A Compact, Large-Aperture Tunable Lens with Adaptive Spherical Correction

In this paper, we present the proof of concept a very fast adaptive glass membrane lens with a large aperture/diameter ratio, spherical aberration correction and integrated actuation. The membrane is directly deformed using two piezo actuators that can tune the focal length and the conical parameter. This operating principle allows for a usable aperture of the whole membrane diameter. Together with the efficient actuation mechanism, the aperture is around 2/3 of the total system diameter -- at a thickness of less than 2mm. The response time is a few milliseconds at 12mm aperture, which is fast compared to similar systems.
Anomaly Detection in Dynamic Networks of Varying Size

Dynamic networks, also called network streams, are an important data representation that applies to many real-world domains. Many sets of network data such as e-mail networks, social networks, or internet traffic networks are best represented by a dynamic network due to the temporal component of the data. One important application in the domain of dynamic network analysis is anomaly detection. Here the task is to identify points in time where the network exhibits behavior radically different from a typical time, either due to some event (like the failure of machines in a computer network) or a shift in the network properties. This problem is made more difficult by the fluid nature of what is considered "normal" network behavior. The volume of traffic on a network, for example, can change over the course of a month or even vary based on the time of the day without being considered unusual. Anomaly detection tests using traditional network statistics have difficulty in these scenarios due to their Density Dependence: as the volume of edges changes the value of the statistics changes as well making it difficult to determine if the change in signal is due to the traffic volume or due to some fundamental shift in the behavior of the network. To more accurately detect anomalies in dynamic networks, we introduce the concept of Density-Consistent network statistics. On synthetically generated graphs anomaly detectors using these statistics show a a 20-400% improvement in the recall when distinguishing graphs drawn from different distributions. When applied to several real datasets Density-Consistent statistics recover multiple network events which standard statistics failed to find.
Two-neutrino double- β decay of Nd 150 to excited final states in Sm 150

In order to understand the nature of the neutrino, a worldwide search for neutrino-less double-$\ensuremath{\beta}$ decay is planned. The present paper measures the half-life for two-neutrino decay of one of a small number of nuclei that are viable candidates for neutrino-less decay. This measurement uses a powerful coincidence technique to determine the two-neutrino half-life of ${}^{150}$Nd, and compare it to predictions. This is an important step in enhancing the potential search for the neutrino-less mode with this nucleus.
Asymmetric Minwise Hashing

Minwise hashing (Minhash) is a widely popular indexing scheme in practice. Minhash is designed for estimating set resemblance and is known to be suboptimal in many applications where the desired measure is set overlap (i.e., inner product between binary vectors) or set containment. Minhash has inherent bias towards smaller sets, which adversely affects its performance in applications where such a penalization is not desirable. In this paper, we propose asymmetric minwise hashing (MH-ALSH), to provide a solution to this problem. The new scheme utilizes asymmetric transformations to cancel the bias of traditional minhash towards smaller sets, making the final "collision probability" monotonic in the inner product. Our theoretical comparisons show that for the task of retrieving with binary inner products asymmetric minhash is provably better than traditional minhash and other recently proposed hashing algorithms for general inner products. Thus, we obtain an algorithmic improvement over existing approaches in the literature. Experimental evaluations on four publicly available high-dimensional datasets validate our claims and the proposed scheme outperforms, often significantly, other hashing algorithms on the task of near neighbor retrieval with set containment. Our proposal is simple and easy to implement in practice.
Tracking Triadic Cardinality Distributions for Burst Detection in Social Activity Streams

In everyday life, we often observe unusually frequent interactions among people before or during important events, i.e., people receive/send more greetings from/to their friends on Christmas Day than regular days. We also observe that some videos suddenly go viral through people's sharing in online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these phenomena are associated with sudden surges of user activities in networks, which we call bursts in this work. We uncover that the emergence of a burst is accompanied with the formation of triangles in networks. This finding motivates us to propose a new and robust method to detect bursts in OSNs. We first introduce a new measure, triadic cardinality distribution, corresponding to the fractions of nodes with different numbers of triangles, i.e., triadic cardinalities, within a network. We demonstrate that this distribution not only changes when a burst occurs, but it also has a robustness property that it is immunized against common spamming social-bot attacks. Hence, by tracking triadic cardinality distributions, we can reliably detect bursts in OSNs. To avoid handling massive activity data generated by OSN users during the triadic tracking, we design an efficient sample-estimate solution to provide maximum likelihood estimate on the triadic cardinality distribution from sampled data. Extensive experiments conducted on real data demonstrate the usefulness of this triadic cardinality distribution and effectiveness of our sample-estimate solution.
Calculation of the graphene C 1$\textit{s}$ core level binding energy

X-ray photoelectron spectroscopy (XPS) combined with first principles modeling is a powerful tool for determining the chemical composition and electronic structure of novel materials. Of these, graphene is an especially important model system for understanding the properties of other carbon nanomaterials. Here, we calculate the carbon 1$\textit{s}$ core level binding energy of pristine graphene using two methods based on density functional theory total energy differences: a calculation with an explicit core-hole ($\Delta$KS), and a novel all-electron extension of the delta self-consistent field ($\Delta$SCF) method. We study systematically their convergence and computational workload, and the dependence of the energies on the chosen exchange-correlation functional. The $\mathrm{\Delta}$SCF method is computationally more expensive, but gives consistently higher C 1$\textit{s}$ binding energies. Although there is a significant functional dependence, the binding energy calculated using the PBE functional is found to be remarkably close to what has been measured for graphite.
1974: the discovery of the first binary pulsar

The 1974 discovery, by Russell A Hulse and Joseph H Taylor, of the first binary pulsar, PSR B1913+16, opened up new possibilities for the study of relativistic gravity.  General relativity has passed all of the binary pulsar tests with flying colors. The discovery of binary pulsars also had very important consequences for astrophysics, leading to accurate measurement of neutron star masses, improved understanding of the possible evolution scenarios for the co-evolution of binary stars, and proof of the existence of binary neutron stars emitting gravitational waves for hundreds of millions of years, before coalescing in catastrophic events radiating intense gravitational wave signals, and probably also leading to important emissions of electromagnetic radiation and neutrinos. This article reviews the history of the discovery of the first binary pulsar, and describes both its immediate impact and its longer-term effect on theoretical and experimental studies of relativistic gravity.
Incorporating Views on Market Dynamics in Options Hedging

We examine the possibility of incorporating information or views of market movements during the holding period of a portfolio, in the hedging of European options with respect to the underlying. Given a fixed holding period interval, we explore whether it is possible to adjust the number of shares needed to effectively hedge our position to account for views on market dynamics from present until the end of our interval, to account for the time-dependence of the options' sensitivity to the underlying. We derive an analytical expression for the number of shares needed by adjusting the standard Black-Scholes-Merton $\Delta$ quantity, in the case of an arbitrary process for implied volatility, and we present numerical results.
Multi-curve HJM modelling for risk management

We present a HJM approach to the projection of multiple yield curves developed to capture the volatility content of historical term structures for risk management purposes. Since we observe the empirical data at daily frequency and only for a finite number of time-to-maturity buckets, we propose a modelling framework which is inherently discrete. In particular, we show how to approximate the HJM continuous time description of the multi-curve dynamics by a Vector Autoregressive process of order one. The resulting dynamics lends itself to a feasible estimation of the model volatility-correlation structure and market risk-premia. Then, resorting to the Principal Component Analysis we further simplify the dynamics reducing the number of covariance components. Applying the constant volatility version of our model on a sample of curves from the Euro area, we demonstrate its forecasting ability through an out-of-sample test.
Spatially structured photons that travel in free space slower than the speed of light

Slowing down light with added structure We are taught that the speed of light in free space is one of the universal physical constants: c. Giovannini et al. now show that there are certain conditions under which such certainty can be broken (see the Perspective by Sambles). Adding spatial structure to an optical beam of single photons reduced the speed of light. The magnitude of the decrease depended on the complexity of the structure imprinted onto the photons. Science, this issue p. 857; see also p. 828 Introducing spatial structure to an optical beam reduces the speed of light.  That the speed of light in free space is constant is a cornerstone of modern physics. However, light beams have finite transverse size, which leads to a modification of their wave vectors resulting in a change to their phase and group velocities. We study the group velocity of single photons by measuring a change in their arrival time that results from changing the beam’s transverse spatial structure. Using time-correlated photon pairs, we show a reduction in the group velocity of photons in both a Bessel beam and photons in a focused Gaussian beam. In both cases, the delay is several micrometers over a propagation distance of ~1 meter. Our work highlights that, even in free space, the invariance of the speed of light only applies to plane waves.
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets

The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems. We argue that this barrier can be effectively overcome. In particular, we develop methods to scale up kernel models to successfully tackle large-scale learning problems that are so far only approachable by deep learning architectures. Based on the seminal work by Rahimi and Recht on approximating kernel functions with features derived from random projections, we advance the state-of-the-art by proposing methods that can efficiently train models with hundreds of millions of parameters, and learn optimal representations from multiple kernels. We conduct extensive empirical studies on problems from image recognition and automatic speech recognition, and show that the performance of our kernel models matches that of well-engineered deep neural nets (DNNs). To the best of our knowledge, this is the first time that a direct comparison between these two methods on large-scale problems is reported. Our kernel methods have several appealing properties: training with convex optimization, cost for training a single model comparable to DNNs, and significantly reduced total cost due to fewer hyperparameters to tune for model selection. Our contrastive study between these two very different but equally competitive models sheds light on fundamental questions such as how to learn good representations.
GUP-Corrected Thermodynamics for all Black Objects and the Existence of Remnants

Based on the universality of the entropy-area relation of a black hole, and the fact that the generalized uncertainty principle (GUP) adds a logarithmic correction term to the entropy in accordance with most approaches to quantum gravity, we argue that the GUP-corrected entropy-area relation is universal for all black objects. This correction to the entropy produces corrections to the thermodynamics. We explicitly calculate these corrections for three types of black holes: Reissner--Nordstr\"{o}m, Kerr, and charged AdS black holes, in addition to spinning black rings. In all cases we find that they produce a remnant. Even though the GUP-corrected entropy-area relation produces the logarithmic term in the series expansion, we need to use the full form of the GUP-corrected entropy-area relation to get remnants for these black holes.