nils-herrmann/clean_scirepeval huggingface dataset:
Super/subradiant second harmonic generation

A scheme for active second harmonics generation is suggested. The system comprises N three-level atoms in ladder configuration, situated into a resonant cavity. The system generates the field whose frequency is twice the frequency of the pumping laser, and the field phase is locked to the phase of the pumping field. It is found that the system can lase in either superradiant or subradiant regime, depending on the number of atoms N. When N passes some critical value the transition from the super to subradiance occurs in a phase-transition-like manner. Stability study of the steady state supports this conclusion. For experimental realization of the super/subradiant second harmonics generation we propose semiconductor quantum well structures, superconducting quantum circuits, and evanescently coupled waveguides in which equally spaced levels relevant to this study exist.
Development of High Precision Timing Counter Based on Plastic Scintillator with SiPM Readout

High-time-resolution counters based on plastic scintillator with silicon photomultiplier (SiPM) readout have been developed for applications to high energy physics experiments for which relatively large-sized counters are required. We have studied counter sizes up to 120 ×40 ×5 mm3 with series connection of multiple SiPMs to increase the sensitive area and thus achieve better time resolution. A readout scheme with analog shaping and digital waveform analysis is optimized to achieve the highest time resolution. The timing performance is measured using electrons from a 90Sr radioactive source, comparing different scintillators, counter dimensions, and types of near-ultraviolet sensitive SiPMs. As a result, a resolution of σ = 42 ±2 ps at 1 MeV energy deposition is obtained for counter size 60 ×30 ×5 mm3 with three SiPMs ( 3 ×3 mm2 each) at each end of the scintillator. The time resolution improves with the number of photons detected by the SiPMs. The SiPMs from Hamamatsu Photonics give the best time resolution because of their high photon detection efficiency in the near-ultraviolet region. Further improvement is possible by increasing the number of SiPMs attached to the scintillator.
Partial correlation analysis: applications for financial markets

The presence of significant cross-correlations between the synchronous time evolution of a pair of equity returns is a well-known empirical fact. The Pearson correlation is commonly used to indicate the level of similarity in the price changes for a given pair of stocks, but it does not measure whether other stocks influence the relationship between them. To explore the influence of a third stock on the relationship between two stocks, we use a partial correlation measurement to determine the underlying relationships between financial assets. Building on previous work, we present a statistically robust approach to extract the underlying relationships between stocks from four different financial markets: the United States, the United Kingdom, Japan, and India. This methodology provides new insights into financial market dynamics and uncovers implicit influences in play between stocks. To demonstrate the capabilities of this methodology, we (i) quantify the influence of different companies and, by studying market similarity across time, present new insights into market structure and market stability, and (ii) we present a practical application, which provides information on the how a company is influenced by different economic sectors, and how the sectors interact with each other. These examples demonstrate the effectiveness of this methodology in uncovering information valuable for a range of individuals, including not only investors and traders but also regulators and policy makers.
Tunguska similar impacts and origin of life

The author suggests new vision of mechanism of initiation of life on the planets after Tunguska similar impacts. This mechanism takes into consideration not only incoming cosmic organic substance but also information, which is connected to this substance. Mathematical metrics of atom of hydrogen is deduced which may be used for pattern recognition algorithm. In accordance to author's opinion, similar algorithm can promote evolution (transformation) of inert organic substance into living substance. The fact of a survival of vegetation after the Tunguska event is analyzed especially. Also the author checked up his probably Tunguska meteorites by strong magnet. The presence of magnetic substance was detected with concentration of 10-2 % during this test.
Globally Causal Solutions for Gravitational Collapse

Through an illuminating thought experiment we demonstrate that the nonsingular "continued collapse" picture of a black hole is the only consistent and physical one. We provide a class exact solutions on the boundary of the space of physical configurations. This will show that all the other known exact solutions are unphysical near the surface of the event horizon or in the interior. This will have important consequences for the no-hair conjecture and the kinds of persistent fields that can emerge from a black hole as well as the evolution during collisions and near grazing events. The interior of these holes are characterized by a limiting degenerate metric and these regions tend to well defined volumes and radii in contrast with what is inferred from singular solutions. Surprisingly, these depend on past history and not simply the mass or external fields of the body. It is shown that there is often a well defined "hidden" flat background that can be used to equivalently reformulate GR in terms of a classical nonlinear gravity field and gives local conservation laws. This has implications for unification efforts and numerical approaches to handle the degenerate metric regions reminiscent of the Rankine-Hugoniot conditions. Possible consistency problems with current numerical approaches to black hole dynamics are discussed.
Dual Query: Practical Private Query Release for High Dimensional Data

We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets. Like all algorithms for this task, ours necessarily has worst-case complexity exponential in the dimension of the data. However, our algorithm packages the computationally hard step into a concisely defined integer program, which can be solved nonprivately using standard solvers. We prove accuracy and privacy theorems for our algorithm, and then demonstrate experimentally that our algorithm performs well in practice. For example, our algorithm can efficiently and accurately answer millions of queries on the Netflix dataset, which has over 17;000 attributes; this is an improvement on the state of the art by multiple orders of magnitude.
TDOA-based Localization in Two Dimensions: the Bifurcation Curve

In this paper, we complete the study of the geometry of the TDOA map that encodes the noiseless model for the localization of a source from the range differences between three receivers in a plane, by computing the Cartesian equation of the bifurcation curve in terms of the positions of the receivers. From that equation, we can compute its real asymptotic lines. The present manuscript completes the analysis of . Our result is useful to check if a source belongs or is closed to the bifurcation curve, where the localization in a noisy scenario is ambiguous.
PRESS: A Novel Framework of Trajectory Compression in Road Networks

Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS ( P aralleled R oad-Network-Based Trajectory Compr ess ion), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal Compression (BTC) algorithm to compress the spatial and temporal information of trajectories respectively. PRESS also supports common spatial-temporal queries without fully decompressing the data. Through an extensive experimental study on real trajectory dataset, PRESS significantly outperforms existing approaches in terms of saving storage cost of trajectory data with bounded errors.
Goethe's Faust and the Minimal Action Principle: a possible Universal message

This short note is devoted to non-proficient in physics. Its purpose is that of proposing a possible universal connection between the definition of Action given by Goethe in his ``Faust'' tragic play and the Hamilton's Principle of the Minimal Action. This Principle is one of the most general ways of formulating the dynamics of a physical system both in classical and in quantum physics. The recent discovery of the long time searched Higgs particle may be seen as the most spectacular successful confirmation of the Standard Model formalism based on the Minimal Action Principle. Supported by this experimental confirmation, we propose a possible general meaning of the Principle, inspired by the Goethe's ``Faust'' tragic play.
Fast Numerical Nonlinear Fourier Transforms

The nonlinear Fourier transform, which is also known as the forward scattering transform, decomposes a periodic signal into nonlinearly interacting waves. In contrast to the common Fourier transform, these waves no longer have to be sinusoidal. Physically relevant waveforms are often available for the analysis instead. The details of the transform depend on the waveforms underlying the analysis, which in turn are specified through the implicit assumption that the signal is governed by a certain evolution equation. For example, water waves generated by the Korteweg-de Vries equation can be expressed in terms of cnoidal waves. Light waves in optical fiber governed by the nonlinear Schrödinger equation (NSE) are another example. Nonlinear analogs of classic problems such as spectral analysis and filtering arise in many applications, with information transmission in optical fiber, as proposed by Yousefi and Kschischang, being a very recent one. The nonlinear Fourier transform is eminently suited to address them - at least from a theoretical point of view. Although numerical algorithms are available for computing the transform, a fast nonlinear Fourier transform that is similarly effective as the fast Fourier transform is for computing the common Fourier transform has not been available so far. The goal of this paper is to address this problem. Two fast numerical methods for computing the nonlinear Fourier transform with respect to the NSE are presented. The first method achieves a runtime of O(D2) floating point operations, where D is the number of sample points. The second method applies only to the case where the NSE is defocusing, but it achieves an O(D log2 D) runtime. Extensions of the results to other evolution equations are discussed as well.
Using Twitter to Model the EUR/USD Exchange Rate

Fast, global, and sensitively reacting to political, economic and social events of any kind, these are attributes that social media like Twitter share with foreign exchange markets. The leading assumption of this paper is that information which can be distilled from public debates on Twitter has predictive content for exchange rate movements. This assumption prompted a Twitter-based exchange rate model that harnesses regARIMA analyses for short-term out-of-sample ex post forecasts of the daily closing prices of EUR/USD spot exchange rates. The analyses used Tweet counts collected from January 1, 2012 - September 27, 2013. To identify concepts mentioned on Twitter with a predictive potential the analysis followed a 2-step selection. Firstly, a heuristic qualitative analysis assembled a long list of 594 concepts, e.g., Merkel, Greece, Cyprus, crisis, chaos, growth, unemployment expected to covary with the ups and downs of the EUR/USD exchange rate. Secondly, cross-validation using window averaging with a fixed-sized rolling origin was deployed to select concepts and corresponding univariate time series that had error scores below chance level as defined by the random walk model. With regard to a short list of 17 concepts (covariates), in particular SP (Standard & Poor's) and risk, the out-of-sample predictive accuracy of the Twitter-based regARIMA model was found to be repeatedly better than that obtained from both the random walk model and a random noise covariate in 1-step ahead forecasts of the EUR/USD exchange rate. This advantage was evident on the level of forecast error metrics (MSFE, MAE) when a majority vote over different estimation windows was conducted. The results challenge the semi-strong form of the efficient market hypothesis (Fama, 1970, 1991) which when applied to the FX market maintains that all publicly available information is already integrated into exchange rates.
Liquid Metal Transformers

The room temperature liquid metal is quickly emerging as an important functional material in a variety of areas like chip cooling, 3D printing or printed electronics etc. With diverse capabilities in electrical, thermal and flowing behaviors, such fluid owns many intriguing properties that had never been anticipated before. Here, we show a group of unconventional phenomena occurring on the liquid metal objects. Through applying electrical field on the liquid metals immersed in water, a series of complex transformation behaviors such as self-assembling of a sheet of liquid metal film into a single sphere, quick mergences of separate metal droplets, controlled self-rotation and planar locomotion of liquid metal objects can be realized. Meanwhile, it was also found that two accompanying water vortexes were induced and reliably swirled near the rotating liquid metal sphere. Further, effects of the shape, size, voltage, orientation and geometries of the electrodes to control the liquid metal transformers were clarified. Such events are hard to achieve otherwise on rigid metal or conventional liquid spheres. This finding has both fundamental and practical significances which suggest a generalized way of making smart soft machine, collecting discrete metal fluids, as well as flexibly manipulating liquid metal objects including accompanying devices.
Cross-phenotype meta-analysis reveals large-scale trans-eQTLs mediating patterns of transcriptional co-regulation

Genetic variation affecting gene regulation is a central driver of phenotypic differences between individuals and can be used to uncover how biological processes are organized in a cell. Although detecting cis-eQTLs is now routine, trans-eQTLs have proven more challenging to find due to the modest variance explained and the multiple tests burden of testing millions of SNPs for association to thousands of transcripts. Here, we successfully map trans-eQTLs with the complementary approach of looking for SNPs associated to the expression of multiple genes simultaneously. We find 732 trans- eQTLs that replicate across two continental populations; each trans-eQTL controls large groups of target transcripts (regulons), which are part of interacting networks controlled by transcription factors. We are thus able to uncover co-regulated gene sets and begin describing the cell circuitry of gene regulation.
Two-stage sampled learning theory on distributions

We focus on the distribution regression problem: regressing to a real-valued response from a probability distribution. Although there exist a large number of similarity measures between distributions, very little is known about their generalization performance in specific learning tasks. Learning problems formulated on distributions have an inherent two-stage sampled difficulty: in practice only samples from sampled distributions are observable, and one has to build an estimate on similarities computed between sets of points. To the best of our knowledge, the only existing method with consistency guarantees for distribution regression requires kernel density estimation as an intermediate step (which suffers from slow convergence issues in high dimensions), and the domain of the distributions to be compact Euclidean. In this paper, we provide theoretical guarantees for a remarkably simple algorithmic alternative to solve the distribution regression problem: embed the distributions to a reproducing kernel Hilbert space, and learn a ridge regressor from the embeddings to the outputs. Our main contribution is to prove the consistency of this technique in the two-stage sampled setting under mild conditions (on separable, topological domains endowed with kernels). For a given total number of observations, we derive convergence rates as an explicit function of the problem difficulty. As a special case, we answer a 15-year-old open question: we establish the consistency of the classical set kernel [Haussler, 1999; Gartner et. al, 2002] in regression, and cover more recent kernels on distributions, including those due to [Christmann and Steinwart, 2010].
A single-molecule approach to ZnO defect studies: Single photons and single defects

Investigations that probe defects one at a time offer a unique opportunity to observe properties and dynamics that are washed out of ensemble measurements. Here, we present confocal fluorescence measurements of individual defects in ZnO nanoparticles and sputtered films that are excited with sub-bandgap energy light. Photon correlation measurements yield both antibunching and bunching, indicative of single-photon emission from isolated defects that possess a metastable shelving state. The single-photon emission is in the range of ∼560–720 nm and typically exhibits two broad spectral peaks separated by ∼150 meV. The excited state lifetimes range from 1 to 13 ns, consistent with the finite-size and surface effects of nanoparticles and small grains. We also observe discrete jumps in the fluorescence intensity between a bright state and a dark state. The dwell times in each state are exponentially distributed and the average dwell time in the bright (dark) state does (may) depend on the power of the exciting ...
Analysis of a Heterogeneous Social Network of Humans and Cultural Objects

Modern online social platforms allow their members to be involved in a broad range of activities including getting friends, joining groups, posting, and commenting resources. In this paper, we investigate whether a correlation emerges across the different activities a user can take part in. For our analysis, we focused on aNobii, a social platform with a world-wide user base of book readers, who post their readings, give ratings, review books, and discuss them with friends and fellow readers. aNobii presents a heterogeneous structure: 1) part social network, with user-to-user interactions; 2) part interest network, with the management of book collections; and 3) part folksonomy, with books that are tagged by the users. We analyzed a complete snapshot of aNobii and we focused on three specific activities a user can perform, namely tagging behavior, tendency to join groups and aptitude to compile a wishlist of the books one is planning to read. For each user, we create a tag-based, a group-based, and a wishlist-based profile. Experimental analysis, which was carried out with information-theory tools like entropy and mutual information, suggests that tag-based and group-based profiles are in general more informative than wishlist-based ones. Furthermore, we discover that the degree of correlation between the three profiles associated with the same user tend to be small. Hence, user profiling cannot be reduced to considering just any one type of user activity (albeit important) but it is crucial to incorporate multiple dimensions to effectively describe users' preferences and behavior.
Active Clustering with Model-Based Uncertainty Reduction

Semi-supervised clustering seeks to augment traditional clustering methods by incorporating side information provided via human expertise in order to increase the semantic meaningfulness of the resulting clusters. However, most current methods are passive in the sense that the side information is provided beforehand and selected randomly. This may require a large number of constraints, some of which could be redundant, unnecessary, or even detrimental to the clustering results. Thus in order to scale such semi-supervised algorithms to larger problems it is desirable to pursue an active clustering method—i.e., an algorithm that maximizes the effectiveness of the available human labor by only requesting human input where it will have the greatest impact. Here, we propose a novel online framework for active semi-supervised spectral clustering that selects pairwise constraints as clustering proceeds, based on the principle of uncertainty reduction. Using a first-order Taylor expansion, we decompose the expected uncertainty reduction problem into a gradient and a step-scale, computed via an application of matrix perturbation theory and cluster-assignment entropy, respectively. The resulting model is used to estimate the uncertainty reduction potential of each sample in the dataset. We then present the human user with pairwise queries with respect to only the best candidate sample. We evaluate our method using three different image datasets (faces, leaves and dogs), a set of common UCI machine learning datasets and a gene dataset. The results validate our decomposition formulation and show that our method is consistently superior to existing state-of-the-art techniques, as well as being robust to noise and to unknown numbers of clusters.
Using Markov state models to study self-assembly.

Markov state models (MSMs) have been demonstrated to be a powerful method for computationally studying intramolecular processes such as protein folding and macromolecular conformational changes. In this article, we present a new approach to construct MSMs that is applicable to modeling a broad class of multi-molecular assembly reactions. Distinct structures formed during assembly are distinguished by their undirected graphs, which are defined by strong subunit interactions. Spatial inhomogeneities of free subunits are accounted for using a recently developed Gaussian-based signature. Simplifications to this state identification are also investigated. The feasibility of this approach is demonstrated on two different coarse-grained models for virus self-assembly. We find good agreement between the dynamics predicted by the MSMs and long, unbiased simulations, and that the MSMs can reduce overall simulation time by orders of magnitude.
Binary Excess Risk for Smooth Convex Surrogates

In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in. This is of more importance if we consider smooth surrogates as witnessed by the fact that the smoothness is further beneficial both computationally- by attaining an {\it optimal} convergence rate for optimization, and in a statistical sense- by providing an improved {\it optimistic} rate for generalization bound. In this paper we investigate the smoothness property from the viewpoint of statistical consistency and show how it affects the binary excess risk. We show that in contrast to optimization and generalization errors that favor the choice of smooth surrogate loss, the smoothness of loss function may degrade the binary excess risk. Motivated by this negative result, we provide a unified analysis that integrates optimization error, generalization bound, and the error in translating convex excess risk into a binary excess risk when examining the impact of smoothness on the binary excess risk. We show that under favorable conditions appropriate choice of smooth convex loss will result in a binary excess risk that is better than $O(1/\sqrt{n})$.