nils-herrmann/clean_scirepeval huggingface dataset:
Non-equilibrium entropy and dynamics in a system with long-range interactions

We extend the core-halo approach of Levin et al (2014  Rep. 535, 1) for the violent relaxation of long-range interacting system with a waterbag initial condition, in the case of a widely studied Hamiltonian mean field model. The Gibbs entropy maximization principle is considered with the constraints of energy conservation and of coarse-grained Casimir invariants of the Vlasov equation. The core-halo distribution function depends only on the one-particle mean-field energy, as is expected from the Jeans theorem, and depends on a set of parameters which in our approach is completely determined without having to solve an envelope equation for the contour of the initial state, as required in the original approach. We also show that a different ansatz can be used for the core-halo distribution with similar results. This work also reveals a link between a parametric resonance causing the non-equilibrium phase transition in the model, a dynamical property, and a discontinuity of the (non-equilibrium) entropy of the system.
Optimal Census by Quorum Sensing

Quorum sensing is the regulation of gene expression in response to changes in cell density. To measure their cell density, bacterial populations produce and detect diffusible molecules called autoinducers. Individual bacteria internally represent the external concentration of autoinducers via the level of monitor proteins. In turn, these monitor proteins typically regulate both their own production and the production of autoinducers, thereby establishing internal and external feedbacks. Here, we ask whether feedbacks can increase the information available to cells about their local density. We quantify available information as the mutual information between the abundance of a monitor protein and the local cell density for biologically relevant models of quorum sensing. Using variational methods, we demonstrate that feedbacks can increase information transmission, allowing bacteria to resolve up to two additional ranges of cell density when compared with bistable quorum-sensing systems. Our analysis is relevant to multi-agent systems that track an external driver implicitly via an endogenously generated signal.
Liquid-state polaron theory of the hydrated electron revisited.

The quantum path integral/classical liquid-state theory of Chandler and co-workers, created to describe an excess electron in solvent, is re-examined for the hydrated electron. The portion that models electron-water density correlations is replaced by two equations: the range optimized random phase approximation (RO-RPA), and the Donley, Rajasekaran, and Liu (DRL) approximation to the "two-chain" equation, both shown previously to describe accurately the static structure and thermodynamics of strongly charged polyelectrolyte solutions. The static equilibrium properties of the hydrated electron are analyzed using five different electron-water pseudopotentials. The theory is then compared with data from mixed quantum/classical Monte Carlo and molecular dynamics simulations using these same pseudopotentials. It is found that the predictions of the RO-RPA and DRL-based polaron theories are similar and improve upon previous theory, with values for almost all properties analyzed in reasonable quantitative agreement with the available simulation data. Also, it is found using the Larsen, Glover, and Schwartz pseudopotential that the theories give values for the solvation free energy that are at least three times larger than that from experiment.
Fast-light-assisted four-wave mixing in the photonic bandgap.

In this Letter, the enhanced four-wave mixing (FWM) in the photonic bandgap of fiber Bragg grating (FBG) has been achieved with an extra phase introduced by fast-light effect in the bandgap. The experiment for phase-matched FWM generation in the bandgap in a special-designed FBG was carried out, as well as simulations. The enhanced energy conversion with such a FWM in the bandgap has been demonstrated but the transmissivity has not. The experimental results show agreement with the simulations.
Plagiarism detection on electronic text based assignments using vector space model

Plagiarism is known as illegal use of others' part of work or whole work as one's own in any field such as art, poetry, literature, cinema, research and other creative forms of study. Plagiarism is one of the important issues in academic and research fields and giving more concern in academic systems. The situation is even worse with the availability of ample resources on the web. This paper focuses on an effective plagiarism detection tool on identifying suitable intra-corpal plagiarism detection for text based assignments by comparing unigram, bigram, trigram of vector space model with cosine similarity measure. Manually evaluated, labelled dataset was tested using unigram, bigram and trigram vector. Even though trigram vector consumes comparatively more time, it shows better results with the labelled data. In addition, the selected trigram vector space model with cosine similarity measure is compared with tri-gram sequence matching technique with Jaccard measure. In the results, cosine similarity score shows slightly higher values than the other. Because, it focuses on giving more weight for terms that do not frequently exist in the dataset and cosine similarity measure using trigram technique is more preferable than the other. Therefore, we present our new tool and it could be used as an effective tool to evaluate text based electronic assignments and minimize the plagiarism among students.
Spatio-TemporalMetasurface for Real-time 2-D Spectrum Analysis

A spatio-temporal metasurface is proposed to decompose in real time the temporal frequencies of electromagnetic waves into spatial frequencies onto a two-dimensional plane. The metasurface is analyzed and demonstrated using Fourier analysis. The required transmittance function is derived from an equivalent free-space optical system consisting of the cascade combination of a wedge, a diffraction grating and a focusing lens. The metasurface must exhibit both multi-resonance over a broad bandwidth and 1-D grating-type scanning to achieve the specified 2-D frequency scanning in space. Compared to state-of-the art related systems, the proposed metasurface system is more compact as it requires only one dispersive structure, while maintaining the high frequency resolution that characterizes 2-D spatial-temporal mapping systems.
A polarization gadget with two quarter wave plates: Application to Mueller Polarimetry

We show that there are number of ways to transform an arbitrary polarization state to another with just two quarter wave plates (QWP). We have verified this geometrically using the trajectories of the initial and final polarization states corresponding to all the fast axis orientations of a QWP on the Poincare sphere. The exact analytical expression for the locus of polarization states has also been given that describes the trajectory. An analytical treatment of the equations obtained through matrix operations corresponding to the transformation supports the geometrical representation. This knowledge can be used to obtain the Mueller matrix by just using quarter wave plates which has been shown experimentally by exploiting projections of the output states on the input states.
Effects of thermal fluctuations on thermal inflation

The mechanism of thermal inflation, a relatively short period of accelerated expansion after primordial inflation, is a desirable ingredient for a certain class of particle physics models if they are not to be in contention with the cosmology of the early Universe. Though thermal inflation is most simply described in terms of a thermal effective potential, a thermal environment also gives rise to thermal fluctuations that must be taken into account. We numerically study the effects of these thermal fluctuations using lattice simulations. We conclude that though they do not ruin the thermal inflation scenario, the phase transition at the end of thermal inflation proceeds through phase mixing and is therefore not accompanied by the formations of bubbles nor appreciable amplitude of gravitational waves.
Thin shell model revisited

We reconsider some fundamental problems of the thin shell model. First, we point out that the "cut and paste" construction does not guarantee a well-defined manifold because there is no overlap of coordinates across the shell. When one requires that the spacetime metric across the thin shell is continuous, it also provides a way to specify the tangent space and the manifold. Other authors have shown that this specification leads to the conservation laws when shells collide. On the other hand, the well-known areal radius $r$ seems to be a perfect coordinate covering all regions of a spherically symmetric spacetime. However, we show by simple but rigorous arguments that $r$ fails to be a coordinate covering a neighborhood of the thin shell if the metric across the shell is continuous. When two spherical shells collide and merge into one, we show that it is possible that $r$ remains to be a good coordinate and the conservation laws hold. To make this happen, different spacetime regions divided by the shells must be glued in a specific way such that some constraints are satisfied. We compare our new construction with the old one by solving constraints numerically.
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion

Low-rank matrix completion is an important problem with extensive real-world applications. When observations are uniformly sampled from the underlying matrix entries, existing methods all require the matrix to be incoherent. This paper provides the first working method for coherent matrix completion under the standard uniform sampling model. Our approach is based on the weighted nuclear norm minimization idea proposed in several recent work, and our key contribution is a practical method to compute the weighting matrices so that the leverage scores become more uniform after weighting. Under suitable conditions, we are able to derive theoretical results, showing the effectiveness of our approach. Experiments on synthetic data show that our approach recovers highly coherent matrices with high precision, whereas the standard unweighted method fails even on noise-free data.
Unsupervised Learning through Prediction in a Model of Cortex

We propose a primitive called PJOIN, for "predictive join," which combines and extends the operations JOIN and LINK, which Valiant proposed as the basis of a computational theory of cortex. We show that PJOIN can be implemented in Valiant's model. We also show that, using PJOIN, certain reasonably complex learning and pattern matching tasks can be performed, in a way that involves phenomena which have been observed in cognition and the brain, namely memory-based prediction and downward traffic in the cortical hierarchy.
Reasoning for Improved Sensor Data Interpretation in a Smart Home

In this paper an ontological representation and reasoning paradigm has been proposed for interpretation of time-series signals. The signals come from sensors observing a smart environment. The signal chosen for the annotation process is a set of unintuitive and complex gas sensor data. The ontology of this paradigm is inspired form the SSN ontology (Semantic Sensor Network) and used for representation of both the sensor data and the contextual information. The interpretation process is mainly done by an incremental ASP solver which as input receives a logic program that is generated from the contents of the ontology. The contextual information together with high level domain knowledge given in the ontology are used to infer explanations (answer sets) for changes in the ambient air detected by the gas sensors.
Virtual reality based approach to protein heavy-atom structure reconstruction

A commonly recurring problem in structural protein studies, is the determination of all heavy atom positions from the knowledge of the central alpha-carbon coordinates. We employ advances in virtual reality to address the problem. The outcome is a 3D visualisation based technique where all the heavy backbone and side chain atoms are treated on equal footing, in terms of the C-alpha coordinates. Each heavy atom can be visualised on the surfaces of the different two-spheres, that are centered at the other heavy backbone and side chain atoms. In particular, the rotamers are visible as clusters which display strong dependence on the underlying backbone secondary structure. Our method easily detects those atoms in a crystallographic protein structure which have been been likely misplaced. Our approach forms a basis for the development of a new generation, visualisation based side chain construction, validation and refinement tools. The heavy atom positions are identified in a manner which accounts for the secondary structure environment, leading to improved accuracy over existing methods.
Sculplexity: Sculptures of Complexity using 3D printing

We show how to convert models of complex systems such as 2D cellular automata into a 3D printed object. Our method takes into account the limitations inherent to 3D printing processes and materials. Our approach automates the greater part of this task, bypassing the use of CAD software and the need for manual design. As a proof of concept, a physical object representing a modified forest fire model was successfully printed. Automated conversion methods similar to the ones developed here can be used to create objects for research, for demonstration and teaching, for outreach, or simply for aesthetic pleasure. As our outputs can be touched, they may be particularly useful for those with visual disabilities.
Exploring Sparsity in Multi-class Linear Discriminant Analysis

Recent studies in the literature have paid much attention to the sparsity in linear classification tasks. One motivation of imposing sparsity assumption on the linear discriminant direction is to rule out the noninformative features, making hardly contribution to the classification problem. Most of those work were focused on the scenarios of binary classification. In the presence of multi-class data, preceding researches recommended individually pairwise sparse linear discriminant analysis(LDA). However, further sparsity should be explored. In this paper, an estimator of grouped LASSO type is proposed to take advantage of sparsity for multi-class data. It enjoys appealing non-asymptotic properties which allows insignificant correlations among features. This estimator exhibits superior capability on both simulated and real data.
Predicting User Engagement in Twitter with Collaborative Ranking

Collaborative Filtering (CF) is a core component of popular web-based services such as Amazon, YouTube, Netflix, and Twitter. Most applications use CF to recommend a small set of items to the user. For instance, YouTube presents to a user a list of top-n videos she would likely watch next based on her rating and viewing history. Current methods of CF evaluation have been focused on assessing the quality of a predicted rating or the ranking performance for top-n recommended items. However, restricting the recommender system evaluation to these two aspects is rather limiting and neglects other dimensions that could better characterize a well-perceived recommendation. In this paper, instead of optimizing rating or top-n recommendation, we focus on the task of predicting which items generate the highest user engagement. In particular, we use Twitter as our testbed and cast the problem as a Collaborative Ranking task where the rich features extracted from the metadata of the tweets help to complement the transaction information limited to user ids, item ids, ratings and timestamps. We learn a scoring function that directly optimizes the user engagement in terms of nDCG@10 on the predicted ranking. Experiments conducted on an extended version of the MovieTweetings dataset, released as part of the RecSys Challenge 2014, show the effectiveness of our approach.
Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method

In the recent decade, with the enormous growth of digital content in internet and databases, sentiment analysis has received more and more attention between information retrieval and natural language processing researchers. Sentiment analysis aims to use automated tools to detect subjective information from reviews. One of the main challenges in sentiment analysis is feature selection. Feature selection is widely used as the first stage of analysis and classification tasks to reduce the dimension of problem, and improve speed by the elimination of irrelevant and redundant features. Up to now as there are few researches conducted on feature selection in sentiment analysis, there are very rare works for Persian sentiment analysis. This paper considers the problem of sentiment classification using different feature selection methods for online customer reviews in Persian language. Three of the challenges of Persian text are using of a wide variety of declensional suffixes, different word spacing and many informal or colloquial words. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on lemmatization and feature selection and is employed Naive Bayes algorithm for classification. We evaluate the performance of the model on a manually gathered collection of cellphone reviews, where the results show the effectiveness of the proposed approaches.
Marginal likelihood and model selection for Gaussian latent tree and forest models

Gaussian latent tree models, or more generally, Gaussian latent forest models have Fisher-information matrices that become singular along interesting submodels, namely, models that correspond to subforests. For these singularities, we compute the real log-canonical thresholds (also known as stochastic complexities or learning coefficients) that quantify the large-sample behavior of the marginal likelihood in Bayesian inference. This provides the information needed for a recently introduced generalization of the Bayesian information criterion. Our mathematical developments treat the general setting of Laplace integrals whose phase functions are sums of squared differences between monomials and constants. We clarify how in this case real log-canonical thresholds can be computed using polyhedral geometry, and we show how to apply the general theory to the Laplace integrals associated with Gaussian latent tree and forest models. In simulations and a data example, we demonstrate how the mathematical knowledge can be applied in model selection.
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

We consider the problem of improving the efficiency of randomized Fourier feature maps to accelerate training and testing speed of kernel methods on large datasets. These approximate feature maps arise as Monte Carlo approximations to integral representations of shift-invariant kernel functions (e.g., Gaussian kernel). In this paper, we propose to use Quasi-Monte Carlo (QMC) approximations instead, where the relevant integrands are evaluated on a low-discrepancy sequence of points as opposed to random point sets as in the Monte Carlo approach. We derive a new discrepancy measure called box discrepancy based on theoretical characterizations of the integration error with respect to a given sequence. We then propose to learn QMC sequences adapted to our setting based on explicit box discrepancy minimization. Our theoretical analyses are complemented with empirical results that demonstrate the effectiveness of classical and adaptive QMC techniques for this problem.
WHAT IS LIFE - Sub-cellular Physics of Live Matter

This is a set of lectures that I presented at the Les Houches 2014 Summer School "Topological Aspects in Condensed Matter Physics". The lectures are an introduction to physics of proteins. To physicists, and by a physicist. My lectures at les Houches were also celebration of the anniversary of Schroedinger's 1944 lectures, and for that reason I decided to share my title with his book.
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding

Data vectors are obtained from multiple domains. They are feature vectors of images or vector representations of words. Domains may have different numbers of data vectors with different dimensions. These data vectors from multiple domains are projected to a common space by linear transformations in order to search closely related vectors across domains. We would like to find projection matrices to minimize distances between closely related data vectors. This formulation of cross-domain matching is regarded as an extension of the spectral graph embedding to multi-domain setting, and it includes several multivariate analysis methods of statistics such as multiset canonical correlation analysis, correspondence analysis, and principal component analysis. Similar approaches are very popular recently in pattern recognition and vision. In this paper, instead of proposing a novel method, we will introduce an embarrassingly simple idea of coding the data vectors for explaining all the above mentioned approaches. A data vector is concatenated with zero vectors from all other domains to make an augmented vector. The cross-domain matching is solved by applying the single-domain version of spectral graph embedding to these augmented vectors of all the domains. An interesting connection to the classical associative memory model of neural networks is also discussed by noticing a coding for association. A cross-validation method for choosing the dimension of the common space and a regularization parameter will be discussed in an illustrative numerical example.
Estimating inter-event time distributions from finite observation periods in communication networks

A diverse variety of processes-including recurrent disease episodes, neuron firing, and communication patterns among humans-can be described using interevent time (IET) distributions. Many such processes are ongoing, although event sequences are only available during a finite observation window. Because the observation time window is more likely to begin or end during long IETs than during short ones, the analysis of such data is susceptible to a bias induced by the finite observation period. In this paper, we illustrate how this length bias is born and how it can be corrected without assuming any particular shape for the IET distribution. To do this, we model event sequences using stationary renewal processes, and we formulate simple heuristics for determining the severity of the bias. To illustrate our results, we focus on the example of empirical communication networks, which are temporal networks that are constructed from communication events. The IET distributions of such systems guide efforts to build models of human behavior, and the variance of IETs is very important for estimating the spreading rate of information in networks of temporal interactions. We analyze several well-known data sets from the literature, and we find that the resulting bias can lead to systematic underestimates of the variance in the IET distributions and that correcting for the bias can lead to qualitatively different results for the tails of the IET distributions.