nils-herrmann/clean_scirepeval huggingface dataset:
pyFRET: A Python Library for Single Molecule Fluorescence Data Analysis

Single molecule F\"orster resonance energy transfer (smFRET) is a powerful experimental technique for studying the properties of individual biological molecules in solution. However, as adoption of smFRET techniques becomes more widespread, the lack of available software, whether open source or commercial, for data analysis, is becoming a significant issue. Here, we present pyFRET, an open source Python package for the analysis of data from single-molecule fluorescence experiments from freely diffusing biomolecules. The package provides methods for the complete analysis of a smFRET dataset, from burst selection and denoising, through data visualisation and model fitting. We provide support for both continuous excitation and alternating laser excitation (ALEX) data analysis. pyFRET is available as a package downloadable from the Python Package Index (PyPI) under the open source three-clause BSD licence, together with links to extensive documentation and tutorials, including example usage and test data. Additional documentation including tutorials is hosted independently on ReadTheDocs. The code is available from the free hosting site Bitbucket. Through distribution of this software, we hope to lower the barrier for the adoption of smFRET experiments by other research groups and we encourage others to contribute modules for specific analysis needs.
A Python-based Post-processing Toolset For Seismic Analyses

This paper discusses the design and implementation of a Python-based toolset to aid in assessing the response of the UK's Advanced Gas Reactor nuclear power stations to earthquakes. The seismic analyses themselves are carried out with a commercial Finite Element solver, but understanding the raw model output this produces requires customised post-processing and visualisation tools. Extending the existing tools had become increasingly difficult and a decision was made to develop a new, Python-based toolset. This comprises of a post-processing framework (aftershock) which includes an embedded Python interpreter, and a plotting package (afterplot) based on numpy and matplotlib. The new toolset had to be significantly more flexible and easier to maintain than the existing code-base, while allowing the majority of development to be carried out by engineers with little training in software development. The resulting architecture will be described with a focus on exploring how the design drivers were met and the successes and challenges arising from the choices made.
Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations

In this work, we propose a new method to integrate two recent lines of work: unsupervised induction of shallow semantics (e.g., semantic roles) and factorization of relations in text and knowledge bases. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexical features; (2) a reconstruction component: a tensor factorization model which relies on roles to predict argument fillers. When the components are estimated jointly to minimize errors in argument reconstruction, the induced roles largely correspond to roles defined in annotated resources. Our method performs on par with most accurate role induction methods on English, even though, unlike these previous approaches, we do not incorporate any prior linguistic knowledge about the language.
Dynamic Conic Finance via Backward Stochastic Difference Equations

We present an arbitrage free theoretical framework for modeling bid and ask prices of dividend paying securities in a discrete time setup using theory of dynamic acceptability indices. In the first part of the paper we develop the theory of dynamic subscale invariant performance measures, on a general probability space, and discrete time setup. We prove a representation theorem of such measures in terms of a family of dynamic convex risk measures, and provide a representation of dynamic risk measures in terms of g-expectations, and solutions of BS$\Delta$Es with convex drivers. We study the existence and uniqueness of the solutions, and derive a comparison theorem for corresponding BS$\Delta$Es. 
In the second part of the paper we discuss a market model for dividend paying securities by introducing the pricing operators that are defined in terms of dynamic acceptability indices, and find various properties of these operators. Using these pricing operators, we define the bid and ask prices for the underlying securities and then for derivatives in this market. We show that the obtained market model is arbitrage free, and we also prove a series of properties of these prices.
A la Carte - Learning Fast Kernels

Kernel methods have great promise for learning rich statistical representations of large modern datasets. However, compared to neural networks, kernel methods have been perceived as lacking in scalability and flexibility. We introduce a family of fast, flexible, lightly parametrized and general purpose kernel learning methods, derived from Fastfood basis function expansions. We provide mechanisms to learn the properties of groups of spectral frequencies in these expansions, which require only O(mlogd) time and O(m) memory, for m basis functions and d input dimensions. We show that the proposed methods can learn a wide class of kernels, outperforming the alternatives in accuracy, speed, and memory consumption.
Cauchy Principal Component Analysis

Principal Component Analysis (PCA) has wide applications in machine learning, text mining and computer vision. Classical PCA based on a Gaussian noise model is fragile to noise of large magnitude. Laplace noise assumption based PCA methods cannot deal with dense noise effectively. In this paper, we propose Cauchy Principal Component Analysis (Cauchy PCA), a very simple yet effective PCA method which is robust to various types of noise. We utilize Cauchy distribution to model noise and derive Cauchy PCA under the maximum likelihood estimation (MLE) framework with low rank constraint. Our method can robustly estimate the low rank matrix regardless of whether noise is large or small, dense or sparse. We analyze the robustness of Cauchy PCA from a robust statistics view and present an efficient singular value projection optimization method. Experimental results on both simulated data and real applications demonstrate the robustness of Cauchy PCA to various noise patterns.
Score Function Features for Discriminative Learning

Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms for extracting discriminative information, given these pre-trained features and labeled samples for any related task. Our class of features are based on higher-order score functions, which capture local variations in the probability density function of the input. We establish a theoretical framework to characterize the nature of discriminative information that can be extracted from score-function features, when used in conjunction with labeled samples. We employ efficient spectral decomposition algorithms (on matrices and tensors) for extracting discriminative components. The advantage of employing tensor-valued features is that we can extract richer discriminative information in the form of an overcomplete representations. Thus, we present a novel framework for employing generative models of the input for discriminative learning.
On distinguishability criteria for estimating generative models

Two recently introduced criteria for estimation of generative models are both based on a reduction to binary classification. Noise-contrastive estimation (NCE) is an estimation procedure in which a generative model is trained to be able to distinguish data samples from noise samples. Generative adversarial networks (GANs) are pairs of generator and discriminator networks, with the generator network learning to generate samples by attempting to fool the discriminator network into believing its samples are real data. Both estimation procedures use the same function to drive learning, which naturally raises questions about how they are related to each other, as well as whether this function is related to maximum likelihood estimation (MLE). NCE corresponds to training an internal data model belonging to the {\em discriminator} network but using a fixed generator network. We show that a variant of NCE, with a dynamic generator network, is equivalent to maximum likelihood estimation. Since pairing a learned discriminator with an appropriate dynamically selected generator recovers MLE, one might expect the reverse to hold for pairing a learned generator with a certain discriminator. However, we show that recovering MLE for a learned generator requires departing from the distinguishability game.  
(i) The expected gradient of the NCE discriminator can be made to match the expected gradient of 
MLE, if one is allowed to use a non-stationary noise distribution for NCE, 
(ii) No choice of discriminator network can make the expected gradient for the GAN generator match that of MLE, and 
(iii) The existing theory does not guarantee that GANs will converge in the non-convex case. 
This suggests that the key next step in GAN research is to determine whether GANs converge, and if not, to modify their training algorithm to force convergence.
Particle partition entanglement of bosonic Luttinger liquids

We consider the R\'enyi entanglement entropy of bosonic Tomonaga-Luttinger liquids under a particle bipartition and demonstrate that the leading order finite-size scaling is logarithmic in the system size with a prefactor equal to the inverse Luttinger parameter. While higher-order corrections involve a microscopic length scale, the leading-order scaling depends only on this sole dimensionless parameter which characterizes the low-energy quantum hydrodynamics. This result contrasts the leading entanglement entropy scaling under a spatial bipartition, for which the coefficient is universal and independent of the Luttinger parameter. Using quantum Monte Carlo calculations, we explicitly confirm the scaling predictions of Tomonaga-Luttinger liquid theory for the Lieb-Liniger model of $\ensuremath{\delta}$-function interacting bosons in the one-dimensional spatial continuum.
A detailed study of the optical attenuation of gamma-ray bursts in the Swift era

We present optical and near-infrared (NIR) photometry of 24 gamma-ray bursts (GRBs) detected by the Swift satellite and rapidly observed by the Reionization and Transients Infrared/Optical (RATIR) camera. We compare the optical flux at a fiducial time of 11 hours after the high-energy trigger to that in the X-ray regime to quantify optical darkness. 50 per cent (12/24) of all bursts in our sample and 55 per cent (12/22) of long GRBs are optically dark, which is statistically consistently with previous studies. Fitting RATIR optical and NIR spectral energy distributions (SEDs) of 16 GRBs, most (6/7) optically dark GRBs either occur at high-redshift (z > 4.5) or have a high dust content in their host galaxies (AV > 0.3). Performing K-S tests, we compare the RATIR sample to those previously presented in the literature, finding our distributions of redshift, optical darkness, host dust extinction and X-ray derived column density to be consistent. The one reported discrepancy is with host galaxy dust content in the BAT6 sample, which appears inconsistent with our sample and other previous literature. Comparing X-ray derived host galaxy hydrogen column densities to host galaxy dust extinction, we find that GRBs tend to occur in host galaxies with a higher metal-to-dust ratio than our own Galaxy, more akin to the Large and Small Magellanic Clouds. Finally, to mitigate time evolution of optical darkness, we measure βOX,rest at a fixed rest frame time, trest = 1.5 hours and fixed rest frame energies in the X-ray and optical regimes. Choosing to evaluate optical flux at λrest = 0.25 µm, we remove high-redshift as a source of optical darkness, demonstrating that optical darkness must result from either high-redshift, dust content in the host galaxy along the GRB sight line, or a combination of the two.
New AC-Powered SFQ Digital Circuits

Recent progress on reciprocal quantum logic (RQL) has renewed interest in ac powering of superconductor digital circuits, which had been abandoned since the famous IBM project of 1970s. In this paper, we propose and demonstrate new ac-biased single flux quantum (SFQ) circuits, and search for synergy of ac and currently dominating dc biasing schemes. As the first step, we suggest an on-chip ac/dc converter capable of feeding a few dc-biased gates surrounded by their ac-biased counterparts. As the second step, we introduce and present the first successful demonstration of a new ac-powered circuit-an 8192-bit shift register with over 32800 Josephson junctions (JJs) and JJ density of about 6 · 105 JJ per cm2. We suggest a few niche applications for this type of ac-biased circuits, which require high clock rates. For example, these circuits, scalable to millions of JJs per chip, can serve as a convenient benchmark for new SFQ fabrication technology nodes, allowing the operating margins of individual cells to be extracted and thus “visualize” individual fabrication defects and flux trapping events. The circuit can also be developed into a megapixel imaging array for a magnetic field microscope.
Random Walk Initialization for Training Very Deep Feedforward Networks

Training very deep networks is an important open problem in machine learning. One of many difficulties is that the norm of the back-propagated error gradient can grow or decay exponentially. Here we show that training very deep feed-forward networks (FFNs) is not as difficult as previously thought. Unlike when back-propagation is applied to a recurrent network, application to an FFN amounts to multiplying the error gradient by a different random matrix at each layer. We show that the successive application of correctly scaled random matrices to an initial vector results in a random walk of the log of the norm of the resulting vectors, and we compute the scaling that makes this walk unbiased. The variance of the random walk grows only linearly with network depth and is inversely proportional to the size of each layer. Practically, this implies a gradient whose log-norm scales with the square root of the network depth and shows that the vanishing gradient problem can be mitigated by increasing the width of the layers. Mathematical analyses and experimental results using stochastic gradient descent to optimize tasks related to the MNIST and TIMIT datasets are provided to support these claims. Equations for the optimal matrix scaling are provided for the linear and ReLU cases.
Cosmological perturbations in the (1 + 3 + 6)-dimensional space-times

Cosmological perturbations in the (1+3+6)-dimensional space-times including photon gas without viscous processes are studied on the basis of Abbott et al.'s formalism. Space-times consist of the outer space (the 3-dimensional expanding section) and the inner space (the 6-dimensional section). The inner space expands initially and contracts later. Abbott et al. derived only power-type solutions in the small wave-number limit which appear at the final stage of the space-times. In this paper, we derive not only small wave-number solutions, but also large wave-number solutions. It is found that the latter solutions depend on the two wave-numbers k_r and k_R (which are defined in the outer and inner spaces, respectively), and that the k_r-dependent and k_R-dependent parts dominate the total perturbations when (k_r/r(t))/(k_R/R(t)) >> 1 or << 1, respectively, where r(t) and R(t) are the scale-factors in the outer and inner spaces. By comparing the behaviors of these perturbations, moreover, changes in the spectrum of perturbations in the outer space with time are discussed.
A Deep-Structured Fully Connected Random Field Model for Structured Inference

There has been significant interest in the use of fully connected graphical models and deep-structured graphical models for the purpose of structured inference. However, fully connected and deep-structured graphical models have been largely explored independently, leaving the unification of these two concepts ripe for exploration. A fundamental challenge with unifying these two types of models is in dealing with computational complexity. In this paper, we investigate the feasibility of unifying fully connected and deep-structured models in a computationally tractable manner for the purpose of structured inference. To accomplish this, we introduce a deep-structured fully connected random field (DFRF) model that integrates a series of intermediate sparse autoencoding layers placed between state layers to significantly reduce the computational complexity. The problem of image segmentation was used to illustrate the feasibility of using the DFRF for structured inference in a computationally tractable manner. Results in this paper show that it is feasible to unify fully connected and deep-structured models in a computationally tractable manner for solving structured inference problems such as image segmentation.
Why does Deep Learning work? - A perspective from Group Theory

Why does Deep Learning work? What representations does it capture? How do higher-order representations emerge? We study these questions from the perspective of group theory, thereby opening a new approach towards a theory of Deep learning. 
One factor behind the recent resurgence of the subject is a key algorithmic step called pre-training: first search for a good generative model for the input samples, and repeat the process one layer at a time. We show deeper implications of this simple principle, by establishing a connection with the interplay of orbits and stabilizers of group actions. Although the neural networks themselves may not form groups, we show the existence of {\em shadow} groups whose elements serve as close approximations. 
Over the shadow groups, the pre-training step, originally introduced as a mechanism to better initialize a network, becomes equivalent to a search for features with minimal orbits. Intuitively, these features are in a way the {\em simplest}. Which explains why a deep learning network learns simple features first. Next, we show how the same principle, when repeated in the deeper layers, can capture higher order representations, and why representation complexity increases as the layers get deeper.
Extended Theories of Gravity with Generalized Energy Conditions

We address the problem of the energy conditions in modified gravity taking into account the additional degrees of freedom related to scalar fields and curvature invariants. The latter are usually interpreted as generalized geometrical fluids that differ in meaning with respect to the matter fluids generally considered as sources of the field equations. In extended gravity theories the curvature terms are encapsulated in a tensor Hab and a coupling g(Ψi) that can be recast as effective Einstein field equations, with corrections to the energy-momentum tensor of matter. The formal validity of standard energy inequalities does not assure basic requirements such as the attractive nature of gravity, so we argue that the energy conditions have to be considered in a wider sense.
Risk measuring under liquidity risk

We present a general framework for measuring the liquidity risk. The theoretical framework defines risk measures that incorporate the liquidity risk into the standard risk measures. We consider a one-period risk measurement model. The liquidity risk is defined as the risk that a security or a portfolio of securities cannot be sold or bought without causing changes in prices. The risk measures are decomposed into two terms, one measuring the risk of the future value of a given position in a security or a portfolio of securities and the other the initial cost of this position. Within the framework of coherent risk measures, the risk measures applied to the random part of the future value of a position in a determinate security are increasing monotonic and convex cash sub-additive on long positions. The contrary, in certain situations, holds for the sell positions. By using convex risk measures, we apply our framework to the situation in which large trades are broken into many small ones. Dual representation results are obtained for both positions in securities and portfolios. We give many examples of risk measures and derive for each of them the respective capital requirement. In particular, we discuss the VaR measure.
SENNS: Sparse Extraction Neural NetworkS for Feature Extraction

By drawing on ideas from optimisation theory, artificial neural networks (ANN), graph embeddings and sparse representations, I develop a novel technique, termed SENNS (Sparse Extraction Neural NetworkS), aimed at addressing the feature extraction problem. The proposed method uses (preferably deep) ANNs for projecting input attribute vectors to an output space wherein pairwise distances are maximized for vectors belonging to different classes, but minimized for those belonging to the same class, while simultaneously enforcing sparsity on the ANN outputs. The vectors that result from the projection can then be used as features in any classifier of choice. Mathematically, I formulate the proposed method as the minimisation of an objective function which can be interpreted, in the ANN output space, as a negative factor of the sum of the squares of the pair-wise distances between output vectors belonging to different classes, added to a positive factor of the sum of squares of the pair-wise distances between output vectors belonging to the same classes, plus sparsity and weight decay terms. To derive an algorithm for minimizing the objective function via gradient descent, I use the multi-variate version of the chain rule to obtain the partial derivatives of the function with respect to ANN weights and biases, and find that each of the required partial derivatives can be expressed as a sum of six terms. As it turns out, four of those six terms can be computed using the standard back propagation algorithm; the fifth can be computed via a slight modification of the standard backpropagation algorithm; while the sixth one can be computed via simple arithmetic. Finally, I propose experiments on the ARABASE Arabic corpora of digits and letters, the CMU PIE database of faces, the MNIST digits database, and other standard machine learning databases.
On Learning Vector Representations in Hierarchical Label Spaces

An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. This paper addresses one such problem, namely how to exploit hierarchical structures over labels. We present a novel method to learn vector representations of a label space given a hierarchy of labels and label co-occurrence patterns. Our experimental results demonstrate qualitatively that the proposed method is able to learn regularities among labels by exploiting a label hierarchy as well as label co-occurrences. It highlights the importance of the hierarchical information in order to obtain regularities which facilitate analogical reasoning over a label space. We also experimentally illustrate the dependency of the learned representations on the label hierarchy.