nils-herrmann/clean_scirepeval huggingface dataset:
Monolayer MXenes: promising half-metals and spin gapless semiconductors.

Half-metals and spin gapless semiconductors are promising candidates for spintronic applications due to the complete (100%) spin polarization of electrons around the Fermi level. Based on recent experimental and theoretical findings of graphene-like monolayer transition metal carbides and nitrides (also known as MXenes), we demonstrate using first-principles calculations that monolayers Ti2C and Ti2N exhibit nearly half-metallic ferromagnetism with the magnetic moments of 1.91 and 1.00μB per formula unit, respectively, while monolayer V2C is a metal with unstable antiferromagnetism, and monolayer V2N is a nonmagnetic metal. Interestingly, under a biaxial strain, there is a phase transition from a nearly half-metal to truly half-metal, spin gapless semiconductor, and metal for monolayer Ti2C. Monolayer Ti2N is still a nearly half-metal under a suitable biaxial strain. Large magnetic moments can be induced by the biaxial tensile and compressive strains for monolayer V2C and V2N, respectively. We also show that the structures of these four monolayer MXenes are stable according to the calculated formation energy and phonon spectrum. Our investigations suggest that, unlike monolayer graphene, monolayer MXenes Ti2C and Ti2N without vacancy, doping or external electric field exhibit intrinsic magnetism, especially the half-metallic ferromagnetism and spin gapless semiconductivity, which will stimulate further studies on possible spintronic applications for new two-dimensional materials of MXenes.
ORBITAL CIRCULARIZATION OF HOT AND COOL KEPLER ECLIPSING BINARIES

The rate of tidal circularization is predicted to be faster for relatively cool stars with convective outer layers, compared to hotter stars with radiative outer layers. Observing this effect is challenging because it requires large and well-characterized samples that include both hot and cool stars. Here we seek evidence of the predicted dependence of circularization upon stellar type, using a sample of 945 eclipsing binaries observed by Kepler. This sample complements earlier studies of this effect, which employed smaller samples of better-characterized stars. For each Kepler binary we measure e cos ω based on the relative timing of the primary and secondary eclipses. We examine the distribution of e cos ω as a function of period for binaries composed of hot stars, cool stars, and mixtures of the two types. At the shortest periods, hot–hot binaries are most likely to be eccentric; for periods shorter than four days, significant eccentricities occur frequently for hot–hot binaries, but not for hot–cool or cool–cool binaries. This is in qualitative agreement with theoretical expectations based on the slower dissipation rates of hot stars. However, the interpretation of our results is complicated by the largely unknown ages and evolutionary states of the stars in our sample.
The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC

Speeding up Markov chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator. The estimator is a product of Poisson estimators, allowing us to update a single block of subsample indicators in each MCMC iteration so that a desired correlation is achieved between the logs of successive likelihood estimates. This is important since pseudo-marginal MCMC with positively correlated likelihood estimates can use substantially smaller subsamples without adversely affecting the sampling efficiency. The block-Poisson estimator is unbiased but not necessarily positive, so the algorithm runs the MCMC on the absolute value of the likelihood estimator and uses an importance sampling correction to obtain consistent estimates of the posterior mean of any function of the parameters. Our article derives guidelines to select the optimal tuning parameters for our method and shows that it compares very favorably to regular MCMC without subsampling, and to two other recently proposed exact subsampling approaches in the literature.
Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention

This paper focuses on two key problems for audio-visual emotion recognition in the video. One is the audio and visual streams temporal alignment for feature level fusion. The other one is locating and re-weighting the perception attentions in the whole audio-visual stream for better recognition. The Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is employed as the main classification architecture. Firstly, soft attention mechanism aligns the audio and visual streams. Secondly, seven emotion embedding vectors, which are corresponding to each classification emotion type, are added to locate the perception attentions. The locating and re-weighting process is also based on the soft attention mechanism. The experiment results on EmotiW2015 dataset and the qualitative analysis show the efficiency of the proposed two techniques.
The unresolved mystery of the great divergence is solved

The so-called great divergence in the income per capita is described in the Unified Growth Theory as the mind-boggling and unresolved mystery about the growth process. This mystery has now been solved: the great divergence never happened. It was created by the manipulation of data. Economic growth in various regions is at different levels of development but it follows similar, non-divergent trajectories. Unified Growth Theory is shown yet again to be incorrect and scientifically unacceptable. It promotes incorrect and even potentially dangerous concepts. The distorted presentation of data supporting the concept of the great divergence shows that economic growth is now developing along moderately-increasing trajectories but mathematical analysis of the same data and even their undistorted presentation shows that these trajectories are now increasing approximately vertically with time. So, while the distorted presentation of data used in the Unified Growth Theory suggests generally sustainable and secure economic growth, the undistorted presentation of data demonstrates that the growth is unsustainable and insecure. The concept of takeoffs from stagnation to the sustained-growth regime promoted in the Unified Growth Theory is also dangerously misleading because it suggests a sustainable and prosperous future while the mathematical analysis of data shows that the current economic growth is insecure and unsustainable.
Modelling income, wealth, and expenditure data by use of Econophysics

In the present paper, we identify several distributions from Physics and study their applicability to phenomena such as distribution of income, wealth, and expenditure. Firstly, we apply logistic distribution to these data and we find that it fits very well the annual data for the entire income interval including for upper income segment of population. Secondly, we apply Fermi-Dirac distribution to these data. We seek to explain possible correlations and analogies between economic systems and statistical thermodynamics systems. We try to explain their behavior and properties when we correlate physical variables with macroeconomic aggregates and indicators. Then we draw some analogies between parameters of the Fermi-Dirac distribution and macroeconomic variables. Thirdly, as complex systems are modeled using polynomial distributions, we apply polynomials to the annual sets of data and we find that it fits very well also the entire income interval. Fourthly, we develop a new methodology to approach dynamically the income, wealth, and expenditure distribution similarly with dynamical complex systems. This methodology was applied to different time intervals consisting of consecutive years up to 35 years. Finally, we develop a mathematical model based on a Hamiltonian that maximizes utility function applied to Ramsey model using Fermi-Dirac and polynomial utility functions. We find some theoretical connections with time preference theory. We apply these distributions to a large pool of data from countries with different levels of development, using different methods for calculation of income, wealth, and expenditure.
Higher order methylation features for clustering and prediction in epigenomic studies

DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands.



Here, we use probabilistic machine learning to extract higher order features associated with the methylation profile across a defined region. These features quantitate precisely notions of shape of a methylation profile, capturing spatial correlations in DNA methylation across genomic regions. Using these higher order features across promoter-proximal regions, we are able to construct a powerful machine learning predictor of gene expression, significantly improving upon the predictive power of average DNA methylation levels. Furthermore, we can use higher order features to cluster promoter-proximal regions, showing that five major patterns of methylation occur at promoters across different cell lines, and we provide evidence that methylation beyond CpG islands may be related to regulation of gene expression. Our results support previous reports of a functional role of spatial correlations in methylation patterns, and provide a mean to quantitate such features for downstream analyses.



https://github.com/andreaskapou/BPRMeth



Supplementary data are available at Bioinformatics online.
Filtering information in imaging connectomics

Mapping the connectome through non-invasive imaging tools allows to study the wiring diagram of the brain in-vivo. The informative value of the resulting brain network depends on how much of the estimated connectivity is retained. We present a method, the efficiency cost optimization (ECO), to filter connectivity based on the optimization of the trade-off between the efficiency of a network and its wiring cost. As a result, we find an optimal connection density threshold that emphasizes the intrinsic properties of a given network, while preserving its sparsity. Such threshold can be determined a-priori, since the number of connections to retain only depends on the network size. We validate this result on connectomes, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering approaches. This method advances our ability to analyze and compare imaging connectomes in a fast and principled way.
Characterization of multiple topological scales in multiplex networks through supra-Laplacian eigengaps.

Multilayer networks have been the subject of intense research during the past few years, as they represent better the interdependent nature of many real-world systems. Here, we address the question of describing the three different structural phases in which a multiplex network might exist. We show that each phase can be characterized by the presence of gaps in the spectrum of the supra-Laplacian of the multiplex network. We therefore unveil the existence of different topological scales in the system, whose relation characterizes each phase. Moreover, by capitalizing on the coarse-grained representation that is given in terms of quotient graphs, we explain the mechanisms that produce those gaps as well as their dynamical consequences.
Deep Embedding for Spatial Role Labeling

This paper introduces the visually informed embedding of word (VIEW), a continuous vector representation for a word extracted from a deep neural model trained using the Microsoft COCO data set to forecast the spatial arrangements between visual objects, given a textual description. The model is composed of a deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory (LSTM) network, the latter being preceded by an embedding layer. The VIEW is applied to transferring multimodal background knowledge to Spatial Role Labeling (SpRL) algorithms, which recognize spatial relations between objects mentioned in the text. This work also contributes with a new method to select complementary features and a fine-tuning method for MLP that improves the $F1$ measure in classifying the words into spatial roles. The VIEW is evaluated with the Task 3 of SemEval-2013 benchmark data set, SpaceEval.
A hybrid algorithm for coupling PDE and compartment-based dynamics

Stochastic simulation methods can be applied successfully to model exact spatiotemporally resolved reaction-diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations. Although the numerical solution of such partial differential equations is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, whilst maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time.
Making Decisions with Spatially and Temporally Uncertain Data

We consider a decision-making problem where the environment varies both in space and time. Such problems arise naturally when considering e.g., the navigation of an underwater robot amidst ocean currents or the navigation of an aerial vehicle in wind. To model such spatiotemporal variation, we extend the standard Markov Decision Process (MDP) to a new framework called the Time-Varying Markov Decision Process (TVMDP). The TVMDP has a time-varying state transition model and transforms the standard MDP that considers only {\em immediate} and {\em static} uncertainty descriptions of state transitions, to a framework that is able to adapt to future time-varying uncertainty over some horizon. We show how to solve a TVMDP via a redesign of the MDP value propagation mechanisms by incorporating the introduced dynamics along the temporal dimension. We validate our framework in a marine robotics navigation setting using real spatiotemporal ocean data and show that it outperforms prior efforts to explicitly accommodate time by including it in the state.
Wide Residual Networks

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL
An Analysis of Deep Neural Network Models for Practical Applications

Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption.  (1) power consumption is independent of batch size and architecture; (2) accuracy and inference time are in a hyperbolic relationship; (3) energy constraint are an upper bound on the maximum achievable accuracy and model complexity; (4) the number of operations is a reliable estimate of the inference time. We believe our analysis provides a compelling set of information that helps design and engineer efficient DNNs.
Broadband Transverse Electric Surface Wave in Silicene

Transverse electric (TE) surface wave in silicine is theoretically investigated. The TE surface wave in silicene is found to exhibit better characteristics compared with that in graphene, in terms of a broader frequency range and more confinement to the surface which originate from the buckled structure of silicene. We found that even undoped silicene can support the TE surface wave. We expect to obtain the similar characteristics of the TE surface wave in other two-dimensional materials that have slightly buckled honeycomb lattice.
Journeys & Notes: Designing Social Computing for Non-Places

In this work we present a mobile application we designed and engineered to enable people to log their travels near and far, leave notes behind, and build a community around spaces in between destinations. Our design explores new ground for location-based social computing systems, identifying opportunities where these systems can foster the growth of on-line communities rooted at non-places. In our work we develop, explore, and evaluate several innovative features designed around four usage scenarios: daily commuting, long-distance traveling, quantified traveling, and journaling. We present the results of two small-scale user studies, and one large-scale, world-wide deployment, synthesizing the results as potential opportunities and lessons learned in designing social computing for non-places.
Autoconvolution for Unsupervised Feature Learning

In visual recognition tasks, supervised learning shows excellent performance. On the other hand, unsupervised learning exploits cheap unlabeled data and can help to solve the same tasks more efficiently. We show that the recursive autoconvolutional operator, adopted from physics, boosts existing unsupervised methods to learn more powerful filters. We use a well established multilayer convolutional network and train filters layer-wise. To build a stronger classifier, we design a very light committee of SVM models. The total number of trainable parameters is also greatly reduced by using shared filters in higher layers. We evaluate our networks on the MNIST, CIFAR-10 and STL-10 benchmarks and report several state of the art results among other unsupervised methods.
Convolutional Imputation of Matrix Network

A matrix network is a family of matrices, where the relationship between them is modeled as a weighted graph. Each node represents a matrix, and the weight on each edge represents the similarity between the two matrices. Suppose that we observe a few entries of each matrix with noise, and the fraction of entries we observe varies from matrix to matrix. Even worse, a subset of matrices in this family may be completely unobserved. How can we recover the entire matrix network from noisy and incomplete observations? One motivating example is the cold start problem, where we need to do inference on new users or items that come with no information. To recover this network of matrices, we propose a structural assumption that the matrix network can be approximated by generalized convolution of low rank matrices living on the same network. We propose an iterative imputation algorithm to complete the matrix network. This algorithm is efficient for large scale applications and is guaranteed to accurately recover all matrices, as long as there are enough observations accumulated over the network.
Dense Associative Memory for Pattern Recognition

A model of associative memory is studied, which stores and reliably retrieves many more patterns than the number of neurons in the network. We propose a simple duality between this dense associative memory and neural networks commonly used in deep learning. On the associative memory side of this duality, a family of models that smoothly interpolates between two limiting cases can be constructed. One limit is referred to as the feature-matching mode of pattern recognition, and the other one as the prototype regime. On the deep learning side of the duality, this family corresponds to feedforward neural networks with one hidden layer and various activation functions, which transmit the activities of the visible neurons to the hidden layer. This family of activation functions includes logistics, rectified linear units, and rectified polynomials of higher degrees. The proposed duality makes it possible to apply energy-based intuition from associative memory to analyze computational properties of neural networks with unusual activation functions - the higher rectified polynomials which until now have not been used in deep learning. The utility of the dense memories is illustrated for two test cases: the logical gate XOR and the recognition of handwritten digits from the MNIST data set.
Landau levels in 2D materials using Wannier Hamiltonians obtained by first principles

We present a method to calculate the Landau levels and the corresponding edge states of two dimensional (2D) crystals using as a starting point their electronic structure as obtained from standard density functional theory (DFT). The DFT Hamiltonian is represented in the basis of maximally localized Wannier functions. This defines a tight-binding Hamiltonian for the bulk that can be used to describe other structures, such as ribbons, provided that atomic scale details of the edges are ignored. The effect of the orbital magnetic field is described using the Peierls substitution in the hopping matrix elements. Implementing this approach in a ribbon geometry, we obtain both the Landau levels and the dispersive edge states for a series of 2D crystals, including graphene, Boron Nitride, MoS2, Black Phosphorous, Indium Selenide and MoO3. Our procedure can readily be used in any other 2D crystal, and provides an alternative to effective mass descriptions.
Absorption of a Particle by a Rotating Black Hole: The Potential Barrier

For a test particle approaching a rapidly rotating black hole we find a range of values of the particle's energy and angular momentum, on the order of 1\% or more of the corresponding values of the hole, such that three conditions are satisfied. 1) The particle can reach the horizon. 2) After absorption the new hole still has a horizon. 3) The area of the new hole is less than the area of the original one, in apparent violation of a theorem of Hawking. We offer support for the claim that the test particle approximation is the cause of the violation.