nils-herrmann/clean_scirepeval huggingface dataset:
Rational design of self-assembly pathways for complex multicomponent structures

Recent experiments have demonstrated that complex, three-dimensional nanostructures can be self-assembled out of thousands of short strands of preprogrammed DNA. However, the mechanism by which robust self-assembly occurs is poorly understood, and the same feat has not yet been achieved using any other molecular building block. Using a new theory of “addressable” self-assembly, we explain how the design of the target structure and the choice of interparticle interactions determine the self-assembly pathway, and, to our knowledge, for the first time predict that a time-dependent protocol, rather than merely a carefully tuned set of conditions, may be necessary to optimize the yield. With an understanding of these design principles, it should be possible to engineer addressable nanostructures using a much wider array of materials. The field of complex self-assembly is moving toward the design of multiparticle structures consisting of thousands of distinct building blocks. To exploit the potential benefits of structures with such “addressable complexity,” we need to understand the factors that optimize the yield and the kinetics of self-assembly. Here we use a simple theoretical method to explain the key features responsible for the unexpected success of DNA-brick experiments, which are currently the only demonstration of reliable self-assembly with such a large number of components. Simulations confirm that our theory accurately predicts the narrow temperature window in which error-free assembly can occur. Even more strikingly, our theory predicts that correct assembly of the complete structure may require a time-dependent experimental protocol. Furthermore, we predict that low coordination numbers result in nonclassical nucleation behavior, which we find to be essential for achieving optimal nucleation kinetics under mild growth conditions. We also show that, rather surprisingly, the use of heterogeneous bond energies improves the nucleation kinetics and in fact appears to be necessary for assembling certain intricate 3D structures. This observation makes it possible to sculpt nucleation pathways by tuning the distribution of interaction strengths. These insights not only suggest how to improve the design of structures based on DNA bricks, but also point the way toward the creation of a much wider class of chemical or colloidal structures with addressable complexity.
Gaussian approximation potentials: A brief tutorial introduction

We present a swift walk-through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian approximation potentials (GAP) framework, discuss a variety of descriptors, how to train the model on total energies and derivatives, and the simultaneous use of multiple models of different complexity. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for noncommercial use.
Sparse Representation Classification Beyond ℓ1 Minimization and the Subspace Assumption

The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of <inline-formula> <tex-math notation="LaTeX">$\ell 1$ </tex-math></inline-formula> minimization and works well for image recognition satisfying a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency under a latent subspace model and contamination. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance and significantly faster.
New knotted solutions of Maxwell's equations

In this paper we have further developed the study of topologically non-trivial solutions of vacuum electrodynamics. We have discovered a novel method of generating such solutions by applying conformal transformations with complex parameters on known solutions expressed in terms of Bateman's variables. This has enabled us to obtain a wide class of solutions from the basic configuration, such as constant electromagnetic fields and plane-waves. We have introduced a covariant formulation of Bateman's construction and discussed the conserved charges associated with the conformal group as well as a set of four types of conserved helicities. We have also given a formulation in terms of quaternions. This led to a simple map between the electromagnetic knotted and linked solutions into flat connections of SU(2) gauge theory. We have computed the corresponding Chern–Simons charge in a class of solutions and the charge takes integer values.
Doppler broadening thermometry based on cavity ring-down spectroscopy

A Doppler broadening thermometry (DBT) instrument is built based on cavity ring-down spectroscopy (CRDS) for the precise determination of the Boltzmann constant. Compared with conventional direct absorption methods, the high-sensitivity of CRDS allows one to reach a satisfactory precision at lower sample pressures, which reduces the influence due to collisions. By recording the spectrum of C2H2 at 787 nm, we demonstrate a statistical uncertainty of 6 ppm (part per million) in the determined linewidth values by several hours’ measurement at a sample pressure of 1.5 Pa. As for the spectroscopy-determined temperatures, although with a reproducibility better than 10 ppm, we found a systematic deviation of about 800 ppm, which is attributed to ‘hidden’ weak lines overlapped with the selected transition at 787 nm. Our analysis indicates that it is feasible to pursue a DBT measurement toward the 1 ppm precision using cavity ring-down spectroscopy of a CO line at 1.57 μ ?>m.
Fundamental constants and cosmic vacuum: the micro and macro connection

The idea that the vacuum energy density $\rho_{\Lambda}$ could be time dependent is a most reasonable one in the expanding Universe; in fact, much more reasonable than just a rigid cosmological constant for the entire cosmic history. Being $\rho_{\Lambda}=\rho_{\Lambda}(t)$ dynamical, it offers a possibility to tackle the cosmological constant problem in its various facets. Furthermore, for a long time (most prominently since Dirac's first proposal on a time variable gravitational coupling) the possibility that the fundamental "constants" of Nature are slowly drifting with the cosmic expansion has been continuously investigated. In the last two decades, and specially in recent times, mounting experimental evidence attests that this could be the case. In this paper, we consider the possibility that these two groups of facts might be intimately connected, namely that the observed acceleration of the Universe and the possible time variation of the fundamental constants are two manifestations of the same underlying dynamics. We call it: the "micro and macro connection", and on its basis we expect that the cosmological term in Einstein's equations, Newton's coupling and the masses of all the particles in the Universe, both the dark matter particles and the ordinary baryons and leptons, should all drift with the cosmic expansion. Here we discuss specific cosmological models realizing such possibility in a way that preserves the principle of covariance of General Relativity.
RELEAF: An Algorithm for Learning and Exploiting Relevance

Recommender systems, medical diagnosis, network security, etc., require on-going learning and decision-making in real time. These-and many others-represent perfect examples of the opportunities and difficulties presented by Big Data: the available information often arrives from a variety of sources and has diverse features so that learning from all the sources may be valuable but integrating what is learned is subject to the curse of dimensionality. This paper develops and analyzes algorithms that allow efficient learning and decision-making while avoiding the curse of dimensionality. We formalize the information available to the learner/decision-maker at a particular time as a context vector which the learner should consider when taking actions. In general the context vector is very high dimensional, but in many settings, the most relevant information is embedded into only a few relevant dimensions. If these relevant dimensions were known in advance, the problem would be simple-but they are not. Moreover, the relevant dimensions may be different for different actions. Our algorithm learns the relevant dimensions for each action, and makes decisions based in what it has learned. Formally, we build on the structure of a contextual multi-armed bandit by adding and exploiting a relevance relation. We prove a general regret bound for our algorithm whose time order depends only on the maximum number of relevant dimensions among all the actions, which in the special case where the relevance relation is single-valued (a function), reduces to \mathtildeO(T2(√2-1)); in the absence of a relevance relation, the best known contextual bandit algorithms achieve regret \mathtildeO(T(D+1)/(D+2)), where D is the full dimension of the context vector. Our algorithm alternates between exploring and exploiting and does not require observing outcomes during exploitation (so allows for active learning). Moreover, during exploitation, suboptimal actions are chosen with arbitrarily low probability. Our algorithm is tested on datasets arising from network security and online news article recommendations.
Nonlinear analysis of magnetization dynamics excited by spin Hall effect

We investigate the possibility of exciting self-oscillation in a perpendicular ferromagnet by the spin Hall effect on the basis of a nonlinear analysis of the Landau-Lifshitz-Gilbert (LLG) equation. In the self-oscillation state, the energy supplied by the spin torque during a precession on a constant energy curve should equal the dissipation due to damping. Also, the current to balance the spin torque and the damping torque in the self-oscillation state should be larger than the critical current to destabilize the initial state. We find that the second condition in the spin Hall system is not satisfied by deriving analytical solutions of the energy supplied by the spin transfer effect and the dissipation due to the damping from the nonlinear LLG equation. This indicates that the self-oscillation of a perpendicular ferromagnet cannot be excited solely by the spin Hall torque.
Provable sparse tensor decomposition

We propose a novel sparse tensor decomposition method, namely the tensor truncated power method, that incorporates variable selection in the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in the tensor power iteration. Our method applies to a broad family of high dimensional latent variable models, including high dimensional Gaussian mixtures and mixtures of sparse regressions. A thorough theoretical investigation is further conducted. In particular, we show that the final decomposition estimator is guaranteed to achieve a local statistical rate, and we further strengthen it to the global statistical rate by introducing a proper initialization procedure. In high dimensional regimes, the statistical rate obtained significantly improves those shown in the existing non‐sparse decomposition methods. The empirical advantages of tensor truncated power are confirmed in extensive simulation results and two real applications of click‐through rate prediction and high dimensional gene clustering.
A mixture Cox-Logistic model for feature selection from survival and classification data

This paper presents an original approach for jointly fitting survival times and classifying samples into subgroups. The Coxlogit model is a generalized linear model with a common set of selected features for both tasks. Survival times and class labels are here assumed to be conditioned by a common risk score which depends on those features. Learning is then naturally expressed as maximizing the joint probability of subgroup labels and the ordering of survival events, conditioned to a common weight vector. The model is estimated by minimizing a regularized log-likelihood through a coordinate descent algorithm. 
Validation on synthetic and breast cancer data shows that the proposed approach outperforms a standard Cox model or logistic regression when both predicting the survival times and classifying new samples into subgroups. It is also better at selecting informative features for both tasks.
X-CSIT: a toolkit for simulating 2D pixel detectors

A new, modular toolkit for creating simulations of 2D X-ray pixel detectors, X-CSIT (X-ray Camera SImulation Toolkit), is being developed. The toolkit uses three sequential simulations of detector processes which model photon interactions, electron charge cloud spreading with a high charge density plasma model and common electronic components used in detector readout. In addition, because of the wide variety in pixel detector design, X-CSIT has been designed as a modular platform so that existing functions can be modified or additional functionality added if the specific design of a detector demands it. X-CSIT will be used to create simulations of the detectors at the European XFEL, including three bespoke 2D detectors: the Adaptive Gain Integrating Pixel Detector (AGIPD), Large Pixel Detector (LPD) and DePFET Sensor with Signal Compression (DSSC). These simulations will be used by the detector group at the European XFEL for detector characterisation and calibration. For this purpose, X-CSIT has been integrated into the European XFEL's software framework, Karabo. This will further make it available to users to aid with the planning of experiments and analysis of data. In addition, X-CSIT will be released as a standalone, open source version for other users, collaborations and groups intending to create simulations of their own detectors.
A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification

Frank-Wolfe algorithms have recently regained the attention of the Machine Learning community. Their solid theoretical properties and sparsity guarantees make them a suitable choice for a wide range of problems in this field. In addition, several variants of the basic procedure exist that improve its theoretical properties and practical performance. In this paper, we investigate the application of some of these techniques to Machine Learning, focusing in particular on a Parallel Tangent (PARTAN) variant of the FW algorithm for SVM classification, which has not been previously suggested or studied for this type of problem. We provide experiments both in a standard setting and using a stochastic speed-up technique, showing that the considered algorithms obtain promising results on several medium and large-scale benchmark datasets.
Heavy Context Dependence - Decisions Of Underground Soldiers

An attempt is made to simulate the disclosure of underground soldiers in terms of theory of networks. The coupling mechanism between the network nodes is the possibility that a disclosed soldier is going to disclose also his acquaintances. We calculate the fraction of disclosed soldiers as dependent on the fraction of those who, once disclosed, reveal also their colleagues. The simulation is immersed in the historical context of the Polish Home Army under the communist rule in 1946-49.
Dark Matter From Spacetime Nonlocality

We propose that dark matter is not yet another new particle in nature, but that it is a remnant of quantum gravitational effects on known fields. We arrive at this possibility in an indirect and surprising manner: by considering retarded, nonlocal, and Lorentzian evolution for quantum fields. This is inspired by recent developments in causal set theory, where such an evolution shows up as the continuum limit of scalar field propagation on a background causal set. Concretely, we study the quantum theory of a massless scalar field whose evolution is given not by the the d'Alembertian $\Box$, but by an operator $\tilde \Box$ which is Lorentz invariant, reduces to $\Box$ at low energies, and defines an explicitly retarded evolution: $(\tilde \Box \phi)(x)$ only depends on $\phi(y)$, with $y$ is in the causal past of $x$. This modification results in the existence of a continuum of massive particles, in addition to the usual massless ones, in the free theory. When interactions are introduced, these massive or off-shell quanta can be produced by the scattering of massless particles, but once produced, they no longer interact, which makes them a natural candidate for dark matter.
Estimating Optimal Active Learning via Model Retraining Improvement

A central question for active learning (AL) is: "what is the optimal selection?" Defining optimality by classifier loss produces a new characterisation of optimal AL behaviour, by treating expected loss reduction as a statistical target for estimation. This target forms the basis of model retraining improvement (MRI), a novel approach providing a statistical estimation framework for AL. This framework is constructed to address the central question of AL optimality, and to motivate the design of estimation algorithms. MRI allows the exploration of optimal AL behaviour, and the examination of AL heuristics, showing precisely how they make sub-optimal selections. The abstract formulation of MRI is used to provide a new guarantee for AL, that an unbiased MRI estimator should outperform random selection. This MRI framework reveals intricate estimation issues that in turn motivate the construction of new statistical AL algorithms. One new algorithm in particular performs strongly in a large-scale experimental study, compared to standard AL methods. This competitive performance suggests that practical efforts to minimise estimation bias may be important for AL applications.
Strong Gravitational Lensing by Kiselev Black Hole

We investigate the gravitational lensing scenario due to Schwarzschild-like black hole surrounded by quintessence (Kiselev black hole). We work for the special case of Kiselev black hole where we take the state parameter $w_{q}=-\frac{2}{3}$. For the detailed derivation and analysis of the bending angle involved in the deflection of light, we discuss three special cases of Kiselev black hole: non-extreme, extreme and naked singularity. We also calculate the approximate bending angle and compare it with exact bending angle. We found the relation of bending angles in the decreasing order as: naked singularity, extreme Kiselev black hole, non-extreme Kiselev black hole and Schwarzschild black hole. In the weak field approximation, we compute the position and total magnification of relativistic images as well.
Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT

This article describes the resource- and system-building efforts of an 8-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation scheme, the creation of a (publicly available) MN lexicon, and two automated MN taggers that we built using the annotation scheme and lexicon. Our annotation scheme isolates three components of modality and negation: a trigger (a word that conveys modality or negation), a target (an action associated with modality or negation), and a holder (an experiencer of modality). We describe how our MN lexicon was semi-automatically produced and we demonstrate that a structure-based MN tagger results in precision around 86% (depending on genre) for tagging of a standard LDC data set.We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations. Syntactic tags enriched with semantic annotations are assigned to parse trees in the target-language training texts through a process of tree grafting. Although the focus of our work is modality and negation, the tree grafting procedure is general and supports other types of semantic information. We exploit this capability by including named entities, produced by a pre-existing tagger, in addition to the MN elements produced by the taggers described here. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu–English test set. This finding supports the hypothesis that both syntactic and semantic information can improve translation quality.
On Anomaly Ranking and Excess-Mass Curves

Learning how to rank multivariate unlabeled observations depending on their degree of abnormality/novelty is a crucial problem in a wide range of applications. In practice, it generally consists in building a real valued "scoring" function on the feature space so as to quantify to which extent observations should be considered as abnormal. In the 1-d situation, measurements are generally considered as "abnormal" when they are remote from central measures such as the mean or the median. Anomaly detection then relies on tail analysis of the variable of interest. Extensions to the multivariate setting are far from straightforward and it is precisely the main purpose of this paper to introduce a novel and convenient (functional) criterion for measuring the performance of a scoring function regarding the anomaly ranking task, referred to as the Excess-Mass curve (EM curve). In addition, an adaptive algorithm for building a scoring function based on unlabeled data X1 , . . . , Xn with a nearly optimal EM is proposed and is analyzed from a statistical perspective.
A Confident Information First Principle for Parameter Reduction and Model Selection of Boltzmann Machines

Typical dimensionality reduction (DR) methods are data-oriented, focusing on directly reducing the number of random variables (or features) while retaining the maximal variations in the high-dimensional data. Targeting unsupervised situations, this paper aims to address the problem from a novel perspective and considers model-oriented DR in parameter spaces of binary multivariate distributions. Specifically, we propose a general parameter reduction criterion, called confident-information-first (CIF) principle, to maximally preserve confident parameters and rule out less confident ones. Formally, the confidence of each parameter can be assessed by its contribution to the expected Fisher information distance within a geometric manifold over the neighborhood of the underlying real distribution. Then, we demonstrate two implementations of CIF in different scenarios. First, when there are no observed samples, we revisit the Boltzmann machines (BMs) from a model selection perspective and theoretically show that both the fully visible BM and the BM with hidden units can be derived from the general binary multivariate distribution using the CIF principle. This finding would help us uncover and formalize the essential parts of the target density that BM aims to capture and the nonessential parts that BM should discard. Second, when there exist observed samples, we apply CIF to the model selection for BM, which is in turn made adaptive to the observed samples. The sample-specific CIF is a heuristic method to decide the priority order of parameters, which can improve the search efficiency without degrading the quality of model selection results as shown in a series of density estimation experiments.
Text Understanding from Scratch

This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.