nils-herrmann/clean_scirepeval huggingface dataset:
Neural correlates of consciousness in humans

The directness and vivid quality of conscious experience belies the complexity of the underlying neural mechanisms, which remain incompletely understood. Recent work has focused on identifying the brain structures and patterns of neural activity within the primate visual system that are correlated with the content of visual consciousness. Functional neuroimaging in humans and electrophysiology in awake mokeys indicate that there are important differences between striate and extrastriate visual cortex in how well neural activity correlates with consciousness. Moreover, recent neuroimaging studies indicate that, in addition to these ventral areas of visual cortex, dorsal prefrontal and parietal areas might contribute to conscious visual experience.
A Transformation Framework for Optimizing Task-Parallel Programs

Task parallelism has increasingly become a trend with programming models such as OpenMP 3.0, Cilk, Java Concurrency, X10, Chapel and Habanero-Java (HJ) to address the requirements of multicore programmers. While task parallelism increases productivity by allowing the programmer to express multiple levels of parallelism, it can also lead to performance degradation due to increased overheads. In this article, we introduce a transformation framework for optimizing task-parallel programs with a focus on task creation and task termination operations. These operations can appear explicitly in constructs such as async, finish in X10 and HJ, task, taskwait in OpenMP 3.0, and spawn, sync in Cilk, or implicitly in composite code statements such as foreach and ateach loops in X10, forall and foreach loops in HJ, and parallel loop in OpenMP.
 Our framework includes a definition of data dependence in task-parallel programs, a happens-before analysis algorithm, and a range of program transformations for optimizing task parallelism. Broadly, our transformations cover three different but interrelated optimizations: (1) finish-elimination, (2) forall-coarsening, and (3) loop-chunking. Finish-elimination removes redundant task termination operations, forall-coarsening replaces expensive task creation and termination operations with more efficient synchronization operations, and loop-chunking extracts useful parallelism from ideal parallelism. All three optimizations are specified in an iterative transformation framework that applies a sequence of relevant transformations until a fixed point is reached. Further, we discuss the impact of exception semantics on the specified transformations, and extend them to handle task-parallel programs with precise exception semantics. Experimental results were obtained for a collection of task-parallel benchmarks on three multicore platforms: a dual-socket 128-thread (16-core) Niagara T2 system, a quad-socket 16-core Intel Xeon SMP, and a quad-socket 32-core Power7 SMP. We have observed that the proposed optimizations interact with each other in a synergistic way, and result in an overall geometric average performance improvement between 6.28× and 10.30×, measured across all three platforms for the benchmarks studied.
Transfer in Reinforcement Learning: A Framework and a Survey

Transfer in reinforcement learning is a novel research area that focuses on the development of methods to transfer knowledge from a se t of source tasks to a target task. Whenever the tasks are similar, the transferred knowledge can be used by a learning algorithm to solve the target task and sign ifica tly improve its performance (e.g., by reducing the number of samples needed to achieve a nearly optimal performance). In this chapter we provide a formaliz ation of the general transfer problem, we identify the main settings which have b e n investigated so far, and we review the most important approaches to transfer in re inforcement learning.
Assorted pixels: multi-sampled imaging with structural models

Multi-sampled imaging is a general framework for using pixels on an image detector to simultaneously sample multiple dimensions of imaging (space, time, spectrum, brightness, polarization, etc.). The mosaic of red, green and blue spectral filters found in most solid-state color cameras is one example of multi-sampled imaging. We briefly describe how multi-sampling can be used to explore other dimensions of imaging. Once such an image is captured, smooth reconstructions along the individual dimensions can be obtained using standard interpolation algorithms. Typically, this results in a substantial reduction of resolution (and hence image quality). One can extract significantly greater resolution in each dimension by noting that the light fields associated with real scenes have enormous redundancies within them, causing different dimensions to be highly correlated. Hence, multi-sampled images can be better interpolated using local structural models that are learned off- line from a diverse set of training images. The specific type of structural models we use are based on polynomial functions of measured image intensities. They are very effective as well as computationally efficient. We demonstrate the benefits of structural interpolation using three specific applications. These are (a) traditional color imaging with a mosaic of color filters, (b) high dynamic range monochrome imaging using a mosaic of exposure filters, and (c) high dynamic range color imaging using a mosaic of overlapping color and exposure filters.
Reinforcement Learning with LSTM in Non-MarkovianTasks with Long-Term

This paper presents reinforcement learning with a Long Short-Term Memory recurrent neural network: RL-LSTM. Model-free RL-LSTM using Advantage() learning and directed exploration can solve non-Markovian tasks with long-term dependencies between relevant events. This is demonstrated in a T-maze task, as well as in a diicult variation of the pole balancing task.
A rank sum test method for informative gene discovery

Finding informative genes from microarray data is an important research problem in bioinformatics research and applications. Most of the existing methods rank features according to their discriminative capability and then find a subset of discriminative genes (usually top k genes). In particular, t-statistic criterion and its variants have been adopted extensively. This kind of methods rely on the statistics principle of t-test, which requires that the data follows a normal distribution. However, according to our investigation, the normality condition often cannot be met in real data sets.To avoid the assumption of the normality condition, in this paper, we propose a rank sum test method for informative gene discovery. The method uses a rank-sum statistic as the ranking criterion. Moreover, we propose using the significance level threshold, instead of the number of informative genes, as the parameter. The significance level threshold as a parameter carries the quality specification in statistics. We follow the Pitman efficiency theory to show that the rank sum method is more accurate and more robust than the t-statistic method in theory.To verify the effectiveness of the rank sum method, we use support vector machine (SVM) to construct classifiers based on the identified informative genes on two well known data sets, namely colon data and leukemia data. The prediction accuracy reaches 96.2% on the colon data and 100% on the leukemia data. The results are clearly better than those from the previous feature ranking methods. By experiments, we also verify that using significance level threshold is more effective than directly specifying an arbitrary k.
Higher-order Web link analysis using multilinear algebra

Linear algebra is a powerful and proven tool in Web search. Techniques, such as the PageRank algorithm of Brin and Page and the HITS algorithm of Kleinberg, score Web pages based on the principal eigenvector (or singular vector) of a particular non-negative matrix that captures the hyperlink structure of the Web graph. We propose and test a new methodology that uses multilinear algebra to elicit more information from a higher-order representation of the hyperlink graph. We start by labeling the edges in our graph with the anchor text of the hyperlinks so that the associated linear algebra representation is a sparse, three-way tensor. The first two dimensions of the tensor represent the Web pages while the third dimension adds the anchor text. We then use the rank-1 factors of a multilinear PARAFAC tensor decomposition, which are akin to singular vectors of the SVD, to automatically identify topics in the collection along with the associated authoritative Web pages.
A survey of research on anomaly detection for time series

Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications, and anomaly detection for time series is becoming a hot research topic recently. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. In this paper, we have discussed the definition of anomaly and grouped existing techniques into different categories based on the underlying approach adopted by each technique. And for each category, we identify the advantages and disadvantages of the techniques in that category. Then, we provide a briefly discussion on the representative methods recently. Furthermore, we also point out some key issues about multivariate time series anomaly. Finally, some suggestions about anomaly detection are discussed and future research trends are also summarized, which is hopefully beneficial to the researchers of time series and other relative domains.
Copper toxicity in a New Zealand dairy herd

Chronic copper toxicity was diagnosed in a Jersey herd in the Waikato region of New Zealand following an investigation into the deaths of six cattle from a herd of 250 dry cows. Clinical signs and post-mortem examination results were consistent with a hepatopathy, and high concentrations of copper in liver and blood samples of clinically affected animals confirmed copper toxicity. Liver copper concentrations and serum gamma-glutamyl transferase activities were both raised in a group of healthy animals sampled at random from the affected herd, indicating an ongoing risk to the remaining cattle; these animals all had serum copper concentrations within normal limits. Serum samples and liver biopsies were also collected and assayed for copper from animals within two other dairy herds on the same farm; combined results from all three herds showed poor correlation between serum and liver copper concentrations. To reduce liver copper concentrations the affected herd was drenched with 0.5 g ammonium molybdate and 1 g sodium sulphate per cow for five days, and the herd was given no supplementary feed or mineral supplements. Liver biopsies were repeated 44 days after the initial biopsies (approximately 1 month after the end of the drenching program); these showed a significant 37.3% decrease in liver copper concentrations (P <0.02). Also there were no further deaths after the start of the drenching program. Since there was no control group it is impossible to quantify the effect of the drenching program in this case, and dietary changes were also made that would have depleted liver copper stores. Historical analysis of the diet was difficult due to poor record keeping, but multiple sources of copper contributed to a long term copper over supplementation of the herd; the biggest source of copper was a mineral supplement. The farmer perceived this herd to have problems with copper deficiency prior to the diagnosis of copper toxicity, so this case demonstrates the importance of monitoring herd copper status regularly. Also the poor correlation between liver and serum copper concentrations in the three herds sampled demonstrates the importance of using liver copper concentration to assess herd copper status.
Decentralized Frank–Wolfe Algorithm for Convex and Nonconvex Problems

Decentralized optimization algorithms have received much attention due to the recent advances in network information processing. However, conventional decentralized algorithms based on projected gradient descent are incapable of handling high-dimensional constrained problems, as the projection step becomes computationally prohibitive. To address this problem, this paper adopts a projection-free optimization approach, a.k.a. the Frank–Wolfe (FW) or conditional gradient algorithm. We first develop a decentralized FW (DeFW) algorithm from the classical FW algorithm. The convergence of the proposed algorithm is studied by viewing the decentralized algorithm as an <italic>inexact </italic> FW algorithm. Using a diminishing step size rule and letting <inline-formula><tex-math notation="LaTeX">$t$ </tex-math></inline-formula> be the iteration number, we show that the DeFW algorithm's convergence rate is <inline-formula><tex-math notation="LaTeX">${\mathcal O}(1/t)$</tex-math></inline-formula> for convex objectives; is <inline-formula><tex-math notation="LaTeX">${\mathcal O}(1/t^2)$</tex-math></inline-formula> for strongly convex objectives with the optimal solution in the interior of the constraint set; and is <inline-formula> <tex-math notation="LaTeX">${\mathcal O}(1/\sqrt{t})$</tex-math></inline-formula> toward a stationary point for smooth but nonconvex objectives. We then show that a consensus-based DeFW algorithm meets the above guarantees with two communication rounds per iteration. We demonstrate the advantages of the proposed DeFW algorithm on low-complexity robust matrix completion and communication efficient sparse learning. Numerical results on synthetic and real data are presented to support our findings.
Simultaneous calibration of odometry and camera for a differential drive mobile robot

Differential-drive mobile robots are usually equipped with video-cameras for navigation purposes. In order to ensure proper operational capabilities of such systems, several calibration steps are required to estimate the following quantities: the video-camera intrinsic and extrinsic parameters, the relative pose between the camera and the vehicle frame and, finally, the odometric parameters of the vehicle. In this paper the simultaneous estimation of the above mentioned quantities is achieved by a systematic and effective calibration procedure that does not require any iterative step. The calibration procedure needs only on-board measurements given by the wheels encoders, the camera and a number of properly taken camera snapshots of a set of known landmarks. Numerical simulations and experimental results with a mobile robot Khepera III equipped with a low-cost camera confirm the effectiveness of the proposed technique.
Human nucleolar protein Nop52 (RRP1/NNP-1) is involved in site 2 cleavage in internal transcribed spacer 1 of pre-rRNAs at early stages of ribosome biogenesis

During the early steps of ribosome biogenesis in mammals, the two ribosomal subunits 40S and 60S are produced via splitting of the large 90S pre-ribosomal particle (90S) into pre-40S and pre-60S pre-ribosomal particles (pre-40S and pre-60S). We previously proposed that replacement of fibrillarin by Nop52 (RRP1/NNP-1) for the binding to p32 (C1QBP) is a key event that drives this splitting process. However, how the replacement by RRP1 is coupled with the endo- and/or exo-ribonucleolytic cleavage of pre-rRNA remains unknown. In this study, we demonstrate that RRP1 deficiency suppressed site 2 cleavage on ITS1 of 47S/45S, 41S and 36S pre-rRNAs in human cells. RRP1 was also present in 90S and was localized in the dense fibrillar component of the nucleolus dependently on active RNA polymerase I transcription. In addition, double knockdown of XRN2 and RRP1 revealed that RRP1 accelerated the site 2 cleavage of 47S, 45S and 41S pre-rRNAs. These data suggest that RRP1 is involved not only in competitive binding with fibrillarin to C1QBP on 90S but also in site 2 cleavage in ITS1 of pre-rRNAs at early stages of human ribosome biogenesis; thus, it is likely that RRP1 integrates the cleavage of site 2 with the physical split of 90S into pre-40S and pre-60S.
Linking DNA methylation and histone modification: patterns and paradigms

Both DNA methylation and histone modification are involved in establishing patterns of gene repression during development. Certain forms of histone methylation cause local formation of heterochromatin, which is readily reversible, whereas DNA methylation leads to stable long-term repression. It has recently become apparent that DNA methylation and histone modification pathways can be dependent on one another, and that this crosstalk can be mediated by biochemical interactions between SET domain histone methyltransferases and DNA methyltransferases. Relationships between DNA methylation and histone modification have implications for understanding normal development as well as somatic cell reprogramming and tumorigenesis.
Network Cosmology

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.
Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications

The Singular Value Decomposition (SVD) of a matrix is a linear algebra tool that has been successfully applied to a wide variety of domains. The present paper is concerned with the problem of estimating the Jacobian of the SVD components of a matrix with respect to the matrix itself. An exact analytic technique is developed that facilitates the estimation of the Jacobian using calculations based on simple linear algebra. Knowledge of the Jacobian of the SVD is very useful in certain applications involving multivariate regression or the computation of the uncertainty related to estimates obtained through the SVD. The usefulness and generality of the proposed technique is demonstrated by applying it to the estimation of the uncertainty for three different vision problems, namely self-calibration, epipole computation and rigid motion estimation.  Singular Value Decomposition, Jacobian, Uncertainty, Calibration, Structure from Motion. M. Lourakis was supported by the VIRGO research network (EC Contract No ERBFMRX-CT96-0049) of the TMR Programme. Calcul de la Jacobienne de la Décomposition en Valeurs Singulières: Théorie et applications Résumé : La technique de Décomposition en Valeurs Singulières (SVD) d’une matrice est un outil algèbrique qui a trouvé de nombreuses applications en vision par ordinateur. Dans ce rapport, nous nous intéressons au problème de l’estimation de la jacobienne de la SVD par rapport aux coefficients de la matrice initiale. Cette jacobienne est très utile pour toute une gamme d’applications faisant intervenir des estimations aux moindres carrés (pour lesquelles on utilise la SVD) ou bien des calculs d’incertitude pour des grandeurs estimées de cette manière. Une solution analytique simple à ce problème est présentée. Elle exprime la jacobienne à partir de la SVD de la matrice à l’aide d’opérations très simples d’algèbre linéaire. L’utilité et la généralité de la technique est démontrée en l’appliquant à trois problèmes de vision: l’auto-calibration, le calcul d’épipoles et l’estimation de mouvements rigides. Mots-clés : Décomposition en valeurs singulières, Jacobienne, Incertitude, Calibration, Structure à partir du mouvement. Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications 3
Defining Locality for Surrogates in Post-hoc Interpretablity

Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box. This paper highlights the importance of defining the right locality, the neighborhood on which a local surrogate is trained, in order to approximate accurately the local black-box decision boundary. Unfortunately, as shown in this paper, this issue is not only a parameter or sampling distribution challenge and has a major impact on the relevance and quality of the approximation of the local black-box decision boundary and thus on the meaning and accuracy of the generated explanation. To overcome the identified problems, quantified with an adapted measure and procedure, we propose to generate surrogate-based explanations for individual predictions based on a sampling centered on particular place of the decision boundary, relevant for the prediction to be explained, rather than on the prediction itself as it is classically done. We evaluate the novel approach compared to state-of-the-art methods and a straightforward improvement thereof on four UCI datasets.
Digital CMOS neuromorphic processor design featuring unsupervised online learning

The compute-intensive and power-efficient brain has been a source of inspiration for a broad range of neural networks to solve recognition and classification tasks. Compared to the supervised deep neural networks (DNNs) that have been very successful on well-defined labeled datasets, bio-plausible spiking neural networks (SNNs) with unsupervised learning rules could be well-suited for training and learning representations from the massive amount of unlabeled data. To design dense and low-power hardware for such unsupervised SNNs, we employ digital CMOS circuits for neuromorphic processors, which can exploit transistor scaling and dynamic voltage scaling to the utmost. As exemplary works, we present two neuromorphic processor designs. First, a 45nm neuromorphic chip is designed for a small-scale network of spiking neurons. Through tight integration of memory (64k SRAM synapses) and computation (256 digital neurons), the chip demonstrates on-chip learning on pattern recognition tasks down to 0.53V supply. Secondly, a 65nm neuromorphic processor that performs unsupervised on-line spike-clustering for brain sensing applications is implemented with 1.2k digital neurons and 4.7k latch-based synapses. The processor exhibits a power consumption of 9.3μW/ch at 0.3V supply. Synapse hardware precision, efficient synapse memory array access, overfitting, and voltage scaling will be discussed for dense and power-efficient on-chip learning for CMOS spiking neural networks.