nils-herrmann/clean_scirepeval huggingface dataset:
Coherent regulation in yeast’s cell-cycle network

We define a measure of coherent activity for gene regulatory networks, a property that reflects the unity of purpose between the regulatory agents with a common target. We propose that such harmonious regulatory action is desirable under a demand for energy efficiency and may be selected for under evolutionary pressures. We consider two recent models of the cell-cycle regulatory network of the yeast, Saccharomyces cerevisiae as a case study and calculate their degree of coherence. A comparison with random networks of similar size and composition reveals that the yeast’s cell-cycle regulation is wired to yield an exceptionally high level of coherent regulatory activity. We also investigate the mean degree of coherence as a function of the network size, connectivity and the fraction of repressory/activatory interactions.
Efficient Learning in Large-Scale Combinatorial Semi-Bandits

A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to combinatorial constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we consider efficient learning in large-scale combinatorial semi-bandits with linear generalization, and as a solution, propose two learning algorithms called Combinatorial Linear Thompson Sampling (CombLinTS) and Combinatorial Linear UCB (CombLinUCB). Both algorithms are computationally efficient as long as the offline version of the combinatorial problem can be solved efficiently. We establish that CombLinTS and CombLinUCB are also provably statistically efficient under reasonable assumptions, by developing regret bounds that are independent of the problem scale (number of items) and sublinear in time. We also evaluate CombLinTS on a variety of problems with thousands of items. Our experiment results demonstrate that CombLinTS is scalable, robust to the choice of algorithm parameters, and significantly outperforms the best of our baselines.
Predictability of Volatility Homogenised Financial Time Series

Modelling financial time series as a time change of a simpler process has been proposed in various forms over the years. One of such recent approaches is called volatility homogenisation decomposition, and has been designed specifically to aid the forecasting of price changes on financial markets. The authors of this method have attempted to prove the its usefulness by applying a specific forecasting procedure and determining the effectiveness of this procedure on the decomposed time series, as compared with the original time series. This is problematic in at least two ways. First, the choice of the forecasting procedure obviously has an effect on the results, rendering them non-exhaustive. Second, the results obtained were not completely convincing, with some values falling under 50% guessing rate. Additionally, only nine Australian stocks were being investigated, which further limits the scope of this proof. In this study we propose to find the usefulness of volatility homogenisation by calculating the predictability of the decomposed time series and comparing it to the predictability of the original time series. We are applying information-theoretic notion of entropy rate to quantify predictability, which guarantees the result is not tied to a specific method of prediction, and additionally we base our calculations on a large number of stocks from the Warsaw Stock Exchange.
Why I-Love-Q: Explaining why universality emerges in compact objects

similar to each other to explain the universality observed. Second, we study the impact on the universality of approximating stellar isodensity contours as self-similar ellipsoids. An analytical calculation in the non-relativistic limit reveals that the shape of the ellipsoids per se does not aect the universal relations much, but relaxing the selfsimilarity assumption can completely destroy it. Third, we investigate the eccentricity proles of rotating relativistic stars and nd that the ellipticity is roughly constant, with variations of roughly (20{30)% in the region that matters to the universal relations. Fourth, we repeat the above analysis for dierentially-rot ating, non-compact, regular stars and nd that this time the ellipticity is not constant, with variations that easily exceed 100%, and moreover, universality is lost. These ndings suggest that universality arises as an emergent approximate symmetry: as one ows in the stellarstructure phase space from non-compact star region to the relativistic star region, the eccentricity variation inside stars decreases, leading to approximate self-similarity in their isodensity contours, which then leads to the universal behavior observed in their exterior multipole moments.
TALEs from a Spring – Superelasticity of Tal Effector Protein Structures

Transcription activator-like effectors (TALEs) are DNA-related proteins that recognise and bind specific target sequences to manipulate gene expression. Recently determined crystal structures show that their common architecture reveals a superhelical overall structure that may undergo drastic conformational changes. To establish a link between structure and dynamics in TALE proteins we have employed coarse-grained elastic-network modelling of currently available structural data and implemented a force-probe setup that allowed us to investigate their mechanical behaviour in computer experiments. Based on the measured force-extension curves we conclude that TALEs exhibit superelastic dynamical properties allowing for large-scale global conformational changes along their helical axis, which represents the soft direction in such proteins. For moderate external forcing the TALE models behave like linear springs, obeying Hooke's law, and the investigated structures can be characterised and compared by a corresponding spring constant. We show that conformational flexibility underlying the large-scale motions is not homogeneously distributed over the TALE structure, but instead soft spot residues around which strain is accumulated and which turn out to represent key agents in the transmission of conformational motions are identified. They correspond to the RVD loop residues that have been experimentally determined to play an eminent role in the binding process of target DNA.
Popularity and Performance: A Large-Scale Study

Social scientists have long sought to understand why certain people, items, or options become more popular than others. One seemingly intuitive theory is that inherent value drives popularity. An alternative theory claims that popularity is driven by the rich-get-richer effect of cumulative advantage---certain options become more popular, not because they are higher quality, but because they are already relatively popular. Realistically, it seems likely that popularity is driven by neither one of these forces alone but rather both together. Recently, researchers have begun using large-scale online experiments to study the effect of cumulative advantage in realistic scenarios, but there have been no large-scale studies of the combination of these two effects. We are interested in studying a case where decision-makers observe explicit signals of both the popularity and the quality of various options. We derive a model for change in popularity as a function of past popularity and past perceived quality. Our model implies that we should expect an interaction between these two forces---popularity should amplify the effect of quality, so that the more popular an option is, the faster we expect it to increase in popularity with better perceived quality. We use a data set from eToro.com, an online social investment platform, to support this hypothesis.
Online popularity and topical interests through the lens of instagram

Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.
Universal Compressed Sensing of Markov Sources

The main promise of compressed sensing is accurate recovery f high-dimensional structured signals from an underdetermined set of randomized linear projections. Sev eral types of structure such as sparsity and low-rankness have already been explored in the literature. For each type o f structure, a recovery algorithm has been developed based on the properties of signals with that type of structur e. Such algorithms recover any signal complying with the assumed model from its sufficient number of linear projectio ns. However, in many practical situations the underlying structure of the signal is not known, or is only partially kno wn. Moreover, it is desirable to have recovery algorithms that can be applied to signals with different types of struct ure. In this paper, the problem of developing “universal” algori thms for compressed sensing of stochastic processes is studied. First, Rényi’s notion of information dimension i s generalized to analog stationary processes. This provide s a measure of complexity for such processes and is connected t o the number of measurements required for their accurate recovery. Then a minimum entropy pursuit (MEP) alg orithm is proposed, and it is proven that it can reliably and robustly recover any Markov process of any order from suf ficient number of randomized linear measurements, without having any prior information about the distributio n f the process.  The decoder is interested in recoverin g the signalxo from the measurements y m o . In CS we are usually interested in the case where xo is high-dimensional and the number of measurements m i much smaller than the ambient dimension of the signal, i.e., m ≪ n. In such a setup, clearly, without any extra information, it is impossible to recover xo from y m o , and the system of linear equations described by y m o = Ax n has infinitely many solutions. However, if some extra information about the str ucture ofxo is available, for instance it is known that xo is sparse, i.e., ‖xo‖0 ≪ n, then it is known that this additional information can be exp loited andxo can be recovered fromy o . For sparse signals, it can be proved that in fact the signal r ecovery can be achieved efficiently and robustly [1]–[3]. This result can be extended to several other structures as well [4]–[11]. In each of these cases, the signal is known to have some specific structure, and the de coder takes advantage of its knowledge about the structure of the signal to recover it from its linear project ions.
Learning Graphs from Signal Observations under Smoothness Prior

The construction of a meaningful graph plays a crucial role in the success of many graph-based data representations and algorithms, especially in the emerging field of signal processing on graphs. However, a meaningful graph is not always readily available from the data, nor easy to define depending on the application domain. In this paper, we address the problem of graph learning, where we are interested in learning graph topologies, namely, the relationships between data entities, that well explain the signal observations. In particular, we want to infer a graph such that the input data forms graph signals with smooth variations on the resulting topology. To this end, we adopt a factor analysis model for the graph signals and impose a Gaussian probabilistic prior on the latent variables that control these graph signals. We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals. We then propose an algorithm for learning graphs that enforce such smoothness property for the signal observations by minimizing the variations of the signals on the learned graph. Experiments on both synthetic and real world data demonstrate that the proposed graph learning framework can efficiently infer meaningful graph topologies from only the signal observations.
Quantifying long-term evolution of intra-urban spatial interactions

Understanding the long-term impact that changes in a city's transportation infrastructure have on its spatial interactions remains a challenge. The difficulty arises from the fact that the real impact may not be revealed in static or aggregated mobility measures, as these are remarkably robust to perturbations. More generally, the lack of longitudinal, cross-sectional data demonstrating the evolution of spatial interactions at a meaningful urban scale also hinders us from evaluating the sensitivity of movement indicators, limiting our capacity to understand the evolution of urban mobility in depth. Using very large mobility records distributed over 3 years, we quantify the impact of the completion of a metro line extension: the Circle Line (CCL) in Singapore. We find that the commonly used movement indicators are almost identical before and after the project was completed. However, in comparing the temporal community structure across years, we do observe significant differences in the spatial reorganization of the affected geographical areas. The completion of CCL enables travellers to re-identify their desired destinations collectively with lower transport cost, making the community structure more consistent. These changes in locality are dynamic and characterized over short timescales, offering us a different approach to identify and analyse the long-term impact of new infrastructures on cities and their evolution dynamics.
World Input-Output Network

Production systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD) is one of the first efforts to construct the global multi-regional input-output (GMRIO) tables. By viewing the world input-output system as an interdependent network where the nodes are the individual industries in different economies and the edges are the monetary goods flows between industries, we analyze respectively the global, regional, and local network properties of the so-called world input-output network (WION) and document its evolution over time. At global level, we find that the industries are highly but asymmetrically connected, which implies that micro shocks can lead to macro fluctuations. At regional level, we find that the world production is still operated nationally or at most regionally as the communities detected are either individual economies or geographically well defined regions. Finally, at local level, for each industry we compare the network-based measures with the traditional methods of backward linkages. We find that the network-based measures such as PageRank centrality and community coreness measure can give valuable insights into identifying the key industries.
Significant Subgraph Mining with Multiple Testing Correction

The problem of finding itemsets that are statistically significantly enriched in a class of transactions is complicated by the need to correct for multiple hypothesis testing. Pruning untestable hypotheses was recently proposed as a strategy for this task of significant itemset mining. It was shown to lead to greater statistical power, the discovery of more truly significant itemsets, than the standard Bonferroni correction on real-world datasets. An open question, however, is whether this strategy of excluding untestable hypotheses also leads to greater statistical power in subgraph mining, in which the number of hypotheses is much larger than in itemset mining. Here we answer this question by an empirical investigation on eight popular graph benchmark datasets. We propose a new efficient search strategy, which always returns the same solution as the state-of-the-art approach and is approximately two orders of magnitude faster. Moreover, we exploit the dependence between subgraphs by considering the effective number of tests and thereby further increase the statistical power.
Emergence and persistence of communities in coevolutionary networks

We investigate the emergence and persistence of communities through a recently proposed mechanism of adaptive rewiring in coevolutionary networks. We characterize the topological structures arising in a coevolutionary network subject to an adaptive rewiring process and a node dynamics given by a simple voter-like rule. We find that, for some values of the parameters describing the adaptive rewiring process, a community structure emerges on a connected network. We show that the emergence of communities is associated to a decrease in the number of active links in the system, i.e. links that connect two nodes in different states. The lifetime of the community structure state scales exponentially with the size of the system. Additionally, we find that a small noise in the node dynamics can sustain a diversity of states and a community structure in time in a finite size system. Thus, large system size and/or local noise can explain the persistence of communities and diversity in many real systems.
Power counting of contact-range currents in effective field theory.

We analyze the power counting of two-body currents in nuclear effective field theories (EFTs). We find that the existence of nonperturbative physics at low energies, which is manifest in the existence of the deuteron and the ^{1}S_{0} NN virtual bound state, combined with the appearance of singular potentials in versions of nuclear EFT that incorporate chiral symmetry, modifies the renormalization-group flow of the couplings associated with contact operators that involve nucleon-nucleon pairs and external fields. The order of these couplings is thereby enhanced with respect to the naive-dimensional-analysis estimate. Consequently, short-range currents enter at a lower order in the chiral EFT than has been appreciated up until now, and their impact on low-energy observables is concomitantly larger. We illustrate the changes in the power counting with a few low-energy processes involving external probes and few-nucleon systems, including electron-deuteron elastic scattering and radiative neutron capture by protons.
AsterixDB: A Scalable, Open Source BDMS

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. 
 
Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.
Stochastic model of a pension plan

Structuring a viable pension plan is a problem that arises in the study of financial contracts pricing and bears special importance these days. Deterministic pension models often rely on projections that are based on several assumptions concerning the "average" long-time behavior of the stock market. Our aim here is to examine some of the popular "average" assumptions in a more realistic setting of a stochastic model. Thus, we examine the contention that investment in the stock market is similar to gambling in a casino, while purchasing companies, after due diligence, is safer under the premise that acting as a holding company that wholly owns other companies avoids some of the stock market risks. We show that the stock market index faithfully reflects its companies' profits at the time of their publication. We compare the shifted historical dynamics of the S\&P500's aggregated financial earnings to its value, and find a high degree of correlation. We conclude that there is no benefit to a pension fund in wholly owning a super trust. We verify, by examining historical data, that stock earnings follow an exponential (geometric) Brownian motion and estimate its parameters. The robustness of this model is examined by an estimate of a pensioner's accumulated assets over a saving period. We also estimate the survival probability and mean survival time of the accumulated individual fund with pension consumption over the residual life of the pensioner.
Non-Cooperativity in Bayesian Social Learning

We describe a Bayesian model for social learning of a random variable in which agents might observe each other over a directed network. The outcomes produced are compared to those from a model in which observations occur randomly over a complete graph. In both cases we observe a nontrivial level of observation which maximizes learning, though individuals have strong incentive to defect from the societal optimum. The implications of such competition over information commons are discussed.
Support Consistency of Direct Sparse-Change Learning in Markov Networks

We study the problem of learning sparse structure changes between two Markov networks P and Q. Rather than fitting two Markov networks separately to two sets of data and figuring out their differences, a recent work proposed to learn changes directly via estimating the ratio between two Markov network models.  Such a direct approach was demonstrated to perform excellently in experiments, although its theoretical properties remained unexplored.  In this paper, we give sufficient conditions for successful change detection with respect to the sample size np, nq, the dimension of data m, and the number of changed edges d.